修复gradio复制按钮BUG

🐛 匹配latex注释的正则表达式

.gitattributes

@@ -0,0 +1,5 @@
+*.h linguist-detectable=false
+*.cpp linguist-detectable=false
+*.tex linguist-detectable=false
+*.cs linguist-detectable=false
+*.tps linguist-detectable=false

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,75 @@
+name: Report Bug | 报告BUG
+description: "Report bug"
+title: "[Bug]: "
+labels: []
+body:
+  - type: dropdown
+    id: download
+    attributes:
+      label: Installation Method | 安装方法与平台
+      options:
+        - Please choose | 请选择
+        - Pip Install (I ignored requirements.txt)
+        - Pip Install (I used latest requirements.txt)
+        - Anaconda (I ignored requirements.txt)
+        - Anaconda (I used latest requirements.txt)
+        - Docker（Windows/Mac）
+        - Docker（Linux）
+        - Docker-Compose（Windows/Mac）
+        - Docker-Compose（Linux）
+        - Huggingface
+        - Others (Please Describe)
+    validations:
+      required: true
+
+  - type: dropdown
+    id: version
+    attributes:
+      label: Version | 版本
+      options:
+        - Please choose | 请选择
+        - Latest | 最新版
+        - Others | 非最新版
+    validations:
+      required: true
+      
+  - type: dropdown
+    id: os
+    attributes:
+      label: OS | 操作系统
+      options:
+        - Please choose | 请选择
+        - Windows
+        - Mac
+        - Linux
+        - Docker
+    validations:
+      required: true
+      
+  - type: textarea
+    id: describe
+    attributes:
+      label: Describe the bug | 简述
+      description: Describe the bug | 简述
+    validations:
+      required: true
+      
+  - type: textarea
+    id: screenshot
+    attributes:
+      label: Screen Shot | 有帮助的截图
+      description: Screen Shot | 有帮助的截图
+    validations:
+      required: true
+      
+  - type: textarea
+    id: traceback
+    attributes:
+      label: Terminal Traceback & Material to Help Reproduce Bugs | 终端traceback（如有） + 帮助我们复现的测试材料样本（如有）
+      description: Terminal Traceback & Material to Help Reproduce Bugs | 终端traceback（如有） + 帮助我们复现的测试材料样本（如有）
+
+
+
+
+
+

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,28 @@
+name: Feature Request | 功能请求
+description: "Feature Request"
+title: "[Feature]: "
+labels: []
+body:
+  - type: dropdown
+    id: download
+    attributes:
+      label: Class | 类型
+      options:
+        - Please choose | 请选择
+        - 其他
+        - 函数插件
+        - 大语言模型
+        - 程序主体
+    validations:
+      required: false
+
+  - type: textarea
+    id: traceback
+    attributes:
+      label: Feature Request | 功能请求
+      description: Feature Request | 功能请求
+
+
+
+
+

.github/workflows/build-with-chatglm.yml

@@ -0,0 +1,44 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: Create and publish a Docker image for ChatGLM support
+
+on:
+  push:
+    branches:
+      - 'master'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_chatglm_moss
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v2
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          push: true
+          file: docs/GithubAction+ChatGLM+Moss
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.github/workflows/build-with-jittorllms.yml

@@ -0,0 +1,44 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: Create and publish a Docker image for ChatGLM support
+
+on:
+  push:
+    branches:
+      - 'master'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_jittorllms
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v2
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          push: true
+          file: docs/GithubAction+JittorLLMs
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.github/workflows/build-without-local-llms.yml

@@ -0,0 +1,44 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: Create and publish a Docker image
+
+on:
+  push:
+    branches:
+      - 'master'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_nolocal
+
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v2
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          push: true
+          file: docs/GithubAction+NoLocal
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.gitignore

@@ -131,6 +131,22 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
+.vscode
+.idea
+
 history
 ssr_conf
 config_private.py
+gpt_log
+private.md
+private_upload
+other_llms
+cradle*
+debug*
+private*
+crazy_functions/test_project/pdf_and_word
+crazy_functions/test_samples
+request_llm/jittorllms
+multi-language
+request_llm/moss
+media

Dockerfile

@@ -1,17 +1,20 @@
-FROM ubuntu:latest
-
-RUN apt-get update && \
-    apt-get install -y python3 python3-pip && \
-    rm -rf /var/lib/apt/lists/*
+# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
+# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic .
+# 如何运行: docker run --rm -it --net=host gpt-academic
+FROM python:3.11
 
 RUN echo '[global]' > /etc/pip.conf && \
     echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
     echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
 
-RUN pip3 install gradio requests[socks] mdtex2html
 
-COPY . /gpt
 WORKDIR /gpt
+COPY requirements.txt .
+RUN pip3 install -r requirements.txt
 
+COPY . .
 
-CMD ["python3", "main.py"]
+# 可选步骤，用于预热模块
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+CMD ["python3", "-u", "main.py"]

README.md

@@ -1,26 +1,330 @@
-# ChatGPT 学术优化
+> **Note**
+>
+> 安装依赖时，请严格选择requirements.txt中**指定的版本**。 
+> 
+> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
+>
 
-**如果喜欢这个项目，请给它一个Star**
+# <img src="docs/logo.png" width="40" > GPT 学术优化 (GPT Academic)
 
-## 使用docker
+**如果喜欢这个项目，请给它一个Star；如果你发明了更好用的快捷键或函数插件，欢迎发pull requests**
 
-``` sh
-# 下载项目
+If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a README in [English|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself.
+To translate this project to arbitary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
+
+> **Note**
+>
+> 1.请注意只有**红颜色**标识的函数插件（按钮）才支持读取文件，部分插件位于插件区的**下拉菜单**中。另外我们以**最高优先级**欢迎和处理任何新插件的PR！
+>
+> 2.本项目中每个文件的功能都在自译解[`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)详细说明。随着版本的迭代，您也可以随时自行点击相关函数插件，调用GPT重新生成项目的自我解析报告。常见问题汇总在[`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)当中。[安装方法](#installation)。
+> 
+> 3.本项目兼容并鼓励尝试国产大语言模型chatglm和RWKV, 盘古等等。支持多个api-key共存，可在配置文件中填写如`API_KEY="openai-key1,openai-key2,api2d-key3"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交后即可生效。
+
+
+ 
+
+<div align="center">
+
+功能 | 描述
+--- | ---
+一键润色 | 支持一键润色、一键查找论文语法错误
+一键中英互译 | 一键中英互译
+一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释
+[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
+模块化设计 | 支持自定义强大的[函数插件](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
+[自我程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] [一键读懂](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)本项目的源代码
+[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] 一键可以剖析其他Python/C/C++/Java/Lua/...项目树
+读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [函数插件] 一键解读latex/pdf论文全文并生成摘要
+Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [函数插件] 一键翻译或润色latex论文
+批量注释生成 | [函数插件] 一键批量生成函数注释
+Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)了吗？
+chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
+[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [函数插件] PDF论文提取题目&摘要+翻译全文（多线程）
+[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
+[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL，让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
+互联网信息聚合+GPT | [函数插件] 一键[让GPT先从互联网获取信息](https://www.bilibili.com/video/BV1om4y127ck)，再回答问题，让信息永不过时
+公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)，支持公式、代码高亮
+多线程函数插件支持 | 支持多线调用chatgpt，一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
+启动暗色gradio[主题](https://github.com/binary-husky/chatgpt_academic/issues/173) | 在浏览器url后面添加```/?__theme=dark```可以切换dark主题
+[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持，[API2D](https://api2d.com/)接口支持 | 同时被GPT3.5、GPT4、[清华ChatGLM](https://github.com/THUDM/ChatGLM-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)同时伺候的感觉一定会很不错吧？
+更多LLM模型接入，支持[huggingface部署](https://huggingface.co/spaces/qingxu98/gpt-academic) | 加入Newbing接口(新必应)，引入清华[Jittorllms](https://github.com/Jittor/JittorLLMs)支持[LLaMA](https://github.com/facebookresearch/llama)，[RWKV](https://github.com/BlinkDL/ChatRWKV)和[盘古α](https://openi.org.cn/pangu/)
+更多新功能展示(图像生成等) …… | 见本文档结尾处 ……
+
+</div>
+
+
+- 新界面（修改`config.py`中的LAYOUT选项即可实现“左右布局”和“上下布局”的切换）
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+</div>
+
+
+- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放粘贴板
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- 润色/纠错
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- 如果输出包含公式，会同时以tex形式和渲染形式显示，方便复制和阅读
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- 懒得看项目代码？整个工程直接给chatgpt炫嘴里
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+# Installation
+## 安装-方法1：直接运行 (Windows, Linux or MacOS) 
+
+1. 下载项目
+```sh
 git clone https://github.com/binary-husky/chatgpt_academic.git
 cd chatgpt_academic
-# 配置 海外Proxy 和 OpenAI API KEY
-config.py
-# 安装
-docker build -t gpt-academic .
-# 运行
+```
+
+2. 配置API_KEY
+
+在`config.py`中，配置API KEY等设置，[特殊网络环境设置](https://github.com/binary-husky/gpt_academic/issues/1) 。
+
+(P.S. 程序运行时会优先检查是否存在名为`config_private.py`的私密配置文件，并用其中的配置覆盖`config.py`的同名配置。因此，如果您能理解我们的配置读取逻辑，我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件，并把`config.py`中的配置转移（复制）到`config_private.py`中。`config_private.py`不受git管控，可以让您的隐私信息更加安全。P.S.项目同样支持通过`环境变量`配置大多数选项，环境变量的书写格式参考`docker-compose`文件。读取优先级: `环境变量` > `config_private.py` > `config.py`)
+
+
+3. 安装依赖
+```sh
+# （选择I: 如熟悉python）（python版本3.9以上，越新越好），备注：使用官方pip源或者阿里pip源,临时换源方法：python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# （选择II: 如不熟悉python）使用anaconda，步骤也是类似的 (https://www.bilibili.com/video/BV1rc411W7Dr)：
+conda create -n gptac_venv python=3.11    # 创建anaconda环境
+conda activate gptac_venv                 # 激活anaconda环境
+python -m pip install -r requirements.txt # 这个步骤和pip安装一样的步骤
+```
+
+<details><summary>如果需要支持清华ChatGLM/复旦MOSS作为后端，请点击展开此处</summary>
+<p>
+
+【可选步骤】如果需要支持清华ChatGLM/复旦MOSS作为后端，需要额外安装更多依赖（前提条件：熟悉Python + 用过Pytorch + 电脑配置够强）：
+```sh
+# 【可选步骤I】支持清华ChatGLM。清华ChatGLM备注：如果遇到"Call ChatGLM fail 不能正常加载ChatGLM的参数" 错误，参考如下： 1：以上默认安装的为torch+cpu版，使用cuda需要卸载torch重新安装torch+cuda； 2：如因本机配置不够无法加载模型，可以修改request_llm/bridge_chatglm.py中的模型精度, 将 AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) 都修改为 AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+python -m pip install -r request_llm/requirements_chatglm.txt  
+
+# 【可选步骤II】支持复旦MOSS
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # 注意执行此行代码时，必须处于项目根路径
+
+# 【可选步骤III】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. 运行
+```sh
+python main.py
+```
+
+5. 测试函数插件
+```
+- 测试函数插件模板函数（要求gpt回答历史上的今天发生了什么），您可以根据此函数为模板，实现更复杂的功能
+    点击 "[函数插件模板Demo] 历史上的今天"
+```
+
+## 安装-方法2：使用Docker
+
+1. 仅ChatGPT（推荐大多数人选择）
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # 下载项目
+cd chatgpt_academic                                 # 进入路径
+nano config.py                                      # 用任意文本编辑器编辑config.py, 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等
+docker build -t gpt-academic .                      # 安装
+
+#（最后一步-选择1）在Linux环境下，用`--net=host`更方便快捷
 docker run --rm -it --net=host gpt-academic
-
+#（最后一步-选择2）在macOS/windows环境下，只能用-p选项将容器上的端口(例如50923)暴露给主机上的端口
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
 ```
 
-## 参考项目
+2. ChatGPT + ChatGLM + MOSS（需要熟悉Docker）
+
+``` sh
+# 修改docker-compose.yml，删除方案1和方案3，保留方案2。修改docker-compose.yml中方案2的配置，参考其中注释即可
+docker-compose up
 ```
-https://github.com/Python-Markdown/markdown
-https://github.com/gradio-app/gradio
-https://github.com/polarwinkel/mdtex2html
+
+3. ChatGPT + LLAMA + 盘古 + RWKV（需要熟悉Docker）
+``` sh
+# 修改docker-compose.yml，删除方案1和方案2，保留方案3。修改docker-compose.yml中方案3的配置，参考其中注释即可
+docker-compose up
+```
+
+
+## 安装-方法3：其他部署姿势
+
+1. 如何使用反代URL/微软云AzureAPI
+按照`config.py`中的说明配置API_URL_REDIRECT即可。
+
+2. 远程云服务器部署（需要云服务器知识与经验）
+请访问[部署wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
+
+3. 使用WSL2（Windows Subsystem for Linux 子系统）
+请访问[部署wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
+
+4. 如何在二级网址（如`http://localhost/subpath`）下运行
+请访问[FastAPI运行说明](docs/WithFastapi.md)
+
+5. 使用docker-compose运行
+请阅读docker-compose.yml后，按照其中的提示操作即可
+---
+# Advanced Usage
+## 自定义新的便捷按钮 / 自定义函数插件
+
+1. 自定义新的便捷按钮（学术快捷键）
+任意文本编辑器打开`core_functional.py`，添加条目如下，然后重启程序即可。（如果按钮已经添加成功并可见，那么前缀、后缀都支持热修改，无需重启程序即可生效。）
+例如
+```
+"超级英译中": {
+    # 前缀，会被加在你的输入之前。例如，用来描述你的要求，例如翻译、解释代码、润色等等
+    "Prefix": "请翻译把下面一段内容成中文，然后用一个markdown表格逐一解释文中出现的专有名词：\n\n", 
+    
+    # 后缀，会被加在你的输入之后。例如，配合前缀可以把你的输入内容用引号圈起来。
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. 自定义函数插件
+
+编写强大的函数插件来执行任何你想得到的和想不到的任务。
+本项目的插件编写、调试难度很低，只要您具备一定的python基础知识，就可以仿照我们提供的模板实现自己的插件功能。
+详情请参考[函数插件指南](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。
+
+---
+# Latest Update
+## 新功能动态
+
+1. 对话保存功能。在函数插件区调用 `保存当前的对话` 即可将当前对话保存为可读+可复原的html文件，
+另外在函数插件区（下拉菜单）调用 `载入对话历史存档` ，即可还原之前的会话。
+Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史html存档缓存，点击 `删除所有本地对话历史记录` 可以删除所有html存档缓存。
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
+</div>
+
+
+
+2. 生成报告。大部分插件都会在执行结束后，生成工作报告
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
+</div>
+
+3. 模块化功能设计，简单的接口却能支持强大的功能
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
+<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
+</div>
+
+4. 这是一个能够“自我译解”的开源项目
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
+</div>
+
+5. 译解其他开源项目，不在话下
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
+</div>
+
+6. 装饰[live2d](https://github.com/fghrsh/live2d_demo)的小功能（默认关闭，需要修改`config.py`）
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
+</div>
+
+7. 新增MOSS大语言模型支持
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
+</div>
+
+8. OpenAI图像生成
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
+</div>
+
+9. OpenAI音频解析与总结
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
+</div>
+
+10. Latex全文校对纠错
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
+</div>
+
+
+## 版本:
+- version 3.5(Todo): 使用自然语言调用本项目的所有函数插件（高优先级）
+- version 3.4(Todo): 完善chatglm本地大模型的多线支持
+- version 3.3: +互联网信息综合功能
+- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合)
+- version 3.1: 支持同时问询多个gpt模型！支持api2d，支持多个apikey负载均衡
+- version 3.0: 对chatglm和其他小型llm的支持
+- version 2.6: 重构了插件结构，提高了交互性，加入更多插件
+- version 2.5: 自更新，解决总结大工程源代码时文本过长、token溢出的问题
+- version 2.4: (1)新增PDF全文翻译功能; (2)新增输入区切换位置的功能; (3)新增垂直布局选项; (4)多线程函数插件优化。
+- version 2.3: 增强多线程交互性
+- version 2.2: 函数插件支持热重载
+- version 2.1: 可折叠式布局
+- version 2.0: 引入模块化函数插件
+- version 1.0: 基础功能
+
+gpt_academic开发者QQ群-2：610599535
+
+- 已知问题
+    - 某些浏览器翻译插件干扰此软件前端的运行
+    - gradio版本过高或过低，都会导致多种异常
+
+## 参考与学习
+
+```
+代码中参考了很多其他优秀项目中的设计，主要包括：
+
+# 项目1：清华ChatGLM-6B:
+https://github.com/THUDM/ChatGLM-6B
+
+# 项目2：清华JittorLLMs:
+https://github.com/Jittor/JittorLLMs
+
+# 项目3：Edge-GPT:
+https://github.com/acheong08/EdgeGPT
+
+# 项目4：ChuanhuChatGPT:
 https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# 项目5：ChatPaper:
+https://github.com/kaixindelele/ChatPaper
+
+# 更多：
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
 ```

check_proxy.py

@@ -0,0 +1,159 @@
+
+def check_proxy(proxies):
+    import requests
+    proxies_https = proxies['https'] if proxies is not None else '无'
+    try:
+        response = requests.get("https://ipapi.co/json/",
+                                proxies=proxies, timeout=4)
+        data = response.json()
+        print(f'查询代理的地理位置，返回的结果是{data}')
+        if 'country_name' in data:
+            country = data['country_name']
+            result = f"代理配置 {proxies_https}, 代理所在地：{country}"
+        elif 'error' in data:
+            result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
+        print(result)
+        return result
+    except:
+        result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
+        print(result)
+        return result
+
+
+def backup_and_download(current_version, remote_version):
+    """
+    一键更新协议：备份和下载
+    """
+    from toolbox import get_conf
+    import shutil
+    import os
+    import requests
+    import zipfile
+    os.makedirs(f'./history', exist_ok=True)
+    backup_dir = f'./history/backup-{current_version}/'
+    new_version_dir = f'./history/new-version-{remote_version}/'
+    if os.path.exists(new_version_dir):
+        return new_version_dir
+    os.makedirs(new_version_dir)
+    shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
+    proxies, = get_conf('proxies')
+    r = requests.get(
+        'https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
+    zip_file_path = backup_dir+'/master.zip'
+    with open(zip_file_path, 'wb+') as f:
+        f.write(r.content)
+    dst_path = new_version_dir
+    with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
+        for zip_info in zip_ref.infolist():
+            dst_file_path = os.path.join(dst_path, zip_info.filename)
+            if os.path.exists(dst_file_path):
+                os.remove(dst_file_path)
+            zip_ref.extract(zip_info, dst_path)
+    return new_version_dir
+
+
+def patch_and_restart(path):
+    """
+    一键更新协议：覆盖和重启
+    """
+    from distutils import dir_util
+    import shutil
+    import os
+    import sys
+    import time
+    import glob
+    from colorful import print亮黄, print亮绿, print亮红
+    # if not using config_private, move origin config.py as config_private.py
+    if not os.path.exists('config_private.py'):
+        print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
+              '另外您可以随时在history子文件夹下找回旧版的程序。')
+        shutil.copyfile('config.py', 'config_private.py')
+    path_new_version = glob.glob(path + '/*-master')[0]
+    dir_util.copy_tree(path_new_version, './')
+    print亮绿('代码已经更新，即将更新pip包依赖……')
+    for i in reversed(range(5)): time.sleep(1); print(i)
+    try: 
+        import subprocess
+        subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
+    except:
+        print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
+    print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
+    print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
+    print(' ------------------------------ -----------------------------------')
+    for i in reversed(range(8)): time.sleep(1); print(i)
+    os.execl(sys.executable, sys.executable, *sys.argv)
+
+
+def get_current_version():
+    import json
+    try:
+        with open('./version', 'r', encoding='utf8') as f:
+            current_version = json.loads(f.read())['version']
+    except:
+        current_version = ""
+    return current_version
+
+
+def auto_update(raise_error=False):
+    """
+    一键更新协议：查询版本和用户意见
+    """
+    try:
+        from toolbox import get_conf
+        import requests
+        import time
+        import json
+        proxies, = get_conf('proxies')
+        response = requests.get(
+            "https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
+        remote_json_data = json.loads(response.text)
+        remote_version = remote_json_data['version']
+        if remote_json_data["show_feature"]:
+            new_feature = "新功能：" + remote_json_data["new_feature"]
+        else:
+            new_feature = ""
+        with open('./version', 'r', encoding='utf8') as f:
+            current_version = f.read()
+            current_version = json.loads(current_version)['version']
+        if (remote_version - current_version) >= 0.01:
+            from colorful import print亮黄
+            print亮黄(
+                f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
+            print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
+            user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
+            if user_instruction in ['Y', 'y']:
+                path = backup_and_download(current_version, remote_version)
+                try:
+                    patch_and_restart(path)
+                except:
+                    msg = '更新失败。'
+                    if raise_error:
+                        from toolbox import trimmed_format_exc
+                        msg += trimmed_format_exc()
+                    print(msg)
+            else:
+                print('自动更新程序：已禁用')
+                return
+        else:
+            return
+    except:
+        msg = '自动更新程序：已禁用'
+        if raise_error:
+            from toolbox import trimmed_format_exc
+            msg += trimmed_format_exc()
+        print(msg)
+
+def warm_up_modules():
+    print('正在执行一些模块的预热...')
+    from request_llm.bridge_all import model_info
+    enc = model_info["gpt-3.5-turbo"]['tokenizer']
+    enc.encode("模块预热", disallowed_special=())
+    enc = model_info["gpt-4"]['tokenizer']
+    enc.encode("模块预热", disallowed_special=())
+
+if __name__ == '__main__':
+    import os
+    os.environ['no_proxy'] = '*'  # 避免代理网络产生意外污染
+    from toolbox import get_conf
+    proxies, = get_conf('proxies')
+    check_proxy(proxies)

colorful.py

@@ -0,0 +1,91 @@
+import platform
+from sys import stdout
+
+if platform.system()=="Linux":
+    pass
+else: 
+    from colorama import init
+    init()
+
+# Do you like the elegance of Chinese characters?
+def print红(*kw,**kargs):
+    print("\033[0;31m",*kw,"\033[0m",**kargs)
+def print绿(*kw,**kargs):
+    print("\033[0;32m",*kw,"\033[0m",**kargs)
+def print黄(*kw,**kargs):
+    print("\033[0;33m",*kw,"\033[0m",**kargs)
+def print蓝(*kw,**kargs):
+    print("\033[0;34m",*kw,"\033[0m",**kargs)
+def print紫(*kw,**kargs):
+    print("\033[0;35m",*kw,"\033[0m",**kargs)
+def print靛(*kw,**kargs):
+    print("\033[0;36m",*kw,"\033[0m",**kargs)
+
+def print亮红(*kw,**kargs):
+    print("\033[1;31m",*kw,"\033[0m",**kargs)
+def print亮绿(*kw,**kargs):
+    print("\033[1;32m",*kw,"\033[0m",**kargs)
+def print亮黄(*kw,**kargs):
+    print("\033[1;33m",*kw,"\033[0m",**kargs)
+def print亮蓝(*kw,**kargs):
+    print("\033[1;34m",*kw,"\033[0m",**kargs)
+def print亮紫(*kw,**kargs):
+    print("\033[1;35m",*kw,"\033[0m",**kargs)
+def print亮靛(*kw,**kargs):
+    print("\033[1;36m",*kw,"\033[0m",**kargs)
+
+
+
+def print亮红(*kw,**kargs):
+    print("\033[1;31m",*kw,"\033[0m",**kargs)
+def print亮绿(*kw,**kargs):
+    print("\033[1;32m",*kw,"\033[0m",**kargs)
+def print亮黄(*kw,**kargs):
+    print("\033[1;33m",*kw,"\033[0m",**kargs)
+def print亮蓝(*kw,**kargs):
+    print("\033[1;34m",*kw,"\033[0m",**kargs)
+def print亮紫(*kw,**kargs):
+    print("\033[1;35m",*kw,"\033[0m",**kargs)
+def print亮靛(*kw,**kargs):
+    print("\033[1;36m",*kw,"\033[0m",**kargs)
+    
+print_red = print红
+print_green = print绿
+print_yellow = print黄
+print_blue = print蓝
+print_purple = print紫
+print_indigo = print靛
+
+print_bold_red = print亮红
+print_bold_green = print亮绿
+print_bold_yellow = print亮黄
+print_bold_blue = print亮蓝
+print_bold_purple = print亮紫
+print_bold_indigo = print亮靛
+    
+if not stdout.isatty():
+    # redirection, avoid a fucked up log file
+    print红 = print
+    print绿 = print
+    print黄 = print
+    print蓝 = print
+    print紫 = print
+    print靛 = print
+    print亮红 = print
+    print亮绿 = print
+    print亮黄 = print
+    print亮蓝 = print
+    print亮紫 = print
+    print亮靛 = print
+    print_red = print
+    print_green = print
+    print_yellow = print
+    print_blue = print
+    print_purple = print
+    print_indigo = print
+    print_bold_red = print
+    print_bold_green = print
+    print_bold_yellow = print
+    print_bold_blue = print
+    print_bold_purple = print
+    print_bold_indigo = print

config.py

@@ -1,11 +1,83 @@
-# my_api_key = "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"
-API_KEY = "sk-此处填API秘钥"
-API_URL = "https://api.openai.com/v1/chat/completions"
+# [step 1]>> 例如： API_KEY = "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" （此key无效）
+API_KEY = "sk-此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey1,fkxxxx-api2dkey2"
 
+# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
 USE_PROXY = False
 if USE_PROXY:
-    proxies = { "http": "socks5h://localhost:11284", "https": "socks5h://localhost:11284", } 
-    print('网络代理状态：运行。')
+    # 填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
+    # 例如    "socks5h://localhost:11284"
+    # [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
+    # [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
+    # [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
+
+    # 代理网络的地址，打开你的*学*网软件查看代理的协议(socks5/http)、地址(localhost)和端口(11284)
+    proxies = {
+        #          [协议]://  [地址]  :[端口]
+        "http":  "socks5h://localhost:11284",  # 再例如  "http":  "http://127.0.0.1:7890",
+        "https": "socks5h://localhost:11284",  # 再例如  "https": "http://127.0.0.1:7890",
+    }
 else:
     proxies = None
-    print('网络代理状态：未配置。无代理状态下很可能无法访问。')
+
+# [step 3]>> 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
+# 一言以蔽之：免费用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
+DEFAULT_WORKER_NUM = 3
+
+
+# [step 4]>> 以下配置可以优化体验，但大部分场合下并不需要修改
+# 对话窗的高度
+CHATBOT_HEIGHT = 1115
+
+# 代码高亮
+CODE_HIGHLIGHT = True
+
+# 窗口布局
+LAYOUT = "LEFT-RIGHT"  # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
+DARK_MODE = True  # "LEFT-RIGHT"（左右布局） # "TOP-DOWN"（上下布局）
+
+# 发送请求到OpenAI后，等待多久判定为超时
+TIMEOUT_SECONDS = 30
+
+# 网页的端口, -1代表随机端口
+WEB_PORT = -1
+
+# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
+MAX_RETRY = 2
+
+# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 同时它必须被包含在AVAIL_LLM_MODELS切换列表中 )
+LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "newbing-free", "stack-claude"]
+# P.S. 其他可用的模型还包括 ["newbing-free", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+
+# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
+LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
+
+# 设置gradio的并行线程数（不需要修改）
+CONCURRENT_COUNT = 100
+
+# 加一个live2d装饰
+ADD_WAIFU = False
+
+# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
+# [("username", "password"), ("username2", "password2"), ...]
+AUTHENTICATION = []
+
+# 重新URL重新定向，实现更换API_URL的作用（常规情况下，不要修改!!）
+# （高危设置！通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
+# 格式 {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"} 
+# 例如 API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://ai.open.com/api/conversation"}
+API_URL_REDIRECT = {}
+
+# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
+CUSTOM_PATH = "/"
+
+# 如果需要使用newbing，把newbing的长长的cookie放到这里
+NEWBING_STYLE = "creative"  # ["creative", "balanced", "precise"]
+# 从现在起，如果您调用"newbing-free"模型，则无需填写NEWBING_COOKIES
+NEWBING_COOKIES = """
+your bing cookies here
+"""
+
+# 如果需要使用Slack Claude，使用教程详情见 request_llm/README.md
+SLACK_CLAUDE_BOT_ID = ''   
+SLACK_CLAUDE_USER_TOKEN = ''

core_functional.py

@@ -0,0 +1,78 @@
+# 'primary' 颜色对应 theme.py 中的 primary_hue
+# 'secondary' 颜色对应 theme.py 中的 neutral_hue
+# 'stop' 颜色对应 theme.py 中的 color_er
+# 默认按钮颜色是 secondary
+from toolbox import clear_line_break
+
+
+def get_core_functions():
+    return {
+        "英语学术润色": {
+            # 前言
+            "Prefix":   r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, " +
+                        r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. " +
+                        r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
+            # 后语
+            "Suffix":   r"",
+            "Color":    r"secondary",    # 按钮颜色
+        },
+        "中文学术润色": {
+            "Prefix":   r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
+                        r"同时分解长句，减少重复，并提供改进建议。请只提供文本的更正版本，避免包括解释。请编辑以下文本" + "\n\n",
+            "Suffix":   r"",
+        },
+        "查找语法错误": {
+            "Prefix":   r"Can you help me ensure that the grammar and the spelling is correct? " +
+                        r"Do not try to polish the text, if no mistake is found, tell me that this paragraph is good." +
+                        r"If you find grammar or spelling mistakes, please list mistakes you find in a two-column markdown table, " +
+                        r"put the original text the first column, " +
+                        r"put the corrected text in the second column and highlight the key words you fixed.""\n"
+                        r"Example:""\n"
+                        r"Paragraph: How is you? Do you knows what is it?""\n"
+                        r"| Original sentence | Corrected sentence |""\n"
+                        r"| :--- | :--- |""\n"
+                        r"| How **is** you? | How **are** you? |""\n"
+                        r"| Do you **knows** what **is** **it**? | Do you **know** what **it** **is** ? |""\n"
+                        r"Below is a paragraph from an academic paper. "
+                        r"You need to report all grammar and spelling mistakes as the example before."
+                        + "\n\n",
+            "Suffix":   r"",
+            "PreProcess": clear_line_break,    # 预处理：清除换行符
+        },
+        "中译英": {
+            "Prefix":   r"Please translate following sentence to English:" + "\n\n",
+            "Suffix":   r"",
+        },
+        "学术中英互译": {
+            "Prefix":   r"I want you to act as a scientific English-Chinese translator, " +
+                        r"I will provide you with some paragraphs in one language " +
+                        r"and your task is to accurately and academically translate the paragraphs only into the other language. " +
+                        r"Do not repeat the original provided paragraphs after translation. " +
+                        r"You should use artificial intelligence tools, " +
+                        r"such as natural language processing, and rhetorical knowledge " +
+                        r"and experience about effective writing techniques to reply. " +
+                        r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n",
+            "Suffix": "",
+            "Color": "secondary",
+        },
+        "英译中": {
+            "Prefix":   r"翻译成地道的中文：" + "\n\n",
+            "Suffix":   r"",
+        },
+        "找图片": {
+            "Prefix":   r"我需要你找一张网络图片。使用Unsplash API(https://source.unsplash.com/960x640/?<英语关键词>)获取图片URL，" +
+                        r"然后请使用Markdown格式封装，并且不要有反斜线，不要用代码块。现在，请按以下描述给我发送图片：" + "\n\n",
+            "Suffix":   r"",
+        },
+        "解释代码": {
+            "Prefix":   r"请解释以下代码：" + "\n```\n",
+            "Suffix":   "\n```\n",
+        },
+        "参考文献转Bib": {
+            "Prefix":   r"Here are some bibliography items, please transform them into bibtex style." +
+                        r"Note that, reference styles maybe more than one kind, you should transform each item correctly." +
+                        r"Items need to be transformed:",
+            "Suffix":   r"",
+            "Visible": False,
+        }
+    }

crazy_functional.py

@@ -0,0 +1,299 @@
+from toolbox import HotReload  # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
+
+
+def get_crazy_functions():
+    ###################### 第一组插件 ###########################
+    from crazy_functions.读文章写摘要 import 读文章写摘要
+    from crazy_functions.生成函数注释 import 批量生成函数注释
+    from crazy_functions.解析项目源代码 import 解析项目本身
+    from crazy_functions.解析项目源代码 import 解析一个Python项目
+    from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
+    from crazy_functions.解析项目源代码 import 解析一个C项目
+    from crazy_functions.解析项目源代码 import 解析一个Golang项目
+    from crazy_functions.解析项目源代码 import 解析一个Rust项目
+    from crazy_functions.解析项目源代码 import 解析一个Java项目
+    from crazy_functions.解析项目源代码 import 解析一个前端项目
+    from crazy_functions.高级功能函数模板 import 高阶功能模板函数
+    from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文
+    from crazy_functions.Latex全文润色 import Latex英文润色
+    from crazy_functions.询问多个大语言模型 import 同时问询
+    from crazy_functions.解析项目源代码 import 解析一个Lua项目
+    from crazy_functions.解析项目源代码 import 解析一个CSharp项目
+    from crazy_functions.总结word文档 import 总结word文档
+    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
+    from crazy_functions.对话历史存档 import 对话历史存档
+    from crazy_functions.对话历史存档 import 载入对话历史存档
+    from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
+    
+    from crazy_functions.批量Markdown翻译 import Markdown英译中
+    function_plugins = {
+        "解析整个Python项目": {
+            "Color": "stop",    # 按钮颜色
+            "Function": HotReload(解析一个Python项目)
+        },
+        "载入对话历史存档（先上传存档或输入路径）": {
+            "Color": "stop",
+            "AsButton":False,
+            "Function": HotReload(载入对话历史存档)
+        },
+        "删除所有本地对话历史记录（请谨慎操作）": {
+            "AsButton":False,
+            "Function": HotReload(删除所有本地对话历史记录)
+        },
+        "[测试功能] 解析Jupyter Notebook文件": {
+            "Color": "stop",
+            "AsButton":False,
+            "Function": HotReload(解析ipynb文件),
+            "AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
+            "ArgsReminder": "若输入0，则不解析notebook中的Markdown块", # 高级参数输入区的显示提示
+        },
+        "批量总结Word文档": {
+            "Color": "stop",
+            "Function": HotReload(总结word文档)
+        },
+        "解析整个C++项目头文件": {
+            "Color": "stop",    # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个C项目的头文件)
+        },
+        "解析整个C++项目（.cpp/.hpp/.c/.h）": {
+            "Color": "stop",    # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个C项目)
+        },
+        "解析整个Go项目": {
+            "Color": "stop",    # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个Golang项目)
+        },
+        "解析整个Rust项目": {
+            "Color": "stop",    # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个Rust项目)
+        },
+        "解析整个Java项目": {
+            "Color": "stop",  # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个Java项目)
+        },
+        "解析整个前端项目（js,ts,css等）": {
+            "Color": "stop",  # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个前端项目)
+        },
+        "解析整个Lua项目": {
+            "Color": "stop",    # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个Lua项目)
+        },
+        "解析整个CSharp项目": {
+            "Color": "stop",    # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析一个CSharp项目)
+        },
+        "读Tex论文写摘要": {
+            "Color": "stop",    # 按钮颜色
+            "Function": HotReload(读文章写摘要)
+        },
+        "Markdown/Readme英译中": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "Function": HotReload(Markdown英译中)
+        },
+        "批量生成函数注释": {
+            "Color": "stop",    # 按钮颜色
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(批量生成函数注释)
+        },
+        "保存当前的对话": {
+            "Function": HotReload(对话历史存档)
+        },
+        "[多线程Demo] 解析此项目本身（源码自译解）": {
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(解析项目本身)
+        },
+        "[老旧的Demo] 把本项目源代码切换成全英文": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(全项目切换英文)
+        },
+        "[插件demo] 历史上的今天": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Function": HotReload(高阶功能模板函数)
+        },
+
+    }
+    ###################### 第二组插件 ###########################
+    # [第二组插件]: 经过充分测试
+    from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
+    from crazy_functions.批量总结PDF文档pdfminer import 批量总结PDF文档pdfminer
+    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
+    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
+    from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
+    from crazy_functions.Latex全文润色 import Latex中文润色
+    from crazy_functions.Latex全文润色 import Latex英文纠错
+    from crazy_functions.Latex全文翻译 import Latex中译英
+    from crazy_functions.Latex全文翻译 import Latex英译中
+    from crazy_functions.批量Markdown翻译 import Markdown中译英
+
+    function_plugins.update({
+        "批量翻译PDF文档（多线程）": {
+            "Color": "stop",
+            "AsButton": True,  # 加入下拉菜单中
+            "Function": HotReload(批量翻译PDF文档)
+        },
+        "询问多个GPT模型": {
+            "Color": "stop",    # 按钮颜色
+            "Function": HotReload(同时问询)
+        },
+        "[测试功能] 批量总结PDF文档": {
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Function": HotReload(批量总结PDF文档)
+        },
+        "[测试功能] 批量总结PDF文档pdfminer": {
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(批量总结PDF文档pdfminer)
+        },
+        "谷歌学术检索助手（输入谷歌学术搜索页url）": {
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(谷歌检索小助手)
+        },
+
+        "理解PDF文档内容 （模仿ChatPDF）": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(理解PDF文档内容标准文件输入)
+        },
+        "英文Latex项目全文润色（输入路径或上传压缩包）": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(Latex英文润色)
+        },
+        "英文Latex项目全文纠错（输入路径或上传压缩包）": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(Latex英文纠错)
+        },
+        "[测试功能] 中文Latex项目全文润色（输入路径或上传压缩包）": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(Latex中文润色)
+        },
+        "Latex项目全文中译英（输入路径或上传压缩包）": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(Latex中译英)
+        },
+        "Latex项目全文英译中（输入路径或上传压缩包）": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(Latex英译中)
+        },
+        "批量Markdown中译英（输入路径或上传压缩包）": {
+            # HotReload 的意思是热更新，修改函数插件代码后，不需要重启程序，代码直接生效
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(Markdown中译英)
+        },
+
+
+    })
+
+    ###################### 第三组插件 ###########################
+    # [第三组插件]: 尚未充分测试的函数插件，放在这里
+    from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
+    function_plugins.update({
+        "一键下载arxiv论文并翻译摘要（先在input输入编号，如1812.10695）": {
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(下载arxiv论文并翻译摘要)
+        }
+    })
+
+    from crazy_functions.联网的ChatGPT import 连接网络回答问题
+    function_plugins.update({
+        "连接网络回答问题（先输入问题，再点击按钮，需要访问谷歌）": {
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(连接网络回答问题)
+        }
+    })
+
+    from crazy_functions.解析项目源代码 import 解析任意code项目
+    function_plugins.update({
+        "解析项目源代码（手动指定和筛选源代码文件类型）": {
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
+            "ArgsReminder": "输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: \"*.c, ^*.cpp, config.toml, ^*.toml\"", # 高级参数输入区的显示提示
+            "Function": HotReload(解析任意code项目)
+        },
+    })
+    from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
+    function_plugins.update({
+        "询问多个GPT模型（手动指定询问哪些模型）": {
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
+            "ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&api2d-gpt-4", # 高级参数输入区的显示提示
+            "Function": HotReload(同时问询_指定模型)
+        },
+    })
+    from crazy_functions.图片生成 import 图片生成
+    function_plugins.update({
+        "图片生成（先切换模型到openai或api2d）": {
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
+            "ArgsReminder": "在这里输入分辨率, 如256x256（默认）", # 高级参数输入区的显示提示
+            "Function": HotReload(图片生成)
+        },
+    })
+    from crazy_functions.总结音视频 import 总结音视频
+    function_plugins.update({
+        "批量总结音视频（输入路径或上传压缩包）": {
+            "Color": "stop",
+            "AsButton": False,
+            "AdvancedArgs": True,
+            "ArgsReminder": "调用openai api 使用whisper-1模型, 目前支持的格式:mp4, m4a, wav, mpga, mpeg, mp3。此处可以输入解析提示，例如：解析为简体中文（默认）。",
+            "Function": HotReload(总结音视频)
+        }
+    })
+    try:
+        from crazy_functions.数学动画生成manim import 动画生成
+        function_plugins.update({
+            "数学动画生成（Manim）": {
+                "Color": "stop",
+                "AsButton": False,
+                "Function": HotReload(动画生成)
+            }
+        })
+    except:
+        print('Load function plugin failed')
+
+    try:
+        from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
+        function_plugins.update({
+            "Markdown翻译（手动指定语言）": {
+                "Color": "stop",
+                "AsButton": False,
+                "AdvancedArgs": True,
+                "ArgsReminder": "请输入要翻译成哪种语言，默认为Chinese。",
+                "Function": HotReload(Markdown翻译指定语言)
+            }
+        })
+    except:
+        print('Load function plugin failed')
+
+    ###################### 第n组插件 ###########################
+    return function_plugins

crazy_functions/Latex全文润色.py

@@ -0,0 +1,240 @@
+from toolbox import update_ui, trimmed_format_exc
+from toolbox import CatchException, report_execption, write_results_to_file, zip_folder
+
+
+class PaperFileGroup():
+    def __init__(self):
+        self.file_paths = []
+        self.file_contents = []
+        self.sp_file_contents = []
+        self.sp_file_index = []
+        self.sp_file_tag = []
+
+        # count_token
+        from request_llm.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        self.get_token_num = get_token_num
+
+    def run_file_split(self, max_token_limit=1900):
+        """
+        将长文本分离开来
+        """
+        for index, file_content in enumerate(self.file_contents):
+            if self.get_token_num(file_content) < max_token_limit:
+                self.sp_file_contents.append(file_content)
+                self.sp_file_index.append(index)
+                self.sp_file_tag.append(self.file_paths[index])
+            else:
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
+                for j, segment in enumerate(segments):
+                    self.sp_file_contents.append(segment)
+                    self.sp_file_index.append(index)
+                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
+
+        print('Segmentation: done')
+    def merge_result(self):
+        self.file_result = ["" for _ in range(len(self.file_paths))]
+        for r, k in zip(self.sp_file_result, self.sp_file_index):
+            self.file_result[k] += r
+
+    def write_result(self):
+        manifest = []
+        for path, res in zip(self.file_paths, self.file_result):
+            with open(path + '.polish.tex', 'w', encoding='utf8') as f:
+                manifest.append(path + '.polish.tex')
+                f.write(res)
+        return manifest
+    
+    def zip_result(self):
+        import os, time
+        folder = os.path.dirname(self.file_paths[0])
+        t = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
+        zip_folder(folder, './gpt_log/', f'{t}-polished.zip')
+
+
+def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='polish'):
+    import time, os, re
+    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+
+
+    #  <-------- 读取Latex文件，删除其中的所有注释 ----------> 
+    pfg = PaperFileGroup()
+
+    for index, fp in enumerate(file_manifest):
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+            # 定义注释的正则表达式
+            comment_pattern = r'(?<!\\)%.*'
+            # 使用正则表达式查找注释，并替换为空字符串
+            clean_tex_content = re.sub(comment_pattern, '', file_content)
+            # 记录删除注释后的文本
+            pfg.file_paths.append(fp)
+            pfg.file_contents.append(clean_tex_content)
+
+    #  <-------- 拆分过长的latex文件 ----------> 
+    pfg.run_file_split(max_token_limit=1024)
+    n_split = len(pfg.sp_file_contents)
+
+
+    #  <-------- 多线程润色开始 ----------> 
+    if language == 'en':
+        if mode == 'polish':
+            inputs_array = ["Below is a section from an academic paper, polish this section to meet the academic standard, " + 
+                            "improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" + 
+                            f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        else:
+            inputs_array = [r"Below is a section from an academic paper, proofread this section." + 
+                            r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + 
+                            r"Answer me only with the revised text:" + 
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        inputs_show_user_array = [f"Polish {f}" for f in pfg.sp_file_tag]
+        sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
+    elif language == 'zh':
+        if mode == 'polish':
+            inputs_array = [f"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
+                            f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        else:
+            inputs_array = [f"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" + 
+                            f"\n\n{frag}" for frag in pfg.sp_file_contents] 
+        inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag]
+        sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)]
+
+
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array=inputs_array,
+        inputs_show_user_array=inputs_show_user_array,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history_array=[[""] for _ in range(n_split)],
+        sys_prompt_array=sys_prompt_array,
+        # max_workers=5,  # 并行任务数量限制，最多同时执行5个，其他的排队等待
+        scroller_max_len = 80
+    )
+
+    #  <-------- 文本碎片重组为完整的tex文件，整理结果为压缩包 ----------> 
+    try:
+        pfg.sp_file_result = []
+        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
+            pfg.sp_file_result.append(gpt_say)
+        pfg.merge_result()
+        pfg.write_result()
+        pfg.zip_result()
+    except:
+        print(trimmed_format_exc())
+
+    #  <-------- 整理结果，退出 ----------> 
+    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
+    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)
+    history = gpt_response_collection
+    chatbot.append((f"{fp}完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+@CatchException
+def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en')
+
+
+
+
+
+
+@CatchException
+def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Latex项目进行润色。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh')
+
+
+
+
+@CatchException
+def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Latex项目进行纠错。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='proofread')

crazy_functions/Latex全文翻译.py

@@ -0,0 +1,175 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+fast_debug = False
+
+class PaperFileGroup():
+    def __init__(self):
+        self.file_paths = []
+        self.file_contents = []
+        self.sp_file_contents = []
+        self.sp_file_index = []
+        self.sp_file_tag = []
+
+        # count_token
+        from request_llm.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        self.get_token_num = get_token_num
+
+    def run_file_split(self, max_token_limit=1900):
+        """
+        将长文本分离开来
+        """
+        for index, file_content in enumerate(self.file_contents):
+            if self.get_token_num(file_content) < max_token_limit:
+                self.sp_file_contents.append(file_content)
+                self.sp_file_index.append(index)
+                self.sp_file_tag.append(self.file_paths[index])
+            else:
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
+                for j, segment in enumerate(segments):
+                    self.sp_file_contents.append(segment)
+                    self.sp_file_index.append(index)
+                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
+
+        print('Segmentation: done')
+
+def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
+    import time, os, re
+    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+
+    #  <-------- 读取Latex文件，删除其中的所有注释 ----------> 
+    pfg = PaperFileGroup()
+
+    for index, fp in enumerate(file_manifest):
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+            # 定义注释的正则表达式
+            comment_pattern = r'(?<!\\)%.*'
+            # 使用正则表达式查找注释，并替换为空字符串
+            clean_tex_content = re.sub(comment_pattern, '', file_content)
+            # 记录删除注释后的文本
+            pfg.file_paths.append(fp)
+            pfg.file_contents.append(clean_tex_content)
+
+    #  <-------- 拆分过长的latex文件 ----------> 
+    pfg.run_file_split(max_token_limit=1024)
+    n_split = len(pfg.sp_file_contents)
+
+    #  <-------- 抽取摘要 ----------> 
+    # if language == 'en':
+    #     abs_extract_inputs = f"Please write an abstract for this paper"
+
+    # # 单线，获取文章meta信息
+    # paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
+    #     inputs=abs_extract_inputs,
+    #     inputs_show_user=f"正在抽取摘要信息。",
+    #     llm_kwargs=llm_kwargs,
+    #     chatbot=chatbot, history=[],
+    #     sys_prompt="Your job is to collect information from materials。",
+    # )
+
+    #  <-------- 多线程润色开始 ----------> 
+    if language == 'en->zh':
+        inputs_array = ["Below is a section from an English academic paper, translate it into Chinese, do not modify any latex command such as \section, \cite and equations:" + 
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+    elif language == 'zh->en':
+        inputs_array = [f"Below is a section from a Chinese academic paper, translate it into English, do not modify any latex command such as \section, \cite and equations:" + 
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array=inputs_array,
+        inputs_show_user_array=inputs_show_user_array,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history_array=[[""] for _ in range(n_split)],
+        sys_prompt_array=sys_prompt_array,
+        # max_workers=5,  # OpenAI所允许的最大并行过载
+        scroller_max_len = 80
+    )
+
+    #  <-------- 整理结果，退出 ----------> 
+    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
+    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)
+    history = gpt_response_collection
+    chatbot.append((f"{fp}完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+
+
+
+@CatchException
+def Latex英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
+
+
+
+
+
+@CatchException
+def Latex中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Latex项目进行翻译。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')

crazy_functions/crazy_functions_test.py

@@ -0,0 +1,135 @@
+"""
+这是什么？
+    这个文件用于函数插件的单元测试
+    运行方法 python crazy_functions/crazy_functions_test.py
+"""
+
+def validate_path():
+    import os, sys
+    dir_name = os.path.dirname(__file__)
+    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
+    os.chdir(root_dir_assume)
+    sys.path.append(root_dir_assume)
+    
+validate_path() # validate path so you can run from base directory
+from colorful import *
+from toolbox import get_conf, ChatBotWithCookies
+proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
+    get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
+
+llm_kwargs = {
+    'api_key': API_KEY,
+    'llm_model': LLM_MODEL,
+    'top_p':1.0, 
+    'max_length': None,
+    'temperature':1.0,
+}
+plugin_kwargs = { }
+chatbot = ChatBotWithCookies(llm_kwargs)
+history = []
+system_prompt = "Serve me as a writing and programming assistant."
+web_port = 1024
+
+
+def test_解析一个Python项目():
+    from crazy_functions.解析项目源代码 import 解析一个Python项目
+    txt = "crazy_functions/test_project/python/dqn"
+    for cookies, cb, hist, msg in 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_解析一个Cpp项目():
+    from crazy_functions.解析项目源代码 import 解析一个C项目
+    txt = "crazy_functions/test_project/cpp/cppipc"
+    for cookies, cb, hist, msg in 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_Latex英文润色():
+    from crazy_functions.Latex全文润色 import Latex英文润色
+    txt = "crazy_functions/test_project/latex/attention"
+    for cookies, cb, hist, msg in Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_Markdown中译英():
+    from crazy_functions.批量Markdown翻译 import Markdown中译英
+    txt = "README.md"
+    for cookies, cb, hist, msg in Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_批量翻译PDF文档():
+    from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
+    txt = "crazy_functions/test_project/pdf_and_word"
+    for cookies, cb, hist, msg in 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_谷歌检索小助手():
+    from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
+    txt = "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG="
+    for cookies, cb, hist, msg in 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_总结word文档():
+    from crazy_functions.总结word文档 import 总结word文档
+    txt = "crazy_functions/test_project/pdf_and_word"
+    for cookies, cb, hist, msg in 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_下载arxiv论文并翻译摘要():
+    from crazy_functions.下载arxiv论文翻译摘要 import 下载arxiv论文并翻译摘要
+    txt = "1812.10695"
+    for cookies, cb, hist, msg in 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+def test_联网回答问题():
+    from crazy_functions.联网的ChatGPT import 连接网络回答问题
+    # txt = "谁是应急食品？"
+    # >>        '根据以上搜索结果可以得知，应急食品是“原神”游戏中的角色派蒙的外号。'
+    # txt = "道路千万条，安全第一条。后面两句是？"
+    # >>        '行车不规范，亲人两行泪。'
+    # txt = "You should have gone for the head. What does that mean?"
+    # >>        The phrase "You should have gone for the head" is a quote from the Marvel movies, Avengers: Infinity War and Avengers: Endgame. It was spoken by the character Thanos in Infinity War and by Thor in Endgame.
+    txt = "AutoGPT是什么？"
+    for cookies, cb, hist, msg in 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): 
+        print("当前问答：", cb[-1][-1].replace("\n"," "))
+    for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])
+
+def test_解析ipynb文件():
+    from crazy_functions.解析JupyterNotebook import 解析ipynb文件
+    txt = "crazy_functions/test_samples"
+    for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+
+def test_数学动画生成manim():
+    from crazy_functions.数学动画生成manim import 动画生成
+    txt = "A ball split into 2, and then split into 4, and finally split into 8."
+    for cookies, cb, hist, msg in 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+        print(cb)
+
+
+
+def test_Markdown多语言():
+    from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
+    txt = "README.md"
+    history = []
+    for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]:
+        plugin_kwargs = {"advanced_arg": lang}
+        for cookies, cb, hist, msg in Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+            print(cb)
+
+
+
+# test_解析一个Python项目()
+# test_Latex英文润色()
+# test_Markdown中译英()
+# test_批量翻译PDF文档()
+# test_谷歌检索小助手()
+# test_总结word文档()
+# test_下载arxiv论文并翻译摘要()
+# test_解析一个Cpp项目()
+# test_联网回答问题()
+# test_解析ipynb文件()
+# test_数学动画生成manim()
+test_Markdown多语言()
+
+input("程序完成，回车退出。")
+print("退出。")

crazy_functions/crazy_utils.py

@@ -0,0 +1,608 @@
+from toolbox import update_ui, get_conf, trimmed_format_exc
+
+def input_clipping(inputs, history, max_token_limit):
+    import numpy as np
+    from request_llm.bridge_all import model_info
+    enc = model_info["gpt-3.5-turbo"]['tokenizer']
+    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+
+    mode = 'input-and-history'
+    # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
+    input_token_num = get_token_num(inputs)
+    if input_token_num < max_token_limit//2: 
+        mode = 'only-history'
+        max_token_limit = max_token_limit - input_token_num
+
+    everything = [inputs] if mode == 'input-and-history' else ['']
+    everything.extend(history)
+    n_token = get_token_num('\n'.join(everything))
+    everything_token = [get_token_num(e) for e in everything]
+    delta = max(everything_token) // 16 # 截断时的颗粒度
+        
+    while n_token > max_token_limit:
+        where = np.argmax(everything_token)
+        encoded = enc.encode(everything[where], disallowed_special=())
+        clipped_encoded = encoded[:len(encoded)-delta]
+        everything[where] = enc.decode(clipped_encoded)[:-1]    # -1 to remove the may-be illegal char
+        everything_token[where] = get_token_num(everything[where])
+        n_token = get_token_num('\n'.join(everything))
+
+    if mode == 'input-and-history':
+        inputs = everything[0]
+    else:
+        pass
+    history = everything[1:]
+    return inputs, history
+
+def request_gpt_model_in_new_thread_with_ui_alive(
+        inputs, inputs_show_user, llm_kwargs, 
+        chatbot, history, sys_prompt, refresh_interval=0.2,
+        handle_token_exceed=True, 
+        retry_times_at_unknown_error=2,
+        ):
+    """
+    Request GPT model，请求GPT模型同时维持用户界面活跃。
+
+    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
+        inputs (string): List of inputs （输入）
+        inputs_show_user (string): List of inputs to show user（展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
+        top_p (float): Top p value for sampling from model distribution （GPT参数，浮点数）
+        temperature (float): Temperature value for sampling from model distribution（GPT参数，浮点数）
+        chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
+        history (list): List of chat history （历史，对话历史列表）
+        sys_prompt (string): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
+        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
+        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
+        retry_times_at_unknown_error：失败时的重试次数
+
+    输出 Returns:
+        future: 输出，GPT返回的结果
+    """
+    import time
+    from concurrent.futures import ThreadPoolExecutor
+    from request_llm.bridge_all import predict_no_ui_long_connection
+    # 用户反馈
+    chatbot.append([inputs_show_user, ""])
+    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+    executor = ThreadPoolExecutor(max_workers=16)
+    mutable = ["", time.time(), ""]
+    def _req_gpt(inputs, history, sys_prompt):
+        retry_op = retry_times_at_unknown_error
+        exceeded_cnt = 0
+        while True:
+            # watchdog error
+            if len(mutable) >= 2 and (time.time()-mutable[1]) > 5: 
+                raise RuntimeError("检测到程序终止。")
+            try:
+                # 【第一种情况】：顺利完成
+                result = predict_no_ui_long_connection(
+                    inputs=inputs, llm_kwargs=llm_kwargs,
+                    history=history, sys_prompt=sys_prompt, observe_window=mutable)
+                return result
+            except ConnectionAbortedError as token_exceeded_error:
+                # 【第二种情况】：Token溢出
+                if handle_token_exceed:
+                    exceeded_cnt += 1
+                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
+                    from toolbox import get_reduce_token_percent
+                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
+                    MAX_TOKEN = 4096
+                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
+                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
+                    mutable[0] += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
+                    continue # 返回重试
+                else:
+                    # 【选择放弃】
+                    tb_str = '```\n' + trimmed_format_exc() + '```'
+                    mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                    return mutable[0] # 放弃
+            except:
+                # 【第三种情况】：其他错误：重试几次
+                tb_str = '```\n' + trimmed_format_exc() + '```'
+                print(tb_str)
+                mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                if retry_op > 0:
+                    retry_op -= 1
+                    mutable[0] += f"[Local Message] 重试中，请稍等 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}：\n\n"
+                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
+                        time.sleep(30)
+                    time.sleep(5)
+                    continue # 返回重试
+                else:
+                    time.sleep(5)
+                    return mutable[0] # 放弃
+
+    # 提交任务
+    future = executor.submit(_req_gpt, inputs, history, sys_prompt)
+    while True:
+        # yield一次以刷新前端页面
+        time.sleep(refresh_interval)
+        # “喂狗”（看门狗）
+        mutable[1] = time.time()
+        if future.done():
+            break
+        chatbot[-1] = [chatbot[-1][0], mutable[0]]
+        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+
+    final_result = future.result()
+    chatbot[-1] = [chatbot[-1][0], final_result]
+    yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
+    return final_result
+
+
+def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array, inputs_show_user_array, llm_kwargs, 
+        chatbot, history_array, sys_prompt_array, 
+        refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
+        handle_token_exceed=True, show_user_at_complete=False,
+        retry_times_at_unknown_error=2,
+        ):
+    """
+    Request GPT model using multiple threads with UI and high efficiency
+    请求GPT模型的[多线程]版。
+    具备以下功能：
+        实时在UI上反馈远程数据流
+        使用线程池，可调节线程池的大小避免openai的流量限制错误
+        处理中途中止的情况
+        网络等出问题时，会把traceback和已经接收的数据转入输出
+
+    输入参数 Args （以_array结尾的输入变量都是列表，列表长度为子任务的数量，执行时，会把列表拆解，放到每个子线程中分别执行）:
+        inputs_array (list): List of inputs （每个子任务的输入）
+        inputs_show_user_array (list): List of inputs to show user（每个子任务展现在报告中的输入，借助此参数，在汇总报告中隐藏啰嗦的真实输入，增强报告的可读性）
+        llm_kwargs: llm_kwargs参数
+        chatbot: chatbot （用户界面对话窗口句柄，用于数据流可视化）
+        history_array (list): List of chat history （历史对话输入，双层列表，第一层列表是子任务分解，第二层列表是对话历史）
+        sys_prompt_array (list): List of system prompts （系统输入，列表，用于输入给GPT的前提提示，比如你是翻译官怎样怎样）
+        refresh_interval (float, optional): Refresh interval for UI (default: 0.2) （刷新时间间隔频率，建议低于1，不可高于3，仅仅服务于视觉效果）
+        max_workers (int, optional): Maximum number of threads (default: see config.py) （最大线程数，如果子任务非常多，需要用此选项防止高频地请求openai导致错误）
+        scroller_max_len (int, optional): Maximum length for scroller (default: 30)（数据流的显示最后收到的多少个字符，仅仅服务于视觉效果）
+        handle_token_exceed (bool, optional): （是否在输入过长时，自动缩减文本）
+        handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
+        show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)
+        retry_times_at_unknown_error：子任务失败时的重试次数
+
+    输出 Returns:
+        list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）
+    """
+    import time, random
+    from concurrent.futures import ThreadPoolExecutor
+    from request_llm.bridge_all import predict_no_ui_long_connection
+    assert len(inputs_array) == len(history_array)
+    assert len(inputs_array) == len(sys_prompt_array)
+    if max_workers == -1: # 读取配置文件
+        try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
+        except: max_workers = 8
+        if max_workers <= 0: max_workers = 3
+    # 屏蔽掉 chatglm的多线程，可能会导致严重卡顿
+    if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
+        max_workers = 1
+        
+    executor = ThreadPoolExecutor(max_workers=max_workers)
+    n_frag = len(inputs_array)
+    # 用户反馈
+    chatbot.append(["请开始多线程操作。", ""])
+    yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+    # 跨线程传递
+    mutable = [["", time.time(), "等待中"] for _ in range(n_frag)]
+
+    # 子线程任务
+    def _req_gpt(index, inputs, history, sys_prompt):
+        gpt_say = ""
+        retry_op = retry_times_at_unknown_error
+        exceeded_cnt = 0
+        mutable[index][2] = "执行中"
+        while True:
+            # watchdog error
+            if len(mutable[index]) >= 2 and (time.time()-mutable[index][1]) > 5: 
+                raise RuntimeError("检测到程序终止。")
+            try:
+                # 【第一种情况】：顺利完成
+                # time.sleep(10); raise RuntimeError("测试")
+                gpt_say = predict_no_ui_long_connection(
+                    inputs=inputs, llm_kwargs=llm_kwargs, history=history, 
+                    sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
+                )
+                mutable[index][2] = "已成功"
+                return gpt_say
+            except ConnectionAbortedError as token_exceeded_error:
+                # 【第二种情况】：Token溢出，
+                if handle_token_exceed:
+                    exceeded_cnt += 1
+                    # 【选择处理】 尝试计算比例，尽可能多地保留文本
+                    from toolbox import get_reduce_token_percent
+                    p_ratio, n_exceed = get_reduce_token_percent(str(token_exceeded_error))
+                    MAX_TOKEN = 4096
+                    EXCEED_ALLO = 512 + 512 * exceeded_cnt
+                    inputs, history = input_clipping(inputs, history, max_token_limit=MAX_TOKEN-EXCEED_ALLO)
+                    gpt_say += f'[Local Message] 警告，文本过长将进行截断，Token溢出数：{n_exceed}。\n\n'
+                    mutable[index][2] = f"截断重试"
+                    continue # 返回重试
+                else:
+                    # 【选择放弃】
+                    tb_str = '```\n' + trimmed_format_exc() + '```'
+                    gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                    if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
+                    mutable[index][2] = "输入过长已放弃"
+                    return gpt_say # 放弃
+            except:
+                # 【第三种情况】：其他错误
+                tb_str = '```\n' + trimmed_format_exc() + '```'
+                print(tb_str)
+                gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
+                if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
+                if retry_op > 0: 
+                    retry_op -= 1
+                    wait = random.randint(5, 20)
+                    if ("Rate limit reached" in tb_str) or ("Too Many Requests" in tb_str):
+                        wait = wait * 3
+                        fail_info = "OpenAI绑定信用卡可解除频率限制 "
+                    else:
+                        fail_info = ""
+                    # 也许等待十几秒后，情况会好转
+                    for i in range(wait):
+                        mutable[index][2] = f"{fail_info}等待重试 {wait-i}"; time.sleep(1)
+                    # 开始重试
+                    mutable[index][2] = f"重试中 {retry_times_at_unknown_error-retry_op}/{retry_times_at_unknown_error}"
+                    continue # 返回重试
+                else:
+                    mutable[index][2] = "已失败"
+                    wait = 5
+                    time.sleep(5)
+                    return gpt_say # 放弃
+
+    # 异步任务开始
+    futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
+        range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
+    cnt = 0
+    while True:
+        # yield一次以刷新前端页面
+        time.sleep(refresh_interval)
+        cnt += 1
+        worker_done = [h.done() for h in futures]
+        # 更好的UI视觉效果
+        observe_win = []
+        # 每个线程都要“喂狗”（看门狗）
+        for thread_index, _ in enumerate(worker_done):
+            mutable[thread_index][1] = time.time()
+        # 在前端打印些好玩的东西
+        for thread_index, _ in enumerate(worker_done):
+            print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
+                replace('\n', '').replace('```', '...').replace(
+                    ' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
+            observe_win.append(print_something_really_funny)
+        # 在前端打印些好玩的东西
+        stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n' 
+                            if not done else f'`{mutable[thread_index][2]}`\n\n' 
+                            for thread_index, done, obs in zip(range(len(worker_done)), worker_done, observe_win)])
+        # 在前端打印些好玩的东西
+        chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
+        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+        if all(worker_done):
+            executor.shutdown()
+            break
+
+    # 异步任务结束
+    gpt_response_collection = []
+    for inputs_show_user, f in zip(inputs_show_user_array, futures):
+        gpt_res = f.result()
+        gpt_response_collection.extend([inputs_show_user, gpt_res])
+    
+    # 是否在结束时，在界面上显示结果
+    if show_user_at_complete:
+        for inputs_show_user, f in zip(inputs_show_user_array, futures):
+            gpt_res = f.result()
+            chatbot.append([inputs_show_user, gpt_res])
+            yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+            time.sleep(0.3)
+    return gpt_response_collection
+
+
+def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
+    def cut(txt_tocut, must_break_at_empty_line):  # 递归
+        if get_token_fn(txt_tocut) <= limit:
+            return [txt_tocut]
+        else:
+            lines = txt_tocut.split('\n')
+            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
+            estimated_line_cut = int(estimated_line_cut)
+            for cnt in reversed(range(estimated_line_cut)):
+                if must_break_at_empty_line:
+                    if lines[cnt] != "":
+                        continue
+                print(cnt)
+                prev = "\n".join(lines[:cnt])
+                post = "\n".join(lines[cnt:])
+                if get_token_fn(prev) < limit:
+                    break
+            if cnt == 0:
+                raise RuntimeError("存在一行极长的文本！")
+            # print(len(post))
+            # 列表递归接龙
+            result = [prev]
+            result.extend(cut(post, must_break_at_empty_line))
+            return result
+    try:
+        return cut(txt, must_break_at_empty_line=True)
+    except RuntimeError:
+        return cut(txt, must_break_at_empty_line=False)
+
+
+def force_breakdown(txt, limit, get_token_fn):
+    """
+    当无法用标点、空行分割时，我们用最暴力的方法切割
+    """
+    for i in reversed(range(len(txt))):
+        if get_token_fn(txt[:i]) < limit:
+            return txt[:i], txt[i:]
+    return "Tiktoken未知错误", "Tiktoken未知错误"
+
+def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
+    # 递归
+    def cut(txt_tocut, must_break_at_empty_line, break_anyway=False):  
+        if get_token_fn(txt_tocut) <= limit:
+            return [txt_tocut]
+        else:
+            lines = txt_tocut.split('\n')
+            estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
+            estimated_line_cut = int(estimated_line_cut)
+            cnt = 0
+            for cnt in reversed(range(estimated_line_cut)):
+                if must_break_at_empty_line:
+                    if lines[cnt] != "":
+                        continue
+                prev = "\n".join(lines[:cnt])
+                post = "\n".join(lines[cnt:])
+                if get_token_fn(prev) < limit:
+                    break
+            if cnt == 0:
+                if break_anyway:
+                    prev, post = force_breakdown(txt_tocut, limit, get_token_fn)
+                else:
+                    raise RuntimeError(f"存在一行极长的文本！{txt_tocut}")
+            # print(len(post))
+            # 列表递归接龙
+            result = [prev]
+            result.extend(cut(post, must_break_at_empty_line, break_anyway=break_anyway))
+            return result
+    try:
+        # 第1次尝试，将双空行（\n\n）作为切分点
+        return cut(txt, must_break_at_empty_line=True)
+    except RuntimeError:
+        try:
+            # 第2次尝试，将单空行（\n）作为切分点
+            return cut(txt, must_break_at_empty_line=False)
+        except RuntimeError:
+            try:
+                # 第3次尝试，将英文句号（.）作为切分点
+                res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在
+                return [r.replace('。\n', '.') for r in res]
+            except RuntimeError as e:
+                try:
+                    # 第4次尝试，将中文句号（。）作为切分点
+                    res = cut(txt.replace('。', '。。\n'), must_break_at_empty_line=False)
+                    return [r.replace('。。\n', '。') for r in res]
+                except RuntimeError as e:
+                    # 第5次尝试，没办法了，随便切一下敷衍吧
+                    return cut(txt, must_break_at_empty_line=False, break_anyway=True)
+
+
+
+def read_and_clean_pdf_text(fp):
+    """
+    这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好
+
+    **输入参数说明**
+    - `fp`：需要读取和清理文本的pdf文件路径
+
+    **输出参数说明**
+    - `meta_txt`：清理后的文本内容字符串
+    - `page_one_meta`：第一页清理后的文本内容列表
+
+    **函数功能**
+    读取pdf文件并清理其中的文本内容，清理规则包括：
+    - 提取所有块元的文本信息，并合并为一个字符串
+    - 去除短块（字符数小于100）并替换为回车符
+    - 清理多余的空行
+    - 合并小写字母开头的段落块并替换为空格
+    - 清除重复的换行
+    - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
+    """
+    import fitz, copy
+    import re
+    import numpy as np
+    from colorful import print亮黄, print亮绿
+    fc = 0  # Index 0 文本
+    fs = 1  # Index 1 字体
+    fb = 2  # Index 2 框框
+    REMOVE_FOOT_NOTE = True # 是否丢弃掉 不是正文的内容 （比正文字体小，如参考文献、脚注、图注等）
+    REMOVE_FOOT_FFSIZE_PERCENT = 0.95 # 小于正文的？时，判定为不是正文（有些文章的正文部分字体大小不是100%统一的，有肉眼不可见的小变化）
+    def primary_ffsize(l):
+        """
+        提取文本块主字体
+        """
+        fsize_statiscs = {}
+        for wtf in l['spans']:
+            if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
+            fsize_statiscs[wtf['size']] += len(wtf['text'])
+        return max(fsize_statiscs, key=fsize_statiscs.get)
+        
+    def ffsize_same(a,b):
+        """
+        提取字体大小是否近似相等
+        """
+        return abs((a-b)/max(a,b)) < 0.02
+
+    with fitz.open(fp) as doc:
+        meta_txt = []
+        meta_font = []
+
+        meta_line = []
+        meta_span = []
+        ############################## <第 1 步，搜集初始信息> ##################################
+        for index, page in enumerate(doc):
+            # file_content += page.get_text()
+            text_areas = page.get_text("dict")  # 获取页面上的文本信息
+            for t in text_areas['blocks']:
+                if 'lines' in t:
+                    pf = 998
+                    for l in t['lines']:
+                        txt_line = "".join([wtf['text'] for wtf in l['spans']])
+                        if len(txt_line) == 0: continue
+                        pf = primary_ffsize(l)
+                        meta_line.append([txt_line, pf, l['bbox'], l])
+                        for wtf in l['spans']: # for l in t['lines']:
+                            meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
+                    # meta_line.append(["NEW_BLOCK", pf])
+            # 块元提取                           for each word segment with in line                       for each line         cross-line words                          for each block
+            meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
+                '- ', '') for t in text_areas['blocks'] if 'lines' in t])
+            meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
+                             for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
+            if index == 0:
+                page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
+                    '- ', '') for t in text_areas['blocks'] if 'lines' in t]
+                
+        ############################## <第 2 步，获取正文主字体> ##################################
+        fsize_statiscs = {}
+        for span in meta_span:
+            if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
+            fsize_statiscs[span[1]] += span[2]
+        main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
+        if REMOVE_FOOT_NOTE:
+            give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
+
+        ############################## <第 3 步，切分和重新整合> ##################################
+        mega_sec = []
+        sec = []
+        for index, line in enumerate(meta_line):
+            if index == 0: 
+                sec.append(line[fc])
+                continue
+            if REMOVE_FOOT_NOTE:
+                if meta_line[index][fs] <= give_up_fize_threshold:
+                    continue
+            if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
+                # 尝试识别段落
+                if meta_line[index][fc].endswith('.') and\
+                    (meta_line[index-1][fc] != 'NEW_BLOCK') and \
+                    (meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
+                    sec[-1] += line[fc]
+                    sec[-1] += "\n\n"
+                else:
+                    sec[-1] += " "
+                    sec[-1] += line[fc]
+            else:
+                if (index+1 < len(meta_line)) and \
+                    meta_line[index][fs] > main_fsize:
+                    # 单行 + 字体大
+                    mega_sec.append(copy.deepcopy(sec))
+                    sec = []
+                    sec.append("# " + line[fc])
+                else:
+                    # 尝试识别section
+                    if meta_line[index-1][fs] > meta_line[index][fs]:
+                        sec.append("\n" + line[fc])
+                    else:
+                        sec.append(line[fc])
+        mega_sec.append(copy.deepcopy(sec))
+
+        finals = []
+        for ms in mega_sec:
+            final = " ".join(ms)
+            final = final.replace('- ', ' ')
+            finals.append(final)
+        meta_txt = finals
+
+        ############################## <第 4 步，乱七八糟的后处理> ##################################
+        def 把字符太少的块清除为回车(meta_txt):
+            for index, block_txt in enumerate(meta_txt):
+                if len(block_txt) < 100:
+                    meta_txt[index] = '\n'
+            return meta_txt
+        meta_txt = 把字符太少的块清除为回车(meta_txt)
+
+        def 清理多余的空行(meta_txt):
+            for index in reversed(range(1, len(meta_txt))):
+                if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
+                    meta_txt.pop(index)
+            return meta_txt
+        meta_txt = 清理多余的空行(meta_txt)
+
+        def 合并小写开头的段落块(meta_txt):
+            def starts_with_lowercase_word(s):
+                pattern = r"^[a-z]+"
+                match = re.match(pattern, s)
+                if match:
+                    return True
+                else:
+                    return False
+            for _ in range(100):
+                for index, block_txt in enumerate(meta_txt):
+                    if starts_with_lowercase_word(block_txt):
+                        if meta_txt[index-1] != '\n':
+                            meta_txt[index-1] += ' '
+                        else:
+                            meta_txt[index-1] = ''
+                        meta_txt[index-1] += meta_txt[index]
+                        meta_txt[index] = '\n'
+            return meta_txt
+        meta_txt = 合并小写开头的段落块(meta_txt)
+        meta_txt = 清理多余的空行(meta_txt)
+
+        meta_txt = '\n'.join(meta_txt)
+        # 清除重复的换行
+        for _ in range(5):
+            meta_txt = meta_txt.replace('\n\n', '\n')
+
+        # 换行 -> 双换行
+        meta_txt = meta_txt.replace('\n', '\n\n')
+
+        ############################## <第 5 步，展示分割效果> ##################################
+        # for f in finals:
+        #    print亮黄(f)
+        #    print亮绿('***************************')
+
+    return meta_txt, page_one_meta
+
+
+def get_files_from_everything(txt, type): # type='.md'
+    """
+    这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
+    下面是对每个参数和返回值的说明：
+    参数 
+    - txt: 路径或网址，表示要搜索的文件或者文件夹路径或网络上的文件。 
+    - type: 字符串，表示要搜索的文件类型。默认是.md。
+    返回值 
+    - success: 布尔值，表示函数是否成功执行。 
+    - file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。 
+    - project_folder: 字符串，表示文件所在的文件夹路径。如果是网络上的文件，就是临时文件夹的路径。
+    该函数详细注释已添加，请确认是否满足您的需要。
+    """
+    import glob, os
+
+    success = True
+    if txt.startswith('http'):
+        # 网络的远程文件
+        import requests
+        from toolbox import get_conf
+        proxies, = get_conf('proxies')
+        r = requests.get(txt, proxies=proxies)
+        with open('./gpt_log/temp'+type, 'wb+') as f: f.write(r.content)
+        project_folder = './gpt_log/'
+        file_manifest = ['./gpt_log/temp'+type]
+    elif txt.endswith(type):
+        # 直接给定文件
+        file_manifest = [txt]
+        project_folder = os.path.dirname(txt)
+    elif os.path.exists(txt):
+        # 本地路径，递归搜索
+        project_folder = txt
+        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
+        if len(file_manifest) == 0:
+            success = False
+    else:
+        project_folder = None
+        file_manifest = []
+        success = False
+
+    return success, file_manifest, project_folder

crazy_functions/test_project/cpp/cppipc/buffer.cpp

@@ -0,0 +1,87 @@
+#include "libipc/buffer.h"
+#include "libipc/utility/pimpl.h"
+
+#include <cstring>
+
+namespace ipc {
+
+bool operator==(buffer const & b1, buffer const & b2) {
+    return (b1.size() == b2.size()) && (std::memcmp(b1.data(), b2.data(), b1.size()) == 0);
+}
+
+bool operator!=(buffer const & b1, buffer const & b2) {
+    return !(b1 == b2);
+}
+
+class buffer::buffer_ : public pimpl<buffer_> {
+public:
+    void*       p_;
+    std::size_t s_;
+    void*       a_;
+    buffer::destructor_t d_;
+
+    buffer_(void* p, std::size_t s, buffer::destructor_t d, void* a)
+        : p_(p), s_(s), a_(a), d_(d) {
+    }
+
+    ~buffer_() {
+        if (d_ == nullptr) return;
+        d_((a_ == nullptr) ? p_ : a_, s_);
+    }
+};
+
+buffer::buffer()
+    : buffer(nullptr, 0, nullptr, nullptr) {
+}
+
+buffer::buffer(void* p, std::size_t s, destructor_t d)
+    : p_(p_->make(p, s, d, nullptr)) {
+}
+
+buffer::buffer(void* p, std::size_t s, destructor_t d, void* additional)
+    : p_(p_->make(p, s, d, additional)) {
+}
+
+buffer::buffer(void* p, std::size_t s)
+    : buffer(p, s, nullptr) {
+}
+
+buffer::buffer(char const & c)
+    : buffer(const_cast<char*>(&c), 1) {
+}
+
+buffer::buffer(buffer&& rhs)
+    : buffer() {
+    swap(rhs);
+}
+
+buffer::~buffer() {
+    p_->clear();
+}
+
+void buffer::swap(buffer& rhs) {
+    std::swap(p_, rhs.p_);
+}
+
+buffer& buffer::operator=(buffer rhs) {
+    swap(rhs);
+    return *this;
+}
+
+bool buffer::empty() const noexcept {
+    return (impl(p_)->p_ == nullptr) || (impl(p_)->s_ == 0);
+}
+
+void* buffer::data() noexcept {
+    return impl(p_)->p_;
+}
+
+void const * buffer::data() const noexcept {
+    return impl(p_)->p_;
+}
+
+std::size_t buffer::size() const noexcept {
+    return impl(p_)->s_;
+}
+
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/ipc.cpp

@@ -0,0 +1,701 @@
+
+#include <type_traits>
+#include <cstring>
+#include <algorithm>
+#include <utility>          // std::pair, std::move, std::forward
+#include <atomic>
+#include <type_traits>      // aligned_storage_t
+#include <string>
+#include <vector>
+#include <array>
+#include <cassert>
+
+#include "libipc/ipc.h"
+#include "libipc/def.h"
+#include "libipc/shm.h"
+#include "libipc/pool_alloc.h"
+#include "libipc/queue.h"
+#include "libipc/policy.h"
+#include "libipc/rw_lock.h"
+#include "libipc/waiter.h"
+
+#include "libipc/utility/log.h"
+#include "libipc/utility/id_pool.h"
+#include "libipc/utility/scope_guard.h"
+#include "libipc/utility/utility.h"
+
+#include "libipc/memory/resource.h"
+#include "libipc/platform/detail.h"
+#include "libipc/circ/elem_array.h"
+
+namespace {
+
+using msg_id_t = std::uint32_t;
+using acc_t    = std::atomic<msg_id_t>;
+
+template <std::size_t DataSize, std::size_t AlignSize>
+struct msg_t;
+
+template <std::size_t AlignSize>
+struct msg_t<0, AlignSize> {
+    msg_id_t     cc_id_;
+    msg_id_t     id_;
+    std::int32_t remain_;
+    bool         storage_;
+};
+
+template <std::size_t DataSize, std::size_t AlignSize>
+struct msg_t : msg_t<0, AlignSize> {
+    std::aligned_storage_t<DataSize, AlignSize> data_ {};
+
+    msg_t() = default;
+    msg_t(msg_id_t cc_id, msg_id_t id, std::int32_t remain, void const * data, std::size_t size)
+        : msg_t<0, AlignSize> {cc_id, id, remain, (data == nullptr) || (size == 0)} {
+        if (this->storage_) {
+            if (data != nullptr) {
+                // copy storage-id
+                *reinterpret_cast<ipc::storage_id_t*>(&data_) =
+                     *static_cast<ipc::storage_id_t const *>(data);
+            }
+        }
+        else std::memcpy(&data_, data, size);
+    }
+};
+
+template <typename T>
+ipc::buff_t make_cache(T& data, std::size_t size) {
+    auto ptr = ipc::mem::alloc(size);
+    std::memcpy(ptr, &data, (ipc::detail::min)(sizeof(data), size));
+    return { ptr, size, ipc::mem::free };
+}
+
+struct cache_t {
+    std::size_t fill_;
+    ipc::buff_t buff_;
+
+    cache_t(std::size_t f, ipc::buff_t && b)
+        : fill_(f), buff_(std::move(b))
+    {}
+
+    void append(void const * data, std::size_t size) {
+        if (fill_ >= buff_.size() || data == nullptr || size == 0) return;
+        auto new_fill = (ipc::detail::min)(fill_ + size, buff_.size());
+        std::memcpy(static_cast<ipc::byte_t*>(buff_.data()) + fill_, data, new_fill - fill_);
+        fill_ = new_fill;
+    }
+};
+
+auto cc_acc() {
+    static ipc::shm::handle acc_h("__CA_CONN__", sizeof(acc_t));
+    return static_cast<acc_t*>(acc_h.get());
+}
+
+IPC_CONSTEXPR_ std::size_t align_chunk_size(std::size_t size) noexcept {
+    return (((size - 1) / ipc::large_msg_align) + 1) * ipc::large_msg_align;
+}
+
+IPC_CONSTEXPR_ std::size_t calc_chunk_size(std::size_t size) noexcept {
+    return ipc::make_align(alignof(std::max_align_t), align_chunk_size(
+           ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>)) + size));
+}
+
+struct chunk_t {
+    std::atomic<ipc::circ::cc_t> &conns() noexcept {
+        return *reinterpret_cast<std::atomic<ipc::circ::cc_t> *>(this);
+    }
+
+    void *data() noexcept {
+        return reinterpret_cast<ipc::byte_t *>(this)
+             + ipc::make_align(alignof(std::max_align_t), sizeof(std::atomic<ipc::circ::cc_t>));
+    }
+};
+
+struct chunk_info_t {
+    ipc::id_pool<> pool_;
+    ipc::spin_lock lock_;
+
+    IPC_CONSTEXPR_ static std::size_t chunks_mem_size(std::size_t chunk_size) noexcept {
+        return ipc::id_pool<>::max_count * chunk_size;
+    }
+
+    ipc::byte_t *chunks_mem() noexcept {
+        return reinterpret_cast<ipc::byte_t *>(this + 1);
+    }
+
+    chunk_t *at(std::size_t chunk_size, ipc::storage_id_t id) noexcept {
+        if (id < 0) return nullptr;
+        return reinterpret_cast<chunk_t *>(chunks_mem() + (chunk_size * id));
+    }
+};
+
+auto& chunk_storages() {
+    class chunk_handle_t {
+        ipc::shm::handle handle_;
+
+    public:
+        chunk_info_t *get_info(std::size_t chunk_size) {
+            if (!handle_.valid() &&
+                !handle_.acquire( ("__CHUNK_INFO__" + ipc::to_string(chunk_size)).c_str(), 
+                                  sizeof(chunk_info_t) + chunk_info_t::chunks_mem_size(chunk_size) )) {
+                ipc::error("[chunk_storages] chunk_shm.id_info_.acquire failed: chunk_size = %zd\n", chunk_size);
+                return nullptr;
+            }
+            auto info = static_cast<chunk_info_t*>(handle_.get());
+            if (info == nullptr) {
+                ipc::error("[chunk_storages] chunk_shm.id_info_.get failed: chunk_size = %zd\n", chunk_size);
+                return nullptr;
+            }
+            return info;
+        }
+    };
+    static ipc::map<std::size_t, chunk_handle_t> chunk_hs;
+    return chunk_hs;
+}
+
+chunk_info_t *chunk_storage_info(std::size_t chunk_size) {
+    auto &storages = chunk_storages();
+    std::decay_t<decltype(storages)>::iterator it;
+    {
+        static ipc::rw_lock lock;
+        IPC_UNUSED_ std::shared_lock<ipc::rw_lock> guard {lock};
+        if ((it = storages.find(chunk_size)) == storages.end()) {
+            using chunk_handle_t = std::decay_t<decltype(storages)>::value_type::second_type;
+            guard.unlock();
+            IPC_UNUSED_ std::lock_guard<ipc::rw_lock> guard {lock};
+            it = storages.emplace(chunk_size, chunk_handle_t{}).first;
+        }
+    }
+    return it->second.get_info(chunk_size);
+}
+
+std::pair<ipc::storage_id_t, void*> acquire_storage(std::size_t size, ipc::circ::cc_t conns) {
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return {};
+
+    info->lock_.lock();
+    info->pool_.prepare();
+    // got an unique id
+    auto id = info->pool_.acquire();
+    info->lock_.unlock();
+
+    auto chunk = info->at(chunk_size, id);
+    if (chunk == nullptr) return {};
+    chunk->conns().store(conns, std::memory_order_relaxed);
+    return { id, chunk->data() };
+}
+
+void *find_storage(ipc::storage_id_t id, std::size_t size) {
+    if (id < 0) {
+        ipc::error("[find_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
+        return nullptr;
+    }
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return nullptr;
+    return info->at(chunk_size, id)->data();
+}
+
+void release_storage(ipc::storage_id_t id, std::size_t size) {
+    if (id < 0) {
+        ipc::error("[release_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
+        return;
+    }
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return;
+    info->lock_.lock();
+    info->pool_.release(id);
+    info->lock_.unlock();
+}
+
+template <ipc::relat Rp, ipc::relat Rc>
+bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::unicast>, 
+            std::atomic<ipc::circ::cc_t> &/*conns*/, ipc::circ::cc_t /*curr_conns*/, ipc::circ::cc_t /*conn_id*/) noexcept {
+    return true;
+}
+
+template <ipc::relat Rp, ipc::relat Rc>
+bool sub_rc(ipc::wr<Rp, Rc, ipc::trans::broadcast>, 
+            std::atomic<ipc::circ::cc_t> &conns, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) noexcept {
+    auto last_conns = curr_conns & ~conn_id;
+    for (unsigned k = 0;;) {
+        auto chunk_conns  = conns.load(std::memory_order_acquire);
+        if (conns.compare_exchange_weak(chunk_conns, chunk_conns & last_conns, std::memory_order_release)) {
+            return (chunk_conns & last_conns) == 0;
+        }
+        ipc::yield(k);
+    }
+}
+
+template <typename Flag>
+void recycle_storage(ipc::storage_id_t id, std::size_t size, ipc::circ::cc_t curr_conns, ipc::circ::cc_t conn_id) {
+    if (id < 0) {
+        ipc::error("[recycle_storage] id is invalid: id = %ld, size = %zd\n", (long)id, size);
+        return;
+    }
+    std::size_t chunk_size = calc_chunk_size(size);
+    auto info = chunk_storage_info(chunk_size);
+    if (info == nullptr) return;
+
+    auto chunk = info->at(chunk_size, id);
+    if (chunk == nullptr) return;
+
+    if (!sub_rc(Flag{}, chunk->conns(), curr_conns, conn_id)) {
+        return;
+    }
+    info->lock_.lock();
+    info->pool_.release(id);
+    info->lock_.unlock();
+}
+
+template <typename MsgT>
+bool clear_message(void* p) {
+    auto msg = static_cast<MsgT*>(p);
+    if (msg->storage_) {
+        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg->remain_;
+        if (r_size <= 0) {
+            ipc::error("[clear_message] invalid msg size: %d\n", (int)r_size);
+            return true;
+        }
+        release_storage(
+            *reinterpret_cast<ipc::storage_id_t*>(&msg->data_),
+            static_cast<std::size_t>(r_size));
+    }
+    return true;
+}
+
+struct conn_info_head {
+
+    ipc::string name_;
+    msg_id_t    cc_id_; // connection-info id
+    ipc::detail::waiter cc_waiter_, wt_waiter_, rd_waiter_;
+    ipc::shm::handle acc_h_;
+
+    conn_info_head(char const * name)
+        : name_     {name}
+        , cc_id_    {(cc_acc() == nullptr) ? 0 : cc_acc()->fetch_add(1, std::memory_order_relaxed)}
+        , cc_waiter_{("__CC_CONN__" + name_).c_str()}
+        , wt_waiter_{("__WT_CONN__" + name_).c_str()}
+        , rd_waiter_{("__RD_CONN__" + name_).c_str()}
+        , acc_h_    {("__AC_CONN__" + name_).c_str(), sizeof(acc_t)} {
+    }
+
+    void quit_waiting() {
+        cc_waiter_.quit_waiting();
+        wt_waiter_.quit_waiting();
+        rd_waiter_.quit_waiting();
+    }
+
+    auto acc() {
+        return static_cast<acc_t*>(acc_h_.get());
+    }
+
+    auto& recv_cache() {
+        thread_local ipc::unordered_map<msg_id_t, cache_t> tls;
+        return tls;
+    }
+};
+
+template <typename W, typename F>
+bool wait_for(W& waiter, F&& pred, std::uint64_t tm) {
+    if (tm == 0) return !pred();
+    for (unsigned k = 0; pred();) {
+        bool ret = true;
+        ipc::sleep(k, [&k, &ret, &waiter, &pred, tm] {
+            ret = waiter.wait_if(std::forward<F>(pred), tm);
+            k   = 0;
+        });
+        if (!ret) return false; // timeout or fail
+        if (k == 0) break; // k has been reset
+    }
+    return true;
+}
+
+template <typename Policy,
+          std::size_t DataSize  = ipc::data_length,
+          std::size_t AlignSize = (ipc::detail::min)(DataSize, alignof(std::max_align_t))>
+struct queue_generator {
+
+    using queue_t = ipc::queue<msg_t<DataSize, AlignSize>, Policy>;
+
+    struct conn_info_t : conn_info_head {
+        queue_t que_;
+
+        conn_info_t(char const * name)
+            : conn_info_head{name}
+            , que_{("__QU_CONN__" +
+                    ipc::to_string(DataSize) + "__" +
+                    ipc::to_string(AlignSize) + "__" + name).c_str()} {
+        }
+
+        void disconnect_receiver() {
+            bool dis = que_.disconnect();
+            this->quit_waiting();
+            if (dis) {
+                this->recv_cache().clear();
+            }
+        }
+    };
+};
+
+template <typename Policy>
+struct detail_impl {
+
+using policy_t    = Policy;
+using flag_t      = typename policy_t::flag_t;
+using queue_t     = typename queue_generator<policy_t>::queue_t;
+using conn_info_t = typename queue_generator<policy_t>::conn_info_t;
+
+constexpr static conn_info_t* info_of(ipc::handle_t h) noexcept {
+    return static_cast<conn_info_t*>(h);
+}
+
+constexpr static queue_t* queue_of(ipc::handle_t h) noexcept {
+    return (info_of(h) == nullptr) ? nullptr : &(info_of(h)->que_);
+}
+
+/* API implementations */
+
+static void disconnect(ipc::handle_t h) {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        return;
+    }
+    que->shut_sending();
+    assert(info_of(h) != nullptr);
+    info_of(h)->disconnect_receiver();
+}
+
+static bool reconnect(ipc::handle_t * ph, bool start_to_recv) {
+    assert(ph != nullptr);
+    assert(*ph != nullptr);
+    auto que = queue_of(*ph);
+    if (que == nullptr) {
+        return false;
+    }
+    if (start_to_recv) {
+        que->shut_sending();
+        if (que->connect()) { // wouldn't connect twice
+            info_of(*ph)->cc_waiter_.broadcast();
+            return true;
+        }
+        return false;
+    }
+    // start_to_recv == false
+    if (que->connected()) {
+        info_of(*ph)->disconnect_receiver();
+    }
+    return que->ready_sending();
+}
+
+static bool connect(ipc::handle_t * ph, char const * name, bool start_to_recv) {
+    assert(ph != nullptr);
+    if (*ph == nullptr) {
+        *ph = ipc::mem::alloc<conn_info_t>(name);
+    }
+    return reconnect(ph, start_to_recv);
+}
+
+static void destroy(ipc::handle_t h) {
+    disconnect(h);
+    ipc::mem::free(info_of(h));
+}
+
+static std::size_t recv_count(ipc::handle_t h) noexcept {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        return ipc::invalid_value;
+    }
+    return que->conn_count();
+}
+
+static bool wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        return false;
+    }
+    return wait_for(info_of(h)->cc_waiter_, [que, r_count] {
+        return que->conn_count() < r_count;
+    }, tm);
+}
+
+template <typename F>
+static bool send(F&& gen_push, ipc::handle_t h, void const * data, std::size_t size) {
+    if (data == nullptr || size == 0) {
+        ipc::error("fail: send(%p, %zd)\n", data, size);
+        return false;
+    }
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        ipc::error("fail: send, queue_of(h) == nullptr\n");
+        return false;
+    }
+    if (que->elems() == nullptr) {
+        ipc::error("fail: send, queue_of(h)->elems() == nullptr\n");
+        return false;
+    }
+    if (!que->ready_sending()) {
+        ipc::error("fail: send, que->ready_sending() == false\n");
+        return false;
+    }
+    ipc::circ::cc_t conns = que->elems()->connections(std::memory_order_relaxed);
+    if (conns == 0) {
+        ipc::error("fail: send, there is no receiver on this connection.\n");
+        return false;
+    }
+    // calc a new message id
+    auto acc = info_of(h)->acc();
+    if (acc == nullptr) {
+        ipc::error("fail: send, info_of(h)->acc() == nullptr\n");
+        return false;
+    }
+    auto msg_id   = acc->fetch_add(1, std::memory_order_relaxed);
+    auto try_push = std::forward<F>(gen_push)(info_of(h), que, msg_id);
+    if (size > ipc::large_msg_limit) {
+        auto   dat = acquire_storage(size, conns);
+        void * buf = dat.second;
+        if (buf != nullptr) {
+            std::memcpy(buf, data, size);
+            return try_push(static_cast<std::int32_t>(size) - 
+                            static_cast<std::int32_t>(ipc::data_length), &(dat.first), 0);
+        }
+        // try using message fragment
+        //ipc::log("fail: shm::handle for big message. msg_id: %zd, size: %zd\n", msg_id, size);
+    }
+    // push message fragment
+    std::int32_t offset = 0;
+    for (std::int32_t i = 0; i < static_cast<std::int32_t>(size / ipc::data_length); ++i, offset += ipc::data_length) {
+        if (!try_push(static_cast<std::int32_t>(size) - offset - static_cast<std::int32_t>(ipc::data_length),
+                      static_cast<ipc::byte_t const *>(data) + offset, ipc::data_length)) {
+            return false;
+        }
+    }
+    // if remain > 0, this is the last message fragment
+    std::int32_t remain = static_cast<std::int32_t>(size) - offset;
+    if (remain > 0) {
+        if (!try_push(remain - static_cast<std::int32_t>(ipc::data_length),
+                      static_cast<ipc::byte_t const *>(data) + offset, 
+                      static_cast<std::size_t>(remain))) {
+            return false;
+        }
+    }
+    return true;
+}
+
+static bool send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return send([tm](auto info, auto que, auto msg_id) {
+        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
+            if (!wait_for(info->wt_waiter_, [&] {
+                    return !que->push(
+                        [](void*) { return true; },
+                        info->cc_id_, msg_id, remain, data, size);
+                }, tm)) {
+                ipc::log("force_push: msg_id = %zd, remain = %d, size = %zd\n", msg_id, remain, size);
+                if (!que->force_push(
+                        clear_message<typename queue_t::value_t>,
+                        info->cc_id_, msg_id, remain, data, size)) {
+                    return false;
+                }
+            }
+            info->rd_waiter_.broadcast();
+            return true;
+        };
+    }, h, data, size);
+}
+
+static bool try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return send([tm](auto info, auto que, auto msg_id) {
+        return [tm, info, que, msg_id](std::int32_t remain, void const * data, std::size_t size) {
+            if (!wait_for(info->wt_waiter_, [&] {
+                    return !que->push(
+                        [](void*) { return true; },
+                        info->cc_id_, msg_id, remain, data, size);
+                }, tm)) {
+                return false;
+            }
+            info->rd_waiter_.broadcast();
+            return true;
+        };
+    }, h, data, size);
+}
+
+static ipc::buff_t recv(ipc::handle_t h, std::uint64_t tm) {
+    auto que = queue_of(h);
+    if (que == nullptr) {
+        ipc::error("fail: recv, queue_of(h) == nullptr\n");
+        return {};
+    }
+    if (!que->connected()) {
+        // hasn't connected yet, just return.
+        return {};
+    }
+    auto& rc = info_of(h)->recv_cache();
+    for (;;) {
+        // pop a new message
+        typename queue_t::value_t msg;
+        if (!wait_for(info_of(h)->rd_waiter_, [que, &msg] {
+                return !que->pop(msg);
+            }, tm)) {
+            // pop failed, just return.
+            return {};
+        }
+        info_of(h)->wt_waiter_.broadcast();
+        if ((info_of(h)->acc() != nullptr) && (msg.cc_id_ == info_of(h)->cc_id_)) {
+            continue; // ignore message to self
+        }
+        // msg.remain_ may minus & abs(msg.remain_) < data_length
+        std::int32_t r_size = static_cast<std::int32_t>(ipc::data_length) + msg.remain_;
+        if (r_size <= 0) {
+            ipc::error("fail: recv, r_size = %d\n", (int)r_size);
+            return {};
+        }
+        std::size_t msg_size = static_cast<std::size_t>(r_size);
+        // large message
+        if (msg.storage_) {
+            ipc::storage_id_t buf_id = *reinterpret_cast<ipc::storage_id_t*>(&msg.data_);
+            void* buf = find_storage(buf_id, msg_size);
+            if (buf != nullptr) {
+                struct recycle_t {
+                    ipc::storage_id_t storage_id;
+                    ipc::circ::cc_t   curr_conns;
+                    ipc::circ::cc_t   conn_id;
+                } *r_info = ipc::mem::alloc<recycle_t>(recycle_t{
+                    buf_id, que->elems()->connections(std::memory_order_relaxed), que->connected_id()
+                });
+                if (r_info == nullptr) {
+                    ipc::log("fail: ipc::mem::alloc<recycle_t>.\n");
+                    return ipc::buff_t{buf, msg_size}; // no recycle
+                } else {
+                    return ipc::buff_t{buf, msg_size, [](void* p_info, std::size_t size) {
+                        auto r_info = static_cast<recycle_t *>(p_info);
+                        IPC_UNUSED_ auto finally = ipc::guard([r_info] {
+                            ipc::mem::free(r_info);
+                        });
+                        recycle_storage<flag_t>(r_info->storage_id, size, r_info->curr_conns, r_info->conn_id);
+                    }, r_info};
+                }
+            } else {
+                ipc::log("fail: shm::handle for large message. msg_id: %zd, buf_id: %zd, size: %zd\n", msg.id_, buf_id, msg_size);
+                continue;
+            }
+        }
+        // find cache with msg.id_
+        auto cac_it = rc.find(msg.id_);
+        if (cac_it == rc.end()) {
+            if (msg_size <= ipc::data_length) {
+                return make_cache(msg.data_, msg_size);
+            }
+            // gc
+            if (rc.size() > 1024) {
+                std::vector<msg_id_t> need_del;
+                for (auto const & pair : rc) {
+                    auto cmp = std::minmax(msg.id_, pair.first);
+                    if (cmp.second - cmp.first > 8192) {
+                        need_del.push_back(pair.first);
+                    }
+                }
+                for (auto id : need_del) rc.erase(id);
+            }
+            // cache the first message fragment
+            rc.emplace(msg.id_, cache_t { ipc::data_length, make_cache(msg.data_, msg_size) });
+        }
+        // has cached before this message
+        else {
+            auto& cac = cac_it->second;
+            // this is the last message fragment
+            if (msg.remain_ <= 0) {
+                cac.append(&(msg.data_), msg_size);
+                // finish this message, erase it from cache
+                auto buff = std::move(cac.buff_);
+                rc.erase(cac_it);
+                return buff;
+            }
+            // there are remain datas after this message
+            cac.append(&(msg.data_), ipc::data_length);
+        }
+    }
+}
+
+static ipc::buff_t try_recv(ipc::handle_t h) {
+    return recv(h, 0);
+}
+
+}; // detail_impl<Policy>
+
+template <typename Flag>
+using policy_t = ipc::policy::choose<ipc::circ::elem_array, Flag>;
+
+} // internal-linkage
+
+namespace ipc {
+
+template <typename Flag>
+ipc::handle_t chan_impl<Flag>::inited() {
+    ipc::detail::waiter::init();
+    return nullptr;
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::connect(ipc::handle_t * ph, char const * name, unsigned mode) {
+    return detail_impl<policy_t<Flag>>::connect(ph, name, mode & receiver);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::reconnect(ipc::handle_t * ph, unsigned mode) {
+    return detail_impl<policy_t<Flag>>::reconnect(ph, mode & receiver);
+}
+
+template <typename Flag>
+void chan_impl<Flag>::disconnect(ipc::handle_t h) {
+    detail_impl<policy_t<Flag>>::disconnect(h);
+}
+
+template <typename Flag>
+void chan_impl<Flag>::destroy(ipc::handle_t h) {
+    detail_impl<policy_t<Flag>>::destroy(h);
+}
+
+template <typename Flag>
+char const * chan_impl<Flag>::name(ipc::handle_t h) {
+    auto info = detail_impl<policy_t<Flag>>::info_of(h);
+    return (info == nullptr) ? nullptr : info->name_.c_str();
+}
+
+template <typename Flag>
+std::size_t chan_impl<Flag>::recv_count(ipc::handle_t h) {
+    return detail_impl<policy_t<Flag>>::recv_count(h);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::wait_for_recv(ipc::handle_t h, std::size_t r_count, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::wait_for_recv(h, r_count, tm);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::send(h, data, size, tm);
+}
+
+template <typename Flag>
+buff_t chan_impl<Flag>::recv(ipc::handle_t h, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::recv(h, tm);
+}
+
+template <typename Flag>
+bool chan_impl<Flag>::try_send(ipc::handle_t h, void const * data, std::size_t size, std::uint64_t tm) {
+    return detail_impl<policy_t<Flag>>::try_send(h, data, size, tm);
+}
+
+template <typename Flag>
+buff_t chan_impl<Flag>::try_recv(ipc::handle_t h) {
+    return detail_impl<policy_t<Flag>>::try_recv(h);
+}
+
+template struct chan_impl<ipc::wr<relat::single, relat::single, trans::unicast  >>;
+// template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::unicast  >>; // TBD
+// template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::unicast  >>; // TBD
+template struct chan_impl<ipc::wr<relat::single, relat::multi , trans::broadcast>>;
+template struct chan_impl<ipc::wr<relat::multi , relat::multi , trans::broadcast>>;
+
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/policy.h

@@ -0,0 +1,25 @@
+#pragma once
+
+#include <type_traits>
+
+#include "libipc/def.h"
+#include "libipc/prod_cons.h"
+
+#include "libipc/circ/elem_array.h"
+
+namespace ipc {
+namespace policy {
+
+template <template <typename, std::size_t...> class Elems, typename Flag>
+struct choose;
+
+template <typename Flag>
+struct choose<circ::elem_array, Flag> {
+    using flag_t = Flag;
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    using elems_t = circ::elem_array<ipc::prod_cons_impl<flag_t>, DataSize, AlignSize>;
+};
+
+} // namespace policy
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/pool_alloc.cpp

@@ -0,0 +1,17 @@
+#include "libipc/pool_alloc.h"
+
+#include "libipc/memory/resource.h"
+
+namespace ipc {
+namespace mem {
+
+void* pool_alloc::alloc(std::size_t size) {
+    return async_pool_alloc::alloc(size);
+}
+
+void pool_alloc::free(void* p, std::size_t size) {
+    async_pool_alloc::free(p, size);
+}
+
+} // namespace mem
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/prod_cons.h

@@ -0,0 +1,433 @@
+#pragma once
+
+#include <atomic>
+#include <utility>
+#include <cstring>
+#include <type_traits>
+#include <cstdint>
+
+#include "libipc/def.h"
+
+#include "libipc/platform/detail.h"
+#include "libipc/circ/elem_def.h"
+#include "libipc/utility/log.h"
+#include "libipc/utility/utility.h"
+
+namespace ipc {
+
+////////////////////////////////////////////////////////////////
+/// producer-consumer implementation
+////////////////////////////////////////////////////////////////
+
+template <typename Flag>
+struct prod_cons_impl;
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
+    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
+
+    constexpr circ::u2_t cursor() const noexcept {
+        return 0;
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* /*wrapper*/, F&& f, E* elems) {
+        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
+        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
+            return false; // full
+        }
+        std::forward<F>(f)(&(elems[cur_wt].data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    /**
+     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
+     * So we could just disconnect all connections of receiver, and return false.
+    */
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
+        return false;
+    }
+
+    template <typename W, typename F, typename R, typename E>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
+        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
+        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
+            return false; // empty
+        }
+        std::forward<F>(f)(&(elems[cur_rd].data_));
+        std::forward<R>(out)(true);
+        rd_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
+     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(1);
+        return false;
+    }
+
+    template <typename W, typename F, typename R, 
+              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
+        byte_t buff[DS];
+        for (unsigned k = 0;;) {
+            auto cur_rd = rd_.load(std::memory_order_relaxed);
+            if (circ::index_of(cur_rd) ==
+                circ::index_of(wt_.load(std::memory_order_acquire))) {
+                return false; // empty
+            }
+            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
+            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
+                std::forward<F>(f)(buff);
+                std::forward<R>(out)(true);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
+     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
+
+    using flag_t = std::uint64_t;
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
+
+    template <typename W, typename F, typename E>
+    bool push(W* /*wrapper*/, F&& f, E* elems) {
+        circ::u2_t cur_ct, nxt_ct;
+        for (unsigned k = 0;;) {
+            cur_ct = ct_.load(std::memory_order_relaxed);
+            if (circ::index_of(nxt_ct = cur_ct + 1) ==
+                circ::index_of(rd_.load(std::memory_order_acquire))) {
+                return false; // full
+            }
+            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        auto* el = elems + circ::index_of(cur_ct);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        while (1) {
+            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
+            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
+                return true;
+            }
+            if ((~cac_ct) != cur_ct) {
+                return true;
+            }
+            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
+                return true;
+            }
+            wt_.store(nxt_ct, std::memory_order_release);
+            cur_ct = nxt_ct;
+            nxt_ct = cur_ct + 1;
+            el = elems + circ::index_of(cur_ct);
+        }
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(1);
+        return false;
+    }
+
+    template <typename W, typename F, typename R, 
+              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
+        byte_t buff[DS];
+        for (unsigned k = 0;;) {
+            auto cur_rd = rd_.load(std::memory_order_relaxed);
+            auto cur_wt = wt_.load(std::memory_order_acquire);
+            auto id_rd  = circ::index_of(cur_rd);
+            auto id_wt  = circ::index_of(cur_wt);
+            if (id_rd == id_wt) {
+                auto* el = elems + id_wt;
+                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
+                if ((~cac_ct) != cur_wt) {
+                    return false; // empty
+                }
+                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
+                    wt_.store(cur_wt + 1, std::memory_order_release);
+                }
+                k = 0;
+            }
+            else {
+                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
+                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
+                    std::forward<F>(f)(buff);
+                    std::forward<R>(out)(true);
+                    return true;
+                }
+                ipc::yield(k);
+            }
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
+
+    using rc_t = std::uint64_t;
+
+    enum : rc_t {
+        ep_mask = 0x00000000ffffffffull,
+        ep_incr = 0x0000000100000000ull
+    };
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<rc_t> rc_ { 0 }; // read-counter
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
+    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
+
+    circ::u2_t cursor() const noexcept {
+        return wt_.load(std::memory_order_acquire);
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & ep_mask;
+            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
+                return false; // has not finished yet
+            }
+            // consider rem_cc to be 0 here
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        std::forward<F>(f)(&(el->data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        epoch_ += ep_incr;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & ep_mask;
+            if (cc & rem_cc) {
+                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
+                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
+                if (cc == 0) return false; // no reader
+            }
+            // just compare & exchange
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        std::forward<F>(f)(&(el->data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename R, typename E>
+    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
+        if (cur == cursor()) return false; // acquire
+        auto* el = elems + circ::index_of(cur++);
+        std::forward<F>(f)(&(el->data_));
+        for (unsigned k = 0;;) {
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            if ((cur_rc & ep_mask) == 0) {
+                std::forward<R>(out)(true);
+                return true;
+            }
+            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
+            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
+                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
+
+    using rc_t   = std::uint64_t;
+    using flag_t = std::uint64_t;
+
+    enum : rc_t {
+        rc_mask = 0x00000000ffffffffull,
+        ep_mask = 0x00ffffffffffffffull,
+        ep_incr = 0x0100000000000000ull,
+        ic_mask = 0xff000000ffffffffull,
+        ic_incr = 0x0000000100000000ull
+    };
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<rc_t  > rc_   { 0 }; // read-counter
+        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
+    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
+
+    circ::u2_t cursor() const noexcept {
+        return ct_.load(std::memory_order_acquire);
+    }
+
+    constexpr static rc_t inc_rc(rc_t rc) noexcept {
+        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
+    }
+
+    constexpr static rc_t inc_mask(rc_t rc) noexcept {
+        return inc_rc(rc) & ~rc_mask;
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        circ::u2_t cur_ct;
+        rc_t epoch = epoch_.load(std::memory_order_acquire);
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
+            circ::cc_t rem_cc = cur_rc & rc_mask;
+            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
+                return false; // has not finished yet
+            }
+            else if (!rem_cc) {
+                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
+                if ((cur_fl != cur_ct) && cur_fl) {
+                    return false; // full
+                }
+            }
+            // consider rem_cc to be 0 here
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
+                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        // only one thread/process would touch here at one time
+        ct_.store(cur_ct + 1, std::memory_order_release);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        circ::u2_t cur_ct;
+        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & rc_mask;
+            if (cc & rem_cc) {
+                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
+                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
+                if (cc == 0) return false; // no reader
+            }
+            // just compare & exchange
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
+                if (epoch == epoch_.load(std::memory_order_acquire)) {
+                    break;
+                }
+                else if (push(wrapper, std::forward<F>(f), elems)) {
+                    return true;
+                }
+                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
+            }
+            ipc::yield(k);
+        }
+        // only one thread/process would touch here at one time
+        ct_.store(cur_ct + 1, std::memory_order_release);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename R, typename E, std::size_t N>
+    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
+        auto* el = elems + circ::index_of(cur);
+        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
+        if (cur_fl != ~static_cast<flag_t>(cur)) {
+            return false; // empty
+        }
+        ++cur;
+        std::forward<F>(f)(&(el->data_));
+        for (unsigned k = 0;;) {
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            if ((cur_rc & rc_mask) == 0) {
+                std::forward<R>(out)(true);
+                el->f_ct_.store(cur + N - 1, std::memory_order_release);
+                return true;
+            }
+            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
+            bool last_one = false;
+            if ((last_one = (nxt_rc & rc_mask) == 0)) {
+                el->f_ct_.store(cur + N - 1, std::memory_order_release);
+            }
+            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
+                std::forward<R>(out)(last_one);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/queue.h

@@ -0,0 +1,216 @@
+#pragma once
+
+#include <type_traits>
+#include <new>
+#include <utility>  // [[since C++14]]: std::exchange
+#include <algorithm>
+#include <atomic>
+#include <tuple>
+#include <thread>
+#include <chrono>
+#include <string>
+#include <cassert>  // assert
+
+#include "libipc/def.h"
+#include "libipc/shm.h"
+#include "libipc/rw_lock.h"
+
+#include "libipc/utility/log.h"
+#include "libipc/platform/detail.h"
+#include "libipc/circ/elem_def.h"
+
+namespace ipc {
+namespace detail {
+
+class queue_conn {
+protected:
+    circ::cc_t connected_ = 0;
+    shm::handle elems_h_;
+
+    template <typename Elems>
+    Elems* open(char const * name) {
+        if (name == nullptr || name[0] == '\0') {
+            ipc::error("fail open waiter: name is empty!\n");
+            return nullptr;
+        }
+        if (!elems_h_.acquire(name, sizeof(Elems))) {
+            return nullptr;
+        }
+        auto elems = static_cast<Elems*>(elems_h_.get());
+        if (elems == nullptr) {
+            ipc::error("fail acquire elems: %s\n", name);
+            return nullptr;
+        }
+        elems->init();
+        return elems;
+    }
+
+    void close() {
+        elems_h_.release();
+    }
+
+public:
+    queue_conn() = default;
+    queue_conn(const queue_conn&) = delete;
+    queue_conn& operator=(const queue_conn&) = delete;
+
+    bool connected() const noexcept {
+        return connected_ != 0;
+    }
+
+    circ::cc_t connected_id() const noexcept {
+        return connected_;
+    }
+
+    template <typename Elems>
+    auto connect(Elems* elems) noexcept
+                         /*needs 'optional' here*/
+     -> std::tuple<bool, bool, decltype(std::declval<Elems>().cursor())> {
+        if (elems == nullptr) return {};
+        // if it's already connected, just return
+        if (connected()) return {connected(), false, 0};
+        connected_ = elems->connect_receiver();
+        return {connected(), true, elems->cursor()};
+    }
+
+    template <typename Elems>
+    bool disconnect(Elems* elems) noexcept {
+        if (elems == nullptr) return false;
+        // if it's already disconnected, just return false
+        if (!connected()) return false;
+        elems->disconnect_receiver(std::exchange(connected_, 0));
+        return true;
+    }
+};
+
+template <typename Elems>
+class queue_base : public queue_conn {
+    using base_t = queue_conn;
+
+public:
+    using elems_t  = Elems;
+    using policy_t = typename elems_t::policy_t;
+
+protected:
+    elems_t * elems_ = nullptr;
+    decltype(std::declval<elems_t>().cursor()) cursor_ = 0;
+    bool sender_flag_ = false;
+
+public:
+    using base_t::base_t;
+
+    queue_base() = default;
+
+    explicit queue_base(char const * name)
+        : queue_base{} {
+        elems_ = open<elems_t>(name);
+    }
+
+    explicit queue_base(elems_t * elems) noexcept
+        : queue_base{} {
+        assert(elems != nullptr);
+        elems_ = elems;
+    }
+
+    /* not virtual */ ~queue_base() {
+        base_t::close();
+    }
+
+    elems_t       * elems()       noexcept { return elems_; }
+    elems_t const * elems() const noexcept { return elems_; }
+
+    bool ready_sending() noexcept {
+        if (elems_ == nullptr) return false;
+        return sender_flag_ || (sender_flag_ = elems_->connect_sender());
+    }
+
+    void shut_sending() noexcept {
+        if (elems_ == nullptr) return;
+        if (!sender_flag_) return;
+        elems_->disconnect_sender();
+    }
+
+    bool connect() noexcept {
+        auto tp = base_t::connect(elems_);
+        if (std::get<0>(tp) && std::get<1>(tp)) {
+            cursor_ = std::get<2>(tp);
+            return true;
+        }
+        return std::get<0>(tp);
+    }
+
+    bool disconnect() noexcept {
+        return base_t::disconnect(elems_);
+    }
+
+    std::size_t conn_count() const noexcept {
+        return (elems_ == nullptr) ? static_cast<std::size_t>(invalid_value) : elems_->conn_count();
+    }
+
+    bool valid() const noexcept {
+        return elems_ != nullptr;
+    }
+
+    bool empty() const noexcept {
+        return !valid() || (cursor_ == elems_->cursor());
+    }
+
+    template <typename T, typename F, typename... P>
+    bool push(F&& prep, P&&... params) {
+        if (elems_ == nullptr) return false;
+        return elems_->push(this, [&](void* p) {
+            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
+        });
+    }
+
+    template <typename T, typename F, typename... P>
+    bool force_push(F&& prep, P&&... params) {
+        if (elems_ == nullptr) return false;
+        return elems_->force_push(this, [&](void* p) {
+            if (prep(p)) ::new (p) T(std::forward<P>(params)...);
+        });
+    }
+
+    template <typename T, typename F>
+    bool pop(T& item, F&& out) {
+        if (elems_ == nullptr) {
+            return false;
+        }
+        return elems_->pop(this, &(this->cursor_), [&item](void* p) {
+            ::new (&item) T(std::move(*static_cast<T*>(p)));
+        }, std::forward<F>(out));
+    }
+};
+
+} // namespace detail
+
+template <typename T, typename Policy>
+class queue final : public detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>> {
+    using base_t = detail::queue_base<typename Policy::template elems_t<sizeof(T), alignof(T)>>;
+
+public:
+    using value_t = T;
+
+    using base_t::base_t;
+
+    template <typename... P>
+    bool push(P&&... params) {
+        return base_t::template push<T>(std::forward<P>(params)...);
+    }
+
+    template <typename... P>
+    bool force_push(P&&... params) {
+        return base_t::template force_push<T>(std::forward<P>(params)...);
+    }
+
+    bool pop(T& item) {
+        return base_t::pop(item, [](bool) {});
+    }
+
+    template <typename F>
+    bool pop(T& item, F&& out) {
+        return base_t::pop(item, std::forward<F>(out));
+    }
+};
+
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/shm.cpp

@@ -0,0 +1,103 @@
+
+#include <string>
+#include <utility>
+
+#include "libipc/shm.h"
+
+#include "libipc/utility/pimpl.h"
+#include "libipc/memory/resource.h"
+
+namespace ipc {
+namespace shm {
+
+class handle::handle_ : public pimpl<handle_> {
+public:
+    shm::id_t id_ = nullptr;
+    void*     m_  = nullptr;
+
+    ipc::string n_;
+    std::size_t s_ = 0;
+};
+
+handle::handle()
+    : p_(p_->make()) {
+}
+
+handle::handle(char const * name, std::size_t size, unsigned mode)
+    : handle() {
+    acquire(name, size, mode);
+}
+
+handle::handle(handle&& rhs)
+    : handle() {
+    swap(rhs);
+}
+
+handle::~handle() {
+    release();
+    p_->clear();
+}
+
+void handle::swap(handle& rhs) {
+    std::swap(p_, rhs.p_);
+}
+
+handle& handle::operator=(handle rhs) {
+    swap(rhs);
+    return *this;
+}
+
+bool handle::valid() const noexcept {
+    return impl(p_)->m_ != nullptr;
+}
+
+std::size_t handle::size() const noexcept {
+    return impl(p_)->s_;
+}
+
+char const * handle::name() const noexcept {
+    return impl(p_)->n_.c_str();
+}
+
+std::int32_t handle::ref() const noexcept {
+    return shm::get_ref(impl(p_)->id_);
+}
+
+void handle::sub_ref() noexcept {
+    shm::sub_ref(impl(p_)->id_);
+}
+
+bool handle::acquire(char const * name, std::size_t size, unsigned mode) {
+    release();
+    impl(p_)->id_ = shm::acquire((impl(p_)->n_ = name).c_str(), size, mode);
+    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
+    return valid();
+}
+
+std::int32_t handle::release() {
+    if (impl(p_)->id_ == nullptr) return -1;
+    return shm::release(detach());
+}
+
+void* handle::get() const {
+    return impl(p_)->m_;
+}
+
+void handle::attach(id_t id) {
+    if (id == nullptr) return;
+    release();
+    impl(p_)->id_ = id;
+    impl(p_)->m_  = shm::get_mem(impl(p_)->id_, &(impl(p_)->s_));
+}
+
+id_t handle::detach() {
+    auto old = impl(p_)->id_;
+    impl(p_)->id_ = nullptr;
+    impl(p_)->m_  = nullptr;
+    impl(p_)->s_  = 0;
+    impl(p_)->n_.clear();
+    return old;
+}
+
+} // namespace shm
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/waiter.h

@@ -0,0 +1,83 @@
+#pragma once
+
+#include <utility>
+#include <string>
+#include <mutex>
+#include <atomic>
+
+#include "libipc/def.h"
+#include "libipc/mutex.h"
+#include "libipc/condition.h"
+#include "libipc/platform/detail.h"
+
+namespace ipc {
+namespace detail {
+
+class waiter {
+    ipc::sync::condition cond_;
+    ipc::sync::mutex     lock_;
+    std::atomic<bool>    quit_ {false};
+
+public:
+    static void init();
+
+    waiter() = default;
+    waiter(char const *name) {
+        open(name);
+    }
+
+    ~waiter() {
+        close();
+    }
+
+    bool valid() const noexcept {
+        return cond_.valid() && lock_.valid();
+    }
+
+    bool open(char const *name) noexcept {
+        quit_.store(false, std::memory_order_relaxed);
+        if (!cond_.open((std::string{"_waiter_cond_"} + name).c_str())) {
+            return false;
+        }
+        if (!lock_.open((std::string{"_waiter_lock_"} + name).c_str())) {
+            cond_.close();
+            return false;
+        }
+        return valid();
+    }
+
+    void close() noexcept {
+        cond_.close();
+        lock_.close();
+    }
+
+    template <typename F>
+    bool wait_if(F &&pred, std::uint64_t tm = ipc::invalid_value) noexcept {
+        IPC_UNUSED_ std::lock_guard<ipc::sync::mutex> guard {lock_};
+        while ([this, &pred] {
+                    return !quit_.load(std::memory_order_relaxed)
+                        && std::forward<F>(pred)();
+                }()) {
+            if (!cond_.wait(lock_, tm)) return false;
+        }
+        return true;
+    }
+
+    bool notify() noexcept {
+        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier
+        return cond_.notify(lock_);
+    }
+
+    bool broadcast() noexcept {
+        std::lock_guard<ipc::sync::mutex>{lock_}; // barrier
+        return cond_.broadcast(lock_);
+    }
+
+    bool quit_waiting() {
+        quit_.store(true, std::memory_order_release);
+        return broadcast();
+    }
+};
+
+} // namespace detail
+} // namespace ipc

crazy_functions/test_project/cpp/cppipc/来源

@@ -0,0 +1,3 @@
+https://github.com/mutouyun/cpp-ipc
+
+A high-performance inter-process communication library using shared memory on Linux/Windows.

crazy_functions/test_project/cpp/libJPG/jpgd.h

@@ -0,0 +1,316 @@
+// jpgd.h - C++ class for JPEG decompression.
+// Public domain, Rich Geldreich <richgel99@gmail.com>
+#ifndef JPEG_DECODER_H
+#define JPEG_DECODER_H
+
+#include <stdlib.h>
+#include <stdio.h>
+#include <setjmp.h>
+
+namespace jpgd
+{
+  typedef unsigned char  uint8;
+  typedef   signed short int16;
+  typedef unsigned short uint16;
+  typedef unsigned int   uint;
+  typedef   signed int   int32;
+
+  // Loads a JPEG image from a memory buffer or a file.
+  // req_comps can be 1 (grayscale), 3 (RGB), or 4 (RGBA).
+  // On return, width/height will be set to the image's dimensions, and actual_comps will be set to the either 1 (grayscale) or 3 (RGB).
+  // Notes: For more control over where and how the source data is read, see the decompress_jpeg_image_from_stream() function below, or call the jpeg_decoder class directly.
+  // Requesting a 8 or 32bpp image is currently a little faster than 24bpp because the jpeg_decoder class itself currently always unpacks to either 8 or 32bpp.
+// BEGIN EPIC MOD
+//unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps);
+  unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format);
+// END EPIC MOD
+  unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps);
+
+  // Success/failure error codes.
+  enum jpgd_status
+  {
+    JPGD_SUCCESS = 0, JPGD_FAILED = -1, JPGD_DONE = 1,
+    JPGD_BAD_DHT_COUNTS = -256, JPGD_BAD_DHT_INDEX, JPGD_BAD_DHT_MARKER, JPGD_BAD_DQT_MARKER, JPGD_BAD_DQT_TABLE, 
+    JPGD_BAD_PRECISION, JPGD_BAD_HEIGHT, JPGD_BAD_WIDTH, JPGD_TOO_MANY_COMPONENTS, 
+    JPGD_BAD_SOF_LENGTH, JPGD_BAD_VARIABLE_MARKER, JPGD_BAD_DRI_LENGTH, JPGD_BAD_SOS_LENGTH,
+    JPGD_BAD_SOS_COMP_ID, JPGD_W_EXTRA_BYTES_BEFORE_MARKER, JPGD_NO_ARITHMITIC_SUPPORT, JPGD_UNEXPECTED_MARKER,
+    JPGD_NOT_JPEG, JPGD_UNSUPPORTED_MARKER, JPGD_BAD_DQT_LENGTH, JPGD_TOO_MANY_BLOCKS,
+    JPGD_UNDEFINED_QUANT_TABLE, JPGD_UNDEFINED_HUFF_TABLE, JPGD_NOT_SINGLE_SCAN, JPGD_UNSUPPORTED_COLORSPACE,
+    JPGD_UNSUPPORTED_SAMP_FACTORS, JPGD_DECODE_ERROR, JPGD_BAD_RESTART_MARKER, JPGD_ASSERTION_ERROR,
+    JPGD_BAD_SOS_SPECTRAL, JPGD_BAD_SOS_SUCCESSIVE, JPGD_STREAM_READ, JPGD_NOTENOUGHMEM
+  };
+    
+  // Input stream interface.
+  // Derive from this class to read input data from sources other than files or memory. Set m_eof_flag to true when no more data is available.
+  // The decoder is rather greedy: it will keep on calling this method until its internal input buffer is full, or until the EOF flag is set.
+  // It the input stream contains data after the JPEG stream's EOI (end of image) marker it will probably be pulled into the internal buffer.
+  // Call the get_total_bytes_read() method to determine the actual size of the JPEG stream after successful decoding.
+  class jpeg_decoder_stream
+  {
+  public:
+    jpeg_decoder_stream() { }
+    virtual ~jpeg_decoder_stream() { }
+
+    // The read() method is called when the internal input buffer is empty.
+    // Parameters:
+    // pBuf - input buffer
+    // max_bytes_to_read - maximum bytes that can be written to pBuf
+    // pEOF_flag - set this to true if at end of stream (no more bytes remaining)
+    // Returns -1 on error, otherwise return the number of bytes actually written to the buffer (which may be 0).
+    // Notes: This method will be called in a loop until you set *pEOF_flag to true or the internal buffer is full.
+    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) = 0;
+  };
+
+  // stdio FILE stream class.
+  class jpeg_decoder_file_stream : public jpeg_decoder_stream
+  {
+    jpeg_decoder_file_stream(const jpeg_decoder_file_stream &);
+    jpeg_decoder_file_stream &operator =(const jpeg_decoder_file_stream &);
+
+    FILE *m_pFile;
+    bool m_eof_flag, m_error_flag;
+
+  public:
+    jpeg_decoder_file_stream();
+    virtual ~jpeg_decoder_file_stream();
+    
+    bool open(const char *Pfilename);
+    void close();
+
+    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
+  };
+
+  // Memory stream class.
+  class jpeg_decoder_mem_stream : public jpeg_decoder_stream
+  {
+    const uint8 *m_pSrc_data;
+    uint m_ofs, m_size;
+
+  public:
+    jpeg_decoder_mem_stream() : m_pSrc_data(NULL), m_ofs(0), m_size(0) { }
+    jpeg_decoder_mem_stream(const uint8 *pSrc_data, uint size) : m_pSrc_data(pSrc_data), m_ofs(0), m_size(size) { }
+
+    virtual ~jpeg_decoder_mem_stream() { }
+
+    bool open(const uint8 *pSrc_data, uint size);
+    void close() { m_pSrc_data = NULL; m_ofs = 0; m_size = 0; }
+    
+    virtual int read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag);
+  };
+
+  // Loads JPEG file from a jpeg_decoder_stream.
+  unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps);
+
+  enum 
+  { 
+    JPGD_IN_BUF_SIZE = 8192, JPGD_MAX_BLOCKS_PER_MCU = 10, JPGD_MAX_HUFF_TABLES = 8, JPGD_MAX_QUANT_TABLES = 4, 
+    JPGD_MAX_COMPONENTS = 4, JPGD_MAX_COMPS_IN_SCAN = 4, JPGD_MAX_BLOCKS_PER_ROW = 8192, JPGD_MAX_HEIGHT = 16384, JPGD_MAX_WIDTH = 16384 
+  };
+          
+  typedef int16 jpgd_quant_t;
+  typedef int16 jpgd_block_t;
+
+  class jpeg_decoder
+  {
+  public:
+    // Call get_error_code() after constructing to determine if the stream is valid or not. You may call the get_width(), get_height(), etc.
+    // methods after the constructor is called. You may then either destruct the object, or begin decoding the image by calling begin_decoding(), then decode() on each scanline.
+    jpeg_decoder(jpeg_decoder_stream *pStream);
+
+    ~jpeg_decoder();
+
+    // Call this method after constructing the object to begin decompression.
+    // If JPGD_SUCCESS is returned you may then call decode() on each scanline.
+    int begin_decoding();
+
+    // Returns the next scan line.
+    // For grayscale images, pScan_line will point to a buffer containing 8-bit pixels (get_bytes_per_pixel() will return 1). 
+    // Otherwise, it will always point to a buffer containing 32-bit RGBA pixels (A will always be 255, and get_bytes_per_pixel() will return 4).
+    // Returns JPGD_SUCCESS if a scan line has been returned.
+    // Returns JPGD_DONE if all scan lines have been returned.
+    // Returns JPGD_FAILED if an error occurred. Call get_error_code() for a more info.
+    int decode(const void** pScan_line, uint* pScan_line_len);
+    
+    inline jpgd_status get_error_code() const { return m_error_code; }
+
+    inline int get_width() const { return m_image_x_size; }
+    inline int get_height() const { return m_image_y_size; }
+
+    inline int get_num_components() const { return m_comps_in_frame; }
+
+    inline int get_bytes_per_pixel() const { return m_dest_bytes_per_pixel; }
+    inline int get_bytes_per_scan_line() const { return m_image_x_size * get_bytes_per_pixel(); }
+
+    // Returns the total number of bytes actually consumed by the decoder (which should equal the actual size of the JPEG file).
+    inline int get_total_bytes_read() const { return m_total_bytes_read; }
+    
+  private:
+    jpeg_decoder(const jpeg_decoder &);
+    jpeg_decoder &operator =(const jpeg_decoder &);
+
+    typedef void (*pDecode_block_func)(jpeg_decoder *, int, int, int);
+
+    struct huff_tables
+    {
+      bool ac_table;
+      uint  look_up[256];
+      uint  look_up2[256];
+      uint8 code_size[256];
+      uint  tree[512];
+    };
+
+    struct coeff_buf
+    {
+      uint8 *pData;
+      int block_num_x, block_num_y;
+      int block_len_x, block_len_y;
+      int block_size;
+    };
+
+    struct mem_block
+    {
+      mem_block *m_pNext;
+      size_t m_used_count;
+      size_t m_size;
+      char m_data[1];
+    };
+
+    jmp_buf m_jmp_state;
+    mem_block *m_pMem_blocks;
+    int m_image_x_size;
+    int m_image_y_size;
+    jpeg_decoder_stream *m_pStream;
+    int m_progressive_flag;
+    uint8 m_huff_ac[JPGD_MAX_HUFF_TABLES];
+    uint8* m_huff_num[JPGD_MAX_HUFF_TABLES];      // pointer to number of Huffman codes per bit size
+    uint8* m_huff_val[JPGD_MAX_HUFF_TABLES];      // pointer to Huffman codes per bit size
+    jpgd_quant_t* m_quant[JPGD_MAX_QUANT_TABLES]; // pointer to quantization tables
+    int m_scan_type;                              // Gray, Yh1v1, Yh1v2, Yh2v1, Yh2v2 (CMYK111, CMYK4114 no longer supported)
+    int m_comps_in_frame;                         // # of components in frame
+    int m_comp_h_samp[JPGD_MAX_COMPONENTS];       // component's horizontal sampling factor
+    int m_comp_v_samp[JPGD_MAX_COMPONENTS];       // component's vertical sampling factor
+    int m_comp_quant[JPGD_MAX_COMPONENTS];        // component's quantization table selector
+    int m_comp_ident[JPGD_MAX_COMPONENTS];        // component's ID
+    int m_comp_h_blocks[JPGD_MAX_COMPONENTS];
+    int m_comp_v_blocks[JPGD_MAX_COMPONENTS];
+    int m_comps_in_scan;                          // # of components in scan
+    int m_comp_list[JPGD_MAX_COMPS_IN_SCAN];      // components in this scan
+    int m_comp_dc_tab[JPGD_MAX_COMPONENTS];       // component's DC Huffman coding table selector
+    int m_comp_ac_tab[JPGD_MAX_COMPONENTS];       // component's AC Huffman coding table selector
+    int m_spectral_start;                         // spectral selection start
+    int m_spectral_end;                           // spectral selection end
+    int m_successive_low;                         // successive approximation low
+    int m_successive_high;                        // successive approximation high
+    int m_max_mcu_x_size;                         // MCU's max. X size in pixels
+    int m_max_mcu_y_size;                         // MCU's max. Y size in pixels
+    int m_blocks_per_mcu;
+    int m_max_blocks_per_row;
+    int m_mcus_per_row, m_mcus_per_col;
+    int m_mcu_org[JPGD_MAX_BLOCKS_PER_MCU];
+    int m_total_lines_left;                       // total # lines left in image
+    int m_mcu_lines_left;                         // total # lines left in this MCU
+    int m_real_dest_bytes_per_scan_line;
+    int m_dest_bytes_per_scan_line;               // rounded up
+    int m_dest_bytes_per_pixel;                   // 4 (RGB) or 1 (Y)
+    huff_tables* m_pHuff_tabs[JPGD_MAX_HUFF_TABLES];
+    coeff_buf* m_dc_coeffs[JPGD_MAX_COMPONENTS];
+    coeff_buf* m_ac_coeffs[JPGD_MAX_COMPONENTS];
+    int m_eob_run;
+    int m_block_y_mcu[JPGD_MAX_COMPONENTS];
+    uint8* m_pIn_buf_ofs;
+    int m_in_buf_left;
+    int m_tem_flag;
+    bool m_eof_flag;
+    uint8 m_in_buf_pad_start[128];
+    uint8 m_in_buf[JPGD_IN_BUF_SIZE + 128];
+    uint8 m_in_buf_pad_end[128];
+    int m_bits_left;
+    uint m_bit_buf;
+    int m_restart_interval;
+    int m_restarts_left;
+    int m_next_restart_num;
+    int m_max_mcus_per_row;
+    int m_max_blocks_per_mcu;
+    int m_expanded_blocks_per_mcu;
+    int m_expanded_blocks_per_row;
+    int m_expanded_blocks_per_component;
+    bool  m_freq_domain_chroma_upsample;
+    int m_max_mcus_per_col;
+    uint m_last_dc_val[JPGD_MAX_COMPONENTS];
+    jpgd_block_t* m_pMCU_coefficients;
+    int m_mcu_block_max_zag[JPGD_MAX_BLOCKS_PER_MCU];
+    uint8* m_pSample_buf;
+    int m_crr[256];
+    int m_cbb[256];
+    int m_crg[256];
+    int m_cbg[256];
+    uint8* m_pScan_line_0;
+    uint8* m_pScan_line_1;
+    jpgd_status m_error_code;
+    bool m_ready_flag;
+    int m_total_bytes_read;
+
+    void free_all_blocks();
+    // BEGIN EPIC MOD
+    UE_NORETURN void stop_decoding(jpgd_status status);
+    // END EPIC MOD
+    void *alloc(size_t n, bool zero = false);
+    void word_clear(void *p, uint16 c, uint n);
+    void prep_in_buffer();
+    void read_dht_marker();
+    void read_dqt_marker();
+    void read_sof_marker();
+    void skip_variable_marker();
+    void read_dri_marker();
+    void read_sos_marker();
+    int next_marker();
+    int process_markers();
+    void locate_soi_marker();
+    void locate_sof_marker();
+    int locate_sos_marker();
+    void init(jpeg_decoder_stream * pStream);
+    void create_look_ups();
+    void fix_in_buffer();
+    void transform_mcu(int mcu_row);
+    void transform_mcu_expand(int mcu_row);
+    coeff_buf* coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y);
+    inline jpgd_block_t *coeff_buf_getp(coeff_buf *cb, int block_x, int block_y);
+    void load_next_row();
+    void decode_next_row();
+    void make_huff_table(int index, huff_tables *pH);
+    void check_quant_tables();
+    void check_huff_tables();
+    void calc_mcu_block_order();
+    int init_scan();
+    void init_frame();
+    void process_restart();
+    void decode_scan(pDecode_block_func decode_block_func);
+    void init_progressive();
+    void init_sequential();
+    void decode_start();
+    void decode_init(jpeg_decoder_stream * pStream);
+    void H2V2Convert();
+    void H2V1Convert();
+    void H1V2Convert();
+    void H1V1Convert();
+    void gray_convert();
+    void expanded_convert();
+    void find_eoi();
+    inline uint get_char();
+    inline uint get_char(bool *pPadding_flag);
+    inline void stuff_char(uint8 q);
+    inline uint8 get_octet();
+    inline uint get_bits(int num_bits);
+    inline uint get_bits_no_markers(int numbits);
+    inline int huff_decode(huff_tables *pH);
+    inline int huff_decode(huff_tables *pH, int& extrabits);
+    static inline uint8 clamp(int i);
+    static void decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+    static void decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+    static void decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+    static void decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y);
+  };
+  
+} // namespace jpgd
+
+#endif // JPEG_DECODER_H

crazy_functions/test_project/cpp/libJPG/jpge.h

@@ -0,0 +1,172 @@
+
+// jpge.h - C++ class for JPEG compression.
+// Public domain, Rich Geldreich <richgel99@gmail.com>
+// Alex Evans: Added RGBA support, linear memory allocator.
+#ifndef JPEG_ENCODER_H
+#define JPEG_ENCODER_H
+
+#include <stdint.h>
+
+namespace jpge
+{
+  typedef unsigned char  uint8;
+  typedef signed short   int16;
+  typedef signed int     int32;
+  typedef unsigned short uint16;
+  typedef unsigned int   uint32;
+  typedef unsigned int   uint;
+
+  // JPEG chroma subsampling factors. Y_ONLY (grayscale images) and H2V2 (color images) are the most common.
+  enum subsampling_t { Y_ONLY = 0, H1V1 = 1, H2V1 = 2, H2V2 = 3 };
+
+  // JPEG compression parameters structure.
+  struct params
+  {
+    inline params() : m_quality(85), m_subsampling(H2V2), m_no_chroma_discrim_flag(false), m_two_pass_flag(false) { }
+
+    inline bool check_valid() const
+    {
+      if ((m_quality < 1) || (m_quality > 100)) return false;
+      if ((uint)m_subsampling > (uint)H2V2) return false;
+      return true;
+    }
+
+    // Quality: 1-100, higher is better. Typical values are around 50-95.
+    int m_quality;
+
+    // m_subsampling:
+    // 0 = Y (grayscale) only
+    // 1 = YCbCr, no subsampling (H1V1, YCbCr 1x1x1, 3 blocks per MCU)
+    // 2 = YCbCr, H2V1 subsampling (YCbCr 2x1x1, 4 blocks per MCU)
+    // 3 = YCbCr, H2V2 subsampling (YCbCr 4x1x1, 6 blocks per MCU-- very common)
+    subsampling_t m_subsampling;
+
+    // Disables CbCr discrimination - only intended for testing.
+    // If true, the Y quantization table is also used for the CbCr channels.
+    bool m_no_chroma_discrim_flag;
+
+    bool m_two_pass_flag;
+  };
+  
+  // Writes JPEG image to a file. 
+  // num_channels must be 1 (Y) or 3 (RGB), image pitch must be width*num_channels.
+  bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
+
+  // Writes JPEG image to memory buffer. 
+  // On entry, buf_size is the size of the output buffer pointed at by pBuf, which should be at least ~1024 bytes. 
+  // If return value is true, buf_size will be set to the size of the compressed data.
+  bool compress_image_to_jpeg_file_in_memory(void *pBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params = params());
+    
+  // Output stream abstract class - used by the jpeg_encoder class to write to the output stream. 
+  // put_buf() is generally called with len==JPGE_OUT_BUF_SIZE bytes, but for headers it'll be called with smaller amounts.
+  class output_stream
+  {
+  public:
+    virtual ~output_stream() { };
+    virtual bool put_buf(const void* Pbuf, int64_t len) = 0;
+    template<class T> inline bool put_obj(const T& obj) { return put_buf(&obj, sizeof(T)); }
+  };
+    
+  // Lower level jpeg_encoder class - useful if more control is needed than the above helper functions.
+  class jpeg_encoder
+  {
+  public:
+    jpeg_encoder();
+    ~jpeg_encoder();
+
+    // Initializes the compressor.
+    // pStream: The stream object to use for writing compressed data.
+    // params - Compression parameters structure, defined above.
+    // width, height  - Image dimensions.
+    // channels - May be 1, or 3. 1 indicates grayscale, 3 indicates RGB source data.
+    // Returns false on out of memory or if a stream write fails.
+    bool init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params = params());
+    
+    const params &get_params() const { return m_params; }
+    
+    // Deinitializes the compressor, freeing any allocated memory. May be called at any time.
+    void deinit();
+
+    uint get_total_passes() const { return m_params.m_two_pass_flag ? 2 : 1; }
+    inline uint get_cur_pass() { return m_pass_num; }
+
+    // Call this method with each source scanline.
+    // width * src_channels bytes per scanline is expected (RGB or Y format).
+    // You must call with NULL after all scanlines are processed to finish compression.
+    // Returns false on out of memory or if a stream write fails.
+    bool process_scanline(const void* pScanline);
+        
+  private:
+    jpeg_encoder(const jpeg_encoder &);
+    jpeg_encoder &operator =(const jpeg_encoder &);
+
+    typedef int32 sample_array_t;
+        
+    output_stream *m_pStream;
+    params m_params;
+    uint8 m_num_components;
+    uint8 m_comp_h_samp[3], m_comp_v_samp[3];
+    int m_image_x, m_image_y, m_image_bpp, m_image_bpl;
+    int m_image_x_mcu, m_image_y_mcu;
+    int m_image_bpl_xlt, m_image_bpl_mcu;
+    int m_mcus_per_row;
+    int m_mcu_x, m_mcu_y;
+    uint8 *m_mcu_lines[16];
+    uint8 m_mcu_y_ofs;
+    sample_array_t m_sample_array[64];
+    int16 m_coefficient_array[64];
+    int32 m_quantization_tables[2][64];
+    uint m_huff_codes[4][256];
+    uint8 m_huff_code_sizes[4][256];
+    uint8 m_huff_bits[4][17];
+    uint8 m_huff_val[4][256];
+    uint32 m_huff_count[4][256];
+    int m_last_dc_val[3];
+    enum { JPGE_OUT_BUF_SIZE = 2048 };
+    uint8 m_out_buf[JPGE_OUT_BUF_SIZE];
+    uint8 *m_pOut_buf;
+    uint m_out_buf_left;
+    uint32 m_bit_buffer;
+    uint m_bits_in;
+    uint8 m_pass_num;
+    bool m_all_stream_writes_succeeded;
+        
+    void optimize_huffman_table(int table_num, int table_len);
+    void emit_byte(uint8 i);
+    void emit_word(uint i);
+    void emit_marker(int marker);
+    void emit_jfif_app0();
+    void emit_dqt();
+    void emit_sof();
+    void emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag);
+    void emit_dhts();
+    void emit_sos();
+    void emit_markers();
+    void compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val);
+    void compute_quant_table(int32 *dst, int16 *src);
+    void adjust_quant_table(int32 *dst, int32 *src);
+    void first_pass_init();
+    bool second_pass_init();
+    bool jpg_open(int p_x_res, int p_y_res, int src_channels);
+    void load_block_8_8_grey(int x);
+    void load_block_8_8(int x, int y, int c);
+    void load_block_16_8(int x, int c);
+    void load_block_16_8_8(int x, int c);
+    void load_quantized_coefficients(int component_num);
+    void flush_output_buffer();
+    void put_bits(uint bits, uint len);
+    void code_coefficients_pass_one(int component_num);
+    void code_coefficients_pass_two(int component_num);
+    void code_block(int component_num);
+    void process_mcu_row();
+    bool terminate_pass_one();
+    bool terminate_pass_two();
+    bool process_end_of_image();
+    void load_mcu(const void* src);
+    void clear();
+    void init();
+  };
+
+} // namespace jpge
+
+#endif // JPEG_ENCODER

crazy_functions/test_project/cpp/libJPG/来源

@@ -0,0 +1,3 @@
+jpge.h - C++ class for JPEG compression.
+Public domain, Rich Geldreich <richgel99@gmail.com>
+Alex Evans: Added RGBA support, linear memory allocator.

crazy_functions/test_project/cpp/longcode/prod_cons.h

@@ -0,0 +1,433 @@
+#pragma once
+
+#include <atomic>
+#include <utility>
+#include <cstring>
+#include <type_traits>
+#include <cstdint>
+
+#include "libipc/def.h"
+
+#include "libipc/platform/detail.h"
+#include "libipc/circ/elem_def.h"
+#include "libipc/utility/log.h"
+#include "libipc/utility/utility.h"
+
+namespace ipc {
+
+////////////////////////////////////////////////////////////////
+/// producer-consumer implementation
+////////////////////////////////////////////////////////////////
+
+template <typename Flag>
+struct prod_cons_impl;
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
+    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
+
+    constexpr circ::u2_t cursor() const noexcept {
+        return 0;
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* /*wrapper*/, F&& f, E* elems) {
+        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
+        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
+            return false; // full
+        }
+        std::forward<F>(f)(&(elems[cur_wt].data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    /**
+     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
+     * So we could just disconnect all connections of receiver, and return false.
+    */
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
+        return false;
+    }
+
+    template <typename W, typename F, typename R, typename E>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
+        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
+        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
+            return false; // empty
+        }
+        std::forward<F>(f)(&(elems[cur_rd].data_));
+        std::forward<R>(out)(true);
+        rd_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
+     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(1);
+        return false;
+    }
+
+    template <typename W, typename F, typename R, 
+              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
+        byte_t buff[DS];
+        for (unsigned k = 0;;) {
+            auto cur_rd = rd_.load(std::memory_order_relaxed);
+            if (circ::index_of(cur_rd) ==
+                circ::index_of(wt_.load(std::memory_order_acquire))) {
+                return false; // empty
+            }
+            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
+            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
+                std::forward<F>(f)(buff);
+                std::forward<R>(out)(true);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
+     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
+
+    using flag_t = std::uint64_t;
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
+
+    template <typename W, typename F, typename E>
+    bool push(W* /*wrapper*/, F&& f, E* elems) {
+        circ::u2_t cur_ct, nxt_ct;
+        for (unsigned k = 0;;) {
+            cur_ct = ct_.load(std::memory_order_relaxed);
+            if (circ::index_of(nxt_ct = cur_ct + 1) ==
+                circ::index_of(rd_.load(std::memory_order_acquire))) {
+                return false; // full
+            }
+            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        auto* el = elems + circ::index_of(cur_ct);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        while (1) {
+            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
+            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
+                return true;
+            }
+            if ((~cac_ct) != cur_ct) {
+                return true;
+            }
+            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
+                return true;
+            }
+            wt_.store(nxt_ct, std::memory_order_release);
+            cur_ct = nxt_ct;
+            nxt_ct = cur_ct + 1;
+            el = elems + circ::index_of(cur_ct);
+        }
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&&, E*) {
+        wrapper->elems()->disconnect_receiver(1);
+        return false;
+    }
+
+    template <typename W, typename F, typename R, 
+              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
+    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
+        byte_t buff[DS];
+        for (unsigned k = 0;;) {
+            auto cur_rd = rd_.load(std::memory_order_relaxed);
+            auto cur_wt = wt_.load(std::memory_order_acquire);
+            auto id_rd  = circ::index_of(cur_rd);
+            auto id_wt  = circ::index_of(cur_wt);
+            if (id_rd == id_wt) {
+                auto* el = elems + id_wt;
+                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
+                if ((~cac_ct) != cur_wt) {
+                    return false; // empty
+                }
+                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
+                    wt_.store(cur_wt + 1, std::memory_order_release);
+                }
+                k = 0;
+            }
+            else {
+                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
+                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
+                    std::forward<F>(f)(buff);
+                    std::forward<R>(out)(true);
+                    return true;
+                }
+                ipc::yield(k);
+            }
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
+
+    using rc_t = std::uint64_t;
+
+    enum : rc_t {
+        ep_mask = 0x00000000ffffffffull,
+        ep_incr = 0x0000000100000000ull
+    };
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<rc_t> rc_ { 0 }; // read-counter
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
+    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
+
+    circ::u2_t cursor() const noexcept {
+        return wt_.load(std::memory_order_acquire);
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & ep_mask;
+            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
+                return false; // has not finished yet
+            }
+            // consider rem_cc to be 0 here
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        std::forward<F>(f)(&(el->data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        epoch_ += ep_incr;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & ep_mask;
+            if (cc & rem_cc) {
+                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
+                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
+                if (cc == 0) return false; // no reader
+            }
+            // just compare & exchange
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        std::forward<F>(f)(&(el->data_));
+        wt_.fetch_add(1, std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename R, typename E>
+    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
+        if (cur == cursor()) return false; // acquire
+        auto* el = elems + circ::index_of(cur++);
+        std::forward<F>(f)(&(el->data_));
+        for (unsigned k = 0;;) {
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            if ((cur_rc & ep_mask) == 0) {
+                std::forward<R>(out)(true);
+                return true;
+            }
+            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
+            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
+                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+template <>
+struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
+
+    using rc_t   = std::uint64_t;
+    using flag_t = std::uint64_t;
+
+    enum : rc_t {
+        rc_mask = 0x00000000ffffffffull,
+        ep_mask = 0x00ffffffffffffffull,
+        ep_incr = 0x0100000000000000ull,
+        ic_mask = 0xff000000ffffffffull,
+        ic_incr = 0x0000000100000000ull
+    };
+
+    template <std::size_t DataSize, std::size_t AlignSize>
+    struct elem_t {
+        std::aligned_storage_t<DataSize, AlignSize> data_ {};
+        std::atomic<rc_t  > rc_   { 0 }; // read-counter
+        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
+    };
+
+    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
+    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
+
+    circ::u2_t cursor() const noexcept {
+        return ct_.load(std::memory_order_acquire);
+    }
+
+    constexpr static rc_t inc_rc(rc_t rc) noexcept {
+        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
+    }
+
+    constexpr static rc_t inc_mask(rc_t rc) noexcept {
+        return inc_rc(rc) & ~rc_mask;
+    }
+
+    template <typename W, typename F, typename E>
+    bool push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        circ::u2_t cur_ct;
+        rc_t epoch = epoch_.load(std::memory_order_acquire);
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
+            circ::cc_t rem_cc = cur_rc & rc_mask;
+            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
+                return false; // has not finished yet
+            }
+            else if (!rem_cc) {
+                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
+                if ((cur_fl != cur_ct) && cur_fl) {
+                    return false; // full
+                }
+            }
+            // consider rem_cc to be 0 here
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
+                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
+                break;
+            }
+            ipc::yield(k);
+        }
+        // only one thread/process would touch here at one time
+        ct_.store(cur_ct + 1, std::memory_order_release);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename E>
+    bool force_push(W* wrapper, F&& f, E* elems) {
+        E* el;
+        circ::u2_t cur_ct;
+        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
+        for (unsigned k = 0;;) {
+            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
+            if (cc == 0) return false; // no reader
+            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
+            // check all consumers have finished reading this element
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            circ::cc_t rem_cc = cur_rc & rc_mask;
+            if (cc & rem_cc) {
+                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
+                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
+                if (cc == 0) return false; // no reader
+            }
+            // just compare & exchange
+            if (el->rc_.compare_exchange_weak(
+                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
+                if (epoch == epoch_.load(std::memory_order_acquire)) {
+                    break;
+                }
+                else if (push(wrapper, std::forward<F>(f), elems)) {
+                    return true;
+                }
+                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
+            }
+            ipc::yield(k);
+        }
+        // only one thread/process would touch here at one time
+        ct_.store(cur_ct + 1, std::memory_order_release);
+        std::forward<F>(f)(&(el->data_));
+        // set flag & try update wt
+        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
+        return true;
+    }
+
+    template <typename W, typename F, typename R, typename E, std::size_t N>
+    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
+        auto* el = elems + circ::index_of(cur);
+        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
+        if (cur_fl != ~static_cast<flag_t>(cur)) {
+            return false; // empty
+        }
+        ++cur;
+        std::forward<F>(f)(&(el->data_));
+        for (unsigned k = 0;;) {
+            auto cur_rc = el->rc_.load(std::memory_order_acquire);
+            if ((cur_rc & rc_mask) == 0) {
+                std::forward<R>(out)(true);
+                el->f_ct_.store(cur + N - 1, std::memory_order_release);
+                return true;
+            }
+            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
+            bool last_one = false;
+            if ((last_one = (nxt_rc & rc_mask) == 0)) {
+                el->f_ct_.store(cur + N - 1, std::memory_order_release);
+            }
+            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
+                std::forward<R>(out)(last_one);
+                return true;
+            }
+            ipc::yield(k);
+        }
+    }
+};
+
+} // namespace ipc

crazy_functions/test_project/latex/attention/background.tex

@@ -0,0 +1,58 @@
+The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\ref{sec:attention}. 
+
+Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.
+
+End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \citep{sukhbaatar2015}.
+
+To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
+In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \citep{neural_gpu, NalBytenet2017} and \citep{JonasFaceNet2017}.
+
+
+%\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
+
+%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.
+
+%A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
+
+%After the seminal models introduced in \citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.
+
+%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.
+
+%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \citep{neural_gpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
+
+%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
+
+
+
+%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   
+
+%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.
+
+%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   
+
+%\begin{table}[h!]
+%\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}
+%\label{tab:op_complexities}
+%\begin{center}
+%\vspace{-5pt}
+%\scalebox{0.75}{
+
+%\begin{tabular}{l|c|c|c}
+%\hline \hline
+%Layer Type & Receptive & Complexity & Sequential  \\
+%           & Field     &            & Operations  \\
+%\hline
+%Pointwise Feed-Forward & $1$ & $O(n \cdot d^2)$ & $O(1)$ \\
+%\hline
+%Recurrent & $n$ & $O(n \cdot d^2)$ & $O(n)$ \\
+%\hline
+%Convolutional & $r$ & $O(r \cdot n \cdot d^2)$ & $O(1)$ \\
+%\hline
+%Convolutional (separable) & $r$ & $O(r \cdot n \cdot d + n %\cdot d^2)$ & $O(1)$ \\
+%\hline
+%Attention & $r$ & $O(r \cdot n \cdot d)$ & $O(1)$ \\
+%\hline \hline
+%\end{tabular}
+%}
+%\end{center}
+%\end{table}

crazy_functions/test_project/latex/attention/introduction.tex

@@ -0,0 +1,18 @@
+Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}.
+
+Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.
+%\marginpar{not sure if the memory constraints are understandable here}
+Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
+
+%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away} 
+
+Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.
+
+%\marginpar{not sure if "cross-positional communication" is understandable without explanation}
+%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}
+
+In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs. 
+%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}
+
+% Just a standard paragraph with citations, rewrite.
+%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do.

crazy_functions/test_project/latex/attention/model_architecture.tex

@@ -0,0 +1,155 @@
+
+\begin{figure}
+  \centering
+  \includegraphics[scale=0.6]{Figures/ModalNet-21}
+  \caption{The Transformer - model architecture.}
+  \label{fig:model-arch}
+\end{figure}
+
+% Although the primary workhorse of our model is attention, 
+%Our model maintains the encoder-decoder structure that is common to many so-called sequence-to-sequence models \citep{bahdanau2014neural,sutskever14}.  As in all such architectures, the encoder computes a representation of the input sequence, and the decoder consumes these representations along with the output tokens to autoregressively produce the output sequence.  Where, traditionally, the encoder and decoder contain stacks of recurrent or convolutional layers, our encoder and decoder stacks are composed of attention layers and position-wise feed-forward layers (Figure~\ref{fig:model-arch}).  The following sections describe the gross architecture and these particular components in detail.
+
+Most competitive neural sequence transduction models have an encoder-decoder structure \citep{cho2014learning,bahdanau2014neural,sutskever14}. Here, the encoder maps an input sequence of symbol representations $(x_1, ..., x_n)$ to a sequence of continuous representations $\mathbf{z} = (z_1, ..., z_n)$. Given $\mathbf{z}$, the decoder then generates an output sequence $(y_1,...,y_m)$ of symbols one element at a time. At each step the model is auto-regressive \citep{graves2013generating}, consuming the previously generated symbols as additional input when generating the next.
+
+The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure~\ref{fig:model-arch}, respectively.
+
+\subsection{Encoder and Decoder Stacks}
+
+\paragraph{Encoder:}The encoder is composed of a stack of $N=6$ identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network.   We employ a residual connection \citep{he2016deep} around each of the two sub-layers, followed by layer normalization \cite{layernorm2016}.  That is, the output of each sub-layer is $\mathrm{LayerNorm}(x + \mathrm{Sublayer}(x))$, where $\mathrm{Sublayer}(x)$ is the function implemented by the sub-layer itself.  To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension $\dmodel=512$.
+
+\paragraph{Decoder:}The decoder is also composed of a stack of $N=6$ identical layers.  In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.  Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.  We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions.  This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position $i$ can depend only on the known outputs at positions less than $i$.
+
+% In our model (Figure~\ref{fig:model-arch}), the encoder and decoder are composed of stacks of alternating self-attention layers (for cross-positional communication) and position-wise feed-forward layers (for in-place computation).  In addition, the decoder stack contains encoder-decoder attention layers.  Since attention is agnostic to the distances between words, our model requires a "positional encoding" to be added to the encoder and decoder input. The following sections describe all of these components in detail.
+
+\subsection{Attention} \label{sec:attention}
+An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors.  The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.
+
+\subsubsection{Scaled Dot-Product Attention} \label{sec:scaled-dot-prod}
+
+% \begin{figure}
+%   \centering
+%   \includegraphics[scale=0.6]{Figures/ModalNet-19}
+%   \caption{Scaled Dot-Product Attention.}
+%   \label{fig:multi-head-att}
+% \end{figure}
+
+We call our particular attention "Scaled Dot-Product Attention" (Figure~\ref{fig:multi-head-att}).   The input consists of queries and keys of dimension $d_k$, and values of dimension $d_v$.  We compute the dot products of the query with all keys, divide each by $\sqrt{d_k}$, and apply a softmax function to obtain the weights on the values.
+
+In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix $Q$.   The keys and values are also packed together into matrices $K$ and $V$.  We compute the matrix of outputs as:
+
+\begin{equation}
+   \mathrm{Attention}(Q, K, V) = \mathrm{softmax}(\frac{QK^T}{\sqrt{d_k}})V
+\end{equation}
+
+The two most commonly used attention functions are additive attention \citep{bahdanau2014neural}, and dot-product (multiplicative) attention.  Dot-product attention is identical to our algorithm, except for the scaling factor of $\frac{1}{\sqrt{d_k}}$. Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.  While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code. 
+
+%We scale the dot products by $1/\sqrt{d_k}$ to limit the magnitude of the dot products, which works well in practice. Otherwise, we found applying the softmax to often result in weights very close to 0 or 1, and hence minuscule gradients.
+
+% Already described in the subsequent section
+%When used as part of decoder self-attention, an optional mask function is applied just before the softmax to prevent positions from attending to subsequent positions.   This mask simply sets the logits corresponding to all illegal connections (those outside of the lower triangle) to $-\infty$.
+
+%\paragraph{Comparison to Additive Attention: } We choose dot product attention over additive attention \citep{bahdanau2014neural} since it can be computed using highly optimized matrix multiplication code.  This optimization is particularly important to us, as we employ many attention layers in our model.
+
+While for small values of $d_k$ the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of $d_k$ \citep{DBLP:journals/corr/BritzGLL17}. We suspect that for large values of $d_k$, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients  \footnote{To illustrate why the dot products get large, assume that the components of $q$ and $k$ are independent random variables with mean $0$ and variance $1$.  Then their dot product, $q \cdot k = \sum_{i=1}^{d_k} q_ik_i$, has mean $0$ and variance $d_k$.}. To counteract this effect, we scale the dot products by $\frac{1}{\sqrt{d_k}}$.
+
+
+%We suspect this to be caused by the dot products growing too large in magnitude to result in useful gradients after applying the softmax function.  To counteract this, we scale the dot product by $1/\sqrt{d_k}$.
+
+
+\subsubsection{Multi-Head Attention} \label{sec:multihead}
+
+\begin{figure}
+\begin{minipage}[t]{0.5\textwidth}
+  \centering
+  Scaled Dot-Product Attention \\
+  \vspace{0.5cm}
+  \includegraphics[scale=0.6]{Figures/ModalNet-19}
+\end{minipage}
+\begin{minipage}[t]{0.5\textwidth}
+  \centering 
+  Multi-Head Attention \\
+  \vspace{0.1cm}
+  \includegraphics[scale=0.6]{Figures/ModalNet-20}  
+\end{minipage}
+
+
+  % \centering
+
+  \caption{(left) Scaled Dot-Product Attention. (right) Multi-Head Attention consists of several attention layers running in parallel.}
+  \label{fig:multi-head-att}
+\end{figure}
+
+Instead of performing a single attention function with $\dmodel$-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values $h$ times with different, learned linear projections to $d_k$, $d_k$ and $d_v$ dimensions, respectively.
+On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding $d_v$-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure~\ref{fig:multi-head-att}.
+
+Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.
+
+\begin{align*}
+    \mathrm{MultiHead}(Q, K, V) &= \mathrm{Concat}(\mathrm{head_1}, ..., \mathrm{head_h})W^O\\
+%    \mathrm{where} \mathrm{head_i} &= \mathrm{Attention}(QW_Q_i^{\dmodel \times d_q}, KW_K_i^{\dmodel \times d_k}, VW^V_i^{\dmodel \times d_v})\\
+    \text{where}~\mathrm{head_i} &= \mathrm{Attention}(QW^Q_i, KW^K_i, VW^V_i)\\
+\end{align*}
+
+Where the projections are parameter matrices $W^Q_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^K_i \in \mathbb{R}^{\dmodel \times d_k}$, $W^V_i \in \mathbb{R}^{\dmodel \times d_v}$ and $W^O \in \mathbb{R}^{hd_v \times \dmodel}$.
+
+
+%find it better (and no more expensive) to have multiple parallel attention layers (each over the full set of positions) with proportionally lower-dimensional keys, values and queries.  We call this "Multi-Head Attention" (Figure~\ref{fig:multi-head-att}).  The keys, values, and queries for each of these parallel attention layers are computed by learned linear transformations of the inputs to the multi-head attention.  We use different linear transformations across different parallel attention layers.  The output of the parallel attention layers are concatenated, and then passed through a final learned linear transformation. 
+
+In this work we employ $h=8$ parallel attention layers, or heads. For each of these we use $d_k=d_v=\dmodel/h=64$.
+Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.
+
+\subsubsection{Applications of Attention in our Model}
+
+The Transformer uses multi-head attention in three different ways: 
+\begin{itemize}
+ \item In "encoder-decoder attention" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.   This allows every position in the decoder to attend over all positions in the input sequence.  This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as \citep{wu2016google, bahdanau2014neural,JonasFaceNet2017}.
+
+ \item The encoder contains self-attention layers.  In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder.   Each position in the encoder can attend to all positions in the previous layer of the encoder.
+
+ \item Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position.  We need to prevent leftward information flow in the decoder to preserve the auto-regressive property.  We implement this inside of scaled dot-product attention by masking out (setting to $-\infty$) all values in the input of the softmax which correspond to illegal connections.  See Figure~\ref{fig:multi-head-att}.
+
+\end{itemize}
+
+\subsection{Position-wise Feed-Forward Networks}\label{sec:ffn}
+
+In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.  This consists of two linear transformations with a ReLU activation in between.
+
+\begin{equation}
+   \mathrm{FFN}(x)=\max(0, xW_1 + b_1) W_2 + b_2
+\end{equation}
+
+While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.  The dimensionality of input and output is $\dmodel=512$, and the inner-layer has dimensionality $d_{ff}=2048$.
+
+
+
+%In the appendix, we describe how the position-wise feed-forward network can also be seen as a form of attention.
+
+%from Jakob: The number of operations required for the model to relate signals from two arbitrary input or output positions grows in the distance between positions in input or output, linearly for ConvS2S and logarithmically for ByteNet, making it harder to learn dependencies between these positions \citep{hochreiter2001gradient}. In the transformer this is reduced to a constant number of operations, albeit at the cost of effective resolution caused by averaging attention-weighted positions, an effect we aim to counteract with multi-headed attention.
+
+
+%Figure~\ref{fig:simple-att} presents a simple attention function, $A$, with a single head, that forms the basis of our multi-head attention. $A$ takes a query key vector $\kq$, matrices of memory keys $\km$ and memory values $\vm$ ,and produces a query value vector $\vq$ as 
+%\begin{equation*} \label{eq:attention}
+%    A(\kq, \km, \vm) = {\vm}^T (Softmax(\km \kq).
+%\end{equation*}
+%We linearly transform $\kq,\,\km$, and $\vm$ with learned matrices ${\Wkq \text{,} \, \Wkm}$, and ${\Wvm}$ before calling the attention function, and transform the output query with $\Wvq$ before handing it to the feed forward layer. Each attention layer has it's own set of transformation matrices, which are shared across all query positions. $A$ is applied in parallel for each query position, and is implemented very efficiently as a batch of matrix multiplies. The self-attention and encoder-decoder attention layers use $A$, but with different arguments. For example, in encdoder self-attention, queries in encoder layer $i$ attention to memories in encoder layer $i-1$. To ensure that decoder self-attention layers do not look at future words, we add $- \inf$ to the softmax logits in positions $j+1$ to query length for query position $l$.  
+
+%In simple attention, the query value is a weighted combination of the memory values where the attention weights sum to one. Although this function performs well in practice, the constraint on attention weights can restrict the amount of information that flows from memories to queries because the query cannot focus on multiple memory positions at once, which might be desirable when translating long sequences. \marginpar{@usz, could you think of an example of this ?} We remedy this by maintaining multiple attention heads at each query position that attend to all memory positions in parallel, with a different set of parameters  per attention head $h$. 
+%\marginpar{}
+
+\subsection{Embeddings and Softmax}
+Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension $\dmodel$.  We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.  In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to \citep{press2016using}.   In the embedding layers, we multiply those weights by $\sqrt{\dmodel}$.
+
+
+\subsection{Positional Encoding}
+Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.  To this end, we add "positional encodings" to the input embeddings at the bottoms of the encoder and decoder stacks.  The positional encodings have the same dimension $\dmodel$ as the embeddings, so that the two can be summed.   There are many choices of positional encodings, learned and fixed \citep{JonasFaceNet2017}.
+
+In this work, we use sine and cosine functions of different frequencies:
+
+\begin{align*}
+    PE_{(pos,2i)} = sin(pos / 10000^{2i/\dmodel}) \\
+    PE_{(pos,2i+1)} = cos(pos / 10000^{2i/\dmodel})
+\end{align*}
+
+where $pos$ is the position and $i$ is the dimension.  That is, each dimension of the positional encoding corresponds to a sinusoid.  The wavelengths form a geometric progression from $2\pi$ to $10000 \cdot 2\pi$.  We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset $k$, $PE_{pos+k}$ can be represented as a linear function of $PE_{pos}$.
+
+We also experimented with using learned positional embeddings \citep{JonasFaceNet2017} instead, and found that the two versions produced nearly identical results (see Table~\ref{tab:variations} row (E)).  We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.

crazy_functions/test_project/latex/attention/parameter_attention.tex

@@ -0,0 +1,45 @@
+\pagebreak
+\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
+
+In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between.  In fact, these networks too can be seen as a form of attention.  Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
+
+\begin{align*}
+    FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
+    A(q, K, V) = Softmax(qK^T)V
+\end{align*}
+
+Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
+
+%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
+
+Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer.  These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
+
+In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head.  The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection.  This matches the number of parameters in the position-wise feed-forward network that we replaced.  While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
+
+In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
+
+Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
+
+\begin{table}[h]
+\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model.  All metrics are on the English-to-German translation development set, newstest2013.}
+\label{tab:parameter_attention}
+\begin{center}
+\vspace{-2mm}
+%\scalebox{1.0}{
+\begin{tabular}{c|cccccc|cccc}
+\hline\rule{0pt}{2.0ex}
+ & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
+\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
+ \multirow{2}{*}{$n_p$} &
+ PPL & BLEU & params & training\\
+ & & & & & &  & (dev) & (dev) & $\times10^6$ & time \\
+\hline\rule{0pt}{2.0ex}
+base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
+\hline\rule{0pt}{2.0ex}
+AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5  & 65 & 16 hours\\
+AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9}  & 65 & 16 hours \\
+\hline
+\end{tabular}
+%}
+\end{center}
+\end{table}

crazy_functions/test_project/latex/attention/来源

@@ -0,0 +1,8 @@
+chatgpt的老祖宗《Attention is all you need》
+
+Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
+
+真实的摘要如下
+The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
+
+https://arxiv.org/abs/1706.03762

crazy_functions/test_project/python/dqn/__init__.py

@@ -0,0 +1,2 @@
+from stable_baselines3.dqn.dqn import DQN
+from stable_baselines3.dqn.policies import CnnPolicy, MlpPolicy

crazy_functions/test_project/python/dqn/dqn.py

@@ -0,0 +1,245 @@
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
+
+import gym
+import numpy as np
+import torch as th
+from torch.nn import functional as F
+
+from stable_baselines3.common import logger
+from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
+from stable_baselines3.common.preprocessing import maybe_transpose
+from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
+from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update
+from stable_baselines3.dqn.policies import DQNPolicy
+
+
+class DQN(OffPolicyAlgorithm):
+    """
+    Deep Q-Network (DQN)
+
+    Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236
+    Default hyperparameters are taken from the nature paper,
+    except for the optimizer and learning rate that were taken from Stable Baselines defaults.
+
+    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
+    :param env: The environment to learn from (if registered in Gym, can be str)
+    :param learning_rate: The learning rate, it can be a function
+        of the current progress remaining (from 1 to 0)
+    :param buffer_size: size of the replay buffer
+    :param learning_starts: how many steps of the model to collect transitions for before learning starts
+    :param batch_size: Minibatch size for each gradient update
+    :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
+    :param gamma: the discount factor
+    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
+        like ``(5, "step")`` or ``(2, "episode")``.
+    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
+        Set to ``-1`` means to do as many gradient steps as steps done in the environment
+        during the rollout.
+    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
+        at a cost of more complexity.
+        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
+    :param target_update_interval: update the target network every ``target_update_interval``
+        environment steps.
+    :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced
+    :param exploration_initial_eps: initial value of random action probability
+    :param exploration_final_eps: final value of random action probability
+    :param max_grad_norm: The maximum value for the gradient clipping
+    :param tensorboard_log: the log location for tensorboard (if None, no logging)
+    :param create_eval_env: Whether to create a second environment that will be
+        used for evaluating the agent periodically. (Only available when passing string for the environment)
+    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
+    :param seed: Seed for the pseudo random generators
+    :param device: Device (cpu, cuda, ...) on which the code should be run.
+        Setting it to auto, the code will be run on the GPU if possible.
+    :param _init_setup_model: Whether or not to build the network at the creation of the instance
+    """
+
+    def __init__(
+        self,
+        policy: Union[str, Type[DQNPolicy]],
+        env: Union[GymEnv, str],
+        learning_rate: Union[float, Schedule] = 1e-4,
+        buffer_size: int = 1000000,
+        learning_starts: int = 50000,
+        batch_size: Optional[int] = 32,
+        tau: float = 1.0,
+        gamma: float = 0.99,
+        train_freq: Union[int, Tuple[int, str]] = 4,
+        gradient_steps: int = 1,
+        optimize_memory_usage: bool = False,
+        target_update_interval: int = 10000,
+        exploration_fraction: float = 0.1,
+        exploration_initial_eps: float = 1.0,
+        exploration_final_eps: float = 0.05,
+        max_grad_norm: float = 10,
+        tensorboard_log: Optional[str] = None,
+        create_eval_env: bool = False,
+        policy_kwargs: Optional[Dict[str, Any]] = None,
+        verbose: int = 0,
+        seed: Optional[int] = None,
+        device: Union[th.device, str] = "auto",
+        _init_setup_model: bool = True,
+    ):
+
+        super(DQN, self).__init__(
+            policy,
+            env,
+            DQNPolicy,
+            learning_rate,
+            buffer_size,
+            learning_starts,
+            batch_size,
+            tau,
+            gamma,
+            train_freq,
+            gradient_steps,
+            action_noise=None,  # No action noise
+            policy_kwargs=policy_kwargs,
+            tensorboard_log=tensorboard_log,
+            verbose=verbose,
+            device=device,
+            create_eval_env=create_eval_env,
+            seed=seed,
+            sde_support=False,
+            optimize_memory_usage=optimize_memory_usage,
+            supported_action_spaces=(gym.spaces.Discrete,),
+        )
+
+        self.exploration_initial_eps = exploration_initial_eps
+        self.exploration_final_eps = exploration_final_eps
+        self.exploration_fraction = exploration_fraction
+        self.target_update_interval = target_update_interval
+        self.max_grad_norm = max_grad_norm
+        # "epsilon" for the epsilon-greedy exploration
+        self.exploration_rate = 0.0
+        # Linear schedule will be defined in `_setup_model()`
+        self.exploration_schedule = None
+        self.q_net, self.q_net_target = None, None
+
+        if _init_setup_model:
+            self._setup_model()
+
+    def _setup_model(self) -> None:
+        super(DQN, self)._setup_model()
+        self._create_aliases()
+        self.exploration_schedule = get_linear_fn(
+            self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction
+        )
+
+    def _create_aliases(self) -> None:
+        self.q_net = self.policy.q_net
+        self.q_net_target = self.policy.q_net_target
+
+    def _on_step(self) -> None:
+        """
+        Update the exploration rate and target network if needed.
+        This method is called in ``collect_rollouts()`` after each step in the environment.
+        """
+        if self.num_timesteps % self.target_update_interval == 0:
+            polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau)
+
+        self.exploration_rate = self.exploration_schedule(self._current_progress_remaining)
+        logger.record("rollout/exploration rate", self.exploration_rate)
+
+    def train(self, gradient_steps: int, batch_size: int = 100) -> None:
+        # Update learning rate according to schedule
+        self._update_learning_rate(self.policy.optimizer)
+
+        losses = []
+        for _ in range(gradient_steps):
+            # Sample replay buffer
+            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
+
+            with th.no_grad():
+                # Compute the next Q-values using the target network
+                next_q_values = self.q_net_target(replay_data.next_observations)
+                # Follow greedy policy: use the one with the highest value
+                next_q_values, _ = next_q_values.max(dim=1)
+                # Avoid potential broadcast issue
+                next_q_values = next_q_values.reshape(-1, 1)
+                # 1-step TD target
+                target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
+
+            # Get current Q-values estimates
+            current_q_values = self.q_net(replay_data.observations)
+
+            # Retrieve the q-values for the actions from the replay buffer
+            current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long())
+
+            # Compute Huber loss (less sensitive to outliers)
+            loss = F.smooth_l1_loss(current_q_values, target_q_values)
+            losses.append(loss.item())
+
+            # Optimize the policy
+            self.policy.optimizer.zero_grad()
+            loss.backward()
+            # Clip gradient norm
+            th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
+            self.policy.optimizer.step()
+
+        # Increase update counter
+        self._n_updates += gradient_steps
+
+        logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
+        logger.record("train/loss", np.mean(losses))
+
+    def predict(
+        self,
+        observation: np.ndarray,
+        state: Optional[np.ndarray] = None,
+        mask: Optional[np.ndarray] = None,
+        deterministic: bool = False,
+    ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+        """
+        Overrides the base_class predict function to include epsilon-greedy exploration.
+
+        :param observation: the input observation
+        :param state: The last states (can be None, used in recurrent policies)
+        :param mask: The last masks (can be None, used in recurrent policies)
+        :param deterministic: Whether or not to return deterministic actions.
+        :return: the model's action and the next state
+            (used in recurrent policies)
+        """
+        if not deterministic and np.random.rand() < self.exploration_rate:
+            if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
+                n_batch = observation.shape[0]
+                action = np.array([self.action_space.sample() for _ in range(n_batch)])
+            else:
+                action = np.array(self.action_space.sample())
+        else:
+            action, state = self.policy.predict(observation, state, mask, deterministic)
+        return action, state
+
+    def learn(
+        self,
+        total_timesteps: int,
+        callback: MaybeCallback = None,
+        log_interval: int = 4,
+        eval_env: Optional[GymEnv] = None,
+        eval_freq: int = -1,
+        n_eval_episodes: int = 5,
+        tb_log_name: str = "DQN",
+        eval_log_path: Optional[str] = None,
+        reset_num_timesteps: bool = True,
+    ) -> OffPolicyAlgorithm:
+
+        return super(DQN, self).learn(
+            total_timesteps=total_timesteps,
+            callback=callback,
+            log_interval=log_interval,
+            eval_env=eval_env,
+            eval_freq=eval_freq,
+            n_eval_episodes=n_eval_episodes,
+            tb_log_name=tb_log_name,
+            eval_log_path=eval_log_path,
+            reset_num_timesteps=reset_num_timesteps,
+        )
+
+    def _excluded_save_params(self) -> List[str]:
+        return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"]
+
+    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
+        state_dicts = ["policy", "policy.optimizer"]
+
+        return state_dicts, []

crazy_functions/test_project/python/dqn/policies.py

@@ -0,0 +1,237 @@
+from typing import Any, Dict, List, Optional, Type
+
+import gym
+import torch as th
+from torch import nn
+
+from stable_baselines3.common.policies import BasePolicy, register_policy
+from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
+from stable_baselines3.common.type_aliases import Schedule
+
+
+class QNetwork(BasePolicy):
+    """
+    Action-Value (Q-Value) network for DQN
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        features_extractor: nn.Module,
+        features_dim: int,
+        net_arch: Optional[List[int]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        normalize_images: bool = True,
+    ):
+        super(QNetwork, self).__init__(
+            observation_space,
+            action_space,
+            features_extractor=features_extractor,
+            normalize_images=normalize_images,
+        )
+
+        if net_arch is None:
+            net_arch = [64, 64]
+
+        self.net_arch = net_arch
+        self.activation_fn = activation_fn
+        self.features_extractor = features_extractor
+        self.features_dim = features_dim
+        self.normalize_images = normalize_images
+        action_dim = self.action_space.n  # number of actions
+        q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
+        self.q_net = nn.Sequential(*q_net)
+
+    def forward(self, obs: th.Tensor) -> th.Tensor:
+        """
+        Predict the q-values.
+
+        :param obs: Observation
+        :return: The estimated Q-Value for each action.
+        """
+        return self.q_net(self.extract_features(obs))
+
+    def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
+        q_values = self.forward(observation)
+        # Greedy action
+        action = q_values.argmax(dim=1).reshape(-1)
+        return action
+
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
+
+        data.update(
+            dict(
+                net_arch=self.net_arch,
+                features_dim=self.features_dim,
+                activation_fn=self.activation_fn,
+                features_extractor=self.features_extractor,
+            )
+        )
+        return data
+
+
+class DQNPolicy(BasePolicy):
+    """
+    Policy class with Q-Value Net and target net for DQN
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param features_extractor_class: Features extractor to use.
+    :param features_extractor_kwargs: Keyword arguments
+        to pass to the features extractor.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        lr_schedule: Schedule,
+        net_arch: Optional[List[int]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
+        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super(DQNPolicy, self).__init__(
+            observation_space,
+            action_space,
+            features_extractor_class,
+            features_extractor_kwargs,
+            optimizer_class=optimizer_class,
+            optimizer_kwargs=optimizer_kwargs,
+        )
+
+        if net_arch is None:
+            if features_extractor_class == FlattenExtractor:
+                net_arch = [64, 64]
+            else:
+                net_arch = []
+
+        self.net_arch = net_arch
+        self.activation_fn = activation_fn
+        self.normalize_images = normalize_images
+
+        self.net_args = {
+            "observation_space": self.observation_space,
+            "action_space": self.action_space,
+            "net_arch": self.net_arch,
+            "activation_fn": self.activation_fn,
+            "normalize_images": normalize_images,
+        }
+
+        self.q_net, self.q_net_target = None, None
+        self._build(lr_schedule)
+
+    def _build(self, lr_schedule: Schedule) -> None:
+        """
+        Create the network and the optimizer.
+
+        :param lr_schedule: Learning rate schedule
+            lr_schedule(1) is the initial learning rate
+        """
+
+        self.q_net = self.make_q_net()
+        self.q_net_target = self.make_q_net()
+        self.q_net_target.load_state_dict(self.q_net.state_dict())
+
+        # Setup optimizer with initial learning rate
+        self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
+
+    def make_q_net(self) -> QNetwork:
+        # Make sure we always have separate networks for features extractors etc
+        net_args = self._update_features_extractor(self.net_args, features_extractor=None)
+        return QNetwork(**net_args).to(self.device)
+
+    def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
+        return self._predict(obs, deterministic=deterministic)
+
+    def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
+        return self.q_net._predict(obs, deterministic=deterministic)
+
+    def _get_constructor_parameters(self) -> Dict[str, Any]:
+        data = super()._get_constructor_parameters()
+
+        data.update(
+            dict(
+                net_arch=self.net_args["net_arch"],
+                activation_fn=self.net_args["activation_fn"],
+                lr_schedule=self._dummy_schedule,  # dummy lr schedule, not needed for loading policy alone
+                optimizer_class=self.optimizer_class,
+                optimizer_kwargs=self.optimizer_kwargs,
+                features_extractor_class=self.features_extractor_class,
+                features_extractor_kwargs=self.features_extractor_kwargs,
+            )
+        )
+        return data
+
+
+MlpPolicy = DQNPolicy
+
+
+class CnnPolicy(DQNPolicy):
+    """
+    Policy class for DQN when using images as input.
+
+    :param observation_space: Observation space
+    :param action_space: Action space
+    :param lr_schedule: Learning rate schedule (could be constant)
+    :param net_arch: The specification of the policy and value networks.
+    :param activation_fn: Activation function
+    :param features_extractor_class: Features extractor to use.
+    :param normalize_images: Whether to normalize images or not,
+         dividing by 255.0 (True by default)
+    :param optimizer_class: The optimizer to use,
+        ``th.optim.Adam`` by default
+    :param optimizer_kwargs: Additional keyword arguments,
+        excluding the learning rate, to pass to the optimizer
+    """
+
+    def __init__(
+        self,
+        observation_space: gym.spaces.Space,
+        action_space: gym.spaces.Space,
+        lr_schedule: Schedule,
+        net_arch: Optional[List[int]] = None,
+        activation_fn: Type[nn.Module] = nn.ReLU,
+        features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
+        features_extractor_kwargs: Optional[Dict[str, Any]] = None,
+        normalize_images: bool = True,
+        optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
+        optimizer_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super(CnnPolicy, self).__init__(
+            observation_space,
+            action_space,
+            lr_schedule,
+            net_arch,
+            activation_fn,
+            features_extractor_class,
+            features_extractor_kwargs,
+            normalize_images,
+            optimizer_class,
+            optimizer_kwargs,
+        )
+
+
+register_policy("MlpPolicy", MlpPolicy)
+register_policy("CnnPolicy", CnnPolicy)

crazy_functions/test_project/python/dqn/来源

@@ -0,0 +1,2 @@
+github stablebaseline3
+https://github.com/DLR-RM/stable-baselines3

crazy_functions/test_project/其他测试

@@ -0,0 +1,27 @@
+"In practice, we found that a high-entropy initial state is more likely to increase the speed of training.
+The entropy is calculated by:
+$$H=-\sum_{k= 1}^{n_k} p(k) \cdot \log p(k), p(k)=\frac{|A_k|}{|\mathcal{A}|}$$
+where $H$ is the entropy, $|A_k|$ is the number of agent nodes in $k$-th cluster, $|\mathcal{A}|$ is the total number of agents.
+To ensure the Cooperation Graph initialization has higher entropy,
+we will randomly generate multiple initial states, 
+rank by their entropy and then pick the one with maximum $H$."
+
+```
+FROM ubuntu:latest
+
+RUN apt-get update && \
+    apt-get install -y python3 python3-pip && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN echo '[global]' > /etc/pip.conf && \
+    echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
+    echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
+
+RUN pip3 install gradio requests[socks] mdtex2html
+
+COPY . /gpt
+WORKDIR /gpt
+
+
+CMD ["python3", "main.py"]
+```

crazy_functions/下载arxiv论文翻译摘要.py

@@ -0,0 +1,194 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file, get_conf
+import re, requests, unicodedata, os
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+def download_arxiv_(url_pdf):
+    if 'arxiv.org' not in url_pdf:
+        if ('.' in url_pdf) and ('/' not in url_pdf):
+            new_url = 'https://arxiv.org/abs/'+url_pdf
+            print('下载编号：', url_pdf, '自动定位：', new_url)
+            # download_arxiv_(new_url)
+            return download_arxiv_(new_url)
+        else:
+            print('不能识别的URL！')
+            return None
+    if 'abs' in url_pdf:
+        url_pdf = url_pdf.replace('abs', 'pdf')
+        url_pdf = url_pdf + '.pdf'
+
+    url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')
+    title, other_info = get_name(_url_=url_abs)
+
+    paper_id = title.split()[0]  # '[1712.00559]'
+    if '2' in other_info['year']:
+        title = other_info['year'] + ' ' + title
+
+    known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']
+    for k in known_conf:
+        if k in other_info['comment']:
+            title = k + ' ' + title
+
+    download_dir = './gpt_log/arxiv/'
+    os.makedirs(download_dir, exist_ok=True)
+
+    title_str = title.replace('?', '？')\
+        .replace(':', '：')\
+        .replace('\"', '“')\
+        .replace('\n', '')\
+        .replace('  ', ' ')\
+        .replace('  ', ' ')
+
+    requests_pdf_url = url_pdf
+    file_path = download_dir+title_str
+    # if os.path.exists(file_path):
+    #     print('返回缓存文件')
+    #     return './gpt_log/arxiv/'+title_str
+
+    print('下载中')
+    proxies, = get_conf('proxies')
+    r = requests.get(requests_pdf_url, proxies=proxies)
+    with open(file_path, 'wb+') as f:
+        f.write(r.content)
+    print('下载完成')
+
+    # print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
+    # subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
+
+    x = "%s  %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
+    x = x.replace('?', '？')\
+        .replace(':', '：')\
+        .replace('\"', '“')\
+        .replace('\n', '')\
+        .replace('  ', ' ')\
+        .replace('  ', ' ')
+    return './gpt_log/arxiv/'+title_str, other_info
+
+
+def get_name(_url_):
+    import os
+    from bs4 import BeautifulSoup
+    print('正在获取文献名！')
+    print(_url_)
+
+    # arxiv_recall = {}
+    # if os.path.exists('./arxiv_recall.pkl'):
+    #     with open('./arxiv_recall.pkl', 'rb') as f:
+    #         arxiv_recall = pickle.load(f)
+
+    # if _url_ in arxiv_recall:
+    #     print('在缓存中')
+    #     return arxiv_recall[_url_]
+
+    proxies, = get_conf('proxies')
+    res = requests.get(_url_, proxies=proxies)
+
+    bs = BeautifulSoup(res.text, 'html.parser')
+    other_details = {}
+
+    # get year
+    try:
+        year = bs.find_all(class_='dateline')[0].text
+        year = re.search(r'(\d{4})', year, re.M | re.I).group(1)
+        other_details['year'] = year
+        abstract = bs.find_all(class_='abstract mathjax')[0].text
+        other_details['abstract'] = abstract
+    except:
+        other_details['year'] = ''
+        print('年份获取失败')
+
+    # get author
+    try:
+        authors = bs.find_all(class_='authors')[0].text
+        authors = authors.split('Authors:')[1]
+        other_details['authors'] = authors
+    except:
+        other_details['authors'] = ''
+        print('authors获取失败')
+
+    # get comment
+    try:
+        comment = bs.find_all(class_='metatable')[0].text
+        real_comment = None
+        for item in comment.replace('\n', ' ').split('   '):
+            if 'Comments' in item:
+                real_comment = item
+        if real_comment is not None:
+            other_details['comment'] = real_comment
+        else:
+            other_details['comment'] = ''
+    except:
+        other_details['comment'] = ''
+        print('年份获取失败')
+
+    title_str = BeautifulSoup(
+        res.text, 'html.parser').find('title').contents[0]
+    print('获取成功：', title_str)
+    # arxiv_recall[_url_] = (title_str+'.pdf', other_details)
+    # with open('./arxiv_recall.pkl', 'wb') as f:
+    #     pickle.dump(arxiv_recall, f)
+
+    return title_str+'.pdf', other_details
+
+
+
+@CatchException
+def 下载arxiv论文并翻译摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+
+    CRAZY_FUNCTION_INFO = "下载arxiv论文并翻译摘要，函数插件作者[binary-husky]。正在提取摘要并下载PDF文档……"
+    import glob
+    import os
+
+    # 基本信息：功能、贡献者
+    chatbot.append(["函数插件功能？", CRAZY_FUNCTION_INFO])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import pdfminer, bs4
+    except:
+        report_execption(chatbot, history, 
+            a = f"解析项目: {txt}", 
+            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+
+    # 提取摘要，下载PDF文档
+    try:
+        pdf_path, info = download_arxiv_(txt)
+    except:
+        report_execption(chatbot, history, 
+            a = f"解析项目: {txt}", 
+            b = f"下载pdf文件未成功")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    
+    # 翻译摘要等
+    i_say =            f"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}"
+    i_say_show_user =  f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'
+    chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    msg = '正常'
+    # ** gpt request **
+    # 单线，获取文章meta信息
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=i_say,
+        inputs_show_user=i_say_show_user,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot, history=[],
+        sys_prompt="Your job is to collect information from materials and translate to Chinese。",
+    )
+
+    chatbot[-1] = (i_say_show_user, gpt_say)
+    history.append(i_say_show_user); history.append(gpt_say)
+    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+    # 写入文件
+    import shutil
+    # 重置文件的创建时间
+    shutil.copyfile(pdf_path, f'./gpt_log/{os.path.basename(pdf_path)}'); os.remove(pdf_path)
+    res = write_results_to_file(history)
+    chatbot.append(("完成了吗？", res + "\n\nPDF文件也已经下载"))
+    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+

crazy_functions/代码重写为全英文_多线程.py

@@ -0,0 +1,138 @@
+import threading
+from request_llm.bridge_all import predict_no_ui_long_connection
+from toolbox import update_ui
+from toolbox import CatchException, write_results_to_file, report_execption
+from .crazy_utils import breakdown_txt_to_satisfy_token_limit
+
+def extract_code_block_carefully(txt):
+    splitted = txt.split('```')
+    n_code_block_seg = len(splitted) - 1
+    if n_code_block_seg <= 1: return txt
+    # 剩下的情况都开头除去 ``` 结尾除去一次 ```
+    txt_out = '```'.join(splitted[1:-1])
+    return txt_out
+
+
+
+def break_txt_into_half_at_some_linebreak(txt):
+    lines = txt.split('\n')
+    n_lines = len(lines)
+    pre = lines[:(n_lines//2)]
+    post = lines[(n_lines//2):]
+    return "\n".join(pre), "\n".join(post)
+
+
+@CatchException
+def 全项目切换英文(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
+    # 第1步：清空历史，以免输入溢出
+    history = []
+
+    # 第2步：尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+    except:
+        report_execption(chatbot, history, 
+            a = f"解析项目: {txt}", 
+            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 第3步：集合文件
+    import time, glob, os, shutil, re
+    os.makedirs('gpt_log/generated_english_version', exist_ok=True)
+    os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)
+    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
+                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
+    # file_manifest = ['./toolbox.py']
+    i_say_show_user_buffer = []
+
+    # 第4步：随便显示点什么防止卡顿的感觉
+    for index, fp in enumerate(file_manifest):
+        # if 'test_project' in fp: continue
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+        i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文，只输出转化后的英文代码，请用代码块输出代码: {os.path.abspath(fp)}'
+        i_say_show_user_buffer.append(i_say_show_user)
+        chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作，中间过程不予显示."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+    # 第5步：Token限制下的截断与处理
+    MAX_TOKEN = 3000
+    from request_llm.bridge_all import model_info
+    enc = model_info["gpt-3.5-turbo"]['tokenizer']
+    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
+
+
+    # 第6步：任务函数
+    mutable_return = [None for _ in file_manifest]
+    observe_window = [[""] for _ in file_manifest]
+    def thread_worker(fp,index):
+        if index > 10: 
+            time.sleep(60)
+            print('Openai 限制免费用户每分钟20次请求，降低请求频率中。')
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+        i_say_template = lambda fp, file_content: f'接下来请将以下代码中包含的所有中文转化为英文，只输出代码，文件名是{fp}，文件代码是 ```{file_content}```'
+        try:
+            gpt_say = ""
+            # 分解代码文件
+            file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN)
+            for file_content_partial in file_content_breakdown:
+                i_say = i_say_template(fp, file_content_partial)
+                # # ** gpt request **
+                gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index])
+                gpt_say_partial = extract_code_block_carefully(gpt_say_partial)
+                gpt_say += gpt_say_partial
+            mutable_return[index] = gpt_say
+        except ConnectionAbortedError as token_exceed_err:
+            print('至少一个线程任务Token溢出而失败', e)
+        except Exception as e:
+            print('至少一个线程任务意外失败', e)
+
+    # 第7步：所有线程同时开始执行任务函数
+    handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]
+    for h in handles:
+        h.daemon = True
+        h.start()
+    chatbot.append(('开始了吗？', f'多线程操作已经开始'))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 第8步：循环轮询各个线程是否执行完毕
+    cnt = 0
+    while True:
+        cnt += 1
+        time.sleep(0.2)
+        th_alive = [h.is_alive() for h in handles]
+        if not any(th_alive): break
+        # 更好的UI视觉效果
+        observe_win = []
+        for thread_index, alive in enumerate(th_alive): 
+            observe_win.append("[ ..."+observe_window[thread_index][0][-60:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"... ]")
+        stat = [f'执行中: {obs}\n\n' if alive else '已完成\n\n' for alive, obs in zip(th_alive, observe_win)]
+        stat_str = ''.join(stat)
+        chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1)))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 第9步：把结果写入文件
+    for index, h in enumerate(handles):
+        h.join() # 这里其实不需要join了，肯定已经都结束了
+        fp = file_manifest[index]
+        gpt_say = mutable_return[index]
+        i_say_show_user = i_say_show_user_buffer[index]
+
+        where_to_relocate = f'gpt_log/generated_english_version/{fp}'
+        if gpt_say is not None:
+            with open(where_to_relocate, 'w+', encoding='utf-8') as f:  
+                f.write(gpt_say)
+        else:  # 失败
+            shutil.copyfile(file_manifest[index], where_to_relocate)
+        chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\n\n存入{os.path.abspath(where_to_relocate)}'))
+        history.append(i_say_show_user); history.append(gpt_say)
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        time.sleep(1)
+
+    # 第10步：备份一个文件
+    res = write_results_to_file(history)
+    chatbot.append(("生成一份任务执行报告", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

crazy_functions/图片生成.py

@@ -0,0 +1,67 @@
+from toolbox import CatchException, update_ui, get_conf, select_api_key
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+import datetime
+
+
+def gen_image(llm_kwargs, prompt, resolution="256x256"):
+    import requests, json, time, os
+    from request_llm.bridge_all import model_info
+
+    proxies, = get_conf('proxies')
+    # Set up OpenAI API key and model 
+    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
+    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+    # 'https://api.openai.com/v1/chat/completions'
+    img_endpoint = chat_endpoint.replace('chat/completions','images/generations')
+    # # Generate the image
+    url = img_endpoint
+    headers = {
+        'Authorization': f"Bearer {api_key}",
+        'Content-Type': 'application/json'
+    }
+    data = {
+        'prompt': prompt,
+        'n': 1,
+        'size': resolution,
+        'response_format': 'url'
+    }
+    response = requests.post(url, headers=headers, json=data, proxies=proxies)
+    print(response.content)
+    image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
+
+    # 文件保存到本地
+    r = requests.get(image_url, proxies=proxies)
+    file_path = 'gpt_log/image_gen/'
+    os.makedirs(file_path, exist_ok=True)
+    file_name = 'Image' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.png'
+    with open(file_path+file_name, 'wb+') as f: f.write(r.content)
+
+
+    return image_url, file_path+file_name
+
+
+
+@CatchException
+def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-xxxx或者api2d-xxxx。如果中文效果不理想, 尝试Prompt。正在处理中 ....."))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    resolution = plugin_kwargs.get("advanced_arg", '256x256')
+    image_url, image_path = gen_image(llm_kwargs, prompt, resolution)
+    chatbot.append([prompt,  
+        f'图像中转网址: <br/>`{image_url}`<br/>'+
+        f'中转网址预览: <br/><div align="center"><img src="{image_url}"></div>'
+        f'本地文件地址: <br/>`{image_path}`<br/>'+
+        f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
+    ])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

crazy_functions/对话历史存档.py

@@ -0,0 +1,143 @@
+from toolbox import CatchException, update_ui
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+import re
+
+def write_chat_to_file(chatbot, history=None, file_name=None):
+    """
+    将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
+    """
+    import os
+    import time
+    if file_name is None:
+        file_name = 'chatGPT对话历史' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
+    os.makedirs('./gpt_log/', exist_ok=True)
+    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
+        from theme import advanced_css
+        f.write(f'<!DOCTYPE html><head><meta charset="utf-8"><title>对话历史</title><style>{advanced_css}</style></head>')
+        for i, contents in enumerate(chatbot):
+            for j, content in enumerate(contents):
+                try:    # 这个bug没找到触发条件，暂时先这样顶一下
+                    if type(content) != str: content = str(content)
+                except:
+                    continue
+                f.write(content)
+                if j == 0:
+                    f.write('<hr style="border-top: dotted 3px #ccc;">')
+            f.write('<hr color="red"> \n\n')
+        f.write('<hr color="blue"> \n\n raw chat context:\n')
+        f.write('<code>')
+        for h in history:
+            f.write("\n>>>" + h)
+        f.write('</code>')
+    res = '对话历史写入：' + os.path.abspath(f'./gpt_log/{file_name}')
+    print(res)
+    return res
+
+def gen_file_preview(file_name):
+    try:
+        with open(file_name, 'r', encoding='utf8') as f:
+            file_content = f.read()
+        # pattern to match the text between <head> and </head>
+        pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
+        file_content = re.sub(pattern, '', file_content)
+        html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
+        history = history.strip('<code>')
+        history = history.strip('</code>')
+        history = history.split("\n>>>")
+        return list(filter(lambda x:x!="", history))[0][:100]
+    except:
+        return ""
+
+def read_file_to_chat(chatbot, history, file_name):
+    with open(file_name, 'r', encoding='utf8') as f:
+        file_content = f.read()
+    # pattern to match the text between <head> and </head>
+    pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
+    file_content = re.sub(pattern, '', file_content)
+    html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
+    history = history.strip('<code>')
+    history = history.strip('</code>')
+    history = history.split("\n>>>")
+    history = list(filter(lambda x:x!="", history))
+    html = html.split('<hr color="red"> \n\n')
+    html = list(filter(lambda x:x!="", html))
+    chatbot.clear()
+    for i, h in enumerate(html):
+        i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
+        chatbot.append([i_say, gpt_say])
+    chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
+    return chatbot, history    
+
+@CatchException
+def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+
+    chatbot.append(("保存当前对话", 
+        f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用“载入对话历史存档”还原当下的对话。\n警告！被保存的对话历史可以被使用该系统的任何人查阅。"))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
+
+def hide_cwd(str):
+    import os
+    current_path = os.getcwd()
+    replace_path = "."
+    return str.replace(current_path, replace_path)
+
+@CatchException
+def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    from .crazy_utils import get_files_from_everything
+    success, file_manifest, _ = get_files_from_everything(txt, type='.html')
+
+    if not success:
+        if txt == "": txt = '空空如也的输入栏'
+        import glob
+        local_history = "<br/>".join(["`"+hide_cwd(f)+f" ({gen_file_preview(f)})"+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
+        chatbot.append([f"正在查找对话历史文件（html格式）: {txt}", f"找不到任何html文件: {txt}。但本地存储了以下历史文件，您可以将任意一个文件路径粘贴到输入区，然后重试：<br/>{local_history}"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    try:
+        chatbot, history = read_file_to_chat(chatbot, history, file_manifest[0])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    except:
+        chatbot.append([f"载入对话历史文件", f"对话历史文件损坏！"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+@CatchException
+def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+
+    import glob, os
+    local_history = "<br/>".join(["`"+hide_cwd(f)+"`" for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True)])
+    for f in glob.glob(f'gpt_log/**/chatGPT对话历史*.html', recursive=True):
+        os.remove(f)
+    chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    return
+
+

crazy_functions/总结word文档.py

@@ -0,0 +1,127 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+fast_debug = False
+
+
+def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import time, os
+    # pip install python-docx 用于docx格式，跨平台
+    # pip install pywin32 用于doc格式，仅支持Win平台
+    for index, fp in enumerate(file_manifest):
+        if fp.split(".")[-1] == "docx":
+            from docx import Document
+            doc = Document(fp)
+            file_content = "\n".join([para.text for para in doc.paragraphs])
+        else:
+            import win32com.client
+            word = win32com.client.Dispatch("Word.Application")
+            word.visible = False
+            # 打开文件
+            print('fp', os.getcwd())
+            doc = word.Documents.Open(os.getcwd() + '/' + fp)
+            # file_content = doc.Content.Text
+            doc = word.ActiveDocument
+            file_content = doc.Range().Text
+            doc.Close()
+            word.Quit()
+
+        print(file_content)
+        # private_upload里面的文件名在解压zip后容易出现乱码（rar和7z格式正常），故可以只分析文章内容，不输入文件名
+        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+        from request_llm.bridge_all import model_info
+        max_token = model_info[llm_kwargs['llm_model']]['max_token']
+        TOKEN_LIMIT_PER_FRAGMENT = max_token * 3 // 4
+        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=file_content,  
+            get_token_fn=model_info[llm_kwargs['llm_model']]['token_cnt'], 
+            limit=TOKEN_LIMIT_PER_FRAGMENT
+        )
+        this_paper_history = []
+        for i, paper_frag in enumerate(paper_fragments):
+            i_say = f'请对下面的文章片段用中文做概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{paper_frag}```'
+            i_say_show_user = f'请对下面的文章片段做概述: {os.path.abspath(fp)}的第{i+1}/{len(paper_fragments)}个片段。'
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say, 
+                inputs_show_user=i_say_show_user, 
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot, 
+                history=[],
+                sys_prompt="总结文章。"
+            )
+
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.extend([i_say_show_user,gpt_say])
+            this_paper_history.extend([i_say_show_user,gpt_say])
+
+        # 已经对该文章的所有片段总结完毕，如果文章被切分了，
+        if len(paper_fragments) > 1:
+            i_say = f"根据以上的对话，总结文章{os.path.abspath(fp)}的主要内容。"
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say, 
+                inputs_show_user=i_say, 
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot, 
+                history=this_paper_history,
+                sys_prompt="总结文章。"
+            )
+
+            history.extend([i_say,gpt_say])
+            this_paper_history.extend([i_say,gpt_say])
+
+        res = write_results_to_file(history)
+        chatbot.append(("完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    res = write_results_to_file(history)
+    chatbot.append(("所有文件都总结完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+@CatchException
+def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    import glob, os
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "批量总结Word文档。函数插件贡献者: JasonGuo1。注意, 如果是.doc文件, 请先转化为.docx格式。"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        from docx import Document
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+
+    # 检测输入参数，如没有给定输入参数，直接退出
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 搜索需要处理的文件清单
+    if txt.endswith('.docx') or txt.endswith('.doc'):
+        file_manifest = [txt]
+    else:
+        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.docx', recursive=True)] + \
+                        [f for f in glob.glob(f'{project_folder}/**/*.doc', recursive=True)]
+
+    # 如果没找到任何文件
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.docx或doc文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 开始正式执行任务
+    yield from 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/总结音视频.py

@@ -0,0 +1,184 @@
+from toolbox import CatchException, report_execption, select_api_key, update_ui, write_results_to_file, get_conf
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+def split_audio_file(filename, split_duration=1000):
+    """
+    根据给定的切割时长将音频文件切割成多个片段。
+
+    Args:
+        filename (str): 需要被切割的音频文件名。
+        split_duration (int, optional): 每个切割音频片段的时长（以秒为单位）。默认值为1000。
+
+    Returns:
+        filelist (list): 一个包含所有切割音频片段文件路径的列表。
+
+    """
+    from moviepy.editor import AudioFileClip
+    import os
+    os.makedirs('gpt_log/mp3/cut/', exist_ok=True)  # 创建存储切割音频的文件夹
+
+    # 读取音频文件
+    audio = AudioFileClip(filename)
+
+    # 计算文件总时长和切割点
+    total_duration = audio.duration
+    split_points = list(range(0, int(total_duration), split_duration))
+    split_points.append(int(total_duration))
+    filelist = []
+
+    # 切割音频文件
+    for i in range(len(split_points) - 1):
+        start_time = split_points[i]
+        end_time = split_points[i + 1]
+        split_audio = audio.subclip(start_time, end_time)
+        split_audio.write_audiofile(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
+        filelist.append(f"gpt_log/mp3/cut/{filename[0]}_{i}.mp3")
+
+    audio.close()
+    return filelist
+
+def AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history):
+    import os, requests
+    from moviepy.editor import AudioFileClip
+    from request_llm.bridge_all import model_info
+
+    # 设置OpenAI密钥和模型
+    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
+    chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+
+    whisper_endpoint = chat_endpoint.replace('chat/completions', 'audio/transcriptions')
+    url = whisper_endpoint
+    headers = {
+        'Authorization': f"Bearer {api_key}"
+    }
+
+    os.makedirs('gpt_log/mp3/', exist_ok=True)
+    for index, fp in enumerate(file_manifest):
+        audio_history = []
+        # 提取文件扩展名
+        ext = os.path.splitext(fp)[1]
+        # 提取视频中的音频
+        if ext not in [".mp3", ".wav", ".m4a", ".mpga"]:
+            audio_clip = AudioFileClip(fp)
+            audio_clip.write_audiofile(f'gpt_log/mp3/output{index}.mp3')
+            fp = f'gpt_log/mp3/output{index}.mp3'
+        # 调用whisper模型音频转文字
+        voice = split_audio_file(fp)
+        for j, i in enumerate(voice):
+            with open(i, 'rb') as f:
+                file_content = f.read()  # 读取文件内容到内存
+                files = {
+                    'file': (os.path.basename(i), file_content),
+                }
+                data = {
+                    "model": "whisper-1",
+                    "prompt": parse_prompt,
+                    'response_format': "text"
+                }
+
+            chatbot.append([f"将 {i} 发送到openai音频解析终端 (whisper)，当前参数：{parse_prompt}", "正在处理 ..."])
+            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+            proxies, = get_conf('proxies')
+            response = requests.post(url, headers=headers, files=files, data=data, proxies=proxies).text
+
+            chatbot.append(["音频解析结果", response])
+            history.extend(["音频解析结果", response])
+            yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+            i_say = f'请对下面的音频片段做概述，音频内容是 ```{response}```'
+            i_say_show_user = f'第{index + 1}段音频的第{j + 1} / {len(voice)}片段。'
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say,
+                inputs_show_user=i_say_show_user,
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot,
+                history=[],
+                sys_prompt=f"总结音频。音频文件名{fp}"
+            )
+
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.extend([i_say_show_user, gpt_say])
+            audio_history.extend([i_say_show_user, gpt_say])
+
+        # 已经对该文章的所有片段总结完毕，如果文章被切分了
+        result = "".join(audio_history)
+        if len(audio_history) > 1:
+            i_say = f"根据以上的对话，使用中文总结音频“{result}”的主要内容。"
+            i_say_show_user = f'第{index + 1}段音频的主要内容：'
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say,
+                inputs_show_user=i_say_show_user,
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot,
+                history=audio_history,
+                sys_prompt="总结文章。"
+            )
+
+            history.extend([i_say, gpt_say])
+            audio_history.extend([i_say, gpt_say])
+
+        res = write_results_to_file(history)
+        chatbot.append((f"第{index + 1}段音频完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    # 删除中间文件夹
+    import shutil
+    shutil.rmtree('gpt_log/mp3')
+    res = write_results_to_file(history)
+    chatbot.append(("所有音频都总结完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history)
+
+
+@CatchException
+def 总结音视频(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, WEB_PORT):
+    import glob, os
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "总结音视频内容，函数插件贡献者: dalvqw & BinaryHusky"])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    try:
+        from moviepy.editor import AudioFileClip
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade moviepy```。")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+
+    # 检测输入参数，如没有给定输入参数，直接退出
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # 搜索需要处理的文件清单
+    extensions = ['.mp4', '.m4a', '.wav', '.mpga', '.mpeg', '.mp3', '.avi', '.mkv', '.flac', '.aac']
+
+    if txt.endswith(tuple(extensions)):
+        file_manifest = [txt]
+    else:
+        file_manifest = []
+        for extension in extensions:
+            file_manifest.extend(glob.glob(f'{project_folder}/**/*{extension}', recursive=True))
+
+    # 如果没找到任何文件
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何音频或视频文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+
+    # 开始正式执行任务
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    parse_prompt = plugin_kwargs.get("advanced_arg", '将音频解析为简体中文')
+    yield from AnalyAudio(parse_prompt, file_manifest, llm_kwargs, chatbot, history)
+
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

crazy_functions/批量Markdown翻译.py

@@ -0,0 +1,247 @@
+from toolbox import update_ui, trimmed_format_exc, gen_time_str
+from toolbox import CatchException, report_execption, write_results_to_file
+fast_debug = False
+
+class PaperFileGroup():
+    def __init__(self):
+        self.file_paths = []
+        self.file_contents = []
+        self.sp_file_contents = []
+        self.sp_file_index = []
+        self.sp_file_tag = []
+
+        # count_token
+        from request_llm.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        self.get_token_num = get_token_num
+
+    def run_file_split(self, max_token_limit=1900):
+        """
+        将长文本分离开来
+        """
+        for index, file_content in enumerate(self.file_contents):
+            if self.get_token_num(file_content) < max_token_limit:
+                self.sp_file_contents.append(file_content)
+                self.sp_file_index.append(index)
+                self.sp_file_tag.append(self.file_paths[index])
+            else:
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(file_content, self.get_token_num, max_token_limit)
+                for j, segment in enumerate(segments):
+                    self.sp_file_contents.append(segment)
+                    self.sp_file_index.append(index)
+                    self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.md")
+        print('Segmentation: done')
+
+    def merge_result(self):
+        self.file_result = ["" for _ in range(len(self.file_paths))]
+        for r, k in zip(self.sp_file_result, self.sp_file_index):
+            self.file_result[k] += r
+
+    def write_result(self, language):
+        manifest = []
+        for path, res in zip(self.file_paths, self.file_result):
+            with open(path + f'.{gen_time_str()}.{language}.md', 'w', encoding='utf8') as f:
+                manifest.append(path + f'.{gen_time_str()}.{language}.md')
+                f.write(res)
+        return manifest
+
+def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
+    import time, os, re
+    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+
+    #  <-------- 读取Markdown文件，删除其中的所有注释 ----------> 
+    pfg = PaperFileGroup()
+
+    for index, fp in enumerate(file_manifest):
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+            # 记录删除注释后的文本
+            pfg.file_paths.append(fp)
+            pfg.file_contents.append(file_content)
+
+    #  <-------- 拆分过长的Markdown文件 ----------> 
+    pfg.run_file_split(max_token_limit=1500)
+    n_split = len(pfg.sp_file_contents)
+
+    #  <-------- 多线程翻译开始 ----------> 
+    if language == 'en->zh':
+        inputs_array = ["This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:" + 
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+    elif language == 'zh->en':
+        inputs_array = [f"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:" + 
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+    else:
+        inputs_array = [f"This is a Markdown file, translate it into {language}, do not modify any existing Markdown commands, only answer me with translated results:" + 
+                        f"\n\n{frag}" for frag in pfg.sp_file_contents]
+        inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
+        sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
+
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array=inputs_array,
+        inputs_show_user_array=inputs_show_user_array,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history_array=[[""] for _ in range(n_split)],
+        sys_prompt_array=sys_prompt_array,
+        # max_workers=5,  # OpenAI所允许的最大并行过载
+        scroller_max_len = 80
+    )
+    try:
+        pfg.sp_file_result = []
+        for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
+            pfg.sp_file_result.append(gpt_say)
+        pfg.merge_result()
+        pfg.write_result(language)
+    except:
+        print(trimmed_format_exc())
+
+    #  <-------- 整理结果，退出 ----------> 
+    create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
+    res = write_results_to_file(gpt_response_collection, file_name=create_report_file_name)
+    history = gpt_response_collection
+    chatbot.append((f"{fp}完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+def get_files_from_everything(txt):
+    import glob, os
+
+    success = True
+    if txt.startswith('http'):
+        # 网络的远程文件
+        txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
+        txt = txt.replace("/blob/", "/")
+        import requests
+        from toolbox import get_conf
+        proxies, = get_conf('proxies')
+        r = requests.get(txt, proxies=proxies)
+        with open('./gpt_log/temp.md', 'wb+') as f: f.write(r.content)
+        project_folder = './gpt_log/'
+        file_manifest = ['./gpt_log/temp.md']
+    elif txt.endswith('.md'):
+        # 直接给定文件
+        file_manifest = [txt]
+        project_folder = os.path.dirname(txt)
+    elif os.path.exists(txt):
+        # 本地路径，递归搜索
+        project_folder = txt
+        file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
+    else:
+        success = False
+
+    return success, file_manifest, project_folder
+
+
+@CatchException
+def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+        import glob, os
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+
+    success, file_manifest, project_folder = get_files_from_everything(txt)
+
+    if not success:
+        # 什么都没有
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
+
+
+
+
+
+@CatchException
+def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+        import glob, os
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+    success, file_manifest, project_folder = get_files_from_everything(txt)
+    if not success:
+        # 什么都没有
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='zh->en')
+
+
+@CatchException
+def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import tiktoken
+        import glob, os
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    history = []    # 清空历史，以免输入溢出
+    success, file_manifest, project_folder = get_files_from_everything(txt)
+    if not success:
+        # 什么都没有
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    language = plugin_kwargs.get("advanced_arg", 'Chinese')
+    yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language=language)

crazy_functions/批量总结PDF文档.py

@@ -0,0 +1,166 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+import re
+import unicodedata
+fast_debug = False
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+def is_paragraph_break(match):
+    """
+    根据给定的匹配结果来判断换行符是否表示段落分隔。
+    如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
+    也可以根据之前的内容长度来判断段落是否已经足够长。
+    """
+    prev_char, next_char = match.groups()
+
+    # 句子结束标志
+    sentence_endings = ".!?"
+
+    # 设定一个最小段落长度阈值
+    min_paragraph_length = 140
+
+    if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
+        return "\n\n" 
+    else:
+        return " "
+
+def normalize_text(text):
+    """
+    通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
+    例如，将连字 "fi" 转换为 "f" 和 "i"。
+    """
+    # 对文本进行归一化处理，分解连字
+    normalized_text = unicodedata.normalize("NFKD", text)
+
+    # 替换其他特殊字符
+    cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
+
+    return cleaned_text
+
+def clean_text(raw_text):
+    """
+    对从 PDF 提取出的原始文本进行清洗和格式化处理。
+    1. 对原始文本进行归一化处理。
+    2. 替换跨行的连词
+    3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换
+    """
+    # 对文本进行归一化处理
+    normalized_text = normalize_text(raw_text)
+
+    # 替换跨行的连词
+    text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text)
+
+    # 根据前后相邻字符的特点，找到原文本中的换行符
+    newlines = re.compile(r'(\S)\n(\S)')
+
+    # 根据 heuristic 规则，用空格或段落分隔符替换原换行符
+    final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text)
+
+    return final_text.strip()
+
+def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import time, glob, os, fitz
+    print('begin analysis on:', file_manifest)
+    for index, fp in enumerate(file_manifest):
+        with fitz.open(fp) as doc:
+            file_content = ""
+            for page in doc:
+                file_content += page.get_text()
+            file_content = clean_text(file_content)
+            print(file_content)
+
+        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
+        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
+        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
+        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        if not fast_debug: 
+            msg = '正常'
+            # ** gpt request **
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say, 
+                inputs_show_user=i_say_show_user, 
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot, 
+                history=[],
+                sys_prompt="总结文章。"
+            )  # 带超时倒计时
+                
+
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.append(i_say_show_user); history.append(gpt_say)
+            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+            if not fast_debug: time.sleep(2)
+
+    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
+    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
+    chatbot.append((i_say, "[Local Message] waiting gpt response."))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    if not fast_debug: 
+        msg = '正常'
+        # ** gpt request **
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=i_say, 
+            inputs_show_user=i_say, 
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot, 
+            history=history,
+            sys_prompt="总结文章。"
+        )  # 带超时倒计时
+
+        chatbot[-1] = (i_say, gpt_say)
+        history.append(i_say); history.append(gpt_say)
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+        res = write_results_to_file(history)
+        chatbot.append(("完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+
+
+@CatchException
+def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    import glob, os
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import fitz
+    except:
+        report_execption(chatbot, history, 
+            a = f"解析项目: {txt}", 
+            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+
+    # 检测输入参数，如没有给定输入参数，直接退出
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 搜索需要处理的文件清单
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
+    
+    # 如果没找到任何文件
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 开始正式执行任务
+    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/批量总结PDF文档pdfminer.py

@@ -0,0 +1,160 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+fast_debug = False
+
+def readPdf(pdfPath):
+    """
+    读取pdf文件，返回文本内容
+    """
+    import pdfminer
+    from pdfminer.pdfparser import PDFParser
+    from pdfminer.pdfdocument import PDFDocument
+    from pdfminer.pdfpage import PDFPage, PDFTextExtractionNotAllowed
+    from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
+    from pdfminer.pdfdevice import PDFDevice
+    from pdfminer.layout import LAParams
+    from pdfminer.converter import PDFPageAggregator
+
+    fp = open(pdfPath, 'rb')
+
+    # Create a PDF parser object associated with the file object
+    parser = PDFParser(fp)
+
+    # Create a PDF document object that stores the document structure.
+    # Password for initialization as 2nd parameter
+    document = PDFDocument(parser)
+    # Check if the document allows text extraction. If not, abort.
+    if not document.is_extractable:
+        raise PDFTextExtractionNotAllowed
+
+    # Create a PDF resource manager object that stores shared resources.
+    rsrcmgr = PDFResourceManager()
+
+    # Create a PDF device object.
+    # device = PDFDevice(rsrcmgr)
+
+    # BEGIN LAYOUT ANALYSIS.
+    # Set parameters for analysis.
+    laparams = LAParams(
+        char_margin=10.0,
+        line_margin=0.2,
+        boxes_flow=0.2,
+        all_texts=False,
+    )
+    # Create a PDF page aggregator object.
+    device = PDFPageAggregator(rsrcmgr, laparams=laparams)
+    # Create a PDF interpreter object.
+    interpreter = PDFPageInterpreter(rsrcmgr, device)
+
+    # loop over all pages in the document
+    outTextList = []
+    for page in PDFPage.create_pages(document):
+        # read the page into a layout object
+        interpreter.process_page(page)
+        layout = device.get_result()
+        for obj in layout._objs:
+            if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
+                # print(obj.get_text())
+                outTextList.append(obj.get_text())
+
+    return outTextList
+
+
+def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import time, glob, os
+    from bs4 import BeautifulSoup
+    print('begin analysis on:', file_manifest)
+    for index, fp in enumerate(file_manifest):
+        if ".tex" in fp:
+            with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+                file_content = f.read()
+        if ".pdf" in fp.lower():
+            file_content = readPdf(fp)
+            file_content = BeautifulSoup(''.join(file_content), features="lxml").body.text.encode('gbk', 'ignore').decode('gbk')
+
+        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
+        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
+        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
+        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        if not fast_debug:
+            msg = '正常'
+            # ** gpt request **
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say, 
+                inputs_show_user=i_say_show_user, 
+                llm_kwargs=llm_kwargs,
+                chatbot=chatbot, 
+                history=[],
+                sys_prompt="总结文章。"
+            )  # 带超时倒计时
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.append(i_say_show_user); history.append(gpt_say)
+            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+            if not fast_debug: time.sleep(2)
+
+    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
+    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
+    chatbot.append((i_say, "[Local Message] waiting gpt response."))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    if not fast_debug:
+        msg = '正常'
+        # ** gpt request **
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=i_say, 
+            inputs_show_user=i_say, 
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot, 
+            history=history,
+            sys_prompt="总结文章。"
+        )  # 带超时倒计时
+        chatbot[-1] = (i_say, gpt_say)
+        history.append(i_say); history.append(gpt_say)
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+        res = write_results_to_file(history)
+        chatbot.append(("完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+
+
+
+@CatchException
+def 批量总结PDF文档pdfminer(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "批量总结PDF文档，此版本使用pdfminer插件，带token约简功能。函数插件贡献者: Euclid-Jie。"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import pdfminer, bs4
+    except:
+        report_execption(chatbot, history, 
+            a = f"解析项目: {txt}", 
+            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pdfminer beautifulsoup4```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+

crazy_functions/批量翻译PDF文档_多线程.py

@@ -0,0 +1,216 @@
+from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import update_ui
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+from .crazy_utils import read_and_clean_pdf_text
+from colorful import *
+
+@CatchException
+def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
+    import glob
+    import os
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import fitz
+        import tiktoken
+    except:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}",
+                         b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+
+    # 检测输入参数，如没有给定输入参数，直接退出
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "":
+            txt = '空空如也的输入栏'
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 搜索需要处理的文件清单
+    file_manifest = [f for f in glob.glob(
+        f'{project_folder}/**/*.pdf', recursive=True)]
+
+    # 如果没找到任何文件
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 开始正式执行任务
+    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
+
+
+def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
+    import os
+    import copy
+    import tiktoken
+    TOKEN_LIMIT_PER_FRAGMENT = 1280
+    generated_conclusion_files = []
+    generated_html_files = []
+    for index, fp in enumerate(file_manifest):
+
+        # 读取PDF文件
+        file_content, page_one = read_and_clean_pdf_text(fp)
+        file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
+        page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars
+        # 递归地切割PDF文件
+        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+        from request_llm.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
+        page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+            txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
+
+        # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
+        paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
+        
+        # 单线，获取文章meta信息
+        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}",
+            inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。",
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot, history=[],
+            sys_prompt="Your job is to collect information from materials。",
+        )
+
+        # 多线，翻译
+        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+            inputs_array=[
+                f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
+            inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')}  \n---\n 翻译：\n " for frag in paper_fragments],
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot,
+            history_array=[[paper_meta] for _ in paper_fragments],
+            sys_prompt_array=[
+                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
+            # max_workers=5  # OpenAI所允许的最大并行过载
+        )
+        gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
+        # 整理报告的格式
+        for i,k in enumerate(gpt_response_collection_md): 
+            if i%2==0:
+                gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')}  \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n "
+            else:
+                gpt_response_collection_md[i] = gpt_response_collection_md[i]
+        final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""]
+        final.extend(gpt_response_collection_md)
+        create_report_file_name = f"{os.path.basename(fp)}.trans.md"
+        res = write_results_to_file(final, file_name=create_report_file_name)
+
+        # 更新UI
+        generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')
+        chatbot.append((f"{fp}完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        # write html
+        try:
+            ch = construct_html() 
+            orig = ""
+            trans = ""
+            gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
+            for i,k in enumerate(gpt_response_collection_html): 
+                if i%2==0:
+                    gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '')
+                else:
+                    gpt_response_collection_html[i] = gpt_response_collection_html[i]
+            final = ["论文概况", paper_meta_info.replace('# ', '### '),  "二、论文翻译",  ""]
+            final.extend(gpt_response_collection_html)
+            for i, k in enumerate(final): 
+                if i%2==0:
+                    orig = k
+                if i%2==1:
+                    trans = k
+                    ch.add_row(a=orig, b=trans)
+            create_report_file_name = f"{os.path.basename(fp)}.trans.html"
+            ch.save_file(create_report_file_name)
+            generated_html_files.append(f'./gpt_log/{create_report_file_name}')
+        except:
+            from toolbox import trimmed_format_exc
+            print('writing html result failed:', trimmed_format_exc())
+
+    # 准备文件的下载
+    import shutil
+    for pdf_path in generated_conclusion_files:
+        # 重命名文件
+        rename_file = f'./gpt_log/翻译-{os.path.basename(pdf_path)}'
+        if os.path.exists(rename_file):
+            os.remove(rename_file)
+        shutil.copyfile(pdf_path, rename_file)
+        if os.path.exists(pdf_path):
+            os.remove(pdf_path)
+    for html_path in generated_html_files:
+        # 重命名文件
+        rename_file = f'./gpt_log/翻译-{os.path.basename(html_path)}'
+        if os.path.exists(rename_file):
+            os.remove(rename_file)
+        shutil.copyfile(html_path, rename_file)
+        if os.path.exists(html_path):
+            os.remove(html_path)
+    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+class construct_html():
+    def __init__(self) -> None:
+        self.css = """
+.row {
+  display: flex;
+  flex-wrap: wrap;
+}
+
+.column {
+  flex: 1;
+  padding: 10px;
+}
+
+.table-header {
+  font-weight: bold;
+  border-bottom: 1px solid black;
+}
+
+.table-row {
+  border-bottom: 1px solid lightgray;
+}
+
+.table-cell {
+  padding: 5px;
+}
+        """
+        self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>'
+
+
+    def add_row(self, a, b):
+        tmp = """
+<div class="row table-row">
+    <div class="column table-cell">REPLACE_A</div>
+    <div class="column table-cell">REPLACE_B</div>
+</div>
+        """
+        from toolbox import markdown_convertion
+        tmp = tmp.replace('REPLACE_A', markdown_convertion(a))
+        tmp = tmp.replace('REPLACE_B', markdown_convertion(b))
+        self.html_string += tmp
+
+
+    def save_file(self, file_name):
+        with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
+            f.write(self.html_string.encode('utf-8', 'ignore').decode())
+

crazy_functions/数学动画生成manim.py

@@ -0,0 +1,187 @@
+from toolbox import CatchException, update_ui, gen_time_str
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from .crazy_utils import input_clipping
+
+def inspect_dependency(chatbot, history):
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import manim
+        return True
+    except:
+        chatbot.append(["导入依赖失败", "使用该模块需要额外依赖，安装方法:```pip install manimgl```"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return False
+
+def eval_manim(code):
+    import subprocess, sys, os, shutil
+
+    with open('gpt_log/MyAnimation.py', 'w', encoding='utf8') as f:
+        f.write(code)
+
+    def get_class_name(class_string):
+        import re
+        # Use regex to extract the class name
+        class_name = re.search(r'class (\w+)\(', class_string).group(1)
+        return class_name
+
+    class_name = get_class_name(code)
+
+    try: 
+        subprocess.check_output([sys.executable, '-c', f"from gpt_log.MyAnimation import {class_name}; {class_name}().render()"])
+        shutil.move('media/videos/1080p60/{class_name}.mp4', f'gpt_log/{class_name}-{gen_time_str()}.mp4')
+        return f'gpt_log/{gen_time_str()}.mp4'
+    except subprocess.CalledProcessError as e:
+        output = e.output.decode()
+        print(f"Command returned non-zero exit status {e.returncode}: {output}.")
+        return f"Evaluating python script failed: {e.output}."
+    except: 
+        print('generating mp4 failed')
+        return "Generating mp4 failed."
+
+
+def get_code_block(reply):
+    import re
+    pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
+    matches = re.findall(pattern, reply) # find all code blocks in text
+    if len(matches) != 1: 
+        raise RuntimeError("GPT is not generating proper code.")
+    return matches[0].strip('python') #  code block
+
+@CatchException
+def 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    # 清空历史，以免输入溢出
+    history = []    
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "生成数学动画, 此插件处于开发阶段, 建议暂时不要使用, 作者: binary-husky, 插件初始化中 ..."
+    ])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖, 如果缺少依赖, 则给出安装建议
+    dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面
+    if not dep_ok: return
+    
+    # 输入
+    i_say = f'Generate a animation to show: ' + txt
+    demo = ["Here is some examples of manim", examples_of_manim()]
+    _, demo = input_clipping(inputs="", history=demo, max_token_limit=2560)
+    # 开始
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=i_say, inputs_show_user=i_say, 
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo, 
+        sys_prompt=
+        r"Write a animation script with 3blue1brown's manim. "+
+        r"Please begin with `from manim import *`. " + 
+        r"Answer me with a code block wrapped by ```."
+    )
+    chatbot.append(["开始生成动画", "..."])
+    history.extend([i_say, gpt_say])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+    
+    # 将代码转为动画
+    code = get_code_block(gpt_say)
+    res = eval_manim(code)
+
+    chatbot.append(("生成的视频文件路径", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+
+# 在这里放一些网上搜集的demo，辅助gpt生成代码
+def examples_of_manim():
+    return r"""
+
+
+```
+
+class MovingGroupToDestination(Scene):
+    def construct(self):
+        group = VGroup(Dot(LEFT), Dot(ORIGIN), Dot(RIGHT, color=RED), Dot(2 * RIGHT)).scale(1.4)
+        dest = Dot([4, 3, 0], color=YELLOW)
+        self.add(group, dest)
+        self.play(group.animate.shift(dest.get_center() - group[2].get_center()))
+        self.wait(0.5)
+
+```
+
+
+```
+
+class LatexWithMovingFramebox(Scene):
+    def construct(self):
+        text=MathTex(
+            "\\frac{d}{dx}f(x)g(x)=","f(x)\\frac{d}{dx}g(x)","+",
+            "g(x)\\frac{d}{dx}f(x)"
+        )
+        self.play(Write(text))
+        framebox1 = SurroundingRectangle(text[1], buff = .1)
+        framebox2 = SurroundingRectangle(text[3], buff = .1)
+        self.play(
+            Create(framebox1),
+        )
+        self.wait()
+        self.play(
+            ReplacementTransform(framebox1,framebox2),
+        )
+        self.wait()
+
+```
+
+
+
+```
+
+class PointWithTrace(Scene):
+    def construct(self):
+        path = VMobject()
+        dot = Dot()
+        path.set_points_as_corners([dot.get_center(), dot.get_center()])
+        def update_path(path):
+            previous_path = path.copy()
+            previous_path.add_points_as_corners([dot.get_center()])
+            path.become(previous_path)
+        path.add_updater(update_path)
+        self.add(path, dot)
+        self.play(Rotating(dot, radians=PI, about_point=RIGHT, run_time=2))
+        self.wait()
+        self.play(dot.animate.shift(UP))
+        self.play(dot.animate.shift(LEFT))
+        self.wait()
+
+```
+
+```
+
+# do not use get_graph, this funciton is deprecated
+
+class ExampleFunctionGraph(Scene):
+    def construct(self):
+        cos_func = FunctionGraph(
+            lambda t: np.cos(t) + 0.5 * np.cos(7 * t) + (1 / 7) * np.cos(14 * t),
+            color=RED,
+        )
+
+        sin_func_1 = FunctionGraph(
+            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
+            color=BLUE,
+        )
+
+        sin_func_2 = FunctionGraph(
+            lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t),
+            x_range=[-4, 4],
+            color=GREEN,
+        ).move_to([0, 1, 0])
+
+        self.add(cos_func, sin_func_1, sin_func_2)
+
+```
+"""

crazy_functions/理解PDF文档内容.py

@@ -0,0 +1,112 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption
+from .crazy_utils import read_and_clean_pdf_text
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+fast_debug = False
+
+
+def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import tiktoken
+    print('begin analysis on:', file_name)
+
+    ############################## <第 0 步，切割PDF> ##################################
+    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
+    # 的长度必须小于 2500 个 Token
+    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
+
+    TOKEN_LIMIT_PER_FRAGMENT = 2500
+
+    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+    from request_llm.bridge_all import model_info
+    enc = model_info["gpt-3.5-turbo"]['tokenizer']
+    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
+    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
+    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
+    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
+    
+    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
+    final_results = []
+    final_results.append(paper_meta)
+
+    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
+    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
+    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
+
+    iteration_results = []
+    last_iteration_result = paper_meta  # 初始值是摘要
+    MAX_WORD_TOTAL = 4096
+    n_fragment = len(paper_fragments)
+    if n_fragment >= 20: print('文章极长，不能达到预期效果')
+    for i in range(n_fragment):
+        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
+        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
+        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
+                                                                           llm_kwargs, chatbot, 
+                                                                           history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
+                                                                           sys_prompt="Extract the main idea of this section."  # 提示
+                                                                        ) 
+        iteration_results.append(gpt_say)
+        last_iteration_result = gpt_say
+
+    ############################## <第 3 步，整理history> ##################################
+    final_results.extend(iteration_results)
+    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
+    # 接下来两句话只显示在界面上，不起实际作用
+    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
+    chatbot.append([i_say_show_user, gpt_say])
+
+    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
+    from .crazy_utils import input_clipping
+    _, final_results = input_clipping("", final_results, max_token_limit=3200)
+    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
+
+
+@CatchException
+def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    import glob, os
+
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import fitz
+    except:
+        report_execption(chatbot, history, 
+            a = f"解析项目: {txt}", 
+            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+
+    # 检测输入参数，如没有给定输入参数，直接退出
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "":
+            txt = '空空如也的输入栏'
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 搜索需要处理的文件清单
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
+    # 如果没找到任何文件
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    txt = file_manifest[0]
+    # 开始正式执行任务
+    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/生成函数注释.py

@@ -0,0 +1,54 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+fast_debug = False
+
+def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import time, os
+    print('begin analysis on:', file_manifest)
+    for index, fp in enumerate(file_manifest):
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+
+        i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'
+        i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
+        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        if not fast_debug: 
+            msg = '正常'
+            # ** gpt request **
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
+
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.append(i_say_show_user); history.append(gpt_say)
+            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+            if not fast_debug: time.sleep(2)
+
+    if not fast_debug: 
+        res = write_results_to_file(history)
+        chatbot.append(("完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+
+
+
+@CatchException
+def 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)]
+
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/联网的ChatGPT.py

@@ -0,0 +1,102 @@
+from toolbox import CatchException, update_ui
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
+import requests
+from bs4 import BeautifulSoup
+from request_llm.bridge_all import model_info
+
+def google(query, proxies):
+    query = query # 在此处替换您要搜索的关键词
+    url = f"https://www.google.com/search?q={query}"
+    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36'}
+    response = requests.get(url, headers=headers, proxies=proxies)
+    soup = BeautifulSoup(response.content, 'html.parser')
+    results = []
+    for g in soup.find_all('div', class_='g'):
+        anchors = g.find_all('a')
+        if anchors:
+            link = anchors[0]['href']
+            if link.startswith('/url?q='):
+                link = link[7:]
+            if not link.startswith('http'):
+                continue
+            title = g.find('h3').text
+            item = {'title': title, 'link': link}
+            results.append(item)
+
+    for r in results:
+        print(r['link'])
+    return results
+
+def scrape_text(url, proxies) -> str:
+    """Scrape text from a webpage
+
+    Args:
+        url (str): The URL to scrape text from
+
+    Returns:
+        str: The scraped text
+    """
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
+        'Content-Type': 'text/plain',
+    }
+    try: 
+        response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
+        if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
+    except: 
+        return "无法连接到该网页"
+    soup = BeautifulSoup(response.text, "html.parser")
+    for script in soup(["script", "style"]):
+        script.extract()
+    text = soup.get_text()
+    lines = (line.strip() for line in text.splitlines())
+    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+    text = "\n".join(chunk for chunk in chunks if chunk)
+    return text
+
+@CatchException
+def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append((f"请结合互联网信息回答以下问题：{txt}", 
+                    "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该模板可以实现ChatGPT联网信息综合。该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。您若希望分享新的功能模组，请不吝PR！"))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
+
+    # ------------- < 第1步：爬取搜索引擎的结果 > -------------
+    from toolbox import get_conf
+    proxies, = get_conf('proxies')
+    urls = google(txt, proxies)
+    history = []
+
+    # ------------- < 第2步：依次访问网页 > -------------
+    max_search_result = 5   # 最多收纳多少个网页的结果
+    for index, url in enumerate(urls[:max_search_result]):
+        res = scrape_text(url['link'], proxies)
+        history.extend([f"第{index}份搜索结果：", res])
+        chatbot.append([f"第{index}份搜索结果：", res[:500]+"......"])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
+
+    # ------------- < 第3步：ChatGPT综合 > -------------
+    i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
+    i_say, history = input_clipping(    # 裁剪输入，从最长的条目开始裁剪，防止爆token
+        inputs=i_say, 
+        history=history, 
+        max_token_limit=model_info[llm_kwargs['llm_model']]['max_token']*3//4
+    )
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=i_say, inputs_show_user=i_say, 
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
+        sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
+    )
+    chatbot[-1] = (i_say, gpt_say)
+    history.append(i_say);history.append(gpt_say)
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+

crazy_functions/解析JupyterNotebook.py

@@ -0,0 +1,146 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+fast_debug = True
+
+
+class PaperFileGroup():
+    def __init__(self):
+        self.file_paths = []
+        self.file_contents = []
+        self.sp_file_contents = []
+        self.sp_file_index = []
+        self.sp_file_tag = []
+
+        # count_token
+        from request_llm.bridge_all import model_info
+        enc = model_info["gpt-3.5-turbo"]['tokenizer']
+        def get_token_num(txt): return len(
+            enc.encode(txt, disallowed_special=()))
+        self.get_token_num = get_token_num
+
+    def run_file_split(self, max_token_limit=1900):
+        """
+        将长文本分离开来
+        """
+        for index, file_content in enumerate(self.file_contents):
+            if self.get_token_num(file_content) < max_token_limit:
+                self.sp_file_contents.append(file_content)
+                self.sp_file_index.append(index)
+                self.sp_file_tag.append(self.file_paths[index])
+            else:
+                from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+                segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
+                    file_content, self.get_token_num, max_token_limit)
+                for j, segment in enumerate(segments):
+                    self.sp_file_contents.append(segment)
+                    self.sp_file_index.append(index)
+                    self.sp_file_tag.append(
+                        self.file_paths[index] + f".part-{j}.txt")
+
+
+
+def parseNotebook(filename, enable_markdown=1):
+    import json
+
+    CodeBlocks = []
+    with open(filename, 'r', encoding='utf-8', errors='replace') as f:
+        notebook = json.load(f)
+    for cell in notebook['cells']:
+        if cell['cell_type'] == 'code' and cell['source']:
+            # remove blank lines
+            cell['source'] = [line for line in cell['source'] if line.strip()
+                              != '']
+            CodeBlocks.append("".join(cell['source']))
+        elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
+            cell['source'] = [line for line in cell['source'] if line.strip()
+                              != '']
+            CodeBlocks.append("Markdown:"+"".join(cell['source']))
+
+    Code = ""
+    for idx, code in enumerate(CodeBlocks):
+        Code += f"This is {idx+1}th code block: \n"
+        Code += code+"\n"
+
+    return Code 
+
+
+def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    enable_markdown = plugin_kwargs.get("advanced_arg", "1")
+    try:
+        enable_markdown = int(enable_markdown)
+    except ValueError:
+        enable_markdown = 1
+
+    pfg = PaperFileGroup()
+
+    for fp in file_manifest:
+        file_content = parseNotebook(fp, enable_markdown=enable_markdown)
+        pfg.file_paths.append(fp)
+        pfg.file_contents.append(file_content)
+
+    #  <-------- 拆分过长的IPynb文件 ---------->
+    pfg.run_file_split(max_token_limit=1024)
+    n_split = len(pfg.sp_file_contents)
+
+    inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
+                    r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
+                    r"Start a new line for a block and block num use Chinese." +
+                    f"\n\n{frag}" for frag in pfg.sp_file_contents]
+    inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
+    sys_prompt_array = ["You are a professional programmer."] * n_split
+
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array=inputs_array,
+        inputs_show_user_array=inputs_show_user_array,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history_array=[[""] for _ in range(n_split)],
+        sys_prompt_array=sys_prompt_array,
+        # max_workers=5,  # OpenAI所允许的最大并行过载
+        scroller_max_len=80
+    )
+
+    #  <-------- 整理结果，退出 ---------->
+    block_result = "  \n".join(gpt_response_collection)
+    chatbot.append(("解析的结果如下", block_result))
+    history.extend(["解析的结果如下", block_result])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    #  <-------- 写入文件，退出 ---------->
+    res = write_results_to_file(history)
+    chatbot.append(("完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+@CatchException
+def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    chatbot.append([
+        "函数插件功能？",
+        "对IPynb文件进行解析。Contributor: codycjy."])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    history = []    # 清空历史
+    import glob
+    import os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "":
+            txt = '空空如也的输入栏'
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+    if txt.endswith('.ipynb'):
+        file_manifest = [txt]
+    else:
+        file_manifest = [f for f in glob.glob(
+            f'{project_folder}/**/*.ipynb', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history,
+                         a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+        return
+    yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )

crazy_functions/解析项目源代码.py

@@ -0,0 +1,352 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+from .crazy_utils import input_clipping
+
+def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import os, copy
+    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+    msg = '正常'
+    summary_batch_isolation = True
+    inputs_array = []
+    inputs_show_user_array = []
+    history_array = []
+    sys_prompt_array = []
+    report_part_1 = []
+
+    assert len(file_manifest) <= 512, "源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。"
+    ############################## <第一步，逐个文件分析，多线程> ##################################
+    for index, fp in enumerate(file_manifest):
+        # 读取文件
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+        prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
+        i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
+        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
+        # 装载请求内容
+        inputs_array.append(i_say)
+        inputs_show_user_array.append(i_say_show_user)
+        history_array.append([])
+        sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
+
+    # 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到chatgpt进行分析
+    gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+        inputs_array = inputs_array,
+        inputs_show_user_array = inputs_show_user_array,
+        history_array = history_array,
+        sys_prompt_array = sys_prompt_array,
+        llm_kwargs = llm_kwargs,
+        chatbot = chatbot,
+        show_user_at_complete = True
+    )
+
+    # 全部文件解析完成，结果写入文件，准备对工程源代码进行汇总分析
+    report_part_1 = copy.deepcopy(gpt_response_collection)
+    history_to_return = report_part_1
+    res = write_results_to_file(report_part_1)
+    chatbot.append(("完成？", "逐个文件分析已完成。" + res + "\n\n正在开始汇总。"))
+    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
+
+    ############################## <第二步，综合，单线程，分组+迭代处理> ##################################
+    batchsize = 16  # 10个文件为一组
+    report_part_2 = []
+    previous_iteration_files = []
+    last_iteration_result = ""
+    while True:
+        if len(file_manifest) == 0: break
+        this_iteration_file_manifest = file_manifest[:batchsize]
+        this_iteration_gpt_response_collection = gpt_response_collection[:batchsize*2]
+        file_rel_path = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
+        # 把“请对下面的程序文件做一个概述” 替换成 精简的 "文件名：{all_file[index]}"
+        for index, content in enumerate(this_iteration_gpt_response_collection):
+            if index%2==0: this_iteration_gpt_response_collection[index] = f"{file_rel_path[index//2]}" # 只保留文件名节省token
+        this_iteration_files = [os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)]
+        previous_iteration_files.extend(this_iteration_files)
+        previous_iteration_files_string = ', '.join(previous_iteration_files)
+        current_iteration_focus = ', '.join(this_iteration_files)
+        if summary_batch_isolation: focus = current_iteration_focus
+        else:                       focus = previous_iteration_files_string
+        i_say = f'用一张Markdown表格简要描述以下文件的功能：{focus}。根据以上分析，用一句话概括程序的整体功能。'
+        if last_iteration_result != "":
+            sys_prompt_additional = "已知某些代码的局部作用是:" + last_iteration_result + "\n请继续分析其他源代码，从而更全面地理解项目的整体功能。"
+        else:
+            sys_prompt_additional = ""
+        inputs_show_user = f'根据以上分析，对程序的整体功能和构架重新做出概括，由于输入长度限制，可能需要分组处理，本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
+        this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
+        this_iteration_history.append(last_iteration_result)
+        # 裁剪input
+        inputs, this_iteration_history_feed = input_clipping(inputs=i_say, history=this_iteration_history, max_token_limit=2560)
+        result = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=inputs, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
+            history=this_iteration_history_feed,   # 迭代之前的分析
+            sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。" + sys_prompt_additional)
+        
+        summary = "请用一句话概括这些文件的整体功能"
+        summary_result = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=summary, 
+            inputs_show_user=summary, 
+            llm_kwargs=llm_kwargs, 
+            chatbot=chatbot,
+            history=[i_say, result],   # 迭代之前的分析
+            sys_prompt="你是一个程序架构分析师，正在分析一个项目的源代码。" + sys_prompt_additional)
+
+        report_part_2.extend([i_say, result])
+        last_iteration_result = summary_result
+        file_manifest = file_manifest[batchsize:]
+        gpt_response_collection = gpt_response_collection[batchsize*2:]
+
+    ############################## <END> ##################################
+    history_to_return.extend(report_part_2)
+    res = write_results_to_file(history_to_return)
+    chatbot.append(("完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
+
+
+@CatchException
+def 解析项目本身(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob
+    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
+                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]+ \
+                    [f for f in glob.glob('./request_llm/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
+    project_folder = './'
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+@CatchException
+def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+
+@CatchException
+def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] #+ \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+@CatchException
+def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.h', recursive=True)]  + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.hpp', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.h头文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+
+@CatchException
+def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []  # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.java', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.jar', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.sh', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何java文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+
+@CatchException
+def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []  # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.ts', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.tsx', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.js', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.vue', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.less', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.sass', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.wxml', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.wxss', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.css', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.jsx', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何前端相关文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+
+@CatchException
+def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []  # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/go.mod', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/go.sum', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/go.work', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+@CatchException
+def 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []  # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.rs', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.lock', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何golang文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+@CatchException
+def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.lua', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.xml', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.json', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.toml', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+
+@CatchException
+def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.cs', recursive=True)] + \
+                    [f for f in glob.glob(f'{project_folder}/**/*.csproj', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
+
+
+@CatchException
+def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    txt_pattern = plugin_kwargs.get("advanced_arg")
+    txt_pattern = txt_pattern.replace("，", ",")
+    # 将要匹配的模式(例如: *.c, *.cpp, *.py, config.toml)
+    pattern_include = [_.lstrip(" ,").rstrip(" ,") for _ in txt_pattern.split(",") if _ != "" and not _.strip().startswith("^")]
+    if not pattern_include: pattern_include = ["*"] # 不输入即全部匹配
+    # 将要忽略匹配的文件后缀(例如: ^*.c, ^*.cpp, ^*.py)
+    pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
+    pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
+    # 将要忽略匹配的文件名(例如: ^README.md)
+    pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
+    # 生成正则表达式
+    pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
+    pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
+
+    history.clear()
+    import glob, os, re
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    # 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件
+    maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
+    if len(maybe_dir)>0 and maybe_dir[0].endswith('.extract'):
+        extract_folder_path = maybe_dir[0]
+    else:
+        extract_folder_path = project_folder
+    # 按输入的匹配模式寻找上传的非压缩文件和已解压的文件
+    file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if "" != extract_folder_path and \
+                      os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/询问多个大语言模型.py

@@ -0,0 +1,60 @@
+from toolbox import CatchException, update_ui
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+import datetime
+@CatchException
+def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，如温度和top_p等，一般原样传递下去就行
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
+
+    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
+    llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=txt, inputs_show_user=txt, 
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
+        sys_prompt=system_prompt,
+        retry_times_at_unknown_error=0
+    )
+
+    history.append(txt)
+    history.append(gpt_say)
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+
+
+@CatchException
+def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，如温度和top_p等，一般原样传递下去就行
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
+
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
+    llm_kwargs['llm_model'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=txt, inputs_show_user=txt, 
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, 
+        sys_prompt=system_prompt,
+        retry_times_at_unknown_error=0
+    )
+
+    history.append(txt)
+    history.append(gpt_say)
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

crazy_functions/读文章写摘要.py

@@ -0,0 +1,67 @@
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+fast_debug = False
+
+
+def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import time, glob, os
+    print('begin analysis on:', file_manifest)
+    for index, fp in enumerate(file_manifest):
+        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
+            file_content = f.read()
+
+        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
+        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
+        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
+        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+        if not fast_debug: 
+            msg = '正常'
+            # ** gpt request **
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
+
+            chatbot[-1] = (i_say_show_user, gpt_say)
+            history.append(i_say_show_user); history.append(gpt_say)
+            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+            if not fast_debug: time.sleep(2)
+
+    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
+    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
+    chatbot.append((i_say, "[Local Message] waiting gpt response."))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    if not fast_debug: 
+        msg = '正常'
+        # ** gpt request **
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时
+
+        chatbot[-1] = (i_say, gpt_say)
+        history.append(i_say); history.append(gpt_say)
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+        res = write_results_to_file(history)
+        chatbot.append(("完成了吗？", res))
+        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+
+
+
+@CatchException
+def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    history = []    # 清空历史，以免输入溢出
+    import glob, os
+    if os.path.exists(txt):
+        project_folder = txt
+    else:
+        if txt == "": txt = '空空如也的输入栏'
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
+                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
+    if len(file_manifest) == 0:
+        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)

crazy_functions/谷歌检索小助手.py

@@ -0,0 +1,112 @@
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from toolbox import CatchException, report_execption, write_results_to_file
+from toolbox import update_ui
+
+def get_meta_information(url, chatbot, history):
+    import requests
+    import arxiv
+    import difflib
+    from bs4 import BeautifulSoup
+    from toolbox import get_conf
+    proxies, = get_conf('proxies')
+    headers = {
+        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',
+    }
+    # 发送 GET 请求
+    response = requests.get(url, proxies=proxies, headers=headers)
+
+    # 解析网页内容
+    soup = BeautifulSoup(response.text, "html.parser")
+
+    def string_similar(s1, s2):
+        return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
+
+    profile = []
+    # 获取所有文章的标题和作者
+    for result in soup.select(".gs_ri"):
+        title = result.a.text.replace('\n', ' ').replace('  ', ' ')
+        author = result.select_one(".gs_a").text
+        try:
+            citation = result.select_one(".gs_fl > a[href*='cites']").text  # 引用次数是链接中的文本，直接取出来
+        except:
+            citation = 'cited by 0'
+        abstract = result.select_one(".gs_rs").text.strip()  # 摘要在 .gs_rs 中的文本，需要清除首尾空格
+        search = arxiv.Search(
+            query = title,
+            max_results = 1,
+            sort_by = arxiv.SortCriterion.Relevance,
+        )
+        try:
+            paper = next(search.results())
+            if string_similar(title, paper.title) > 0.90: # same paper
+                abstract = paper.summary.replace('\n', ' ')
+                is_paper_in_arxiv = True
+            else:   # different paper
+                abstract = abstract
+                is_paper_in_arxiv = False
+            paper = next(search.results())
+        except:
+            abstract = abstract
+            is_paper_in_arxiv = False
+        print(title)
+        print(author)
+        print(citation)
+        profile.append({
+            'title':title,
+            'author':author,
+            'citation':citation,
+            'abstract':abstract,
+            'is_paper_in_arxiv':is_paper_in_arxiv,
+        })
+
+        chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
+        yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
+    return profile
+
+@CatchException
+def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    # 基本信息：功能、贡献者
+    chatbot.append([
+        "函数插件功能？",
+        "分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        import arxiv
+        import math
+        from bs4 import BeautifulSoup
+    except:
+        report_execption(chatbot, history, 
+            a = f"解析项目: {txt}", 
+            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        return
+
+    # 清空历史，以免输入溢出
+    history = []
+    meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
+    batchsize = 5
+    for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
+        if len(meta_paper_info_list[:batchsize]) > 0:
+            i_say = "下面是一些学术文献的数据，提取出以下内容：" + \
+            "1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
+            f"以下是信息源：{str(meta_paper_info_list[:batchsize])}" 
+
+            inputs_show_user = f"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批"
+            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs=i_say, inputs_show_user=inputs_show_user,
+                llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
+                sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
+            )
+
+            history.extend([ f"第{batch+1}批", gpt_say ])
+            meta_paper_info_list = meta_paper_info_list[batchsize:]
+
+    chatbot.append(["状态？", 
+        "已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
+    msg = '正常'
+    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+    res = write_results_to_file(history)
+    chatbot.append(("完成了吗？", res)); 
+    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面

crazy_functions/高级功能函数模板.py

@@ -0,0 +1,29 @@
+from toolbox import CatchException, update_ui
+from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+import datetime
+@CatchException
+def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数，暂时没有用武之地
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    web_port        当前软件运行的端口号
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板（该函数只有20多行代码）。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
+    for i in range(5):
+        currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
+        currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
+        i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=i_say, inputs_show_user=i_say, 
+            llm_kwargs=llm_kwargs, chatbot=chatbot, history=[], 
+            sys_prompt="当你想发送一张照片时，请使用Markdown, 并且不要有反斜线, 不要用代码块。使用 Unsplash API (https://source.unsplash.com/1280x720/? < PUT_YOUR_QUERY_HERE >)。"
+        )
+        chatbot[-1] = (i_say, gpt_say)
+        history.append(i_say);history.append(gpt_say)
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

docker-compose.yml

@@ -0,0 +1,104 @@
+#【请修改完参数后，删除此行】请在以下方案中选择一种，然后删除其他的方案，最后docker-compose up运行 | Please choose from one of these options below, delete other options as well as This Line
+
+## ===================================================
+## 【方案一】 如果不需要运行本地模型（仅chatgpt,newbing类远程服务）
+## ===================================================
+version: '3'
+services:
+  gpt_academic_nolocalllms:
+    image: ghcr.io/binary-husky/gpt_academic_nolocal:master
+    environment:
+      # 请查阅 `config.py` 以查看所有的配置信息
+      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx                                            '
+      USE_PROXY:                '    True                                                                                           '
+      proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }                 '
+      LLM_MODEL:                '    gpt-3.5-turbo                                                                                  '
+      AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "newbing"]                    '
+      WEB_PORT:                 '    22303                                                                                          '
+      ADD_WAIFU:                '    True                                                                                           '
+      # DEFAULT_WORKER_NUM:       '    10                                                                                             '
+      # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
+
+    # 与宿主的网络融合
+    network_mode: "host"
+
+    # 不使用代理网络拉取最新代码
+    command: >
+      bash -c "python3 -u main.py"
+
+
+### ===================================================
+### 【方案二】 如果需要运行ChatGLM本地模型
+### ===================================================
+version: '3'
+services:
+  gpt_academic_with_chatglm:
+    image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master
+    environment:
+      # 请查阅 `config.py` 以查看所有的配置信息
+      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  '
+      USE_PROXY:                '    True                                                                                           '
+      proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }                 '
+      LLM_MODEL:                '    gpt-3.5-turbo                                                                                  '
+      AVAIL_LLM_MODELS:         '    ["chatglm", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"]                                       '
+      LOCAL_MODEL_DEVICE:       '    cuda                                                                                           '
+      DEFAULT_WORKER_NUM:       '    10                                                                                             '
+      WEB_PORT:                 '    12303                                                                                          '
+      ADD_WAIFU:                '    True                                                                                           '
+      # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
+
+    # 显卡的使用，nvidia0指第0个GPU
+    runtime: nvidia
+    devices:
+      - /dev/nvidia0:/dev/nvidia0
+      
+    # 与宿主的网络融合
+    network_mode: "host"
+    command: >
+      bash -c "python3 -u main.py"
+
+### ===================================================
+### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
+### ===================================================
+version: '3'
+services:
+  gpt_academic_with_rwkv:
+    image: fuqingxu/gpt_academic:jittorllms    # [option 2] 如果需要运行ChatGLM本地模型
+    environment:
+      # 请查阅 `config.py` 以查看所有的配置信息
+      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  '
+      USE_PROXY:                '    True                                                                                           '
+      proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }                 '
+      LLM_MODEL:                '    gpt-3.5-turbo                                                                                  '
+      AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "newbing", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]   '
+      LOCAL_MODEL_DEVICE:       '    cuda                                                                                           '
+      DEFAULT_WORKER_NUM:       '    10                                                                                             '
+      WEB_PORT:                 '    12305                                                                                          '
+      ADD_WAIFU:                '    True                                                                                           '
+      # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
+
+    # 显卡的使用，nvidia0指第0个GPU
+    runtime: nvidia
+    devices:
+      - /dev/nvidia0:/dev/nvidia0
+      
+    # 与宿主的网络融合
+    network_mode: "host"
+
+    # 使用代理网络拉取最新代码
+    # command: >
+    #   bash -c "       truncate -s -1 /etc/proxychains.conf &&
+    #                   echo \"socks5 127.0.0.1 10880\" >> /etc/proxychains.conf &&
+    #                   echo '[gpt-academic] 正在从github拉取最新代码...' &&
+    #                   proxychains git pull &&
+    #                   echo '[jittorllms] 正在从github拉取最新代码...' &&
+    #                   proxychains git --git-dir=request_llm/jittorllms/.git --work-tree=request_llm/jittorllms pull --force &&
+    #                   python3 -u main.py"
+
+    # 不使用代理网络拉取最新代码
+    command: >
+      bash -c "       echo '[gpt-academic] 正在从github拉取最新代码...' &&
+                      git pull &&
+                      echo '[jittorllms] 正在从github拉取最新代码...' &&
+                      git --git-dir=request_llm/jittorllms/.git --work-tree=request_llm/jittorllms pull --force &&
+                      python3 -u main.py"

docs/Dockerfile+ChatGLM

@@ -0,0 +1,62 @@
+# How to build | 如何构建: docker build -t gpt-academic --network=host  -f Dockerfile+ChatGLM .
+# How to run | (1) 我想直接一键运行（选择0号GPU）: docker run --rm -it --net=host --gpus \"device=0\" gpt-academic
+# How to run | (2) 我想运行之前进容器做一些调整（选择1号GPU）: docker run --rm -it --net=host --gpus \"device=1\" gpt-academic bash
+ 
+# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
+FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
+ARG useProxyNetwork=''
+RUN apt-get update
+RUN apt-get install -y curl proxychains curl 
+RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
+
+# 配置代理网络（构建Docker镜像时使用）
+# # comment out below if you do not need proxy network | 如果不需要翻墙 - 从此行向下删除
+RUN $useProxyNetwork curl cip.cc
+RUN sed -i '$ d' /etc/proxychains.conf
+RUN sed -i '$ d' /etc/proxychains.conf
+# 在这里填写主机的代理协议（用于从github拉取代码）
+RUN echo "socks5 127.0.0.1 10880" >> /etc/proxychains.conf
+ARG useProxyNetwork=proxychains
+# # comment out above if you do not need proxy network | 如果不需要翻墙 - 从此行向上删除
+
+
+# use python3 as the system default python
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
+# 下载pytorch
+RUN $useProxyNetwork python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
+# 下载分支
+WORKDIR /gpt
+RUN $useProxyNetwork git clone https://github.com/binary-husky/chatgpt_academic.git
+WORKDIR /gpt/chatgpt_academic
+RUN $useProxyNetwork python3 -m pip install -r requirements.txt
+RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_chatglm.txt
+RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_newbing.txt
+
+# 预热CHATGLM参数（非必要 可选步骤）
+RUN echo ' \n\
+from transformers import AutoModel, AutoTokenizer \n\
+chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) \n\
+chatglm_model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float() ' >> warm_up_chatglm.py
+RUN python3 -u warm_up_chatglm.py
+
+# 禁用缓存，确保更新代码
+ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
+RUN $useProxyNetwork git pull
+
+# 预热Tiktoken模块
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+# 为chatgpt-academic配置代理和API-KEY （非必要 可选步骤）
+# 可同时填写多个API-KEY，支持openai的key和api2d的key共存，用英文逗号分割，例如API_KEY = "sk-openaikey1,fkxxxx-api2dkey2,........"
+# LLM_MODEL 是选择初始的模型
+# LOCAL_MODEL_DEVICE 是选择chatglm等本地模型运行的设备，可选 cpu 和 cuda
+# [说明: 以下内容与`config.py`一一对应，请查阅config.py来完成一下配置的填写]
+RUN echo ' \n\
+API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \n\
+USE_PROXY = True \n\
+LLM_MODEL = "chatglm" \n\
+LOCAL_MODEL_DEVICE = "cuda" \n\
+proxies = { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' >> config_private.py
+
+# 启动
+CMD ["python3", "-u", "main.py"]

docs/Dockerfile+JittorLLM

@@ -0,0 +1,59 @@
+# How to build | 如何构建: docker build -t gpt-academic-jittor --network=host  -f Dockerfile+ChatGLM .
+# How to run | (1) 我想直接一键运行（选择0号GPU）: docker run --rm -it --net=host --gpus \"device=0\" gpt-academic-jittor bash
+# How to run | (2) 我想运行之前进容器做一些调整（选择1号GPU）: docker run --rm -it --net=host --gpus \"device=1\" gpt-academic-jittor bash
+ 
+# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
+FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
+ARG useProxyNetwork=''
+RUN apt-get update
+RUN apt-get install -y curl proxychains curl g++
+RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
+
+# 配置代理网络（构建Docker镜像时使用）
+# # comment out below if you do not need proxy network | 如果不需要翻墙 - 从此行向下删除
+RUN $useProxyNetwork curl cip.cc
+RUN sed -i '$ d' /etc/proxychains.conf
+RUN sed -i '$ d' /etc/proxychains.conf
+# 在这里填写主机的代理协议（用于从github拉取代码）
+RUN echo "socks5 127.0.0.1 10880" >> /etc/proxychains.conf
+ARG useProxyNetwork=proxychains
+# # comment out above if you do not need proxy network | 如果不需要翻墙 - 从此行向上删除
+
+
+# use python3 as the system default python
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
+# 下载pytorch
+RUN $useProxyNetwork python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
+# 下载分支
+WORKDIR /gpt
+RUN $useProxyNetwork git clone https://github.com/binary-husky/chatgpt_academic.git -b jittor
+WORKDIR /gpt/chatgpt_academic
+RUN $useProxyNetwork python3 -m pip install -r requirements.txt
+RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_chatglm.txt
+RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_newbing.txt
+RUN $useProxyNetwork python3 -m pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
+
+# 下载JittorLLMs
+RUN $useProxyNetwork git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llm/jittorllms
+
+# 禁用缓存，确保更新代码
+ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
+RUN $useProxyNetwork git pull
+
+# 预热Tiktoken模块
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+# 为chatgpt-academic配置代理和API-KEY （非必要 可选步骤）
+# 可同时填写多个API-KEY，支持openai的key和api2d的key共存，用英文逗号分割，例如API_KEY = "sk-openaikey1,fkxxxx-api2dkey2,........"
+# LLM_MODEL 是选择初始的模型
+# LOCAL_MODEL_DEVICE 是选择chatglm等本地模型运行的设备，可选 cpu 和 cuda
+# [说明: 以下内容与`config.py`一一对应，请查阅config.py来完成一下配置的填写]
+RUN echo ' \n\
+API_KEY = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \n\
+USE_PROXY = True \n\
+LLM_MODEL = "chatglm" \n\
+LOCAL_MODEL_DEVICE = "cuda" \n\
+proxies = { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", } ' >> config_private.py
+
+# 启动
+CMD ["python3", "-u", "main.py"]

docs/GithubAction+ChatGLM+Moss

@@ -0,0 +1,30 @@
+
+# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
+FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
+ARG useProxyNetwork=''
+RUN apt-get update
+RUN apt-get install -y curl proxychains curl gcc
+RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
+
+
+# use python3 as the system default python
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
+# 下载pytorch
+RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
+# 下载分支
+WORKDIR /gpt
+RUN git clone https://github.com/binary-husky/chatgpt_academic.git
+WORKDIR /gpt/chatgpt_academic
+RUN git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss
+RUN python3 -m pip install -r requirements.txt
+RUN python3 -m pip install -r request_llm/requirements_moss.txt
+RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
+RUN python3 -m pip install -r request_llm/requirements_newbing.txt
+
+
+
+# 预热Tiktoken模块
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+# 启动
+CMD ["python3", "-u", "main.py"]

docs/GithubAction+JittorLLMs

@@ -0,0 +1,34 @@
+# 从NVIDIA源，从而支持显卡运损（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
+FROM nvidia/cuda:11.3.1-runtime-ubuntu20.04
+ARG useProxyNetwork=''
+RUN apt-get update
+RUN apt-get install -y curl proxychains curl g++
+RUN apt-get install -y git python python3 python-dev python3-dev --fix-missing
+
+# use python3 as the system default python
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
+
+# 下载pytorch
+RUN python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
+
+# 下载分支
+WORKDIR /gpt
+RUN git clone https://github.com/binary-husky/chatgpt_academic.git -b jittor
+WORKDIR /gpt/chatgpt_academic
+RUN python3 -m pip install -r requirements.txt
+RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
+RUN python3 -m pip install -r request_llm/requirements_newbing.txt
+RUN python3 -m pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I
+
+# 下载JittorLLMs
+RUN git clone https://github.com/binary-husky/JittorLLMs.git --depth 1 request_llm/jittorllms
+
+# 禁用缓存，确保更新代码
+ADD "https://www.random.org/cgi-bin/randbyte?nbytes=10&format=h" skipcache
+RUN git pull
+
+# 预热Tiktoken模块
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+# 启动
+CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal

@@ -0,0 +1,20 @@
+# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
+# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal -f docs/Dockerfile+NoLocal .
+# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal
+FROM python:3.11
+
+# 指定路径
+WORKDIR /gpt
+
+# 装载项目文件
+COPY . .
+
+# 安装依赖
+RUN pip3 install -r requirements.txt
+
+
+# 可选步骤，用于预热模块
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+# 启动
+CMD ["python3", "-u", "main.py"]

docs/README.md.German.md

@@ -0,0 +1,307 @@
+> **Hinweis**
+>
+> Bei der Installation von Abhängigkeiten sollten nur die in **requirements.txt** **angegebenen Versionen** streng ausgewählt werden.
+>
+> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
+
+# <img src="docs/logo.png" width="40" > GPT Akademisch optimiert (GPT Academic)
+
+**Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Stern; wenn Sie bessere Tastenkombinationen oder Funktions-Plugins entwickelt haben, können Sie gerne einen Pull Request eröffnen.**
+
+Wenn Sie dieses Projekt mögen, geben Sie ihm bitte einen Stern. Wenn Sie weitere nützliche wissenschaftliche Abkürzungen oder funktionale Plugins entwickelt haben, können Sie gerne ein Problem oder eine Pull-Anforderung öffnen. Wir haben auch ein README in [Englisch|](docs/README_EN.md)[日本語|](docs/README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md), das von diesem Projekt selbst übersetzt wurde.
+Um dieses Projekt in eine beliebige Sprache mit GPT zu übersetzen, lesen Sie `multi_language.py` (experimentell).
+
+> **Hinweis**
+>
+> 1. Beachten Sie bitte, dass nur Funktionserweiterungen (Schaltflächen) mit **roter Farbe** Dateien lesen können und einige Erweiterungen im **Dropdown-Menü** des Erweiterungsbereichs zu finden sind. Außerdem begrüßen wir jede neue Funktionserweiterung mit **höchster Priorität** und bearbeiten sie.
+>
+> 2. Die Funktionalität jeder Datei in diesem Projekt wird in der Selbstanalyse [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) detailliert beschrieben. Mit der Weiterentwicklung der Versionen können Sie jederzeit die zugehörigen Funktions-Erweiterungen aufrufen, um durch Aufruf von GPT einen Selbstanalysebericht des Projekts zu erstellen. Häufig gestellte Fragen finden Sie in der [`Wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installationsanweisungen](#Installation).
+>
+> 3. Dieses Projekt ist kompatibel und fördert die Verwendung von inländischen Sprachmodellen wie ChatGLM und RWKV, Pangu, etc. Es unterstützt das Vorhandensein mehrerer api-keys, die in der Konfigurationsdatei wie folgt angegeben werden können: `API_KEY="openai-key1,openai-key2,api2d-key3"`. Wenn ein `API_KEY` temporär geändert werden muss, geben Sie den temporären `API_KEY` im Eingabebereich ein und drücken Sie dann die Eingabetaste, um ihn zu übernehmen.Funktion | Beschreibung
+--- | ---
+Ein-Klick-Polieren | Unterstützt ein-Klick-Polieren und ein-Klick-Suche nach grammatikalischen Fehlern in wissenschaftlichen Arbeiten
+Ein-Klick Chinesisch-Englisch Übersetzung | Ein-Klick Chinesisch-Englisch Übersetzung
+Ein-Klick-Code-Erklärung | Zeigt Code, erklärt Code, erzeugt Code und fügt Kommentare zum Code hinzu
+[Benutzerdefinierte Tastenkombinationen](https://www.bilibili.com/video/BV14s4y1E7jN) | Unterstützt benutzerdefinierte Tastenkombinationen
+Modulare Gestaltung | Unterstützt leistungsstarke individuelle [Funktions-Plugins](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions). Plugins unterstützen [Hot-Updates](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
+[Selbstprogramm-Analyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] [Ein-Klick Verstehen](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) der Quellcode dieses Projekts
+[Programmanalyse](https://www.bilibili.com/video/BV1cj411A7VW) | [Funktions-Plugin] Ein-Klick-Analyse des Projektbaums anderer Python/C/C++/Java/Lua/...-Projekte
+Lesen von Papieren, [Übersetzen](https://www.bilibili.com/video/BV1KT411x7Wn) von Papieren | [Funktions-Plugin] Ein-Klick Erklärung des gesamten LaTeX/PDF-Artikels und Erstellung einer Zusammenfassung
+LaTeX-Volltext-Übersetzung und [Polieren](https://www.bilibili.com/video/BV1FT411H7c5/) | [Funktions-Plugin] Ein-Klick-Übersetzung oder-Polieren des LaTeX-Artikels
+Bulk-Kommentargenerierung | [Funktions-Plugin] Ein-Klick Massenerstellung von Funktionskommentaren
+Markdown [Chinesisch-Englisch Übersetzung](https://www.bilibili.com/video/BV1yo4y157jV/) | [Funktions-Plugin] Haben Sie die [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) in den oben genannten 5 Sprachen gesehen?
+Analyse-Berichtserstellung von chat | [Funktions-Plugin] Automatische Zusammenfassung nach der Ausführung
+[Funktion zur vollständigen Übersetzung von PDF-Artikeln](https://www.bilibili.com/video/BV1KT411x7Wn) | [Funktions-Plugin] Extrahiert Titel und Zusammenfassung der PDF-Artikel und übersetzt den gesamten Text (mehrere Threads)
+[Arxiv-Assistent](https://www.bilibili.com/video/BV1LM4y1279X) | [Funktions-Plugin] Geben Sie die Arxiv-Artikel-URL ein und klicken Sie auf Eine-Klick-Übersetzung-Zusammenfassung + PDF-Download
+[Google Scholar Integrations-Assistent](https://www.bilibili.com/video/BV19L411U7ia) | [Funktions-Plugin] Geben Sie eine beliebige Google Scholar Such-URL ein und lassen Sie gpt Ihnen bei der Erstellung von [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/) helfen
+Internet-Informationen Aggregation + GPT | [Funktions-Plugin] Lassen Sie GPT eine Frage beantworten, indem es [zuerst Informationen aus dem Internet](https://www.bilibili.com/video/BV1om4y127ck/) sammelt und so die Informationen nie veralten
+Anzeige von Formeln / Bildern / Tabellen | Zeigt Formeln in beiden Formen, [TeX-Format und gerendeter Form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), unterstützt Formeln und Code-Highlights
+Unterstützung von PlugIns mit mehreren Threads | Unterstützt den Aufruf mehrerer Threads in Chatgpt, um Text oder Programme [Batch zu verarbeiten](https://www.bilibili.com/video/BV1FT411H7c5/)
+Starten Sie das dunkle Gradio-[Thema](https://github.com/binary-husky/chatgpt_academic/issues/173) | Fügen Sie ```/?__theme=dark``` an das Ende der Browser-URL an, um das dunkle Thema zu aktivieren
+[Unterstützung für mehrere LLM-Modelle](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) Interface-Unterstützung | Das Gefühl, gleichzeitig von GPT3.5, GPT4, [Tshinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) bedient zu werden, muss toll sein, oder?
+Zugriff auf weitere LLM-Modelle, Unterstützung von [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Hinzufügen der Newbing-Schnittstelle (neues Bing), Einführung der Unterstützung von [Jittorllms](https://github.com/Jittor/JittorLLMs) der Tsinghua-Universität, [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) und [Pangu alpha](https://openi.org.cn/pangu/)
+Weitere neue Funktionen (wie Bildgenerierung) …… | Siehe Ende dieses Dokuments ……
+
+- Neue Oberfläche (Ändern Sie die LAYOUT-Option in `config.py`, um zwischen "Seitenlayout" und "Oben-unten-Layout" zu wechseln)
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+</div>- All buttons are dynamically generated by reading `functional.py`, and custom functions can be easily added, freeing up the clipboard.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- Proofreading/Correcting
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- If the output contains formulas, they will be displayed in both tex format and rendered format for easy copying and reading.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- Don't feel like reading the project code? Show off the entire project to chatgpt. 
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+- Multiple large language models are mixed and called together (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4).
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+# Installation
+## Installation-Method 1: Run directly (Windows, Linux or MacOS)
+
+1. Download the project
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. Configure API_KEY
+
+Configure API KEY and other settings in `config.py`. [Special Network Environment Settings](https://github.com/binary-husky/gpt_academic/issues/1).
+
+(P.S. When the program is running, it will first check whether there is a "config_private.py" private configuration file, and use the configuration defined in it to override the configuration of "config.py". Therefore, if you understand our configuration reading logic, we strongly recommend that you create a new configuration file named "config_private.py" next to "config.py" and transfer (copy) the configurations in "config.py" to "config_private.py". "config_private.py" is not controlled by git, which can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` >`config.py`)
+
+
+3. Install dependencies
+```sh
+# (Option I: If familar with Python) (Python version 3.9 or above, the newer the better), Note: Use the official pip source or Ali pip source, temporary switching method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# (Option II: If not familiar with Python) Use anaconda with similar steps (https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # Create an anaconda environment
+conda activate gptac_venv                 # Activate the anaconda environment
+python -m pip install -r requirements.txt # Same step as pip installation
+```
+
+<details><summary>Click to expand if supporting Tsinghua ChatGLM/Fudan MOSS as backend</summary>
+<p>
+
+[Optional Step] If supporting Tsinghua ChatGLM/Fudan MOSS as backend, additional dependencies need to be installed (Prerequisites: Familiar with Python + Used Pytorch + Sufficient computer configuration):
+```sh
+# [Optional Step I] Support Tsinghua ChatGLM. Remark: If encountering "Call ChatGLM fail Cannot load ChatGLM parameters", please refer to the following: 1: The above default installation is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If the model cannot be loaded due to insufficient machine configuration, you can modify the model precision in `request_llm/bridge_chatglm.py`, and modify all AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+python -m pip install -r request_llm/requirements_chatglm.txt  
+
+# [Optional Step II] Support Fudan MOSS
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # When executing this line of code, you must be in the project root path
+
+# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently supported models are as follows (jittorllms series currently only supports docker solutions):
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. Run
+```sh
+python main.py
+```5. Testing Function Plugin
+```
+- Test function plugin template function (requires gpt to answer what happened today in history), you can use this function as a template to implement more complex functions
+   Click "[Function Plugin Template Demo] Today in History"
+```
+
+## Installation-Method 2: Using Docker
+
+1. Only ChatGPT (Recommended for most people)
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # Download the project
+cd chatgpt_academic                                 # Enter the path
+nano config.py                                      # Edit config.py with any text editor, Configure "Proxy","API_KEY"and"WEB_PORT" (e.g 50923) etc.
+docker build -t gpt-academic .                      # Install
+
+# (Last step-option 1) Under Linux environment, use `--net=host` is more convenient and quick
+docker run --rm -it --net=host gpt-academic
+# (Last step-option 2) Under macOS/windows environment, can only use the -p option to expose the container's port(eg.50923) to the port on the host.
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (Requires familiarity with Docker)
+
+``` sh
+# Modify docker-compose.yml, delete solution 1 and solution 3, and retain solution 2. Modify the configuration of solution 2 in docker-compose.yml, referring to the comments in it.
+docker-compose up
+```
+
+3. ChatGPT+LLAMA+Pangu+RWKV(Requires familiarity with Docker)
+``` sh
+# Modify docker-compose.yml, delete solution 1 and solution 2, and retain solution 3. Modify the configuration of solution 3 in docker-compose.yml, referring to the comments in it.
+docker-compose up
+```
+
+
+## Installation-Method 3: Other Deployment Options
+
+1. How to use reverse proxy URL/Microsoft Azure API
+Configure API_URL_REDIRECT according to the instructions in `config.py`.
+
+2. Remote cloud server deployment (requires cloud server knowledge and experience)
+Please visit [Deployment wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
+
+3. Using WSL 2 (Windows subsystem for Linux)
+Please visit [Deployment wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
+
+4. How to run at a secondary URL (such as `http://localhost/subpath`)
+Please visit [FastAPI operating instructions](docs/WithFastapi.md)
+
+5. Use docker-compose to run
+Please read docker-compose.yml and follow the prompts to operate.
+
+---
+# Advanced Usage
+## Customize new convenience buttons / custom function plugins.
+
+1. Customize new convenience buttons (Academic Shortcut Keys)
+Open `core_functional.py` with any text editor, add an entry as follows, and then restart the program. (If the button has been added successfully and is visible, then the prefix and suffix can be hot-modified, and it will take effect without restarting the program.)
+For example
+```
+"Super English to Chinese": {
+   # Prefix, will be added before your input. For example, used to describe your requirements, such as translation, explaining code, polishing, etc.
+    "Prefix": "Please translate the following content into Chinese, and then use a markdown table to explain the proper nouns that appear in the text one by one:\n\n", 
+    
+    # Suffix, will be added after your input. For example, combined with prefix, you can enclose your input content in quotes.
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. Custom function plugins
+
+Write powerful function plugins to perform any task you want and can't think of.
+The difficulty of plugin writing and debugging is very low in this project. As long as you have a certain knowledge of Python, you can implement your own plugin functions by imitating the template we provided.
+For more information, please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+
+---
+# Latest Update
+## New feature dynamics1. Funktion zur Speicherung von Dialogen. Rufen Sie im Bereich der Funktions-Plugins "Aktuellen Dialog speichern" auf, um den aktuellen Dialog als lesbares und wiederherstellbares HTML-Datei zu speichern. Darüber hinaus können Sie im Funktions-Plugin-Bereich (Dropdown-Menü) "Laden von Dialogverlauf" aufrufen, um den vorherigen Dialog wiederherzustellen. Tipp: Wenn Sie keine Datei angeben und stattdessen direkt auf "Laden des Dialogverlaufs" klicken, können Sie das HTML-Cache-Archiv anzeigen. Durch Klicken auf "Löschen aller lokalen Dialogverlaufsdatensätze" können alle HTML-Archiv-Caches gelöscht werden.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
+</div>
+
+2. Berichterstellung. Die meisten Plugins generieren nach Abschluss der Ausführung einen Arbeitsbericht.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
+</div>
+
+3. Modularisierte Funktionsgestaltung, einfache Schnittstellen mit leistungsstarken Funktionen.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
+<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
+</div>
+
+4. Dies ist ein Open-Source-Projekt, das sich "selbst übersetzen" kann.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
+</div>
+
+5. Die Übersetzung anderer Open-Source-Projekte ist kein Problem.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
+</div>
+
+6. Dekorieren Sie [`live2d`](https://github.com/fghrsh/live2d_demo) mit kleinen Funktionen (standardmäßig deaktiviert, Änderungen an `config.py` erforderlich).
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
+</div>
+
+7. Neue MOSS-Sprachmodellunterstützung.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
+</div>
+
+8. OpenAI-Bildgenerierung.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
+</div>
+
+9. OpenAI-Audio-Analyse und Zusammenfassung.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
+</div>
+
+10. Latex-Proofreading des gesamten Textes.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
+</div>
+
+
+## Version:
+- Version 3.5 (Todo): Rufen Sie alle Funktionserweiterungen dieses Projekts mit natürlicher Sprache auf (hohe Priorität).
+- Version 3.4 (Todo): Verbesserte Unterstützung mehrerer Threads für Local Large Model (LLM).
+- Version 3.3: + Internet-Informationssynthese-Funktion
+- Version 3.2: Funktionserweiterungen unterstützen mehr Parameter-Schnittstellen (Speicherung von Dialogen, Interpretation beliebigen Sprachcodes + gleichzeitige Abfrage jeder LLM-Kombination)
+- Version 3.1: Unterstützung mehrerer GPT-Modelle gleichzeitig! Unterstützung für API2D, Unterstützung für Lastenausgleich von mehreren API-Schlüsseln.
+- Version 3.0: Unterstützung von Chatglm und anderen kleinen LLMs
+- Version 2.6: Umstrukturierung der Plugin-Struktur zur Verbesserung der Interaktivität, Einführung weiterer Plugins
+- Version 2.5: Automatische Aktualisierung, Problembehebung bei Quelltexten großer Projekte, wenn der Text zu lang ist oder Token überlaufen.
+- Version 2.4: (1) Neue Funktion zur Übersetzung des gesamten PDF-Texts; (2) Neue Funktion zum Wechseln der Position des Eingabebereichs; (3) Neue Option für vertikales Layout; (4) Optimierung von Multithread-Funktions-Plugins.
+- Version 2.3: Verbesserte Interaktivität mit mehreren Threads
+- Version 2.2: Funktionserweiterungen unterstützen "Hot-Reload"
+- Version 2.1: Faltbares Layout
+- Version 2.0: Einführung von modularisierten Funktionserweiterungen
+- Version 1.0: Grundlegende Funktionengpt_academic Entwickler QQ-Gruppe-2: 610599535
+
+- Bekannte Probleme
+    - Einige Browser-Übersetzungs-Plugins können die Frontend-Ausführung dieser Software stören.
+    - Sowohl eine zu hohe als auch eine zu niedrige Version von Gradio führt zu verschiedenen Ausnahmen.
+
+## Referenz und Lernen
+
+```
+Der Code bezieht sich auf viele Designs von anderen herausragenden Projekten, insbesondere:
+
+# Projekt 1: ChatGLM-6B der Tsinghua Universität:
+https://github.com/THUDM/ChatGLM-6B
+
+# Projekt 2: JittorLLMs der Tsinghua Universität:
+https://github.com/Jittor/JittorLLMs
+
+# Projekt 3: Edge-GPT:
+https://github.com/acheong08/EdgeGPT
+
+# Projekt 4: ChuanhuChatGPT:
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# Projekt 5: ChatPaper:
+https://github.com/kaixindelele/ChatPaper
+
+# Mehr:
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/README.md.Italian.md

@@ -0,0 +1,310 @@
+> **Nota**
+>
+> Durante l'installazione delle dipendenze, selezionare rigorosamente le **versioni specificate** nel file requirements.txt.
+>
+> ` pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
+
+# <img src="docs/logo.png" width="40" > GPT Ottimizzazione Accademica (GPT Academic)
+
+**Se ti piace questo progetto, ti preghiamo di dargli una stella. Se hai sviluppato scorciatoie accademiche o plugin funzionali più utili, non esitare ad aprire una issue o pull request. Abbiamo anche una README in [Inglese|](docs/README_EN.md)[Giapponese|](docs/README_JP.md)[Coreano|](https://github.com/mldljyh/ko_gpt_academic)[Russo|](docs/README_RS.md)[Francese](docs/README_FR.md) tradotta da questo stesso progetto.
+Per tradurre questo progetto in qualsiasi lingua con GPT, leggere e eseguire [`multi_language.py`](multi_language.py) (sperimentale).
+
+> **Nota**
+>
+> 1. Si prega di notare che solo i plugin (pulsanti) contrassegnati in **rosso** supportano la lettura di file, alcuni plugin sono posizionati nel **menu a discesa** nella zona dei plugin. Accettiamo e gestiamo PR per qualsiasi nuovo plugin con **massima priorità**!
+>
+> 2. Le funzionalità di ogni file di questo progetto sono descritte dettagliatamente nella propria analisi di autotraduzione [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). Con l'iterazione delle versioni, è possibile fare clic sui plugin funzionali correlati in qualsiasi momento per richiamare GPT e generare nuovamente il rapporto di analisi automatica del progetto. Le domande frequenti sono riassunte nella [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Metodo di installazione] (#installazione).
+>
+> 3. Questo progetto è compatibile e incoraggia l'utilizzo di grandi modelli di linguaggio di produzione nazionale come chatglm, RWKV, Pangu ecc. Supporta la coesistenza di più api-key e può essere compilato nel file di configurazione come `API_KEY="openai-key1,openai-key2,api2d-key3"`. Per sostituire temporaneamente `API_KEY`, inserire `API_KEY` temporaneo nell'area di input e premere Invio per renderlo effettivo. 
+
+<div align="center">Funzione | Descrizione
+--- | ---
+Correzione immediata | Supporta correzione immediata e ricerca degli errori di grammatica del documento con un solo clic
+Traduzione cinese-inglese immediata | Traduzione cinese-inglese immediata con un solo clic
+Spiegazione del codice immediata | Visualizzazione del codice, spiegazione del codice, generazione del codice, annotazione del codice con un solo clic
+[Scorciatoie personalizzate](https://www.bilibili.com/video/BV14s4y1E7jN) | Supporta scorciatoie personalizzate
+Design modularizzato | Supporta potenti [plugin di funzioni](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) personalizzati, i plugin supportano l'[aggiornamento in tempo reale](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
+[Auto-profiling del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] [Comprensione immediata](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) del codice sorgente di questo progetto
+[Analisi del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] Un clic può analizzare l'albero di altri progetti Python/C/C++/Java/Lua/...
+Lettura del documento, [traduzione](https://www.bilibili.com/video/BV1KT411x7Wn) del documento | [Plugin di funzioni] La lettura immediata dell'intero documento latex/pdf di un documento e la generazione di un riassunto
+Traduzione completa di un documento Latex, [correzione immediata](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin di funzioni] Una traduzione o correzione immediata di un documento Latex
+Generazione di annotazioni in batch | [Plugin di funzioni] Generazione automatica delle annotazioni di funzione con un solo clic
+[Traduzione cinese-inglese di Markdown](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin di funzioni] Hai letto il [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) delle cinque lingue sopra?
+Generazione di report di analisi di chat | [Plugin di funzioni] Generazione automatica di un rapporto di sintesi dopo l'esecuzione
+[Funzione di traduzione di tutto il documento PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin di funzioni] Estrarre il titolo e il sommario dell'articolo PDF + tradurre l'intero testo (multithreading)
+[Assistente di Arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugin di funzioni] Inserire l'URL dell'articolo di Arxiv e tradurre il sommario con un clic + scaricare il PDF
+[Assistente integrato di Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plugin di funzioni] Con qualsiasi URL di pagina di ricerca di Google Scholar, lascia che GPT ti aiuti a scrivere il tuo [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
+Aggregazione delle informazioni su Internet + GPT | [Plugin di funzioni] Fai in modo che GPT rilevi le informazioni su Internet prima di rispondere alle domande, senza mai diventare obsolete
+Visualizzazione di formule/img/tabelle | È possibile visualizzare un'equazione in forma [tex e render](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) contemporaneamente, supporta equazioni e evidenziazione del codice
+Supporto per plugin di funzioni multithreading | Supporto per chiamata multithreaded di chatgpt, elaborazione con un clic di grandi quantità di testo o di un programma
+Avvia il tema di gradio [scuro](https://github.com/binary-husky/chatgpt_academic/issues/173) | Aggiungere ```/?__theme=dark``` dopo l'URL del browser per passare a un tema scuro
+Supporto per maggiori modelli LLM, supporto API2D | Sentirsi serviti simultaneamente da GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) deve essere una grande sensazione, giusto?
+Ulteriori modelli LLM supportat,i supporto per l'implementazione di Huggingface | Aggiunta di un'interfaccia Newbing (Nuovo Bing), introdotta la compatibilità con Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) e [PanGu-α](https://openi.org.cn/pangu/)
+Ulteriori dimostrazioni di nuove funzionalità (generazione di immagini, ecc.)... | Vedere la fine di questo documento...
+ 
+- Nuova interfaccia (modificare l'opzione LAYOUT in `config.py` per passare dal layout a sinistra e a destra al layout superiore e inferiore)
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+</div>Sei un traduttore professionista di paper accademici.
+
+- Tutti i pulsanti vengono generati dinamicamente leggendo il file functional.py, e aggiungerci nuove funzionalità è facile, liberando la clipboard.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- Revisione/Correzione
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- Se l'output contiene una formula, viene visualizzata sia come testo che come formula renderizzata, per facilitare la copia e la visualizzazione.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- Non hai tempo di leggere il codice del progetto? Passa direttamente a chatgpt e chiedi informazioni.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+- Chiamata mista di vari modelli di lingua di grandi dimensioni (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+# Installazione
+## Installazione - Metodo 1: Esecuzione diretta (Windows, Linux o MacOS)
+
+1. Scarica il progetto
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. Configura API_KEY
+
+In `config.py`, configura la tua API KEY e altre impostazioni, [configs for special network environments](https://github.com/binary-husky/gpt_academic/issues/1).
+
+(N.B. Quando il programma viene eseguito, verifica prima se esiste un file di configurazione privato chiamato `config_private.py` e sovrascrive le stesse configurazioni in `config.py`.  Pertanto, se capisci come funziona la nostra logica di lettura della configurazione, ti consigliamo vivamente di creare un nuovo file di configurazione chiamato `config_private.py` accanto a `config.py`, e spostare (copiare) le configurazioni di `config.py` in `config_private.py`. 'config_private.py' non è sotto la gestione di git e può proteggere ulteriormente le tue informazioni personali. NB Il progetto supporta anche la configurazione della maggior parte delle opzioni tramite "variabili d'ambiente". La sintassi della variabile d'ambiente è descritta nel file `docker-compose`. Priorità di lettura: "variabili d'ambiente" > "config_private.py" > "config.py")
+
+
+3. Installa le dipendenze
+```sh
+# (Scelta I: se sei familiare con python) (python 3.9 o superiore, più nuovo è meglio), N.B.: utilizza il repository ufficiale pip o l'aliyun pip repository, metodo temporaneo per cambiare il repository: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# (Scelta II: se non conosci Python) utilizza anaconda, il processo è simile (https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # crea l'ambiente anaconda
+conda activate gptac_venv                 # attiva l'ambiente anaconda
+python -m pip install -r requirements.txt # questo passaggio funziona allo stesso modo dell'installazione con pip
+```
+
+<details><summary>Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, fare clic qui per espandere</summary>
+<p>
+
+【Passaggio facoltativo】 Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, è necessario installare ulteriori dipendenze (prerequisiti: conoscenza di Python, esperienza con Pytorch e computer sufficientemente potente):
+```sh
+# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+python -m pip install -r request_llm/requirements_chatglm.txt  
+
+# 【Passaggio facoltativo II】 Supporto a MOSS di Fudan
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
+
+# 【Passaggio facoltativo III】 Assicurati che il file di configurazione config.py includa tutti i modelli desiderati, al momento tutti i modelli supportati sono i seguenti (i modelli della serie jittorllms attualmente supportano solo la soluzione docker):
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. Esegui
+```sh
+python main.py
+```5. Plugin di test delle funzioni
+```
+- Funzione plugin di test (richiede una risposta gpt su cosa è successo oggi in passato), puoi utilizzare questa funzione come template per implementare funzionalità più complesse
+    Clicca su "[Demo del plugin di funzione] Oggi nella storia"
+```
+
+## Installazione - Metodo 2: Utilizzo di Docker
+
+1. Solo ChatGPT (consigliato per la maggior parte delle persone)
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # scarica il progetto
+cd chatgpt_academic                                 # entra nel percorso
+nano config.py                                      # con un qualsiasi editor di testo, modifica config.py configurando "Proxy", "API_KEY" e "WEB_PORT" (ad esempio 50923)
+docker build -t gpt-academic .                      # installa
+
+#(ultimo passaggio - selezione 1) In un ambiente Linux, utilizzare '--net=host' è più conveniente e veloce
+docker run --rm -it --net=host gpt-academic
+#(ultimo passaggio - selezione 2) In un ambiente MacOS/Windows, l'opzione -p può essere utilizzata per esporre la porta del contenitore (ad es. 50923) alla porta della macchina
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (richiede familiarità con Docker)
+
+``` sh
+# Modifica docker-compose.yml, elimina i piani 1 e 3, mantieni il piano 2. Modifica la configurazione del piano 2 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
+docker-compose up
+```
+
+3. ChatGPT + LLAMA + Pangu + RWKV (richiede familiarità con Docker)
+
+``` sh
+# Modifica docker-compose.yml, elimina i piani 1 e 2, mantieni il piano 3. Modifica la configurazione del piano 3 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
+docker-compose up
+```
+
+
+## Installazione - Metodo 3: Altre modalità di distribuzione
+
+1. Come utilizzare un URL di reindirizzamento / AzureAPI Cloud Microsoft
+Configura API_URL_REDIRECT seguendo le istruzioni nel file `config.py`.
+
+2. Distribuzione su un server cloud remoto (richiede conoscenze ed esperienza di server cloud)
+Si prega di visitare [wiki di distribuzione-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
+
+3. Utilizzo di WSL2 (Windows Subsystem for Linux)
+Si prega di visitare [wiki di distribuzione-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
+
+4. Come far funzionare ChatGPT all'interno di un sottodominio (ad es. `http://localhost/subpath`)
+Si prega di visitare [Istruzioni per l'esecuzione con FastAPI] (docs/WithFastapi.md)
+
+5. Utilizzo di docker-compose per l'esecuzione
+Si prega di leggere il file docker-compose.yml e seguire le istruzioni fornite.
+
+---
+# Uso avanzato
+## Personalizzazione dei pulsanti / Plugin di funzione personalizzati
+
+1. Personalizzazione dei pulsanti (scorciatoie accademiche)
+Apri `core_functional.py` con qualsiasi editor di testo e aggiungi la voce seguente, quindi riavvia il programma (se il pulsante è già stato aggiunto con successo e visibile, il prefisso e il suffisso supportano la modifica in tempo reale, senza bisogno di riavviare il programma).
+
+ad esempio
+```
+"超级英译中": {
+    # Prefisso, verrà aggiunto prima del tuo input. Ad esempio, descrivi la tua richiesta, come tradurre, spiegare il codice, correggere errori, ecc.
+    "Prefix": "Per favore traduci questo testo in Cinese, e poi spiega tutti i termini tecnici nel testo con una tabella markdown:\n\n", 
+    
+    # Suffisso, verrà aggiunto dopo il tuo input. Ad esempio, con il prefisso puoi circondare il tuo input con le virgolette.
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. Plugin di funzione personalizzati
+
+Scrivi plugin di funzione personalizzati e esegui tutte le attività che desideri o non hai mai pensato di fare.
+La difficoltà di scrittura e debug dei plugin del nostro progetto è molto bassa. Se si dispone di una certa conoscenza di base di Python, è possibile realizzare la propria funzione del plugin seguendo il nostro modello. Per maggiori dettagli, consultare la [guida al plugin per funzioni] (https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+
+---
+# Ultimo aggiornamento
+## Nuove funzionalità dinamiche1. Funzionalità di salvataggio della conversazione. Nell'area dei plugin della funzione, fare clic su "Salva la conversazione corrente" per salvare la conversazione corrente come file html leggibile e ripristinabile, inoltre, nell'area dei plugin della funzione (menu a discesa), fare clic su "Carica la cronologia della conversazione archiviata" per ripristinare la conversazione precedente. Suggerimento: fare clic su "Carica la cronologia della conversazione archiviata" senza specificare il file consente di visualizzare la cache degli archivi html di cronologia, fare clic su "Elimina tutti i record di cronologia delle conversazioni locali" per eliminare tutte le cache degli archivi html.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
+</div>
+
+2. Generazione di rapporti. La maggior parte dei plugin genera un rapporto di lavoro dopo l'esecuzione.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
+</div>
+
+3. Progettazione modulare delle funzioni, semplici interfacce ma in grado di supportare potenti funzionalità.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
+<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
+</div>
+
+4. Questo è un progetto open source che può "tradursi da solo".
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
+</div>
+
+5. Tradurre altri progetti open source è semplice.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
+</div>
+
+6. Piccola funzione decorativa per [live2d](https://github.com/fghrsh/live2d_demo) (disattivata per impostazione predefinita, è necessario modificare `config.py`).
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
+</div>
+
+7. Supporto del grande modello linguistico MOSS
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
+</div>
+
+8. Generazione di immagini OpenAI
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
+</div>
+
+9. Analisi e sintesi audio OpenAI
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
+</div>
+
+10. Verifica completa dei testi in LaTeX
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
+</div>
+
+
+## Versione:
+- versione 3.5(Todo): utilizzo del linguaggio naturale per chiamare tutti i plugin di funzioni del progetto (alta priorità)
+- versione 3.4(Todo): supporto multi-threading per il grande modello linguistico locale Chatglm
+- versione 3.3: +funzionalità di sintesi delle informazioni su Internet
+- versione 3.2: i plugin di funzioni supportano più interfacce dei parametri (funzionalità di salvataggio della conversazione, lettura del codice in qualsiasi lingua + richiesta simultanea di qualsiasi combinazione di LLM)
+- versione 3.1: supporto per interrogare contemporaneamente più modelli gpt! Supporto api2d, bilanciamento del carico per più apikey
+- versione 3.0: supporto per Chatglm e altri piccoli LLM
+- versione 2.6: ristrutturazione della struttura del plugin, miglioramento dell'interattività, aggiunta di più plugin
+- versione 2.5: auto-aggiornamento, risoluzione del problema di testo troppo lungo e overflow del token durante la sintesi di grandi progetti di ingegneria
+- versione 2.4: (1) funzionalità di traduzione dell'intero documento in formato PDF aggiunta; (2) funzionalità di scambio dell'area di input aggiunta; (3) opzione di layout verticale aggiunta; (4) ottimizzazione della funzione di plugin multi-threading.
+- versione 2.3: miglioramento dell'interattività multi-threading
+- versione 2.2: i plugin di funzioni supportano l'hot-reload
+- versione 2.1: layout ripiegabile
+- versione 2.0: introduzione di plugin di funzioni modulari
+- versione 1.0: funzione di basegpt_academic sviluppatori gruppo QQ-2: 610599535
+
+- Problemi noti
+    - Alcuni plugin di traduzione del browser interferiscono con l'esecuzione del frontend di questo software
+    - La versione di gradio troppo alta o troppo bassa può causare diversi malfunzionamenti
+
+## Riferimenti e apprendimento
+
+```
+Il codice fa riferimento a molte altre eccellenti progettazioni di progetti, principalmente:
+
+# Progetto 1: ChatGLM-6B di Tsinghua:
+https://github.com/THUDM/ChatGLM-6B
+
+# Progetto 2: JittorLLMs di Tsinghua:
+https://github.com/Jittor/JittorLLMs
+
+# Progetto 3: Edge-GPT:
+https://github.com/acheong08/EdgeGPT
+
+# Progetto 4: ChuanhuChatGPT:
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# Progetto 5: ChatPaper:
+https://github.com/kaixindelele/ChatPaper
+
+# Altro:
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/README.md.Korean.md

@@ -0,0 +1,268 @@
+> **노트**
+>
+> 의존성을 설치할 때는 반드시 requirements.txt에서 **지정된 버전**을 엄격하게 선택하십시오.
+> 
+> `pip install -r requirements.txt`
+
+# <img src="docs/logo.png" width="40" > GPT 학술 최적화 (GPT Academic)
+
+**이 프로젝트가 마음에 드신다면 Star를 주세요. 추가로 유용한 학술 단축키나 기능 플러그인이 있다면 이슈나 pull request를 남기세요. 이 프로젝트에 대한 [영어 |](docs/README_EN.md)[일본어 |](docs/README_JP.md)[한국어 |](https://github.com/mldljyh/ko_gpt_academic)[러시아어 |](docs/README_RS.md)[프랑스어](docs/README_FR.md)로 된 README도 있습니다. 
+GPT를 이용하여 프로젝트를 임의의 언어로 번역하려면 [`multi_language.py`](multi_language.py)를 읽고 실행하십시오. (실험적)
+
+> **노트**
+>
+> 1. 파일을 읽기 위해 **빨간색**으로 표시된 기능 플러그인 (버튼) 만 지원됩니다. 일부 플러그인은 플러그인 영역의 **드롭다운 메뉴**에 있습니다. 또한 새로운 플러그인은 **가장 높은 우선순위**로 환영하며 처리합니다!
+>
+> 2. 이 프로젝트의 각 파일의 기능을 [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)에서 자세히 설명합니다. 버전이 업데이트 됨에 따라 관련된 기능 플러그인을 클릭하고 GPT를 호출하여 프로젝트의 자체 분석 보고서를 다시 생성할 수도 있습니다. 자주 묻는 질문은 [`위키`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)에서 볼 수 있습니다. [설치 방법](#installation).
+> 
+> 3. 이 프로젝트는 국내 언어 모델 chatglm과 RWKV, 판고 등의 시도와 호환 가능합니다. 여러 개의 api-key를 지원하며 설정 파일에 "API_KEY="openai-key1,openai-key2,api2d-key3""와 같이 작성할 수 있습니다. `API_KEY`를 임시로 변경해야하는 경우 입력 영역에 임시 `API_KEY`를 입력 한 후 엔터 키를 누르면 즉시 적용됩니다. 
+
+<div align="center">기능 | 설명
+--- | ---
+원 키워드 | 원 키워드 및 논문 문법 오류를 찾는 기능 지원
+한-영 키워드 | 한-영 키워드 지원
+코드 설명 | 코드 표시, 코드 설명, 코드 생성, 코드에 주석 추가
+[사용자 정의 바로 가기 키](https://www.bilibili.com/video/BV14s4y1E7jN) | 사용자 정의 바로 가기 키 지원
+모듈식 설계 | 강력한[함수 플러그인](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) 지원, 플러그인이 [램 업데이트](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)를 지원합니다.
+[자체 프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] [원 키 우드] 프로젝트 소스 코드의 내용을 이해하는 기능을 제공
+[프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] 프로젝트 트리를 분석할 수 있습니다 (Python/C/C++/Java/Lua/...)
+논문 읽기, 번역 | [함수 플러그인] LaTex/PDF 논문의 전문을 읽고 요약을 생성합니다.
+LaTeX 텍스트[번역](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [원 키워드](https://www.bilibili.com/video/BV1FT411H7c5/) | [함수 플러그인] LaTeX 논문의 번역 또는 개량을 위해 일련의 모드를 번역할 수 있습니다.
+대량의 주석 생성 | [함수 플러그인] 함수 코멘트를 대량으로 생성할 수 있습니다.
+Markdown 한-영 번역 | [함수 플러그인] 위의 5 종 언어의 [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)를 볼 수 있습니다.
+chat 분석 보고서 생성 | [함수 플러그인] 수행 후 요약 보고서를 자동으로 생성합니다.
+[PDF 논문 번역](https://www.bilibili.com/video/BV1KT411x7Wn) | [함수 플러그인] PDF 논문이 제목 및 요약을 추출한 후 번역됩니다. (멀티 스레드)
+[Arxiv 도우미](https://www.bilibili.com/video/BV1LM4y1279X) | [함수 플러그인] Arxiv 논문 URL을 입력하면 요약을 번역하고 PDF를 다운로드 할 수 있습니다.
+[Google Scholar 통합 도우미](https://www.bilibili.com/video/BV19L411U7ia) | [함수 플러그인] Google Scholar 검색 페이지 URL을 제공하면 gpt가 [Related Works 작성](https://www.bilibili.com/video/BV1GP411U7Az/)을 도와줍니다.
+인터넷 정보 집계+GPT | [함수 플러그인] 먼저 GPT가 인터넷에서 정보를 수집하고 질문에 대답 할 수 있도록합니다. 정보가 절대적으로 구식이 아닙니다.
+수식/이미지/표 표시 | 급여, 코드 강조 기능 지원
+멀티 스레드 함수 플러그인 지원 | Chatgpt를 여러 요청에서 실행하여 [대량의 텍스트](https://www.bilibili.com/video/BV1FT411H7c5/) 또는 프로그램을 처리 할 수 있습니다.
+다크 그라디오 테마 시작 | 어둡게 주제를 변경하려면 브라우저 URL 끝에 ```/?__theme=dark```을 추가하면됩니다.
+[다중 LLM 모델](https://www.bilibili.com/video/BV1wT411p7yf) 지원, [API2D](https://api2d.com/) 인터페이스 지원됨 | GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS)가 모두 동시에 작동하는 것처럼 느낄 수 있습니다!
+LLM 모델 추가 및[huggingface 배치](https://huggingface.co/spaces/qingxu98/gpt-academic) 지원 | 새 Bing 인터페이스 (새 Bing) 추가, Clearing House [Jittorllms](https://github.com/Jittor/JittorLLMs) 지원 [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) 및 [盘古α](https://openi.org.cn/pangu/)
+기타 새로운 기능 (이미지 생성 등) ... | 이 문서의 끝부분을 참조하세요. ...- 모든 버튼은 functional.py를 동적으로 읽어와서 사용자 정의 기능을 자유롭게 추가할 수 있으며, 클립 보드를 해제합니다.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- 검수/오타 교정
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- 출력에 수식이 포함되어 있으면 텍스와 렌더링의 형태로 동시에 표시되어 복사 및 읽기가 용이합니다.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- 프로젝트 코드를 볼 시간이 없습니까? 전체 프로젝트를 chatgpt에 직접 표시하십시오
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+- 다양한 대형 언어 모델 범용 요청 (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+# 설치
+## Installation-Method 1: Run directly (Windows, Linux or MacOS) 
+
+1. 프로젝트 다운로드
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. API_KEY 구성
+
+`config.py`에서 API KEY 등 설정을 구성합니다. [특별한 네트워크 환경 설정](https://github.com/binary-husky/gpt_academic/issues/1) .
+
+(P.S. 프로그램이 실행될 때, 이름이 `config_private.py`인 기밀 설정 파일이 있는지 우선적으로 확인하고 해당 설정으로 `config.py`의 동일한 이름의 설정을 덮어씁니다. 따라서 구성 읽기 논리를 이해할 수 있다면, `config.py` 옆에 `config_private.py`라는 새 구성 파일을 만들고 `config.py`의 구성을 `config_private.py`로 이동(복사)하는 것이 좋습니다. `config_private.py`는 git으로 관리되지 않으며 개인 정보를 더 안전하게 보호할 수 있습니다. P.S. 프로젝트는 또한 대부분의 옵션을 `환경 변수`를 통해 설정할 수 있으며, `docker-compose` 파일을 참조하여 환경 변수 작성 형식을 확인할 수 있습니다. 우선순위: `환경 변수` > `config_private.py` > `config.py`)
+
+
+3. 의존성 설치
+```sh
+# (I 선택: 기존 python 경험이 있다면) (python 버전 3.9 이상, 최신 버전이 좋습니다), 참고: 공식 pip 소스 또는 알리 pip 소스 사용, 일시적인 교체 방법: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# (II 선택: Python에 익숙하지 않은 경우) anaconda 사용 방법은 비슷함(https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # anaconda 환경 만들기
+conda activate gptac_venv                 # anaconda 환경 활성화
+python -m pip install -r requirements.txt # 이 단계도 pip install의 단계와 동일합니다.
+```
+
+<details><summary>추가지원을 위해 Tsinghua ChatGLM / Fudan MOSS를 사용해야하는 경우 지원을 클릭하여 이 부분을 확장하세요.</summary>
+<p>
+
+[Tsinghua ChatGLM] / [Fudan MOSS]를 백엔드로 사용하려면 추가적인 종속성을 설치해야합니다 (전제 조건 : Python을 이해하고 Pytorch를 사용한 적이 있으며, 컴퓨터가 충분히 강력한 경우) :
+```sh
+# [선택 사항 I] Tsinghua ChatGLM을 지원합니다. Tsinghua ChatGLM에 대한 참고사항 : "Call ChatGLM fail cannot load ChatGLM parameters normally" 오류 발생시 다음 참조: 
+# 1 : 기본 설치된 것들은 torch + cpu 버전입니다. cuda를 사용하려면 torch를 제거한 다음 torch + cuda를 다시 설치해야합니다.
+# 2 : 모델을 로드할 수 없는 기계 구성 때문에, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)를
+# AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)로 변경합니다.
+python -m pip install -r request_llm/requirements_chatglm.txt  
+
+# [선택 사항 II] Fudan MOSS 지원
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # 다음 코드 줄을 실행할 때 프로젝트 루트 경로에 있어야합니다.
+
+# [선택 사항III] AVAIL_LLM_MODELS config.py 구성 파일에 기대하는 모델이 포함되어 있는지 확인하십시오.
+# 현재 지원되는 전체 모델 :
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. 실행
+```sh
+python main.py
+```5. 테스트 함수 플러그인
+```
+- 테스트 함수 플러그인 템플릿 함수 (GPT에게 오늘의 역사에서 무슨 일이 일어났는지 대답하도록 요청)를 구현하는 데 사용할 수 있습니다. 이 함수를 기반으로 더 복잡한 기능을 구현할 수 있습니다.
+    "[함수 플러그인 템플릿 데모] 오늘의 역사"를 클릭하세요.
+```
+
+## 설치 - 방법 2 : 도커 사용
+
+1. ChatGPT 만 (대부분의 사람들이 선택하는 것을 권장합니다.)
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # 다운로드
+cd chatgpt_academic                                 # 경로 이동
+nano config.py                                      # 아무 텍스트 에디터로 config.py를 열고 "Proxy","API_KEY","WEB_PORT" (예 : 50923) 등을 구성합니다.
+docker build -t gpt-academic .                      # 설치
+
+#(마지막 단계-1 선택) Linux 환경에서는 --net=host를 사용하면 더 편리합니다.
+docker run --rm -it --net=host gpt-academic
+#(마지막 단계-2 선택) macOS / windows 환경에서는 -p 옵션을 사용하여 컨테이너의 포트 (예 : 50923)를 호스트의 포트로 노출해야합니다.
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (Docker에 익숙해야합니다.)
+
+``` sh
+#docker-compose.yml을 수정하여 계획 1 및 계획 3을 삭제하고 계획 2를 유지합니다. docker-compose.yml에서 계획 2의 구성을 수정하면 됩니다. 주석을 참조하십시오.
+docker-compose up
+```
+
+3. ChatGPT + LLAMA + Pangu + RWKV (Docker에 익숙해야합니다.)
+``` sh
+#docker-compose.yml을 수정하여 계획 1 및 계획 2을 삭제하고 계획 3을 유지합니다. docker-compose.yml에서 계획 3의 구성을 수정하면 됩니다. 주석을 참조하십시오.
+docker-compose up
+```
+
+
+## 설치 - 방법 3 : 다른 배치 방법
+
+1. 리버스 프록시 URL / Microsoft Azure API 사용 방법
+API_URL_REDIRECT를 `config.py`에 따라 구성하면됩니다.
+
+2. 원격 클라우드 서버 배치 (클라우드 서버 지식과 경험이 필요합니다.)
+[배치위키-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)에 방문하십시오.
+
+3. WSL2 사용 (Windows Subsystem for Linux 하위 시스템)
+[배치 위키-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)에 방문하십시오.
+
+4. 2 차 URL (예 : `http : //localhost/subpath`)에서 실행하는 방법
+[FastAPI 실행 설명서] (docs / WithFastapi.md)를 참조하십시오.
+
+5. docker-compose 실행
+docker-compose.yml을 읽은 후 지시 사항에 따라 작업하십시오.
+---
+# 고급 사용법
+## 사용자 정의 바로 가기 버튼 / 사용자 정의 함수 플러그인
+
+1. 사용자 정의 바로 가기 버튼 (학술 바로 가기)
+임의의 텍스트 편집기로 'core_functional.py'를 엽니다. 엔트리 추가, 그런 다음 프로그램을 다시 시작하면됩니다. (버튼이 이미 추가되어 보이고 접두사, 접미사가 모두 변수가 효과적으로 수정되면 프로그램을 다시 시작하지 않아도됩니다.)
+예 :
+```
+"超级英译中": {
+    # 접두사. 당신이 요구하는 것을 설명하는 데 사용됩니다. 예를 들어 번역, 코드를 설명, 다듬기 등
+    "Prefix": "下面翻译成中文，然后用一个 markdown 表格逐一解释文中出现的专有名词：\n\n", 
+    
+    # 접미사는 입력 내용 앞뒤에 추가됩니다. 예를 들어 전위를 사용하여 입력 내용을 따옴표로 묶는데 사용할 수 있습니다.
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. 사용자 지정 함수 플러그인
+강력한 함수 플러그인을 작성하여 원하는 작업을 수행하십시오.
+이 프로젝트의 플러그인 작성 및 디버깅 난이도는 매우 낮으며, 일부 파이썬 기본 지식만 있으면 제공된 템플릿을 모방하여 플러그인 기능을 구현할 수 있습니다. 자세한 내용은 [함수 플러그인 가이드]를 참조하십시오. (https://github.com/binary -husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E 4%BB%B6%E6%8C%87%E5%8D%97).
+---
+# 최신 업데이트
+## 새로운 기능 동향1. 대화 저장 기능. 
+
+1. 함수 플러그인 영역에서 '현재 대화 저장'을 호출하면 현재 대화를 읽을 수 있고 복원 가능한 HTML 파일로 저장할 수 있습니다. 또한 함수 플러그인 영역(드롭다운 메뉴)에서 '대화 기록 불러오기'를 호출하면 이전 대화를 복원할 수 있습니다. 팁: 파일을 지정하지 않고 '대화 기록 불러오기'를 클릭하면 기록된 HTML 캐시를 볼 수 있으며 '모든 로컬 대화 기록 삭제'를 클릭하면 모든 HTML 캐시를 삭제할 수 있습니다.
+
+2. 보고서 생성. 대부분의 플러그인은 실행이 끝난 후 작업 보고서를 생성합니다.
+
+3. 모듈화 기능 설계, 간단한 인터페이스로도 강력한 기능을 지원할 수 있습니다.
+
+4. 자체 번역이 가능한 오픈 소스 프로젝트입니다.
+
+5. 다른 오픈 소스 프로젝트를 번역하는 것은 어렵지 않습니다.
+
+6. [live2d](https://github.com/fghrsh/live2d_demo) 장식 기능(기본적으로 비활성화되어 있으며 `config.py`를 수정해야 합니다.)
+
+7. MOSS 대 언어 모델 지원 추가
+
+8. OpenAI 이미지 생성
+
+9. OpenAI 음성 분석 및 요약
+
+10. LaTeX 전체적인 교정 및 오류 수정
+
+## 버전:
+- version 3.5 (TODO): 자연어를 사용하여 이 프로젝트의 모든 함수 플러그인을 호출하는 기능(우선순위 높음)
+- version 3.4(TODO): 로컬 대 모듈의 다중 스레드 지원 향상
+- version 3.3: 인터넷 정보 종합 기능 추가
+- version 3.2: 함수 플러그인이 더 많은 인수 인터페이스를 지원합니다.(대화 저장 기능, 임의의 언어 코드 해석 및 동시에 임의의 LLM 조합을 확인하는 기능)
+- version 3.1: 여러 개의 GPT 모델에 대한 동시 쿼리 지원! api2d 지원, 여러 개의 apikey 로드 밸런싱 지원
+- version 3.0: chatglm 및 기타 소형 llm의 지원
+- version 2.6: 플러그인 구조를 재구성하여 상호 작용성을 향상시켰습니다. 더 많은 플러그인을 추가했습니다.
+- version 2.5: 자체 업데이트, 전체 프로젝트를 요약할 때 텍스트가 너무 길어지고 토큰이 오버플로우되는 문제를 해결했습니다.
+- version 2.4: (1) PDF 전체 번역 기능 추가; (2) 입력 영역 위치 전환 기능 추가; (3) 수직 레이아웃 옵션 추가; (4) 다중 스레드 함수 플러그인 최적화.
+- version 2.3: 다중 스레드 상호 작용성 강화
+- version 2.2: 함수 플러그인 히트 리로드 지원
+- version 2.1: 접는 레이아웃 지원
+- version 2.0: 모듈화 함수 플러그인 도입
+- version 1.0: 기본 기능
+
+gpt_academic 개발자 QQ 그룹-2 : 610599535
+
+- 알려진 문제
+    - 일부 브라우저 번역 플러그인이이 소프트웨어의 프론트 엔드 작동 방식을 방해합니다.
+    - gradio 버전이 너무 높거나 낮으면 여러 가지 이상이 발생할 수 있습니다.
+
+## 참고 및 학습 자료
+
+```
+많은 우수 프로젝트의 디자인을 참고했습니다. 주요 항목은 다음과 같습니다.
+
+# 프로젝트 1 : Tsinghua ChatGLM-6B :
+https://github.com/THUDM/ChatGLM-6B
+
+# 프로젝트 2 : Tsinghua JittorLLMs:
+https://github.com/Jittor/JittorLLMs
+
+# 프로젝트 3 : Edge-GPT :
+https://github.com/acheong08/EdgeGPT
+
+# 프로젝트 4 : ChuanhuChatGPT:
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# 프로젝트 5 : ChatPaper :
+https://github.com/kaixindelele/ChatPaper
+
+# 더 많은 :
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/README.md.Portuguese.md

@@ -0,0 +1,320 @@
+> **Nota**
+>
+> Ao instalar as dependências, por favor, selecione rigorosamente as versões **especificadas** no arquivo requirements.txt.
+>
+> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
+>
+
+# <img src="logo.png" width="40" > Otimização acadêmica GPT (GPT Academic)
+
+**Se você gostou deste projeto, por favor dê um Star. Se você criou atalhos acadêmicos mais úteis ou plugins funcionais, sinta-se livre para abrir uma issue ou pull request. Nós também temos um README em [Inglês|](README_EN.md)[日本語|](README_JP.md)[한국어|](https://github.com/mldljyh/ko_gpt_academic)[Русский|](README_RS.md)[Français](README_FR.md) traduzidos por este próprio projeto.
+Para traduzir este projeto para qualquer idioma com o GPT, leia e execute [`multi_language.py`](multi_language.py) (experimental).
+
+> **Nota**
+>
+> 1. Por favor, preste atenção que somente os plugins de funções (botões) com a cor **vermelha** podem ler arquivos. Alguns plugins estão localizados no **menu suspenso** na área de plugins. Além disso, nós damos as boas-vindas com a **maior prioridade** e gerenciamos quaisquer novos plugins PR!
+>
+> 2. As funções de cada arquivo neste projeto são detalhadas em [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A), auto-análises do projeto geradas pelo GPT também estão podem ser chamadas a qualquer momento ao clicar nos plugins relacionados. As perguntas frequentes estão resumidas no [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Instruções de Instalação](#installation).
+>
+> 3. Este projeto é compatível com e incentiva o uso de modelos de linguagem nacionais, como chatglm e RWKV, Pangolin, etc. Suporta a coexistência de várias chaves de API e pode ser preenchido no arquivo de configuração como `API_KEY="openai-key1,openai-key2,api2d-key3"`. Quando precisar alterar temporariamente o `API_KEY`, basta digitar o `API_KEY` temporário na área de entrada e pressionar Enter para que ele entre em vigor. 
+
+<div align="center">Funcionalidade | Descrição
+--- | ---
+Um clique de polimento | Suporte a um clique polimento, um clique encontrar erros de gramática no artigo
+Tradução chinês-inglês de um clique | Tradução chinês-inglês de um clique
+Explicação de código de um único clique | Exibir código, explicar código, gerar código, adicionar comentários ao código
+[Teclas de atalho personalizadas](https://www.bilibili.com/video/BV14s4y1E7jN) | Suporte a atalhos personalizados
+Projeto modular | Suporte para poderosos plugins[de função personalizada](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), os plugins suportam[hot-reload](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
+[Análise automática do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função][um clique para entender](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) o código-fonte do projeto
+[Análise do programa](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin de função] Um clique pode analisar a árvore de projetos do Python/C/C++/Java/Lua/...
+Leitura de artigos, [tradução](https://www.bilibili.com/video/BV1KT411x7Wn) de artigos | [Plugin de função] um clique para interpretar o resumo de artigos LaTeX/PDF e gerar resumo
+Tradução completa LATEX, polimento|[Plugin de função] Uma clique para traduzir ou polir um artigo LATEX
+Geração em lote de comentários | [Plugin de função] Um clique gera comentários de função em lote
+[Tradução chinês-inglês](https://www.bilibili.com/video/BV1yo4y157jV/) markdown | [Plugin de função] Você viu o README em 5 linguagens acima?
+Relatório de análise de chat | [Plugin de função] Gera automaticamente um resumo após a execução
+[Funcionalidade de tradução de artigos completos em PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin de função] Extrai o título e o resumo do artigo PDF e traduz o artigo completo (multithread)
+Assistente arXiv | [Plugin de função] Insira o url do artigo arXiv para traduzir o resumo + baixar PDF
+Assistente de integração acadêmica do Google | [Plugin de função] Dê qualquer URL de página de pesquisa acadêmica do Google e deixe o GPT escrever[trabalhos relacionados](https://www.bilibili.com/video/BV1GP411U7Az/)
+Agregação de informações da Internet + GPT | [Plugin de função] Um clique para obter informações do GPT através da Internet e depois responde a perguntas para informações nunca ficarem desatualizadas
+Exibição de fórmulas/imagem/tabela | Pode exibir simultaneamente a forma de renderização e[TEX] das fórmulas, suporte a fórmulas e realce de código
+Suporte de plugins de várias linhas | Suporte a várias chamadas em linha do chatgpt, um clique para processamento[de massa de texto](https://www.bilibili.com/video/BV1FT411H7c5/) ou programa
+Tema gradio escuro | Adicione ``` /?__theme=dark``` ao final da url do navegador para ativar o tema escuro
+[Suporte para vários modelos LLM](https://www.bilibili.com/video/BV1wT411p7yf), suporte para a nova interface API2D | A sensação de ser atendido simultaneamente por GPT3.5, GPT4, [Chatglm THU](https://github.com/THUDM/ChatGLM-6B), [Moss Fudan](https://github.com/OpenLMLab/MOSS) deve ser ótima, certo?
+Mais modelos LLM incorporados, suporte para a implantação[huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Adicione interface Newbing (New Bing), suporte [JittorLLMs](https://github.com/Jittor/JittorLLMs) THU Introdução ao suporte do LLaMA, RWKV e Pan Gu Alpha
+Mais recursos novos mostrados (geração de imagens, etc.) ... | Consulte o final deste documento ...  
+
+</div>
+
+- Nova interface (Modifique a opção LAYOUT em `config.py` para alternar entre o layout esquerdo/direito e o layout superior/inferior)
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+</div>- All buttons are dynamically generated by reading functional.py, and you can add custom functions at will, liberating the clipboard
+
+<div align="center">
+<img src = "https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700">
+</div>
+
+- Proofreading/errors correction
+
+
+<div align="center">
+<img src = "https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700">
+</div>
+
+- If the output contains formulas, it will be displayed in both tex and rendering format at the same time, which is convenient for copying and reading
+
+
+<div align="center">
+<img src = "https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700">
+</div>
+
+- Don't want to read the project code? Just show the whole project to chatgpt
+
+
+<div align="center">
+<img src = "https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700">
+</div>
+
+- Mix the use of multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
+
+
+<div align="center">
+<img src = "https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700">
+</div>
+
+---
+# Instalação
+## Installation-Method 1: Run directly (Windows, Linux or MacOS)
+
+1. Download the project
+
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. Configure the API KEY
+
+In `config.py`, configure API KEY and other settings, [Special Network Environment Settings] (https://github.com/binary-husky/gpt_academic/issues/1).
+
+(P.S. When the program runs, it will first check whether there is a private configuration file named `config_private.py`, and use the configuration in it to cover the configuration with the same name in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`. The writing format of environment variables is referenced to the `docker-compose` file. Reading priority: `environment variable` > `config_private.py` > `config.py`)
+
+
+3. Install dependencies
+
+```sh
+# (Option I: for those familiar with python)(python version is 3.9 or above, the newer the better), note: use the official pip source or the Alibaba pip source. Temporary solution for changing source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# (Option II: for those who are unfamiliar with python) use anaconda, the steps are also similar (https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # create anaconda environment
+conda activate gptac_venv                 # activate anaconda environment
+python -m pip install -r requirements.txt # This step is the same as the pip installation step
+```
+
+<details><summary>If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, click to expand here</summary>
+<p>
+
+[Optional Step] If you need to support Tsinghua ChatGLM / Fudan MOSS as the backend, you need to install more dependencies (prerequisite: familiar with Python + used Pytorch + computer configuration is strong):
+```sh
+# 【Optional Step I】support Tsinghua ChatGLM。Tsinghua ChatGLM Note: If you encounter a "Call ChatGLM fails cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installed is torch+cpu version, and using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+python -m pip install -r request_llm/requirements_chatglm.txt
+
+# 【Optional Step II】support Fudan MOSS
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note: When executing this line of code, you must be in the project root path
+
+# 【Optional Step III】Make sure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports docker solutions):
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+4. Run
+
+```sh
+python main.py
+```5. Plugin de Função de Teste
+```
+- Função de modelo de plug-in de teste (exige que o GPT responda ao que aconteceu hoje na história), você pode usar esta função como modelo para implementar funções mais complexas
+    Clique em "[Função de plug-in de modelo de demonstração] O que aconteceu hoje na história?"
+```
+
+## Instalação - Método 2: Usando o Docker
+
+1. Apenas ChatGPT (recomendado para a maioria das pessoas)
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # Baixar o projeto
+cd chatgpt_academic                                 # Entrar no caminho
+nano config.py                                       # Editar config.py com qualquer editor de texto configurando "Proxy", "API_KEY" e "WEB_PORT" (por exemplo, 50923), etc.
+docker build -t gpt-academic .                      # Instale
+
+# (Ùltima etapa - escolha 1) Dentro do ambiente Linux, é mais fácil e rápido usar `--net=host`
+docker run --rm -it --net=host gpt-academic
+# (Última etapa - escolha 2) Em ambientes macOS/windows, você só pode usar a opção -p para expor a porta do contêiner (por exemplo, 50923) para a porta no host
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (conhecimento de Docker necessário)
+
+``` sh
+# Edite o arquivo docker-compose.yml, remova as soluções 1 e 3, mantenha a solução 2, e siga as instruções nos comentários do arquivo
+docker-compose up
+```
+
+3. ChatGPT + LLAMA + Pangu + RWKV (conhecimento de Docker necessário)
+``` sh
+# Edite o arquivo docker-compose.yml, remova as soluções 1 e 2, mantenha a solução 3, e siga as instruções nos comentários do arquivo 
+docker-compose up
+```
+
+
+## Instalação - Método 3: Outros Métodos de Implantação
+
+1. Como usar URLs de proxy inverso/microsoft Azure API
+Basta configurar o API_URL_REDIRECT de acordo com as instruções em `config.py`.
+
+2. Implantação em servidores em nuvem remotos (requer conhecimento e experiência de servidores em nuvem)
+Acesse [Wiki de implementação remota do servidor em nuvem](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
+
+3. Usando a WSL2 (sub-sistema do Windows para Linux)
+Acesse [Wiki da implantação da WSL2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
+
+4. Como executar em um subdiretório (ex. `http://localhost/subpath`)
+Acesse [Instruções de execução FastAPI](docs/WithFastapi.md)
+
+5. Execute usando o docker-compose
+Leia o arquivo docker-compose.yml e siga as instruções.
+
+# Uso Avançado
+## Customize novos botões de acesso rápido / plug-ins de função personalizados
+
+1. Personalizar novos botões de acesso rápido (atalhos acadêmicos)
+Abra `core_functional.py` em qualquer editor de texto e adicione os seguintes itens e reinicie o programa (Se o botão já foi adicionado e pode ser visto, prefixos e sufixos são compatíveis com modificações em tempo real e não exigem reinício do programa para ter efeito.)
+Por exemplo,
+```
+"Super Eng:": {
+  # Prefixo, será adicionado antes da sua entrada. Por exemplo, para descrever sua solicitação, como tradução, explicação de código, polimento, etc.
+  "Prefix": "Por favor, traduza o seguinte conteúdo para chinês e use uma tabela em Markdown para explicar termos próprios no texto: \n \n",
+
+  # Sufixo, será adicionado após a sua entrada. Por exemplo, emparelhado com o prefixo, pode colocar sua entrada entre aspas.
+  "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. Personalizar plug-ins de função
+
+Escreva plug-ins de função poderosos para executar tarefas que você deseja e não pensava possível.
+A dificuldade geral de escrever e depurar plug-ins neste projeto é baixa e, se você tem algum conhecimento básico de python, pode implementar suas próprias funções sobre o modelo que fornecemos.
+Para mais detalhes, consulte o [Guia do plug-in de função.](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+
+---
+# Última atualização
+## Novas funções dinâmicas.1. Função de salvamento de diálogo. Ao chamar o plug-in de função "Salvar diálogo atual", é possível salvar o diálogo atual em um arquivo html legível e reversível. Além disso, ao chamar o plug-in de função "Carregar arquivo de histórico de diálogo" no menu suspenso da área de plug-in, é possível restaurar uma conversa anterior. Dica: clicar em "Carregar arquivo de histórico de diálogo" sem especificar um arquivo permite visualizar o cache do arquivo html de histórico. Clicar em "Excluir todo o registro de histórico de diálogo local" permite excluir todo o cache de arquivo html. 
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
+</div>
+
+
+2. Geração de relatório. A maioria dos plug-ins gera um relatório de trabalho após a conclusão da execução.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
+</div>
+
+3. Design modular de funcionalidades, com interfaces simples, mas suporte a recursos poderosos
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
+<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
+</div>
+
+4. Este é um projeto de código aberto que é capaz de "auto-traduzir-se".
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
+</div>
+
+5. A tradução de outros projetos de código aberto é simples.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
+</div>
+
+6. Recursos decorativos para o [live2d](https://github.com/fghrsh/live2d_demo) (desativados por padrão, é necessário modificar o arquivo `config.py`)
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
+</div>
+
+7. Suporte ao modelo de linguagem MOSS
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
+</div>
+
+8. Geração de imagens pelo OpenAI
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
+</div>
+
+9. Análise e resumo de áudio pelo OpenAI
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
+</div>
+
+10. Revisão e correção de erros de texto em Latex.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
+</div>
+
+## Versão:
+- Versão 3.5(Todo): Usar linguagem natural para chamar todas as funções do projeto (prioridade alta)
+- Versão 3.4(Todo): Melhorar o suporte à multithread para o chatglm local
+- Versão 3.3: +Funções integradas de internet
+- Versão 3.2: Suporte a mais interfaces de parâmetros de plug-in (função de salvar diálogo, interpretação de códigos de várias linguagens, perguntas de combinações LLM arbitrárias ao mesmo tempo)
+- Versão 3.1: Suporte a perguntas a vários modelos de gpt simultaneamente! Suporte para api2d e balanceamento de carga para várias chaves api
+- Versão 3.0: Suporte ao chatglm e outros LLMs de pequeno porte
+- Versão 2.6: Refatoração da estrutura de plug-in, melhoria da interatividade e adição de mais plug-ins
+- Versão 2.5: Autoatualização, resolvendo problemas de token de texto excessivamente longo e estouro ao compilar grandes projetos
+- Versão 2.4: (1) Adição de funcionalidade de tradução de texto completo em PDF; (2) Adição de funcionalidade de mudança de posição da área de entrada; (3) Adição de opção de layout vertical; (4) Otimização de plug-ins de multithread.
+- Versão 2.3: Melhoria da interatividade de multithread
+- Versão 2.2: Suporte à recarga a quente de plug-ins
+- Versão 2.1: Layout dobrável
+- Versão 2.0: Introdução de plug-ins de função modular
+- Versão 1.0: Funcionalidades básicasgpt_academic desenvolvedores QQ grupo-2: 610599535
+
+- Problemas conhecidos
+    - Extensões de tradução de alguns navegadores podem interferir na execução do front-end deste software
+    - Uma versão muito alta ou muito baixa do Gradio pode causar vários erros
+
+## Referências e Aprendizado
+
+```
+Foi feita referência a muitos projetos excelentes em código, principalmente:
+
+# Projeto1: ChatGLM-6B da Tsinghua:
+https://github.com/THUDM/ChatGLM-6B
+
+# Projeto2: JittorLLMs da Tsinghua:
+https://github.com/Jittor/JittorLLMs
+
+# Projeto3: Edge-GPT:
+https://github.com/acheong08/EdgeGPT
+
+# Projeto4: ChuanhuChatGPT:
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# Projeto5: ChatPaper:
+https://github.com/kaixindelele/ChatPaper
+
+# Mais:
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/README_EN.md

@@ -0,0 +1,322 @@
+> **Note**
+>
+> This English README is automatically generated by the markdown translation plugin in this project, and may not be 100% correct.
+>
+> When installing dependencies, **please strictly select the versions** specified in requirements.txt.
+> 
+> `pip install -r requirements.txt`
+
+# GPT Academic Optimization (GPT Academic)
+
+**If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request.
+To translate this project to arbitary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).**
+
+> Note:
+> 
+> 1. Please note that only the function plugins (buttons) marked in **red** support reading files. Some plugins are in the **drop-down menu** in the plugin area. We welcome and process any new plugins with the **highest priority**!
+> 2. The function of each file in this project is detailed in the self-translation analysis [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). With version iteration, you can also click on related function plugins at any time to call GPT to regenerate the project's self-analysis report. Common questions are summarized in the [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Installation method](#installation).
+> 3. This project is compatible with and encourages trying domestic large language models such as chatglm, RWKV, Pangu, etc. Multiple API keys are supported and can be filled in the configuration file like `API_KEY="openai-key1,openai-key2,api2d-key3"`. When temporarily changing `API_KEY`, enter the temporary `API_KEY` in the input area and press enter to submit, which will take effect. 
+
+<div align="center">
+
+Function | Description
+--- | ---
+One-click polishing | Supports one-click polishing and one-click searching for grammar errors in papers.
+One-click Chinese-English translation | One-click Chinese-English translation.
+One-click code interpretation | Displays, explains, generates, and adds comments to code.
+[Custom shortcut keys](https://www.bilibili.com/video/BV14s4y1E7jN) | Supports custom shortcut keys.
+Modular design | Supports custom powerful [function plug-ins](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), plug-ins support [hot update](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+[Self-program profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Function plug-in] [One-click understanding](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) of the source code of this project
+[Program profiling](https://www.bilibili.com/video/BV1cj411A7VW) | [Function plug-in] One-click profiling of other project trees in Python/C/C++/Java/Lua/...
+Reading papers, [translating](https://www.bilibili.com/video/BV1KT411x7Wn) papers | [Function Plug-in] One-click interpretation of latex/pdf full-text papers and generation of abstracts.
+Latex full-text [translation](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [polishing](https://www.bilibili.com/video/BV1FT411H7c5/) | [Function plug-in] One-click translation or polishing of latex papers.
+Batch annotation generation | [Function plug-in] One-click batch generation of function annotations.
+Markdown [Chinese-English translation](https://www.bilibili.com/video/BV1yo4y157jV/) | [Function plug-in] Have you seen the [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) in the five languages above?
+Chat analysis report generation | [Function plug-in] Automatically generate summary reports after running.
+[PDF full-text translation function](https://www.bilibili.com/video/BV1KT411x7Wn) | [Function plug-in] PDF paper extract title & summary + translate full text (multi-threaded)
+[Arxiv Assistant](https://www.bilibili.com/video/BV1LM4y1279X) | [Function plug-in] Enter the arxiv article url and you can translate abstracts and download PDFs with one click.
+[Google Scholar Integration Assistant](https://www.bilibili.com/video/BV19L411U7ia) | [Function plug-in] Given any Google Scholar search page URL, let GPT help you [write relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
+Internet information aggregation+GPT | [Function plug-in] One-click [let GPT get information from the Internet first](https://www.bilibili.com/video/BV1om4y127ck), then answer questions, and let the information never be outdated.
+Formula/image/table display | Can display formulas in both [tex form and render form](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), support formulas and code highlighting.
+Multi-threaded function plug-in support | Supports multi-threaded calling of chatgpt, and can process [massive text](https://www.bilibili.com/video/BV1FT411H7c5/) or programs with one click.
+Start Dark Gradio [theme](https://github.com/binary-husky/chatgpt_academic/issues/173) | Add ```/?__theme=dark``` after the browser URL to switch to the dark theme.
+[Multiple LLM models](https://www.bilibili.com/video/BV1wT411p7yf) support, [API2D](https://api2d.com/) interface support | The feeling of being served by GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), and [Fudan MOSS](https://github.com/OpenLMLab/MOSS) at the same time must be great, right?
+More LLM model access, support [huggingface deployment](https://huggingface.co/spaces/qingxu98/gpt-academic) | Add Newbing interface (New Bing), introduce Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs) to support [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) and [Panguα](https://openi.org.cn/pangu/)
+More new feature displays (image generation, etc.)…… | See the end of this document for more... 
+</div>
+
+- New interface (modify the LAYOUT option in `config.py` to switch between "left and right layout" and "up and down layout")
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+</div>- All buttons are dynamically generated by reading `functional.py`, and you can add custom functions freely to unleash the power of clipboard.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- polishing/correction
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- If the output contains formulas, they will be displayed in both `tex` and render form, making it easy to copy and read.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- Tired of reading the project code? ChatGPT can explain it all.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+- Multiple large language models are mixed, such as ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+# Installation
+## Method 1: Directly running (Windows, Linux or MacOS) 
+
+1. Download the project
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. Configure the API_KEY
+
+Configure the API KEY in `config.py`, [special network environment settings](https://github.com/binary-husky/gpt_academic/issues/1).
+
+(P.S. When the program is running, it will first check if there is a private configuration file named `config_private.py` and use the configurations in it to override the same configurations in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py` and transfer (copy) the configurations in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your private information more secure. P.S. The project also supports configuring most options through `environment variables`. Please refer to the format of `docker-compose` file when writing. Reading priority: `environment variables` > `config_private.py` > `config.py`)
+
+
+3. Install the dependencies
+```sh
+# (Option I: If familiar with python) (python version 3.9 or above, the newer the better), note: use official pip source or Ali pip source, temporary switching method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# (Option II: If not familiar with python) Use anaconda, the steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # create anaconda environment
+conda activate gptac_venv                 # activate anaconda environment
+python -m pip install -r requirements.txt # this step is the same as pip installation
+```
+
+<details><summary>If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, click to expand</summary>
+<p>
+
+[Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (prerequisites: familiar with Python + used Pytorch + computer configuration is strong enough):
+```sh
+# [Optional Step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: if you encounter the "Call ChatGLM fail cannot load ChatGLM parameters" error, refer to this: 1: The default installation above is torch + cpu version, to use cuda, you need to uninstall torch and reinstall torch + cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code = True)
+python -m pip install -r request_llm/requirements_chatglm.txt 
+
+# [Optional Step II] Support Fudan MOSS
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # When executing this line of code, you must be in the root directory of the project
+
+# [Optional Step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file includes the expected models. Currently supported models are as follows (the jittorllms series only supports the docker solution for the time being):
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. Run it
+```sh
+python main.py
+```5. Test Function Plugin
+```
+- Test function plugin template function (ask GPT what happened today in history), based on which you can implement more complex functions as a template
+    Click "[Function Plugin Template Demo] Today in History"
+```
+
+## Installation - Method 2: Using Docker
+
+1. ChatGPT Only (Recommended for Most People)
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # Download project
+cd chatgpt_academic                                 # Enter path
+nano config.py                                      # Edit config.py with any text editor, configure "Proxy", "API_KEY" and "WEB_PORT" (e.g. 50923), etc.
+docker build -t gpt-academic .                      # Install
+
+#(Last step - option 1) In a Linux environment, use `--net=host` for convenience and speed.
+docker run --rm -it --net=host gpt-academic
+#(Last step - option 2) On macOS/windows environment, only -p option can be used to expose the container's port (e.g. 50923) to the port of the main machine.
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (Requires Docker Knowledge)
+
+``` sh
+# Modify docker-compose.yml, delete Plan 1 and Plan 3, and keep Plan 2. Modify the configuration of Plan 2 in docker-compose.yml, refer to the comments in it for configuration.
+docker-compose up
+```
+
+3. ChatGPT + LLAMA + Pangu + RWKV (Requires Docker Knowledge)
+
+``` sh
+# Modify docker-compose.yml, delete Plan 1 and Plan 2, and keep Plan 3. Modify the configuration of Plan 3 in docker-compose.yml, refer to the comments in it for configuration.
+docker-compose up
+```
+
+## Installation - Method 3: Other Deployment Options
+
+1. How to Use Reverse Proxy URL/Microsoft Cloud Azure API
+Configure API_URL_REDIRECT according to the instructions in 'config.py'.
+
+2. Deploy to a Remote Server (Requires Knowledge and Experience with Cloud Servers)
+Please visit [Deployment Wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
+
+3. Using WSL2 (Windows Subsystem for Linux)
+Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
+
+4. How to Run Under a Subdomain (e.g. `http://localhost/subpath`)
+Please visit [FastAPI Running Instructions](docs/WithFastapi.md)
+
+5. Using docker-compose to Run
+Read the docker-compose.yml and follow the prompts.
+
+---
+# Advanced Usage
+## Custom New Shortcut Buttons / Custom Function Plugins
+
+1. Custom New Shortcut Buttons (Academic Hotkey)
+Open `core_functional.py` with any text editor, add an entry as follows and restart the program. (If the button has been successfully added and is visible, the prefix and suffix can be hot-modified without having to restart the program.)
+For example,
+```
+"Super English-to-Chinese": {
+    # Prefix, which will be added before your input. For example, used to describe your requests, such as translation, code explanation, polishing, etc.
+    "Prefix": "Please translate the following content into Chinese and then use a markdown table to explain the proprietary terms that appear in the text：\n\n", 
+    
+    # Suffix, which is added after your input. For example, with the prefix, your input content can be surrounded by quotes.
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. Custom Function Plugins
+
+Write powerful function plugins to perform any task you can think of, even those you cannot think of.
+The difficulty of plugin writing and debugging in this project is very low. As long as you have a certain knowledge of Python, you can implement your own plug-in functions based on the template we provide.
+For details, please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+
+---
+# Latest Update
+## New Feature Dynamics
+1. Conversation saving function. Call `Save current conversation` in the function plugin area to save the current conversation as a readable and recoverable HTML file. In addition, call `Load conversation history archive` in the function plugin area (dropdown menu) to restore previous sessions. Tip: Clicking `Load conversation history archive` without specifying a file will display the cached history of HTML archives, and clicking `Delete all local conversation history` will delete all HTML archive caches.
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
+</div>
+
+
+2. Report generation. Most plugins will generate work reports after execution. 
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
+</div>
+
+
+3. Modular function design with simple interfaces that support powerful functions.
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
+<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
+</div>
+
+
+4. This is an open-source project that can "self-translate".
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
+</div>
+
+5. Translating other open-source projects is a piece of cake.
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
+</div>
+
+6. A small feature decorated with [live2d](https://github.com/fghrsh/live2d_demo) (disabled by default, need to modify `config.py`).
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
+</div>
+
+7. Added MOSS large language model support.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
+</div>
+
+8. OpenAI image generation.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
+</div>
+
+9. OpenAI audio parsing and summarization.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
+</div>
+
+10. Full-text proofreading and error correction of LaTeX.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
+</div>
+
+
+## Versions:
+- version 3.5(Todo): Use natural language to call all function plugins of this project (high priority).
+- version 3.4(Todo): Improve multi-threading support for chatglm local large models.
+- version 3.3: +Internet information integration function.
+- version 3.2: Function plugin supports more parameter interfaces (save conversation function, interpretation of any language code + simultaneous inquiry of any LLM combination).
+- version 3.1: Support simultaneous inquiry of multiple GPT models! Support api2d, and support load balancing of multiple apikeys.
+- version 3.0: Support chatglm and other small LLM models.
+- version 2.6: Refactored plugin structure, improved interactivity, and added more plugins.
+- version 2.5: Self-updating, solving the problem of text overflow and token overflow when summarizing large engineering source codes.
+- version 2.4: (1) Added PDF full-text translation function; (2) Added the function of switching the position of the input area; (3) Added vertical layout option; (4) Optimized multi-threading function plugins.
+- version 2.3: Enhanced multi-threading interactivity.
+- version 2.2: Function plugin supports hot reloading.
+- version 2.1: Collapsible layout.
+- version 2.0: Introduction of modular function plugins.
+- version 1.0: Basic functions.
+
+gpt_academic Developer QQ Group-2: 610599535
+
+- Known Issues
+    - Some browser translation plugins interfere with the front-end operation of this software.
+    - Both high and low versions of gradio can lead to various exceptions.
+
+## Reference and Learning
+
+```
+Many other excellent designs have been referenced in the code, mainly including:
+
+# Project 1: THU ChatGLM-6B:
+https://github.com/THUDM/ChatGLM-6B
+
+# Project 2: THU JittorLLMs:
+https://github.com/Jittor/JittorLLMs
+
+# Project 3: Edge-GPT:
+https://github.com/acheong08/EdgeGPT
+
+# Project 4: ChuanhuChatGPT:
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# Project 5: ChatPaper:
+https://github.com/kaixindelele/ChatPaper
+
+# More:
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/README_FR.md

@@ -0,0 +1,323 @@
+> **Note**
+>
+> Ce fichier README est généré automatiquement par le plugin de traduction markdown de ce projet et n'est peut - être pas correct à 100%.
+>
+> During installation, please strictly select the versions **specified** in requirements.txt. 
+>
+> `pip install -r requirements.txt`
+>
+
+# <img src="logo.png" width="40" > Optimisation académique GPT (GPT Academic)
+
+**Si vous aimez ce projet, veuillez lui donner une étoile. Si vous avez trouvé des raccourcis académiques ou des plugins fonctionnels plus utiles, n'hésitez pas à ouvrir une demande ou une pull request. 
+Pour traduire ce projet dans une langue arbitraire avec GPT, lisez et exécutez [`multi_language.py`](multi_language.py) (expérimental).
+
+> **Note**
+>
+> 1. Veuillez noter que seuls les plugins de fonctions (boutons) **en rouge** prennent en charge la lecture de fichiers. Certains plugins se trouvent dans le **menu déroulant** de la zone de plugins. De plus, nous accueillons et traitons les nouvelles pull requests pour les plugins avec **la plus haute priorité**!
+>
+> 2. Les fonctions de chaque fichier de ce projet sont expliquées en détail dans l'auto-analyse [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). Avec l'itération des versions, vous pouvez également cliquer sur les plugins de fonctions pertinents et appeler GPT pour régénérer le rapport d'auto-analyse du projet à tout moment. Les FAQ sont résumées dans [le wiki](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Méthode d'installation](#installation).
+>
+> 3. Ce projet est compatible avec et encourage l'utilisation de grands modèles de langage nationaux tels que chatglm, RWKV, Pangu, etc. La coexistence de plusieurs clés API est prise en charge et peut être remplie dans le fichier de configuration, tel que `API_KEY="openai-key1,openai-key2,api2d-key3"`. Lorsque vous souhaitez remplacer temporairement `API_KEY`, saisissez temporairement `API_KEY` dans la zone de saisie, puis appuyez sur Entrée pour soumettre et activer. 
+
+<div align="center">
+
+Functionnalité | Description
+--- | ---
+Révision en un clic | prend en charge la révision en un clic et la recherche d'erreurs de syntaxe dans les articles
+Traduction chinois-anglais en un clic | Traduction chinois-anglais en un clic
+Explication de code en un clic | Affichage, explication, génération et ajout de commentaires de code
+[Raccourcis personnalisés](https://www.bilibili.com/video/BV14s4y1E7jN) | prend en charge les raccourcis personnalisés
+Conception modulaire | prend en charge de puissants plugins de fonction personnalisée, les plugins prennent en charge la [mise à jour à chaud](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)  
+[Autoscanner](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] [Compréhension instantanée](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) du code source de ce projet
+[Analyse de programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plug-in de fonction] Analyse en un clic de la structure d'autres projets Python / C / C ++ / Java / Lua / ...
+Lecture d'articles, [traduction](https://www.bilibili.com/video/BV1KT411x7Wn) d'articles | [Plug-in de fonction] Compréhension instantanée de l'article latex / pdf complet et génération de résumés
+[Traduction](https://www.bilibili.com/video/BV1nk4y1Y7Js/) et [révision](https://www.bilibili.com/video/BV1FT411H7c5/) complets en latex | [Plug-in de fonction] traduction ou révision en un clic d'articles en latex
+Génération de commentaires en masse | [Plug-in de fonction] Génération en un clic de commentaires de fonction en masse
+Traduction [chinois-anglais](https://www.bilibili.com/video/BV1yo4y157jV/) en Markdown | [Plug-in de fonction] avez-vous vu la [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) pour les 5 langues ci-dessus?
+Génération de rapports d'analyse de chat | [Plug-in de fonction] Génère automatiquement un rapport de résumé après l'exécution
+[Traduction intégrale en pdf](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plug-in de fonction] Extraction de titre et de résumé de l'article pdf + traduction intégrale (multi-thread)
+[Aide à arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plug-in de fonction] Entrer l'url de l'article arxiv pour traduire et télécharger le résumé en un clic
+[Aide à la recherche Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plug-in de fonction] Donnez l'URL de la page de recherche Google Scholar, laissez GPT vous aider à [écrire des ouvrages connexes](https://www.bilibili.com/video/BV1GP411U7Az/)
+Aggrégation d'informations en ligne et GPT | [Plug-in de fonction] Permet à GPT de [récupérer des informations en ligne](https://www.bilibili.com/video/BV1om4y127ck), puis de répondre aux questions, afin que les informations ne soient jamais obsolètes
+Affichage d'équations / images / tableaux | Fournit un affichage simultané de [la forme tex et de la forme rendue](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), prend en charge les formules mathématiques et la coloration syntaxique du code
+Prise en charge des plugins à plusieurs threads | prend en charge l'appel multithread de chatgpt, un clic pour traiter [un grand nombre d'articles](https://www.bilibili.com/video/BV1FT411H7c5/) ou de programmes
+Thème gradio sombre en option de démarrage | Ajoutez```/?__theme=dark``` à la fin de l'URL du navigateur pour basculer vers le thème sombre
+[Prise en charge de plusieurs modèles LLM](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) | Sera probablement très agréable d'être servi simultanément par GPT3.5, GPT4, [ChatGLM de Tsinghua](https://github.com/THUDM/ChatGLM-6B), [MOSS de Fudan](https://github.com/OpenLMLab/MOSS)
+Plus de modèles LLM, déploiement de [huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Ajout prise en charge de l'interface Newbing (nouvelle bing), introduction du support de [Jittorllms de Tsinghua](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) et [Panguα](https://openi.org.cn/pangu/)
+Plus de nouvelles fonctionnalités (génération d'images, etc.) ... | Voir la fin de ce document pour plus de détails ...
+
+</div>
+
+
+- Nouvelle interface (modifier l'option LAYOUT de `config.py` pour passer d'une disposition ``gauche-droite`` à une disposition ``haut-bas``)
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+</div>- Tous les boutons sont générés dynamiquement en lisant functional.py et peuvent être facilement personnalisés pour ajouter des fonctionnalités personnalisées, ce qui facilite l'utilisation du presse-papiers.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- Correction d'erreurs/lissage du texte.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- Si la sortie contient des équations, elles sont affichées à la fois sous forme de tex et sous forme rendue pour faciliter la lecture et la copie.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- Pas envie de lire les codes de ce projet? Tout le projet est directement exposé par ChatGPT.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+- Appel à une variété de modèles de langage de grande envergure (ChatGLM + OpenAI-GPT3.5 + [API2D] (https://api2d.com/)-GPT4).
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+# Installation
+## Installation-Method 1: running directly (Windows, Linux or MacOS)
+
+1. Télécharger le projet
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. Configuration de la clé API
+
+Dans `config.py`, configurez la clé API et d'autres paramètres. Consultez [Special network environment settings] (https://github.com/binary-husky/gpt_academic/issues/1).
+
+(P.S. Lorsque le programme est exécuté, il vérifie en premier s'il existe un fichier de configuration privé nommé `config_private.py` et remplace les paramètres portant le même nom dans `config.py` par les paramètres correspondants dans `config_private.py`. Par conséquent, si vous comprenez la logique de lecture de nos configurations, nous vous recommandons vivement de créer un nouveau fichier de configuration nommé `config_private.py` à côté de `config.py` et de transférer (copier) les configurations de `config.py`. `config_private.py` n'est pas contrôlé par Git et peut garantir la sécurité de vos informations privées. P.S. Le projet prend également en charge la configuration de la plupart des options via "variables d'environnement", le format d'écriture des variables d'environnement est référencé dans le fichier `docker-compose`. Priorité de lecture: "variables d'environnement" > `config_private.py` > `config.py`)
+
+
+3. Installer les dépendances
+```sh
+# (Option I: python users instalation) (Python version 3.9 or higher, the newer the better). Note: use official pip source or ali pip source. To temporarily change the source: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# (Option II: non-python users instalation) Use Anaconda, the steps are similar (https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # Create anaconda env
+conda activate gptac_venv                 # Activate anaconda env
+python -m pip install -r requirements.txt # Same step as pip instalation
+```
+
+<details><summary>Cliquez ici pour afficher le texte si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend.</summary>
+<p>
+
+【Optional】 Si vous souhaitez prendre en charge THU ChatGLM/FDU MOSS en tant que backend, des dépendances supplémentaires doivent être installées (prérequis: compétent en Python + utilisez Pytorch + configuration suffisante de l'ordinateur):
+```sh
+# 【Optional Step I】 Support THU ChatGLM. Remarque sur THU ChatGLM: Si vous rencontrez l'erreur "Appel à ChatGLM échoué, les paramètres ChatGLM ne peuvent pas être chargés normalement", reportez-vous à ce qui suit: 1: La version par défaut installée est torch+cpu, si vous souhaitez utiliser cuda, vous devez désinstaller torch et réinstaller torch+cuda; 2: Si le modèle ne peut pas être chargé en raison d'une configuration insuffisante de l'ordinateur local, vous pouvez modifier la précision du modèle dans request_llm/bridge_chatglm.py, modifier AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) par AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+python -m pip install -r request_llm/requirements_chatglm.txt  
+
+# 【Optional Step II】 Support FDU MOSS
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note: When running this line of code, you must be in the project root path.
+
+# 【Optional Step III】Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the desired model. Currently, all models supported are as follows (the jittorllms series currently only supports the docker scheme):
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. Exécution
+```sh
+python main.py
+```5. Plugin de fonction de test
+```
+- Fonction de modèle de plugin de test (requiert que GPT réponde à ce qui s'est passé dans l'histoire aujourd'hui), vous pouvez utiliser cette fonction comme modèle pour mettre en œuvre des fonctionnalités plus complexes.
+    Cliquez sur "[Démo de modèle de plugin de fonction] Aujourd'hui dans l'histoire"
+```
+
+## Installation - Méthode 2: Utilisation de Docker
+
+1. ChatGPT uniquement (recommandé pour la plupart des gens)
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # Télécharger le projet
+cd chatgpt_academic                                 # Accéder au chemin
+nano config.py                                      # Editez config.py avec n'importe quel éditeur de texte en configurant "Proxy", "API_KEY" et "WEB_PORT" (p. ex. 50923)
+docker build -t gpt-academic .                      # Installer
+
+# (Dernière étape - choix1) Dans un environnement Linux, l'utilisation de `--net=host` est plus facile et rapide
+docker run --rm -it --net=host gpt-academic
+# (Dernière étape - choix 2) Dans un environnement macOS/Windows, seule l'option -p permet d'exposer le port du récipient (p.ex. 50923) au port de l'hôte.
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (il faut connaître Docker)
+
+``` sh
+# Modifiez docker-compose.yml, supprimez la solution 1 et la solution 3, conservez la solution 2. Modifiez la configuration de la solution 2 dans docker-compose.yml en suivant les commentaires.
+docker-compose up
+```
+
+3. ChatGPT + LLAMA + PanGu + RWKV (il faut connaître Docker)
+``` sh
+# Modifiez docker-compose.yml, supprimez la solution 1 et la solution 2, conservez la solution 3. Modifiez la configuration de la solution 3 dans docker-compose.yml en suivant les commentaires.
+docker-compose up
+```
+
+
+## Installation - Méthode 3: Autres méthodes de déploiement
+
+1. Comment utiliser une URL de proxy inversé / Microsoft Azure Cloud API
+Configurez simplement API_URL_REDIRECT selon les instructions de config.py.
+
+2. Déploiement distant sur un serveur cloud (connaissance et expérience des serveurs cloud requises)
+Veuillez consulter [Wiki de déploiement-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97).
+
+3. Utilisation de WSL2 (sous-système Windows pour Linux)
+Veuillez consulter [Wiki de déploiement-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2).
+
+4. Comment exécuter sous un sous-répertoire (tel que `http://localhost/subpath`)
+Veuillez consulter les [instructions d'exécution de FastAPI] (docs/WithFastapi.md).
+
+5. Utilisation de docker-compose
+Veuillez lire docker-compose.yml, puis suivre les instructions fournies.
+
+# Utilisation avancée
+## Personnalisation de nouveaux boutons pratiques / Plugins de fonctions personnalisées
+
+1. Personnalisation de nouveaux boutons pratiques (raccourcis académiques)
+Ouvrez core_functional.py avec n'importe quel éditeur de texte, ajoutez une entrée comme suit, puis redémarrez le programme. (Si le bouton a été ajouté avec succès et est visible, le préfixe et le suffixe prennent en charge les modifications à chaud et ne nécessitent pas le redémarrage du programme pour prendre effet.)
+Par exemple
+```
+"Super coller sens": {
+    # Préfixe, sera ajouté avant votre entrée. Par exemple, pour décrire votre demande, telle que traduire, expliquer du code, faire la mise en forme, etc.
+    "Prefix": "Veuillez traduire le contenu suivant en chinois, puis expliquer chaque terme proprement nommé qui y apparaît avec un tableau markdown:\n\n", 
+    
+    # Suffixe, sera ajouté après votre entrée. Par exemple, en utilisant le préfixe, vous pouvez entourer votre contenu d'entrée de guillemets.
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. Plugins de fonctions personnalisées
+
+Écrivez des plugins de fonctions puissants pour effectuer toutes les tâches que vous souhaitez ou que vous ne pouvez pas imaginer.
+Les plugins de ce projet ont une difficulté de programmation et de débogage très faible. Si vous avez des connaissances de base en Python, vous pouvez simuler la fonctionnalité de votre propre plugin en suivant le modèle que nous avons fourni.
+Veuillez consulter le [Guide du plugin de fonction] (https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) pour plus de détails.
+
+---
+# Latest Update
+
+## Nouvelles fonctionnalités en cours de déploiement.
+
+1. Fonction de sauvegarde de la conversation.
+Appelez simplement "Enregistrer la conversation actuelle" dans la zone de plugin de fonction pour enregistrer la conversation actuelle en tant que fichier html lisible et récupérable. De plus, dans la zone de plugin de fonction (menu déroulant), appelez "Charger une archive de l'historique de la conversation" pour restaurer la conversation précédente. Astuce : cliquer directement sur "Charger une archive de l'historique de la conversation" sans spécifier de fichier permet de consulter le cache d'archive html précédent. Cliquez sur "Supprimer tous les enregistrements locaux de l'historique de la conversation" pour supprimer le cache d'archive html.
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
+</div>
+
+
+
+2. Générer un rapport. La plupart des plugins génèrent un rapport de travail après l'exécution.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
+<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
+</div>
+
+3. Conception de fonctionnalités modulaires avec une interface simple mais capable d'une fonctionnalité puissante.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
+<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
+</div>
+
+4. C'est un projet open source qui peut "se traduire de lui-même".
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
+</div>
+
+5. Traduire d'autres projets open source n'est pas un problème.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
+</div>
+
+6. Fonction de décoration de live2d (désactivée par défaut, nécessite une modification de config.py).
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
+</div>
+
+7. Prise en charge du modèle de langue MOSS.
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
+</div>
+
+8. Génération d'images OpenAI.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
+</div>
+
+9. Analyse et synthèse vocales OpenAI.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
+</div>
+
+10. Correction de la totalité des erreurs de Latex.
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
+</div>
+
+
+## Versions :
+- version 3.5 (À faire) : appel de toutes les fonctions de plugin de ce projet en langage naturel (priorité élevée)
+- version 3.4 (À faire) : amélioration du support multi-thread de chatglm en local
+- version 3.3 : Fonctionnalité intégrée d'informations d'internet 
+- version 3.2 : La fonction du plugin de fonction prend désormais en charge des interfaces de paramètres plus nombreuses (fonction de sauvegarde, décodage de n'importe quel langage de code + interrogation simultanée de n'importe quelle combinaison de LLM)
+- version 3.1 : Prise en charge de l'interrogation simultanée de plusieurs modèles GPT ! Support api2d, équilibrage de charge multi-clé api.
+- version 3.0 : Prise en charge de chatglm et autres LLM de petite taille.
+- version 2.6 : Refonte de la structure des plugins, amélioration de l'interactivité, ajout de plus de plugins.
+- version 2.5 : Auto-mise à jour, résolution des problèmes de texte trop long et de dépassement de jetons lors de la compilation du projet global.
+- version 2.4 : (1) Nouvelle fonction de traduction de texte intégral PDF ; (2) Nouvelle fonction de permutation de position de la zone d'entrée ; (3) Nouvelle option de mise en page verticale ; (4) Amélioration des fonctions multi-thread de plug-in.
+- version 2.3 : Amélioration de l'interactivité multithread.
+- version 2.2 : Les plugins de fonctions peuvent désormais être rechargés à chaud.
+- version 2.1 : Disposition pliable
+- version 2.0 : Introduction de plugins de fonctions modulaires
+- version 1.0 : Fonctionnalités de base
+
+gpt_academic développeur QQ groupe-2：610599535
+
+- Problèmes connus
+    - Certains plugins de traduction de navigateur perturbent le fonctionnement de l'interface frontend de ce logiciel
+    - Des versions gradio trop hautes ou trop basses provoquent de nombreuses anomalies
+
+## Référence et apprentissage
+
+```
+De nombreux autres excellents projets ont été référencés dans le code, notamment :
+
+# Projet 1 : ChatGLM-6B de Tsinghua :
+https://github.com/THUDM/ChatGLM-6B
+
+# Projet 2 : JittorLLMs de Tsinghua :
+https://github.com/Jittor/JittorLLMs
+
+# Projet 3 : Edge-GPT :
+https://github.com/acheong08/EdgeGPT
+
+# Projet 4 : ChuanhuChatGPT :
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# Projet 5 : ChatPaper :
+https://github.com/kaixindelele/ChatPaper
+
+# Plus :
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/README_JP.md

@@ -0,0 +1,329 @@
+> **Note**
+>
+> このReadmeファイルは、このプロジェクトのmarkdown翻訳プラグインによって自動的に生成されたもので、100%正確ではない可能性があります。
+>
+> When installing dependencies, please strictly choose the versions specified in `requirements.txt`. 
+> 
+> `pip install -r requirements.txt`
+>
+
+# <img src="logo.png" width="40" > GPT 学术优化 (GPT Academic)
+
+**もしこのプロジェクトが好きなら、星をつけてください。もしあなたがより良いアカデミックショートカットまたは機能プラグインを思いついた場合、Issueをオープンするか pull request を送信してください。私たちはこのプロジェクト自体によって翻訳された[英語 |](README_EN.md)[日本語 |](README_JP.md)[한국어 |](https://github.com/mldljyh/ko_gpt_academic)[Русский |](README_RS.md)[Français](README_FR.md)のREADMEも用意しています。
+GPTを使った任意の言語にこのプロジェクトを翻訳するには、[`multi_language.py`](multi_language.py)を読んで実行してください。 (experimental)。
+
+> **注意**
+>
+> 1. **赤色**で表示された関数プラグイン(ボタン)のみ、ファイルの読み取りをサポートしています。一部のプラグインは、プラグインエリアの**ドロップダウンメニュー**内にあります。また、私たちはどんな新しいプラグインのPRでも、**最優先**で歓迎し、処理します！
+>
+> 2. このプロジェクトの各ファイルの機能は、自己解析の詳細説明書である[`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)で説明されています。バージョンが進化するにつれて、関連する関数プラグインをいつでもクリックし、GPTを呼び出してプロジェクトの自己解析レポートを再生成することができます。よくある問題は[`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)にまとめられています。[インストール方法](#installation)。
+
+> 3. このプロジェクトは、chatglmやRWKV、パンクなど、国内の大規模自然言語モデルを利用することをサポートし、試みることを奨励します。複数のAPIキーを共存することができ、設定ファイルに`API_KEY="openai-key1,openai-key2,api2d-key3"`のように記入することができます。`API_KEY`を一時的に変更する場合は、入力エリアに一時的な`API_KEY`を入力してEnterキーを押せば、それが有効になります。
+
+
+<div align="center">
+
+機能 | 説明
+--- | ---
+一键校正 | 一键で校正可能、論文の文法エラーを検索することができる
+一键中英翻訳 | 一键で中英翻訳可能
+一键コード解説 | コードを表示し、解説し、生成し、コードに注釈をつけることができる
+[自分でカスタマイズ可能なショートカットキー](https://www.bilibili.com/video/BV14s4y1E7jN) | 自分でカスタマイズ可能なショートカットキーをサポートする
+モジュール化された設計 | カスタマイズ可能な[強力な関数プラグイン](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions)をサポートし、プラグインは[ホットアップデート](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)に対応している
+[自己プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン] [一键読解](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)このプロジェクトのソースコード
+プログラム解析 | [関数プラグイン] 一鍵で他のPython/C/C++/Java/Lua/...プロジェクトを分析できる
+論文の読み、[翻訳](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] LaTex/ PDF論文の全文を一鍵で読み解き、要約を生成することができる
+LaTex全文[翻訳](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[校正](https://www.bilibili.com/video/BV1FT411H7c5/) | [関数プラグイン] LaTex論文の翻訳または校正を一鍵で行うことができる
+一括で注釈を生成 | [関数プラグイン] 一鍵で関数に注釈をつけることができる
+Markdown[中英翻訳](https://www.bilibili.com/video/BV1yo4y157jV/) | [関数プラグイン] 上記の5種類の言語の[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)を見たことがありますか？
+チャット分析レポート生成 | [関数プラグイン] 実行後、自動的に概要報告書を生成する
+[PDF論文全文翻訳機能](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] PDF論文からタイトルと要約を抽出し、全文を翻訳する（マルチスレッド）
+[Arxivアシスタント](https://www.bilibili.com/video/BV1LM4y1279X) | [関数プラグイン] arxiv記事のURLを入力するだけで、要約を一鍵翻訳し、PDFをダウンロードできる
+[Google Scholar 総合アシスタント](https://www.bilibili.com/video/BV19L411U7ia) | [関数プラグイン] 任意のGoogle Scholar検索ページURLを指定すると、gptが[related works](https://www.bilibili.com/video/BV1GP411U7Az/)を作成する
+インターネット情報収集＋GPT | [関数プラグイン] まずGPTに[インターネットから情報を収集](https://www.bilibili.com/video/BV1om4y127ck)してから質問に回答させ、情報が常に最新であるようにする
+数式/画像/表表示 | 数式の[tex形式とレンダリング形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png)を同時に表示し、数式、コードハイライトをサポートしている
+マルチスレッド関数プラグインがサポートされている | chatgptをマルチスレッドで呼び出し、[大量のテキスト](https://www.bilibili.com/video/BV1FT411H7c5/)またはプログラムを一鍵で処理できる
+ダークグラジオ[テーマの起動](https://github.com/binary-husky/chatgpt_academic/issues/173) | ブラウザのURLの後ろに```/?__theme=dark```を追加すると、ダークテーマを切り替えることができます。
+[多数のLLMモデル](https://www.bilibili.com/video/BV1wT411p7yf)がサポートされ、[API2D](https://api2d.com/)がサポートされている | 同時にGPT3.5、GPT4、[清華ChatGLM](https://github.com/THUDM/ChatGLM-6B)、[復旦MOSS](https://github.com/OpenLMLab/MOSS)に対応
+より多くのLLMモデルが接続され、[huggingfaceデプロイ](https://huggingface.co/spaces/qingxu98/gpt-academic)がサポートされている | Newbingインターフェイス（Newbing）、清華大学の[Jittorllm](https://github.com/Jittor/JittorLLMs)のサポート[LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV)と[盘古α](https://openi.org.cn/pangu/)
+さらに多くの新機能（画像生成など）を紹介する... | この文書の最後に示す... 
+</div>
+
+- 新しいインターフェース（`config.py`のLAYOUTオプションを変更することで、「左右配置」と「上下配置」を切り替えることができます）
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
+</div>- All buttons are dynamically generated by reading functional.py, and custom functions can be freely added to free the clipboard.
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- Polishing/Correction
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- If the output contains formulas, they are displayed in both TeX and rendering forms, making it easy to copy and read.
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- Don't feel like looking at the project code? Just ask chatgpt directly.
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+
+- Mixed calls of multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+
+# Installation
+
+## Installation-Method 1: Directly run (Windows, Linux or MacOS)
+
+1. Download the project.
+
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. Configure the API_KEY.
+
+Configure the API KEY and other settings in `config.py` and [special network environment settings](https://github.com/binary-husky/gpt_academic/issues/1).
+
+(P.S. When the program is running, it will first check if there is a private configuration file named `config_private.py`, and use the configuration in it to override the same name configuration in `config.py`. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Reading priority: `environment variables` > `config_private.py` > `config.py`)
+
+3. Install dependencies.
+
+```sh
+# （Choose I: If familiar with Python）(Python version 3.9 or above, the newer the better) Note: Use the official pip source or Ali pip source. Temporary switching source method: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# (Choose II: If not familiar with Python) Use anaconda, the steps are the same (https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # Create anaconda environment.
+conda activate gptac_venv                 # Activate the anaconda environment.
+python -m pip install -r requirements.txt # This step is the same as the pip installation step.
+```
+
+<details><summary>If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, click to expand.</summary>
+<p>
+
+[Optional Steps] If you need to support Tsinghua ChatGLM/Fudan MOSS as a backend, you need to install more dependencies (precondition: familiar with Python + used Pytorch + computer configuration). Strong enough):
+
+```sh
+# Optional step I: support Tsinghua ChatGLM. Tsinghua ChatGLM remarks: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The version installed above is torch+cpu version, using cuda requires uninstalling torch and reinstalling torch+cuda; 2: If the model cannot be loaded due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, and change AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).
+python -m pip install -r request_llm/requirements_chatglm.txt  
+
+# Optional Step II: Support Fudan MOSS.
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note that when executing this line of code, it must be in the project root.
+
+# 【Optional Step III】Ensure that the AVAIL_LLM_MODELS in the config.py configuration file contains the expected model. Currently, all supported models are as follows (jittorllms series currently only supports the docker solution):
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. Run.
+
+```sh
+python main.py
+```5. Testing Function Plugin
+```
+- Test function plugin template function (requires gpt to answer what happened today in history), you can use this function as a template to implement more complex functions
+    Click "[Function Plugin Template Demo] Today in History"
+```
+
+## Installation-Methods 2: Using Docker
+
+1. Only ChatGPT (recommended for most people)
+
+ ``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # Download project
+cd chatgpt_academic                                 # Enter path
+nano config.py                                      # Edit config.py with any text editor ‑ configure "Proxy," "API_KEY," "WEB_PORT" (e.g., 50923) and more
+docker build -t gpt-academic .                      # installation
+
+#(Last step-Option 1) In a Linux environment, `--net=host` is more convenient and quick
+docker run --rm -it --net=host gpt-academic
+#(Last step-Option 2) In a macOS/windows environment, the -p option must be used to expose the container port (e.g., 50923) to the port on the host.
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (requires familiarity with Docker)
+
+``` sh
+# Modify docker-compose.yml, delete plans 1 and 3, and retain plan 2. Modify the configuration of plan 2 in docker-compose.yml, and reference the comments for instructions.
+docker-compose up
+```
+
+3. ChatGPT + LLAMA + Pangu + RWKV (requires familiarity with Docker)
+``` sh
+# Modify docker-compose.yml, delete plans 1 and 2, and retain plan 3. Modify the configuration of plan 3 in docker-compose.yml, and reference the comments for instructions.
+docker-compose up
+```
+
+
+## Installation-Method 3: Other Deployment Methods
+
+1. How to use proxy URL/Microsoft Azure API
+Configure API_URL_REDIRECT according to the instructions in `config.py`.
+
+2. Remote Cloud Server Deployment (requires cloud server knowledge and experience)
+Please visit [Deployment Wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
+
+3. Using WSL2 (Windows Subsystem for Linux Subsystem)
+Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
+
+4. How to run on a secondary URL (such as `http://localhost/subpath`)
+Please visit [FastAPI Running Instructions](docs/WithFastapi.md)
+
+5. Run with docker-compose
+Please read docker-compose.yml and follow the instructions provided therein.
+---
+# Advanced Usage
+## Customize new convenience buttons/custom function plugins
+
+1. Custom new convenience buttons (academic shortcut keys)
+Open `core_functional.py` with any text editor, add the item as follows, and restart the program. (If the button has been added successfully and is visible, the prefix and suffix support hot modification without restarting the program.)
+example:
+```
+"Super English to Chinese Translation": {
+    # Prefix, which will be added before your input. For example, used to describe your request, such as translation, code interpretation, polish, etc.
+    "Prefix": "Please translate the following content into Chinese, and explain the proper nouns in the text in a markdown table one by one:\n\n", 
+    
+    # Suffix, which will be added after your input. For example, in combination with the prefix, you can surround your input content with quotation marks.
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. Custom function plugins
+
+Write powerful function plugins to perform any task you can and cannot think of.
+The difficulty of writing and debugging plugins in this project is low, and as long as you have a certain amount of python basic knowledge, you can follow the template provided by us to achieve your own plugin functions.
+For details, please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
+
+---
+# Latest Update
+## New feature dynamics.
+1. ダイアログの保存機能。関数プラグインエリアで '現在の会話を保存' を呼び出すと、現在のダイアログを読み取り可能で復元可能なHTMLファイルとして保存できます。さらに、関数プラグインエリア（ドロップダウンメニュー）で 'ダイアログの履歴保存ファイルを読み込む' を呼び出すことで、以前の会話を復元することができます。Tips:ファイルを指定せずに 'ダイアログの履歴保存ファイルを読み込む' をクリックすることで、過去のHTML保存ファイルのキャッシュを表示することができます。'すべてのローカルダイアログの履歴を削除' をクリックすることで、すべてのHTML保存ファイルのキャッシュを削除できます。
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500">
+</div>
+
+
+2. 報告書を生成します。ほとんどのプラグインは、実行が終了した後に作業報告書を生成します。
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300">
+<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300">
+<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300">
+</div>
+
+3. モジュール化された機能設計、簡単なインターフェースで強力な機能をサポートする。
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400">
+<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400">
+</div>
+
+4. 自己解決可能なオープンソースプロジェクトです。
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500">
+</div>
+
+
+5. 他のオープンソースプロジェクトの解読、容易である。
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500">
+</div>
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500">
+</div>
+
+6. [Live2D](https://github.com/fghrsh/live2d_demo)のデコレート小機能です。（デフォルトでは閉じてますが、 `config.py`を変更する必要があります。）
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500">
+</div>
+
+7. 新たにMOSS大言語モデルのサポートを追加しました。
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500">
+</div>
+
+8. OpenAI画像生成
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500">
+</div>
+
+9. OpenAIオーディオの解析とサマリー
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500">
+</div>
+
+10. 全文校正されたLaTeX
+<div align="center">
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500">
+</div>
+
+
+## バージョン：
+- version 3.5（作業中）：すべての関数プラグインを自然言語で呼び出すことができるようにする（高い優先度）。
+- version 3.4（作業中）：chatglmのローカルモデルのマルチスレッドをサポートすることで、機能を改善する。
+- version 3.3：+Web情報の総合機能
+- version 3.2：関数プラグインでさらに多くのパラメータインターフェイスをサポートする（ダイアログの保存機能、任意の言語コードの解読+同時に任意のLLM組み合わせに関する問い合わせ）
+- version 3.1：複数のGPTモデルを同時に質問できるようになりました！ api2dをサポートし、複数のAPIキーを均等に負荷分散することができます。
+- version 3.0：chatglmとその他の小型LLMのサポート。
+- version 2.6：プラグイン構造を再構築し、対話内容を高め、より多くのプラグインを追加しました。
+- version 2.5：自己アップデートし、長文書やトークンのオーバーフローの問題を解決しました。
+- version 2.4：（1）全文翻訳のPDF機能を追加しました。（2）入力エリアの位置切り替え機能を追加しました。（3）垂直レイアウトオプションを追加しました。（4）マルチスレッド関数プラグインを最適化しました。
+- version 2.3：マルチスレッド性能の向上。
+- version 2.2：関数プラグインのホットリロードをサポートする。
+- version 2.1：折りたたみ式レイアウト。
+- version 2.0：モジュール化された関数プラグインを導入。
+- version 1.0：基本機能
+
+gpt_academic開発者QQグループ-2：610599535
+
+- 既知の問題
+    - 一部のブラウザ翻訳プラグインが、このソフトウェアのフロントエンドの実行を妨害する
+    - gradioバージョンが高すぎるか低すぎると、多くの異常が引き起こされる
+
+## 参考学習
+
+```
+コードの中には、他の優れたプロジェクトの設計から参考にしたものがたくさん含まれています：
+
+# プロジェクト1：清華ChatGLM-6B:
+https://github.com/THUDM/ChatGLM-6B
+
+# プロジェクト2：清華JittorLLMs:
+https://github.com/Jittor/JittorLLMs
+
+# プロジェクト3：Edge-GPT:
+https://github.com/acheong08/EdgeGPT
+
+# プロジェクト4：ChuanhuChatGPT:
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# プロジェクト5：ChatPaper:
+https://github.com/kaixindelele/ChatPaper
+
+# その他：
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/README_RS.md

@@ -0,0 +1,278 @@
+> **Note**
+>
+> Этот файл самовыражения автоматически генерируется модулем перевода markdown в этом проекте и может быть не на 100% правильным.
+>
+# <img src="logo.png" width="40" > GPT Академическая оптимизация (GPT Academic)
+
+**Если вам нравится этот проект, пожалуйста, поставьте ему звезду. Если вы придумали более полезные языковые ярлыки или функциональные плагины, не стесняйтесь открывать issue или pull request. 
+Чтобы перевести этот проект на произвольный язык с помощью GPT, ознакомьтесь и запустите [`multi_language.py`](multi_language.py) (экспериментальный).
+
+> **Примечание**
+> 
+> 1. Обратите внимание, что только функциональные плагины (кнопки), помеченные **красным цветом**, поддерживают чтение файлов, некоторые плагины находятся в **выпадающем меню** в области плагинов. Кроме того, мы с наивысшим приоритетом рады и обрабатываем pull requests для любых новых плагинов!
+> 
+> 2. В каждом файле проекта функциональность описана в документе самоанализа [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). С каждой итерацией выполнения версии вы можете в любое время вызвать повторное создание отчета о самоанализе этого проекта, щелкнув соответствующий функциональный плагин и вызвав GPT. Вопросы сборки описаны в [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Метод установки](#installation).
+> 
+> 3. Этот проект совместим и поощряет использование китайских языковых моделей chatglm и RWKV, пангу и т. Д. Поддержка нескольких api-key, которые могут существовать одновременно, может быть указан в файле конфигурации, например `API_KEY="openai-key1,openai-key2,api2d-key3"`. Если требуется временно изменить `API_KEY`, введите временный `API_KEY` в области ввода и нажмите клавишу Enter, чтобы он вступил в силу. 
+
+> **Примечание**
+>
+> При установке зависимостей строго выбирайте версии, **указанные в файле requirements.txt**.
+> 
+> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`## Задание
+
+Вы профессиональный переводчик научных статей.
+
+Переведите этот файл в формате Markdown на русский язык. Не изменяйте существующие команды Markdown, ответьте только переведенными результатами.
+
+## Результат
+
+Функция | Описание
+--- | ---
+Однокнопочный стиль | Поддержка однокнопочного стиля и поиска грамматических ошибок в научных статьях
+Однокнопочный перевод на английский и китайский | Однокнопочный перевод на английский и китайский
+Однокнопочное объяснение кода | Показ кода, объяснение его, генерация кода, комментирование кода
+[Настройка быстрых клавиш](https://www.bilibili.com/video/BV14s4y1E7jN) | Поддержка настройки быстрых клавиш
+Модульный дизайн | Поддержка пользовательских функциональных плагинов мощных [функциональных плагинов](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions), плагины поддерживают [горячую замену](https://github.com/binary-husky/chatgpt_academic/wiki/Function-Plug-in-Guide)
+[Анализ своей программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] [Однокнопочный просмотр](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academicProject-Self-analysis-Report) исходного кода этого проекта
+[Анализ программы](https://www.bilibili.com/video/BV1cj411A7VW) | [Функциональный плагин] Однокнопочный анализ дерева других проектов Python/C/C++/Java/Lua/...
+Чтение статей, [перевод](https://www.bilibili.com/video/BV1KT411x7Wn) статей | [Функциональный плагин] Однокнопочное чтение полного текста научных статей и генерация резюме
+Полный перевод [LaTeX](https://www.bilibili.com/video/BV1nk4y1Y7Js/) и совершенствование | [Функциональный плагин] Однокнопочный перевод или совершенствование LaTeX статьи
+Автоматическое комментирование | [Функциональный плагин] Однокнопочное автоматическое генерирование комментариев функций
+[Перевод](https://www.bilibili.com/video/BV1yo4y157jV/) Markdown на английский и китайский | [Функциональный плагин] Вы видели обе версии файлов [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) для этих 5 языков?
+Отчет о чат-анализе | [Функциональный плагин] После запуска будет автоматически сгенерировано сводное извещение
+Функция перевода полного текста [PDF-статьи](https://www.bilibili.com/video/BV1KT411x7Wn) | [Функциональный плагин] Извлечение заголовка и резюме [PDF-статьи](https://www.bilibili.com/video/BV1KT411x7Wn) и перевод всего документа (многопоточность)
+[Arxiv Helper](https://www.bilibili.com/video/BV1LM4y1279X) | [Функциональный плагин] Введите URL статьи на arxiv и одним щелчком мыши переведите резюме и загрузите PDF
+[Google Scholar Integration Helper](https://www.bilibili.com/video/BV19L411U7ia) | [Функциональный плагин] При заданном любом URL страницы поиска в Google Scholar позвольте gpt вам помочь [написать обзор](https://www.bilibili.com/video/BV1GP411U7Az/)
+Сбор Интернет-информации + GPT | [Функциональный плагин] Однокнопочный [запрос информации из Интернета GPT](https://www.bilibili.com/video/BV1om4y127ck), затем ответьте на вопрос, чтобы информация не устарела никогда
+Отображение формул / изображений / таблиц | Может одновременно отображать формулы в [формате Tex и рендеринге](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png), поддерживает формулы, подсвечивает код
+Поддержка функций с многопоточностью | Поддержка многопоточного вызова chatgpt, однокнопочная обработка [больших объемов текста](https://www.bilibili.com/video/BV1FT411H7c5/) или программ
+Темная тема gradio для запуска приложений | Добавьте ```/?__theme=dark``` после URL в браузере, чтобы переключиться на темную тему
+[Поддержка нескольких моделей LLM](https://www.bilibili.com/video/BV1wT411p7yf), [API2D](https://api2d.com/) | Они одновременно обслуживаются GPT3.5, GPT4, [Clear ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS)
+Подключение нескольких новых моделей LLM, поддержка деплоя[huggingface](https://huggingface.co/spaces/qingxu98/gpt-academic) | Подключение интерфейса Newbing (новый Bing), подключение поддержки [LLaMA](https://github.com/facebookresearch/llama), поддержка [RWKV](https://github.com/BlinkDL/ChatRWKV) и [Pangu α](https://openi.org.cn/pangu/)
+Больше новых функций (генерация изображения и т. д.) | См. на конце этого файла…- All buttons are dynamically generated by reading functional.py, and custom functions can be freely added to liberate the clipboard
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
+</div>
+
+- Revision/Correction
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
+</div>
+
+- If the output contains formulas, they will be displayed in both tex and rendered form for easy copying and reading
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
+</div>
+
+- Don't feel like looking at project code? Show the entire project directly in chatgpt
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
+</div>
+
+- Mixing multiple large language models (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4）
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
+</div>
+
+---
+# Installation
+## Installation-Method 1: Run directly (Windows, Linux or MacOS) 
+
+1. Download the project
+```sh
+git clone https://github.com/binary-husky/chatgpt_academic.git
+cd chatgpt_academic
+```
+
+2. Configure API_KEY
+
+In `config.py`, configure API KEY and other settings, [special network environment settings] (https://github.com/binary-husky/gpt_academic/issues/1).
+
+(P.S. When the program is running, it will first check whether there is a secret configuration file named `config_private.py` and use the configuration in it to replace the same name in` config.py`. Therefore, if you understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py`, and transfer (copy) the configuration in `config.py` to `config_private.py`. `config_private.py` is not controlled by git, which can make your privacy information more secure. P.S. The project also supports configuring most options through `environment variables`, and the writing format of environment variables refers to the `docker-compose` file. Priority of read: `environment variable`>`config_private.py`>`config.py`)
+
+
+3. Install dependencies
+```sh
+# （Option I: If familiar with Python）(Python version 3.9 or above, the newer the better), note: use the official pip source or the aliyun pip source, temporary switching source method: python -m pip install -r requirements.txt - i https://mirrors.aliyun.com/pypi/simple/
+python -m pip install -r requirements.txt
+
+# （Option II: If unfamiliar with Python）Use Anaconda, the steps are also similar (https://www.bilibili.com/video/BV1rc411W7Dr):
+conda create -n gptac_venv python=3.11    # create an Anaconda environment
+conda activate gptac_venv                 # activate Anaconda environment
+python -m pip install -r requirements.txt # This step is the same as the pip installation
+```
+
+<details><summary> If you need to support Tsinghua ChatGLM/Fudan MOSS as backend, click here to expand </summary>
+<p>
+
+[Optional step] If you need to support Tsinghua ChatGLM/Fudan MOSS as backend, you need to install more dependencies (prerequisites: familiar with Python + have used Pytorch + computer configuration is strong):
+```sh
+# [Optional step I] Support Tsinghua ChatGLM. Tsinghua ChatGLM note: If you encounter the "Call ChatGLM fail cannot load ChatGLM parameters normally" error, refer to the following: 1: The default installation above is torch+cpu version, and cuda is used Need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient local configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) Modify to AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
+python -m pip install -r request_llm/requirements_chatglm.txt  
+
+# [Optional step II] Support Fudan MOSS
+python -m pip install -r request_llm/requirements_moss.txt
+git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Note that when executing this line of code, you must be in the project root path
+
+# [Optional step III] Make sure the AVAIL_LLM_MODELS in the config.py configuration file contains the expected models. Currently, all supported models are as follows (the jittorllms series currently only supports the docker solution):
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+```
+
+</p>
+</details>
+
+
+
+4. Run
+```sh
+python main.py
+```5. Testing Function Plugin
+```
+- Testing function plugin template function (requires GPT to answer what happened in history today), you can use this function as a template to implement more complex functions
+    Click "[Function plugin Template Demo] On this day in history"
+```
+
+## Installation - Method 2: Using Docker
+
+1. ChatGPT only (recommended for most people)
+
+``` sh
+git clone https://github.com/binary-husky/chatgpt_academic.git  # download the project
+cd chatgpt_academic                                 # enter the path
+nano config.py                                      # edit config.py with any text editor to configure "Proxy", "API_KEY", and "WEB_PORT" (eg 50923)
+docker build -t gpt-academic .                      # install
+
+# (Last step-Option 1) In a Linux environment, using `--net=host` is more convenient and faster
+docker run --rm -it --net=host gpt-academic
+# (Last step-Option 2) In macOS/windows environment, only -p option can be used to expose the port on the container (eg 50923) to the port on the host
+docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
+```
+
+2. ChatGPT + ChatGLM + MOSS (requires familiarity with Docker)
+
+``` sh
+# Edit docker-compose.yml, delete solutions 1 and 3, and keep solution 2. Modify the configuration of solution 2 in docker-compose.yml, refer to the comments in it
+docker-compose up
+```
+
+3. ChatGPT + LLAMA + PanGu + RWKV (requires familiarity with Docker)
+``` sh
+# Edit docker-compose.yml, delete solutions 1 and 2, and keep solution 3. Modify the configuration of solution 3 in docker-compose.yml, refer to the comments in it
+docker-compose up
+```
+
+
+## Installation Method 3: Other Deployment Methods
+
+1. How to use reverse proxy URL/Microsoft Azure API
+Configure API_URL_REDIRECT according to the instructions in `config.py`.
+
+2. Remote Cloud Server Deployment (Requires Knowledge and Experience of Cloud Servers)
+Please visit [Deployment Wiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
+
+3. Using WSL2 (Windows Subsystem for Linux subsystem)
+Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
+
+4. How to run at the secondary URL (such as `http://localhost/subpath`)
+Please visit [FastAPI Operation Instructions](docs/WithFastapi.md)
+
+5. Using docker-compose to run
+Please read docker-compose.yml and follow the prompts to operate.
+
+---
+# Advanced Usage
+## Customize new convenient buttons / custom function plugins
+
+1. Customize new convenient buttons (academic shortcuts)
+Open `core_functional.py` with any text editor, add an entry as follows, and then restart the program. (If the button has been added successfully and is visible, both prefixes and suffixes can be hot-modified without having to restart the program.)
+For example:
+```
+"Super English to Chinese": {
+    # Prefix, will be added before your input. For example, describe your requirements, such as translation, code interpretation, polishing, etc.
+    "Prefix": "Please translate the following content into Chinese, and then explain each proper noun that appears in the text with a markdown table:\n\n", 
+    
+    # Suffix, will be added after your input. For example, with the prefix, you can enclose your input content in quotes.
+    "Suffix": "",
+},
+```
+<div align="center">
+<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
+</div>
+
+2. Custom function plugin
+
+Write powerful function plugins to perform any task you can and can't imagine.
+The difficulty of debugging and writing plugins in this project is very low. As long as you have a certain knowledge of python, you can implement your own plugin function by imitating the template we provide.
+Please refer to the [Function Plugin Guide](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97) for details.
+
+---
+# Latest Update
+## New feature dynamic
+
+1. Сохранение диалогов. Вызовите "Сохранить текущий диалог" в разделе функций-плагина, чтобы сохранить текущий диалог как файл HTML, который можно прочитать и восстановить. Кроме того, вызовите «Загрузить архив истории диалога» в меню функций-плагина, чтобы восстановить предыдущую сессию. Совет: если нажать кнопку "Загрузить исторический архив диалога" без указания файла, можно просмотреть кэш исторических файлов HTML. Щелкните "Удалить все локальные записи истории диалогов", чтобы удалить все файловые кэши HTML.
+
+2. Создание отчетов. Большинство плагинов создают рабочий отчет после завершения выполнения.
+ 
+3. Модульный дизайн функций, простой интерфейс, но сильный функционал.
+
+4. Это проект с открытым исходным кодом, который может «сам переводить себя».
+
+5. Перевод других проектов с открытым исходным кодом - это не проблема.
+
+6. Мелкие функции декорирования [live2d](https://github.com/fghrsh/live2d_demo) (по умолчанию отключены, нужно изменить `config.py`).
+
+7. Поддержка большой языковой модели MOSS.
+
+8. Генерация изображений с помощью OpenAI.
+
+9. Анализ и подведение итогов аудиофайлов с помощью OpenAI.
+
+10. Полный цикл проверки правописания с использованием LaTeX.
+
+## Версии:
+- Версия 3.5 (Todo): использование естественного языка для вызова функций-плагинов проекта (высокий приоритет)
+- Версия 3.4 (Todo): улучшение многопоточной поддержки локальных больших моделей чата.
+- Версия 3.3: добавлена функция объединения интернет-информации.
+- Версия 3.2: функции-плагины поддерживают большое количество параметров (сохранение диалогов, анализирование любого языка программирования и одновременное запрос LLM-групп).
+- Версия 3.1: поддержка одновременного запроса нескольких моделей GPT! Поддержка api2d, сбалансированное распределение нагрузки по нескольким ключам api.
+- Версия 3.0: поддержка chatglm и других небольших LLM.
+- Версия 2.6: перестройка структуры плагинов, улучшение интерактивности, добавлено больше плагинов.
+- Версия 2.5: автоматическое обновление для решения проблемы длинного текста и переполнения токенов при обработке больших проектов.
+- Версия 2.4: (1) добавлена функция полного перевода PDF; (2) добавлена функция переключения положения ввода; (3) добавлена опция вертикального макета; (4) оптимизация многопоточности плагинов.
+- Версия 2.3: улучшение многопоточной интерактивности.
+- Версия 2.2: функции-плагины поддерживают горячую перезагрузку.
+- Версия 2.1: раскрывающийся макет.
+- Версия 2.0: использование модульных функций-плагинов.
+- Версия 1.0: базовые функции.
+
+gpt_academic Разработчик QQ-группы-2: 610599535
+
+- Известные проблемы
+    - Некоторые плагины перевода в браузерах мешают работе фронтенда этого программного обеспечения
+    - Высокая или низкая версия gradio может вызвать множество исключений
+
+## Ссылки и учебные материалы
+
+```
+Мы использовали многие концепты кода из других отличных проектов, включая:
+
+# Проект 1: Qinghua ChatGLM-6B:
+https://github.com/THUDM/ChatGLM-6B
+
+# Проект 2: Qinghua JittorLLMs:
+https://github.com/Jittor/JittorLLMs
+
+# Проект 3: Edge-GPT:
+https://github.com/acheong08/EdgeGPT
+
+# Проект 4: Chuanhu ChatGPT:
+https://github.com/GaiZhenbiao/ChuanhuChatGPT
+
+# Проект 5: ChatPaper:
+https://github.com/kaixindelele/ChatPaper
+
+# Больше:
+https://github.com/gradio-app/gradio
+https://github.com/fghrsh/live2d_demo
+```

docs/WithFastapi.md

@@ -0,0 +1,43 @@
+# Running with fastapi
+
+We currently support fastapi in order to solve sub-path deploy issue.
+
+1. change CUSTOM_PATH setting in `config.py`
+
+``` sh
+nano config.py
+```
+
+2. Edit main.py
+
+```diff
+    auto_opentab_delay()
+    - demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
+    + demo.queue(concurrency_count=CONCURRENT_COUNT)
+
+    - # 如果需要在二级路径下运行
+    - # CUSTOM_PATH, = get_conf('CUSTOM_PATH')
+    - # if CUSTOM_PATH != "/": 
+    - #     from toolbox import run_gradio_in_subpath
+    - #     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
+    - # else: 
+    - #     demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
+
+    + 如果需要在二级路径下运行
+    + CUSTOM_PATH, = get_conf('CUSTOM_PATH')
+    + if CUSTOM_PATH != "/": 
+    +     from toolbox import run_gradio_in_subpath
+    +     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
+    + else: 
+    +     demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
+
+if __name__ == "__main__":
+    main()
+```
+
+
+3. Go!
+
+``` sh
+python main.py
+```

docs/self_analysis.md

@@ -0,0 +1,378 @@
+# chatgpt-academic项目自译解报告
+（Author补充：以下分析均由本项目调用ChatGPT一键生成，如果有不准确的地方，全怪GPT😄）
+
+
+| 文件名 | 功能描述 |
+| ------ | ------ |
+| check_proxy.py | 检查代理有效性及地理位置 |
+| colorful.py | 控制台打印彩色文字 |
+| config.py | 配置和参数设置 |
+| config_private.py | 私人配置和参数设置 |
+| core_functional.py | 核心函数和参数设置 |
+| crazy_functional.py | 高级功能插件集合 |
+| main.py | 一个 Chatbot 程序，提供各种学术翻译、文本处理和其他查询服务 |
+| multi_language.py | 识别和翻译不同语言 |
+| theme.py | 自定义 gradio 应用程序主题 |
+| toolbox.py | 工具类库，用于协助实现各种功能 |
+| crazy_functions\crazy_functions_test.py | 测试 crazy_functions 中的各种函数 |
+| crazy_functions\crazy_utils.py | 工具函数，用于字符串处理、异常检测、Markdown 格式转换等 |
+| crazy_functions\Latex全文润色.py | 对整个 Latex 项目进行润色和纠错 |
+| crazy_functions\Latex全文翻译.py | 对整个 Latex 项目进行翻译 |
+| crazy_functions\\_\_init\_\_.py | 模块初始化文件，标识 `crazy_functions` 是一个包 |
+| crazy_functions\下载arxiv论文翻译摘要.py | 下载 `arxiv` 论文的 PDF 文件，并提取摘要和翻译 |
+| crazy_functions\代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
+| crazy_functions\图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
+| crazy_functions\对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
+| crazy_functions\总结word文档.py | 对输入的word文档进行摘要生成 |
+| crazy_functions\总结音视频.py | 对输入的音视频文件进行摘要生成 |
+| crazy_functions\批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
+| crazy_functions\批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
+| crazy_functions\批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
+| crazy_functions\批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
+| crazy_functions\理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
+| crazy_functions\生成函数注释.py | 自动生成Python函数的注释 |
+| crazy_functions\联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |
+| crazy_functions\解析JupyterNotebook.py | 对Jupyter Notebook进行代码解析 |
+| crazy_functions\解析项目源代码.py | 对指定编程语言的源代码进行解析 |
+| crazy_functions\询问多个大语言模型.py | 使用多个大语言模型对输入进行处理和回复 |
+| crazy_functions\读文章写摘要.py | 对论文进行解析和全文摘要生成 |
+| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
+| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
+| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
+| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
+| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
+| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
+| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
+| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
+| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
+| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
+| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
+| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
+| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
+| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
+| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
+| request_llm\test_llms.py | 对llm模型进行单元测试。 |
+
+## 接下来请你逐文件分析下面的工程[0/48] 请对下面的程序文件做一个概述: check_proxy.py
+
+这个文件主要包含了五个函数：
+
+1. `check_proxy`：用于检查代理的有效性及地理位置，输出代理配置和所在地信息。
+
+2. `backup_and_download`：用于备份当前版本并下载新版本。
+
+3. `patch_and_restart`：用于覆盖更新当前版本并重新启动程序。
+
+4. `get_current_version`：用于获取当前程序的版本号。
+
+5. `auto_update`：用于自动检查新版本并提示用户更新。如果用户选择更新，则备份并下载新版本，覆盖更新当前版本并重新启动程序。如果更新失败，则输出错误信息，并不会向用户进行任何提示。
+
+还有一个没有函数名的语句`os.environ['no_proxy'] = '*'`，用于设置环境变量，避免代理网络产生意外污染。
+
+此外，该文件导入了以下三个模块/函数：
+
+- `requests`
+- `shutil`
+- `os`
+
+## [1/48] 请对下面的程序文件做一个概述: colorful.py
+
+该文件是一个Python脚本，用于在控制台中打印彩色文字。该文件包含了一些函数，用于以不同颜色打印文本。其中，红色、绿色、黄色、蓝色、紫色、靛色分别以函数 print红、print绿、print黄、print蓝、print紫、print靛 的形式定义；亮红色、亮绿色、亮黄色、亮蓝色、亮紫色、亮靛色分别以 print亮红、print亮绿、print亮黄、print亮蓝、print亮紫、print亮靛 的形式定义。它们使用 ANSI Escape Code 将彩色输出从控制台突出显示。如果运行在 Linux 操作系统上，文件所执行的操作被留空；否则，该文件导入了 colorama 库并调用 init() 函数进行初始化。最后，通过一系列条件语句，该文件通过将所有彩色输出函数的名称重新赋值为 print 函数的名称来避免输出文件的颜色问题。
+
+## [2/48] 请对下面的程序文件做一个概述: config.py
+
+这个程序文件是用来配置和参数设置的。它包含了许多设置，如API key，使用代理，线程数，默认模型，超时时间等等。此外，它还包含了一些高级功能，如URL重定向等。这些设置将会影响到程序的行为和性能。
+
+## [3/48] 请对下面的程序文件做一个概述: config_private.py
+
+这个程序文件是一个Python脚本，文件名为config_private.py。其中包含以下变量的赋值：
+
+1. API_KEY：API密钥。
+2. USE_PROXY：是否应用代理。
+3. proxies：如果使用代理，则设置代理网络的协议(socks5/http)、地址(localhost)和端口(11284)。
+4. DEFAULT_WORKER_NUM：默认的工作线程数量。
+5. SLACK_CLAUDE_BOT_ID：Slack机器人ID。
+6. SLACK_CLAUDE_USER_TOKEN：Slack用户令牌。
+
+## [4/48] 请对下面的程序文件做一个概述: core_functional.py
+
+这是一个名为core_functional.py的源代码文件，该文件定义了一个名为get_core_functions()的函数，该函数返回一个字典，该字典包含了各种学术翻译润色任务的说明和相关参数，如颜色、前缀、后缀等。这些任务包括英语学术润色、中文学术润色、查找语法错误、中译英、学术中英互译、英译中、找图片和参考文献转Bib。其中，一些任务还定义了预处理函数用于处理任务的输入文本。
+
+## [5/48] 请对下面的程序文件做一个概述: crazy_functional.py
+
+此程序文件（crazy_functional.py）是一个函数插件集合，包含了多个函数插件的定义和调用。这些函数插件旨在提供一些高级功能，如解析项目源代码、批量翻译PDF文档和Latex全文润色等。其中一些插件还支持热更新功能，不需要重启程序即可生效。文件中的函数插件按照功能进行了分类（第一组和第二组），并且有不同的调用方式（作为按钮或下拉菜单）。
+
+## [6/48] 请对下面的程序文件做一个概述: main.py
+
+这是一个Python程序文件，文件名为main.py。该程序包含一个名为main的函数，程序会自动运行该函数。程序要求已经安装了gradio、os等模块，会根据配置文件加载代理、model、API Key等信息。程序提供了Chatbot功能，实现了一个对话界面，用户可以输入问题，然后Chatbot可以回答问题或者提供相关功能。程序还包含了基础功能区、函数插件区、更换模型 & SysPrompt & 交互界面布局、备选输入区，用户可以在这些区域选择功能和插件进行使用。程序中还包含了一些辅助模块，如logging等。
+
+## [7/48] 请对下面的程序文件做一个概述: multi_language.py
+
+该文件multi_language.py是用于将项目翻译成不同语言的程序。它包含了以下函数和变量：lru_file_cache、contains_chinese、split_list、map_to_json、read_map_from_json、advanced_split、trans、trans_json、step_1_core_key_translate、CACHE_FOLDER、blacklist、LANG、TransPrompt、cached_translation等。注释和文档字符串提供了有关程序的说明，例如如何使用该程序，如何修改“LANG”和“TransPrompt”变量等。
+
+## [8/48] 请对下面的程序文件做一个概述: theme.py
+
+这是一个Python源代码文件，文件名为theme.py。此文件中定义了一个函数adjust_theme，其功能是自定义gradio应用程序的主题，包括调整颜色、字体、阴影等。如果允许，则添加一个看板娘。此文件还包括变量advanced_css，其中包含一些CSS样式，用于高亮显示代码和自定义聊天框样式。此文件还导入了get_conf函数和gradio库。
+
+## [9/48] 请对下面的程序文件做一个概述: toolbox.py
+
+toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和小工具函数，用于协助实现聊天机器人所需的各种功能，包括文本处理、功能插件加载、异常检测、Markdown格式转换，文件读写等等。此外，该库还包含一些依赖、参数配置等信息。该库易于理解和维护。
+
+## [10/48] 请对下面的程序文件做一个概述: crazy_functions\crazy_functions_test.py
+
+这个文件是一个Python测试模块，用于测试crazy_functions中的各种函数插件。这些函数包括：解析Python项目源代码、解析Cpp项目源代码、Latex全文润色、Markdown中译英、批量翻译PDF文档、谷歌检索小助手、总结word文档、下载arxiv论文并翻译摘要、联网回答问题、和解析Jupyter Notebooks。对于每个函数插件，都有一个对应的测试函数来进行测试。
+
+## [11/48] 请对下面的程序文件做一个概述: crazy_functions\crazy_utils.py
+
+这个Python文件中包括了两个函数：
+
+1. `input_clipping`: 该函数用于裁剪输入文本长度，使其不超过一定的限制。
+2. `request_gpt_model_in_new_thread_with_ui_alive`: 该函数用于请求 GPT 模型并保持用户界面的响应，支持多线程和实时更新用户界面。
+
+这两个函数都依赖于从 `toolbox` 和 `request_llm` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
+
+## [12/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文润色.py
+
+这是一个Python程序文件，文件名为crazy_functions\Latex全文润色.py。文件包含了一个PaperFileGroup类和三个函数Latex英文润色，Latex中文润色和Latex英文纠错。程序使用了字符串处理、正则表达式、文件读写、多线程等技术，主要作用是对整个Latex项目进行润色和纠错。其中润色和纠错涉及到了对文本的语法、清晰度和整体可读性等方面的提升。此外，该程序还参考了第三方库，并封装了一些工具函数。
+
+## [13/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文翻译.py
+
+这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llm` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
+
+## [14/48] 请对下面的程序文件做一个概述: crazy_functions\__init__.py
+
+这是一个Python模块的初始化文件（__init__.py），命名为"crazy_functions"。该模块包含了一些疯狂的函数，但该文件并没有实现这些函数，而是作为一个包（package）来导入其它的Python模块以实现这些函数。在该文件中，没有定义任何类或函数，它唯一的作用就是标识"crazy_functions"模块是一个包。
+
+## [15/48] 请对下面的程序文件做一个概述: crazy_functions\下载arxiv论文翻译摘要.py
+
+这是一个 Python 程序文件，文件名为 `下载arxiv论文翻译摘要.py`。程序包含多个函数，其中 `下载arxiv论文并翻译摘要` 函数的作用是下载 `arxiv` 论文的 PDF 文件，提取摘要并使用 GPT 对其进行翻译。其他函数包括用于下载 `arxiv` 论文的 `download_arxiv_` 函数和用于获取文章信息的 `get_name` 函数，其中涉及使用第三方库如 requests, BeautifulSoup 等。该文件还包含一些用于调试和存储文件的代码段。
+
+## [16/48] 请对下面的程序文件做一个概述: crazy_functions\代码重写为全英文_多线程.py
+
+该程序文件是一个多线程程序，主要功能是将指定目录下的所有Python代码文件中的中文内容转化为英文，并将转化后的代码存储到一个新的文件中。其中，程序使用了GPT-3等技术进行中文-英文的转化，同时也进行了一些Token限制下的处理，以防止程序发生错误。程序在执行过程中还会输出一些提示信息，并将所有转化过的代码文件存储到指定目录下。在程序执行结束后，还会生成一个任务执行报告，记录程序运行的详细信息。
+
+## [17/48] 请对下面的程序文件做一个概述: crazy_functions\图片生成.py
+
+该程序文件提供了一个用于生成图像的函数`图片生成`。函数实现的过程中，会调用`gen_image`函数来生成图像，并返回图像生成的网址和本地文件地址。函数有多个参数，包括`prompt`(激励文本)、`llm_kwargs`(GPT模型的参数)、`plugin_kwargs`(插件模型的参数)等。函数核心代码使用了`requests`库向OpenAI API请求图像，并做了简单的处理和保存。函数还更新了交互界面，清空聊天历史并显示正在生成图像的消息和最终的图像网址和预览。
+
+## [18/48] 请对下面的程序文件做一个概述: crazy_functions\对话历史存档.py
+
+这个文件是名为crazy_functions\对话历史存档.py的Python程序文件，包含了4个函数：
+
+1. write_chat_to_file(chatbot, history=None, file_name=None)：用来将对话记录以Markdown格式写入文件中，并且生成文件名，如果没指定文件名则用当前时间。写入完成后将文件路径打印出来。
+
+2. gen_file_preview(file_name)：从传入的文件中读取内容，解析出对话历史记录并返回前100个字符，用于文件预览。
+
+3. read_file_to_chat(chatbot, history, file_name)：从传入的文件中读取内容，解析出对话历史记录并更新聊天显示框。
+
+4. 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)：一个主要函数，用于保存当前对话记录并提醒用户。如果用户希望加载历史记录，则调用read_file_to_chat()来更新聊天显示框。如果用户希望删除历史记录，调用删除所有本地对话历史记录()函数完成删除操作。
+
+## [19/48] 请对下面的程序文件做一个概述: crazy_functions\总结word文档.py
+
+该程序文件实现了一个总结Word文档的功能，使用Python的docx库读取docx格式的文件，使用pywin32库读取doc格式的文件。程序会先根据传入的txt参数搜索需要处理的文件，并逐个解析其中的内容，将内容拆分为指定长度的文章片段，然后使用另一个程序文件中的request_gpt_model_in_new_thread_with_ui_alive函数进行中文概述。最后将所有的总结结果写入一个文件中，并在界面上进行展示。
+
+## [20/48] 请对下面的程序文件做一个概述: crazy_functions\总结音视频.py
+
+该程序文件包括两个函数：split_audio_file()和AnalyAudio()，并且导入了一些必要的库并定义了一些工具函数。split_audio_file用于将音频文件分割成多个时长相等的片段，返回一个包含所有切割音频片段文件路径的列表，而AnalyAudio用来分析音频文件，通过调用whisper模型进行音频转文字并使用GPT模型对音频内容进行概述，最终将所有总结结果写入结果文件中。
+
+## [21/48] 请对下面的程序文件做一个概述: crazy_functions\批量Markdown翻译.py
+
+该程序文件名为`批量Markdown翻译.py`，包含了以下功能：读取Markdown文件，将长文本分离开来，将Markdown文件进行翻译（英译中和中译英），整理结果并退出。程序使用了多线程以提高效率。程序使用了`tiktoken`依赖库，可能需要额外安装。文件中还有一些其他的函数和类，但与文件名所描述的功能无关。
+
+## [22/48] 请对下面的程序文件做一个概述: crazy_functions\批量总结PDF文档.py
+
+该文件是一个Python脚本，名为crazy_functions\批量总结PDF文档.py。在导入了一系列库和工具函数后，主要定义了5个函数，其中包括一个错误处理装饰器（@CatchException），用于批量总结PDF文档。该函数主要实现对PDF文档的解析，并调用模型生成中英文摘要。
+
+## [23/48] 请对下面的程序文件做一个概述: crazy_functions\批量总结PDF文档pdfminer.py
+
+该程序文件是一个用于批量总结PDF文档的函数插件，使用了pdfminer插件和BeautifulSoup库来提取PDF文档的文本内容，对每个PDF文件分别进行处理并生成中英文摘要。同时，该程序文件还包括一些辅助工具函数和处理异常的装饰器。
+
+## [24/48] 请对下面的程序文件做一个概述: crazy_functions\批量翻译PDF文档_多线程.py
+
+这个程序文件是一个Python脚本，文件名为“批量翻译PDF文档_多线程.py”。它主要使用了“toolbox”、“request_gpt_model_in_new_thread_with_ui_alive”、“request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency”、“colorful”等Python库和自定义的模块“crazy_utils”的一些函数。程序实现了一个批量翻译PDF文档的功能，可以自动解析PDF文件中的基础信息，递归地切割PDF文件，翻译和处理PDF论文中的所有内容，并生成相应的翻译结果文件（包括md文件和html文件）。功能比较复杂，其中需要调用多个函数和依赖库，涉及到多线程操作和UI更新。文件中有详细的注释和变量命名，代码比较清晰易读。
+
+## [25/48] 请对下面的程序文件做一个概述: crazy_functions\理解PDF文档内容.py
+
+该程序文件实现了一个名为“理解PDF文档内容”的函数，该函数可以为输入的PDF文件提取摘要以及正文各部分的主要内容，并在提取过程中根据上下文关系进行学术性问题解答。该函数依赖于多个辅助函数和第三方库，并在执行过程中针对可能出现的异常进行了处理。
+
+## [26/48] 请对下面的程序文件做一个概述: crazy_functions\生成函数注释.py
+
+该程序文件是一个Python模块文件，文件名为“生成函数注释.py”，定义了两个函数：一个是生成函数注释的主函数“生成函数注释”，另一个是通过装饰器实现异常捕捉的函数“批量生成函数注释”。该程序文件依赖于“toolbox”和本地“crazy_utils”模块，并且在运行时使用了多线程技术和GPT模型来生成注释。函数生成的注释结果使用Markdown表格输出并写入历史记录文件。
+
+## [27/48] 请对下面的程序文件做一个概述: crazy_functions\联网的ChatGPT.py
+
+这是一个名为`联网的ChatGPT.py`的Python程序文件，其中定义了一个函数`连接网络回答问题`。该函数通过爬取搜索引擎的结果和访问网页来综合回答给定的问题，并使用ChatGPT模型完成回答。此外，该文件还包括一些工具函数，例如从网页中抓取文本和使用代理访问网页。
+
+## [28/48] 请对下面的程序文件做一个概述: crazy_functions\解析JupyterNotebook.py
+
+这个程序文件包含了两个函数： `parseNotebook()`和`解析ipynb文件()`，并且引入了一些工具函数和类。`parseNotebook()`函数将Jupyter Notebook文件解析为文本代码块，`解析ipynb文件()`函数则用于解析多个Jupyter Notebook文件，使用`parseNotebook()`解析每个文件和一些其他的处理。函数中使用了多线程处理输入和输出，并且将结果写入到文件中。
+
+## [29/48] 请对下面的程序文件做一个概述: crazy_functions\解析项目源代码.py
+
+这是一个源代码分析的Python代码文件，其中定义了多个函数，包括解析一个Python项目、解析一个C项目、解析一个C项目的头文件和解析一个Java项目等。其中解析源代码新函数是实际处理源代码分析并生成报告的函数。该函数首先会逐个读取传入的源代码文件，生成对应的请求内容，通过多线程发送到chatgpt进行分析。然后将结果写入文件，并进行汇总分析。最后通过调用update_ui函数刷新界面，完整实现了源代码的分析。
+
+## [30/48] 请对下面的程序文件做一个概述: crazy_functions\询问多个大语言模型.py
+
+该程序文件包含两个函数：同时问询()和同时问询_指定模型()，它们的作用是使用多个大语言模型同时对用户输入进行处理，返回对应模型的回复结果。同时问询()会默认使用ChatGPT和ChatGLM两个模型，而同时问询_指定模型()则可以指定要使用的模型。该程序文件还引用了其他的模块和函数库。
+
+## [31/48] 请对下面的程序文件做一个概述: crazy_functions\读文章写摘要.py
+
+这个程序文件是一个Python模块，文件名为crazy_functions\读文章写摘要.py。该模块包含了两个函数，其中主要函数是"读文章写摘要"函数，其实现了解析给定文件夹中的tex文件，对其中每个文件的内容进行摘要生成，并根据各论文片段的摘要，最终生成全文摘要。第二个函数是"解析Paper"函数，用于解析单篇论文文件。其中用到了一些工具函数和库，如update_ui、CatchException、report_execption、write_results_to_file等。
+
+## [32/48] 请对下面的程序文件做一个概述: crazy_functions\谷歌检索小助手.py
+
+该文件是一个Python模块，文件名为“谷歌检索小助手.py”。该模块包含两个函数，一个是“get_meta_information()”，用于从提供的网址中分析出所有相关的学术文献的元数据信息；另一个是“谷歌检索小助手()”，是主函数，用于分析用户提供的谷歌学术搜索页面中出现的文章，并提取相关信息。其中，“谷歌检索小助手()”函数依赖于“get_meta_information()”函数，并调用了其他一些Python模块，如“arxiv”、“math”、“bs4”等。
+
+## [33/48] 请对下面的程序文件做一个概述: crazy_functions\高级功能函数模板.py
+
+该程序文件定义了一个名为高阶功能模板函数的函数，该函数接受多个参数，包括输入的文本、gpt模型参数、插件模型参数、聊天显示框的句柄、聊天历史等，并利用送出请求，使用 Unsplash API 发送相关图片。其中，为了避免输入溢出，函数会在开始时清空历史。函数也有一些 UI 更新的语句。该程序文件还依赖于其他两个模块：CatchException 和 update_ui，以及一个名为 request_gpt_model_in_new_thread_with_ui_alive 的来自 crazy_utils 模块（应该是自定义的工具包）的函数。
+
+## [34/48] 请对下面的程序文件做一个概述: request_llm\bridge_all.py
+
+该文件包含两个函数：predict和predict_no_ui_long_connection，用于基于不同的LLM模型进行对话。该文件还包含一个lazyloadTiktoken类和一个LLM_CATCH_EXCEPTION修饰器函数。其中lazyloadTiktoken类用于懒加载模型的tokenizer，LLM_CATCH_EXCEPTION用于错误处理。整个文件还定义了一些全局变量和模型信息字典，用于引用和配置LLM模型。
+
+## [35/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatglm.py
+
+这是一个Python程序文件，名为`bridge_chatglm.py`，其中定义了一个名为`GetGLMHandle`的类和三个方法：`predict_no_ui_long_connection`、 `predict`和 `stream_chat`。该文件依赖于多个Python库，如`transformers`和`sentencepiece`。该文件实现了一个聊天机器人，使用ChatGLM模型来生成回复，支持单线程和多线程方式。程序启动时需要加载ChatGLM的模型和tokenizer，需要一段时间。在配置文件`config.py`中设置参数会影响模型的内存和显存使用，因此程序可能会导致低配计算机卡死。
+
+## [36/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatgpt.py
+
+该文件为 Python 代码文件，文件名为 request_llm\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
+
+## [37/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_llama.py
+
+该代码文件实现了一个聊天机器人，其中使用了 JittorLLMs 模型。主要包括以下几个部分：
+1. GetGLMHandle 类：一个进程类，用于加载 JittorLLMs 模型并接收并处理请求。
+2. predict_no_ui_long_connection 函数：一个多线程方法，用于在后台运行聊天机器人。
+3. predict 函数：一个单线程方法，用于在前端页面上交互式调用聊天机器人，以获取用户输入并返回相应的回复。
+
+这个文件中还有一些辅助函数和全局变量，例如 importlib、time、threading 等。
+
+## [38/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_pangualpha.py
+
+这个文件是为了实现使用jittorllms（一种机器学习模型）来进行聊天功能的代码。其中包括了模型加载、模型的参数加载、消息的收发等相关操作。其中使用了多进程和多线程来提高性能和效率。代码中还包括了处理依赖关系的函数和预处理函数等。
+
+## [39/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_rwkv.py
+
+这个文件是一个Python程序，文件名为request_llm\bridge_jittorllms_rwkv.py。它依赖transformers、time、threading、importlib、multiprocessing等库。在文件中，通过定义GetGLMHandle类加载jittorllms模型参数和定义stream_chat方法来实现与jittorllms模型的交互。同时，该文件还定义了predict_no_ui_long_connection和predict方法来处理历史信息、调用jittorllms模型、接收回复信息并输出结果。
+
+## [40/48] 请对下面的程序文件做一个概述: request_llm\bridge_moss.py
+
+该文件为一个Python源代码文件，文件名为 request_llm\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
+
+GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个子进程并加载 MOSS 模型参数，通过 Pipe 进行主子进程的通信。该类还定义了 check_dependency、moss_init、run 和 stream_chat 等方法，其中 check_dependency 和 moss_init 是子进程的初始化方法，run 是子进程运行方法，stream_chat 实现了主进程和子进程的交互过程。
+
+函数 predict_no_ui_long_connection 是多线程方法，调用 GetGLMHandle 类加载 MOSS 参数后使用 stream_chat 实现主进程和子进程的交互过程。
+
+函数 predict 是单线程方法，通过调用 update_ui 将交互过程中 MOSS 的回复实时更新到UI（User Interface）中，并执行一个 named function（additional_fn）指定的函数对输入进行预处理。
+
+## [41/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbing.py
+
+这是一个名为`bridge_newbing.py`的程序文件，包含三个部分：
+
+第一部分使用from语句导入了`edge_gpt`模块的`NewbingChatbot`类。
+
+第二部分定义了一个名为`NewBingHandle`的继承自进程类的子类，该类会检查依赖性并启动进程。同时，该部分还定义了一个名为`predict_no_ui_long_connection`的多线程方法和一个名为`predict`的单线程方法，用于与NewBing进行通信。
+
+第三部分定义了一个名为`newbing_handle`的全局变量，并导出了`predict_no_ui_long_connection`和`predict`这两个方法，以供其他程序可以调用。
+
+## [42/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbingfree.py
+
+这个Python文件包含了三部分内容。第一部分是来自edge_gpt_free.py文件的聊天机器人程序。第二部分是子进程Worker，用于调用主体。第三部分提供了两个函数：predict_no_ui_long_connection和predict用于调用NewBing聊天机器人和返回响应。其中predict函数还提供了一些参数用于控制聊天机器人的回复和更新UI界面。
+
+## [43/48] 请对下面的程序文件做一个概述: request_llm\bridge_stackclaude.py
+
+这是一个Python源代码文件，文件名为request_llm\bridge_stackclaude.py。代码分为三个主要部分：
+
+第一部分定义了Slack API Client类，实现Slack消息的发送、接收、循环监听，用于与Slack API进行交互。
+
+第二部分定义了ClaudeHandle类，继承Process类，用于创建子进程Worker，调用主体，实现Claude与用户交互的功能。
+
+第三部分定义了predict_no_ui_long_connection和predict两个函数，主要用于通过调用ClaudeHandle对象的stream_chat方法来获取Claude的回复，并更新ui以显示相关信息。其中predict函数采用单线程方法，而predict_no_ui_long_connection函数使用多线程方法。
+
+## [44/48] 请对下面的程序文件做一个概述: request_llm\bridge_tgui.py
+
+该文件是一个Python代码文件，名为request_llm\bridge_tgui.py。它包含了一些函数用于与chatbot UI交互，并通过WebSocket协议与远程LLM模型通信完成文本生成任务，其中最重要的函数是predict()和predict_no_ui_long_connection()。这个程序还有其他的辅助函数，如random_hash()。整个代码文件在协作的基础上完成了一次修改。
+
+## [45/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt.py
+
+该文件是一个用于调用Bing chatbot API的Python程序，它由多个类和辅助函数构成，可以根据给定的对话连接在对话中提出问题，使用websocket与远程服务通信。程序实现了一个聊天机器人，可以为用户提供人工智能聊天。
+
+## [46/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt_free.py
+
+该代码文件为一个会话API，可通过Chathub发送消息以返回响应。其中使用了 aiohttp 和 httpx 库进行网络请求并发送。代码中包含了一些函数和常量，多数用于生成请求数据或是请求头信息等。同时该代码文件还包含了一个 Conversation 类，调用该类可实现对话交互。
+
+## [47/48] 请对下面的程序文件做一个概述: request_llm\test_llms.py
+
+这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llm.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
+
+## 用一张Markdown表格简要描述以下文件的功能：
+check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, multi_language.py, theme.py, toolbox.py, crazy_functions\crazy_functions_test.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py。根据以上分析，用一句话概括程序的整体功能。
+
+| 文件名 | 功能描述 |
+| ------ | ------ |
+| check_proxy.py | 检查代理有效性及地理位置 |
+| colorful.py | 控制台打印彩色文字 |
+| config.py | 配置和参数设置 |
+| config_private.py | 私人配置和参数设置 |
+| core_functional.py | 核心函数和参数设置 |
+| crazy_functional.py | 高级功能插件集合 |
+| main.py | 一个 Chatbot 程序，提供各种学术翻译、文本处理和其他查询服务 |
+| multi_language.py | 识别和翻译不同语言 |
+| theme.py | 自定义 gradio 应用程序主题 |
+| toolbox.py | 工具类库，用于协助实现各种功能 |
+| crazy_functions\crazy_functions_test.py | 测试 crazy_functions 中的各种函数 |
+| crazy_functions\crazy_utils.py | 工具函数，用于字符串处理、异常检测、Markdown 格式转换等 |
+| crazy_functions\Latex全文润色.py | 对整个 Latex 项目进行润色和纠错 |
+| crazy_functions\Latex全文翻译.py | 对整个 Latex 项目进行翻译 |
+| crazy_functions\__init__.py | 模块初始化文件，标识 `crazy_functions` 是一个包 |
+| crazy_functions\下载arxiv论文翻译摘要.py | 下载 `arxiv` 论文的 PDF 文件，并提取摘要和翻译 |
+
+这些程序源文件提供了基础的文本和语言处理功能、工具函数和高级插件，使 Chatbot 能够处理各种复杂的学术文本问题，包括润色、翻译、搜索、下载、解析等。
+
+## 用一张Markdown表格简要描述以下文件的功能：
+crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生成.py, crazy_functions\对话历史存档.py, crazy_functions\总结word文档.py, crazy_functions\总结音视频.py, crazy_functions\批量Markdown翻译.py, crazy_functions\批量总结PDF文档.py, crazy_functions\批量总结PDF文档pdfminer.py, crazy_functions\批量翻译PDF文档_多线程.py, crazy_functions\理解PDF文档内容.py, crazy_functions\生成函数注释.py, crazy_functions\联网的ChatGPT.py, crazy_functions\解析JupyterNotebook.py, crazy_functions\解析项目源代码.py, crazy_functions\询问多个大语言模型.py, crazy_functions\读文章写摘要.py。根据以上分析，用一句话概括程序的整体功能。
+
+| 文件名 | 功能简述 |
+| --- | --- |
+| 代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
+| 图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
+| 对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
+| 总结word文档.py | 对输入的word文档进行摘要生成 |
+| 总结音视频.py | 对输入的音视频文件进行摘要生成 |
+| 批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
+| 批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
+| 批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
+| 批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
+| 理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
+| 生成函数注释.py | 自动生成Python函数的注释 |
+| 联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |
+| 解析JupyterNotebook.py | 对Jupyter Notebook进行代码解析 |
+| 解析项目源代码.py | 对指定编程语言的源代码进行解析 |
+| 询问多个大语言模型.py | 使用多个大语言模型对输入进行处理和回复 |
+| 读文章写摘要.py | 对论文进行解析和全文摘要生成 |
+
+概括程序的整体功能：提供了一系列处理文本、文件和代码的功能，使用了各类语言模型、多线程、网络请求和数据解析技术来提高效率和精度。
+
+## 用一张Markdown表格简要描述以下文件的功能：
+crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_jittorllms_llama.py, request_llm\bridge_jittorllms_pangualpha.py, request_llm\bridge_jittorllms_rwkv.py, request_llm\bridge_moss.py, request_llm\bridge_newbing.py, request_llm\bridge_newbingfree.py, request_llm\bridge_stackclaude.py, request_llm\bridge_tgui.py, request_llm\edge_gpt.py, request_llm\edge_gpt_free.py, request_llm\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
+
+| 文件名 | 功能描述 |
+| --- | --- |
+| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
+| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
+| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
+| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
+| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
+| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
+| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
+| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
+| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
+| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
+| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
+| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
+| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
+| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
+| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
+| request_llm\test_llms.py | 对llm模型进行单元测试。 |
+| 程序整体功能 | 实现不同种类的聊天机器人，可以根据输入进行文本生成。 |

docs/test_markdown_format.py

@@ -0,0 +1,130 @@
+sample = """
+[1]: https://baike.baidu.com/item/%E8%B4%A8%E8%83%BD%E6%96%B9%E7%A8%8B/1884527 "质能方程（质能方程式）_百度百科"
+[2]: https://www.zhihu.com/question/348249281 "如何理解质能方程 E＝mc²？ - 知乎"
+[3]: https://zhuanlan.zhihu.com/p/32597385 "质能方程的推导与理解 - 知乎 - 知乎专栏"
+
+你好，这是必应。质能方程是描述质量与能量之间的当量关系的方程[^1^][1]。用tex格式，质能方程可以写成$$E=mc^2$$，其中$E$是能量，$m$是质量，$c$是光速[^2^][2] [^3^][3]。
+"""
+import re
+
+def preprocess_newbing_out(s):
+    pattern = r'\^(\d+)\^' # 匹配^数字^
+    pattern2 = r'\[(\d+)\]' # 匹配^数字^
+    sub = lambda m: '\['+m.group(1)+'\]' # 将匹配到的数字作为替换值
+    result = re.sub(pattern, sub, s) # 替换操作
+    if '[1]' in result:
+        result += '<br/><hr style="border-top: dotted 1px #44ac5c;"><br/><small>' + "<br/>".join([re.sub(pattern2, sub, r) for r in result.split('\n') if r.startswith('[')]) + '</small>'
+    return result
+
+
+def close_up_code_segment_during_stream(gpt_reply):
+    """
+    在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
+
+    Args:
+        gpt_reply (str): GPT模型返回的回复字符串。
+
+    Returns:
+        str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。
+
+    """
+    if '```' not in gpt_reply:
+        return gpt_reply
+    if gpt_reply.endswith('```'):
+        return gpt_reply
+
+    # 排除了以上两个情况，我们
+    segments = gpt_reply.split('```')
+    n_mark = len(segments) - 1
+    if n_mark % 2 == 1:
+        # print('输出代码片段中！')
+        return gpt_reply+'\n```'
+    else:
+        return gpt_reply
+    
+import markdown
+from latex2mathml.converter import convert as tex2mathml
+from functools import wraps, lru_cache
+def markdown_convertion(txt):
+    """
+    将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
+    """
+    pre = '<div class="markdown-body">'
+    suf = '</div>'
+    if txt.startswith(pre) and txt.endswith(suf):
+        # print('警告，输入了已经经过转化的字符串，二次转化可能出问题')
+        return txt # 已经被转化过，不需要再次转化
+    
+    markdown_extension_configs = {
+        'mdx_math': {
+            'enable_dollar_delimiter': True,
+            'use_gitlab_delimiters': False,
+        },
+    }
+    find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
+
+    def tex2mathml_catch_exception(content, *args, **kwargs):
+        try:
+            content = tex2mathml(content, *args, **kwargs)
+        except:
+            content = content
+        return content
+
+    def replace_math_no_render(match):
+        content = match.group(1)
+        if 'mode=display' in match.group(0):
+            content = content.replace('\n', '</br>')
+            return f"<font color=\"#00FF00\">$$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$$</font>"
+        else:
+            return f"<font color=\"#00FF00\">$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$</font>"
+
+    def replace_math_render(match):
+        content = match.group(1)
+        if 'mode=display' in match.group(0):
+            if '\\begin{aligned}' in content:
+                content = content.replace('\\begin{aligned}', '\\begin{array}')
+                content = content.replace('\\end{aligned}', '\\end{array}')
+                content = content.replace('&', ' ')
+            content = tex2mathml_catch_exception(content, display="block")
+            return content
+        else:
+            return tex2mathml_catch_exception(content)
+
+    def markdown_bug_hunt(content):
+        """
+        解决一个mdx_math的bug（单$包裹begin命令时多余<script>）
+        """
+        content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
+        content = content.replace('</script>\n</script>', '</script>')
+        return content
+
+
+    if ('$' in txt) and ('```' not in txt):  # 有$标识的公式符号，且没有代码段```的标识
+        # convert everything to html format
+        split = markdown.markdown(text='---')
+        convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
+        convert_stage_1 = markdown_bug_hunt(convert_stage_1)
+        # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
+        # 1. convert to easy-to-copy tex (do not render math)
+        convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
+        # 2. convert to rendered equation
+        convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
+        # cat them together
+        return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
+    else:
+        return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
+
+
+sample = preprocess_newbing_out(sample)
+sample = close_up_code_segment_during_stream(sample)
+sample = markdown_convertion(sample)
+with open('tmp.html', 'w', encoding='utf8') as f:
+    f.write("""
+
+<head>
+    <title>My Website</title>
+    <link rel="stylesheet" type="text/css" href="style.css">
+</head>
+
+    """)
+    f.write(sample)

docs/waifu_plugin/autoload.js

@@ -0,0 +1,30 @@
+try {
+    $("<link>").attr({href: "file=docs/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css"}).appendTo('head');
+    $('body').append('<div class="waifu"><div class="waifu-tips"></div><canvas id="live2d" class="live2d"></canvas><div class="waifu-tool"><span class="fui-home"></span> <span class="fui-chat"></span> <span class="fui-eye"></span> <span class="fui-user"></span> <span class="fui-photo"></span> <span class="fui-info-circle"></span> <span class="fui-cross"></span></div></div>');
+    $.ajax({url: "file=docs/waifu_plugin/waifu-tips.js", dataType:"script", cache: true, success: function() {
+        $.ajax({url: "file=docs/waifu_plugin/live2d.js", dataType:"script", cache: true, success: function() {
+            /* 可直接修改部分参数 */
+            live2d_settings['hitokotoAPI'] = "hitokoto.cn";  // 一言 API
+            live2d_settings['modelId'] = 5;                  // 默认模型 ID
+            live2d_settings['modelTexturesId'] = 1;          // 默认材质 ID
+            live2d_settings['modelStorage'] = false;         // 不储存模型 ID
+            live2d_settings['waifuSize']            = '210x187';  
+            live2d_settings['waifuTipsSize']        = '187x52';  
+            live2d_settings['canSwitchModel']       = true;
+            live2d_settings['canSwitchTextures']    = true;
+            live2d_settings['canSwitchHitokoto']    = false;
+            live2d_settings['canTakeScreenshot']    = false;
+            live2d_settings['canTurnToHomePage']    = false;
+            live2d_settings['canTurnToAboutPage']   = false;
+            live2d_settings['showHitokoto']         = false;         // 显示一言
+            live2d_settings['showF12Status']        = false;         // 显示加载状态
+            live2d_settings['showF12Message']       = false;        // 显示看板娘消息
+            live2d_settings['showF12OpenMsg']       = false;         // 显示控制台打开提示
+            live2d_settings['showCopyMessage']      = false;         // 显示 复制内容 提示
+            live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
+
+            /* 在 initModel 前添加 */
+            initModel("file=docs/waifu_plugin/waifu-tips.json");
+        }});
+    }});
+} catch(err) { console.log("[Error] JQuery is not defined.") }

docs/waifu_plugin/flat-ui-icons-regular.svg

@@ -0,0 +1,126 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg xmlns="http://www.w3.org/2000/svg">
+<metadata>
+<json>
+{
+	"fontFamily": "flat-ui-icons",
+	"majorVersion": 1,
+	"minorVersion": 1,
+	"fontURL": "http://designmodo.com/flat",
+	"designer": "Sergey Shmidt",
+	"designerURL": "http://designmodo.com",
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docs/waifu_plugin/source

@@ -0,0 +1 @@
+https://github.com/fghrsh/live2d_demo

docs/waifu_plugin/waifu-tips.js

@@ -0,0 +1,405 @@
+window.live2d_settings = Array(); /*
+
+    く__,.ヘヽ.　　　　/　,ー､ 〉
+    　　　　　＼ ', !-─‐-i　/　/´
+    　　　 　 ／｀ｰ'　　　 L/／｀ヽ､            Live2D 看板娘 参数设置
+    　　 　 /　 ／,　 /|　 ,　 ,　　　 ',                                           Version 1.4.2
+    　　　ｲ 　/ /-‐/　ｉ　L_ ﾊ ヽ!　 i                            Update 2018.11.12
+    　　　 ﾚ ﾍ 7ｲ｀ﾄ　 ﾚ'ｧ-ﾄ､!ハ|　 |  
+    　　　　 !,/7 '0'　　 ´0iソ| 　 |　　　
+    　　　　 |.从"　　_　　 ,,,, / |./ 　 |             网页添加 Live2D 看板娘
+    　　　　 ﾚ'| i＞.､,,__　_,.イ / 　.i 　|                    https://www.fghrsh.net/post/123.html
+    　　　　　 ﾚ'| | / k_７_/ﾚ'ヽ,　ﾊ.　|           
+    　　　　　　 | |/i 〈|/　 i　,.ﾍ |　i　|    Thanks
+    　　　　　　.|/ /　ｉ： 　 ﾍ!　　＼　|          journey-ad / https://github.com/journey-ad/live2d_src
+    　　　 　 　 kヽ>､ﾊ 　 _,.ﾍ､ 　 /､!            xiazeyu / https://github.com/xiazeyu/live2d-widget.js
+    　　　　　　 !'〈//｀Ｔ´', ＼ ｀'7'ｰr'          Live2d Cubism SDK WebGL 2.1 Projrct & All model authors.
+    　　　　　　 ﾚ'ヽL__|___i,___,ンﾚ|ノ
+    　　　　　 　　　ﾄ-,/　|___./
+    　　　　　 　　　'ｰ'　　!_,.:*********************************************************************************/
+
+
+// 后端接口
+live2d_settings['modelAPI']             = '//live2d.fghrsh.net/api/';   // 自建 API 修改这里
+live2d_settings['tipsMessage']          = 'waifu-tips.json';            // 同目录下可省略路径
+live2d_settings['hitokotoAPI']          = 'lwl12.com';                  // 一言 API，可选 'lwl12.com', 'hitokoto.cn', 'jinrishici.com'(古诗词)
+
+// 默认模型
+live2d_settings['modelId']              = 1;            // 默认模型 ID，可在 F12 控制台找到
+live2d_settings['modelTexturesId']      = 53;           // 默认材质 ID，可在 F12 控制台找到
+
+// 工具栏设置
+live2d_settings['showToolMenu']         = true;         // 显示 工具栏          ，可选 true(真), false(假)
+live2d_settings['canCloseLive2d']       = true;         // 显示 关闭看板娘  按钮，可选 true(真), false(假)
+live2d_settings['canSwitchModel']       = true;         // 显示 模型切换    按钮，可选 true(真), false(假)
+live2d_settings['canSwitchTextures']    = true;         // 显示 材质切换    按钮，可选 true(真), false(假)
+live2d_settings['canSwitchHitokoto']    = true;         // 显示 一言切换    按钮，可选 true(真), false(假)
+live2d_settings['canTakeScreenshot']    = true;         // 显示 看板娘截图  按钮，可选 true(真), false(假)
+live2d_settings['canTurnToHomePage']    = true;         // 显示 返回首页    按钮，可选 true(真), false(假)
+live2d_settings['canTurnToAboutPage']   = true;         // 显示 跳转关于页  按钮，可选 true(真), false(假)
+
+// 模型切换模式
+live2d_settings['modelStorage']         = true;         // 记录 ID (刷新后恢复)，可选 true(真), false(假)
+live2d_settings['modelRandMode']        = 'switch';     // 模型切换，可选 'rand'(随机), 'switch'(顺序)
+live2d_settings['modelTexturesRandMode']= 'rand';       // 材质切换，可选 'rand'(随机), 'switch'(顺序)
+
+// 提示消息选项
+live2d_settings['showHitokoto']         = true;         // 显示一言
+live2d_settings['showF12Status']        = true;         // 显示加载状态
+live2d_settings['showF12Message']       = false;        // 显示看板娘消息
+live2d_settings['showF12OpenMsg']       = true;         // 显示控制台打开提示
+live2d_settings['showCopyMessage']      = true;         // 显示 复制内容 提示
+live2d_settings['showWelcomeMessage']   = true;         // 显示进入面页欢迎词
+
+//看板娘样式设置
+live2d_settings['waifuSize']            = '280x250';    // 看板娘大小，例如 '280x250', '600x535'
+live2d_settings['waifuTipsSize']        = '250x70';     // 提示框大小，例如 '250x70', '570x150'
+live2d_settings['waifuFontSize']        = '12px';       // 提示框字体，例如 '12px', '30px'
+live2d_settings['waifuToolFont']        = '14px';       // 工具栏字体，例如 '14px', '36px'
+live2d_settings['waifuToolLine']        = '20px';       // 工具栏行高，例如 '20px', '36px'
+live2d_settings['waifuToolTop']         = '0px'         // 工具栏顶部边距，例如 '0px', '-60px'
+live2d_settings['waifuMinWidth']        = '768px';      // 面页小于 指定宽度 隐藏看板娘，例如 'disable'(禁用), '768px'
+live2d_settings['waifuEdgeSide']        = 'left:0';     // 看板娘贴边方向，例如 'left:0'(靠左 0px), 'right:30'(靠右 30px)
+live2d_settings['waifuDraggable']       = 'disable';    // 拖拽样式，例如 'disable'(禁用), 'axis-x'(只能水平拖拽), 'unlimited'(自由拖拽)
+live2d_settings['waifuDraggableRevert'] = true;         // 松开鼠标还原拖拽位置，可选 true(真), false(假)
+
+// 其他杂项设置
+live2d_settings['l2dVersion']           = '1.4.2';        // 当前版本
+live2d_settings['l2dVerDate']           = '2018.11.12'; // 版本更新日期
+live2d_settings['homePageUrl']          = 'auto';       // 主页地址，可选 'auto'(自动), '{URL 网址}'
+live2d_settings['aboutPageUrl']         = 'https://www.fghrsh.net/post/123.html';   // 关于页地址, '{URL 网址}'
+live2d_settings['screenshotCaptureName']= 'live2d.png'; // 看板娘截图文件名，例如 'live2d.png'
+
+/****************************************************************************************************/
+
+String.prototype.render = function(context) {
+    var tokenReg = /(\\)?\{([^\{\}\\]+)(\\)?\}/g;
+
+    return this.replace(tokenReg, function (word, slash1, token, slash2) {
+        if (slash1 || slash2) { return word.replace('\\', ''); }
+        
+        var variables = token.replace(/\s/g, '').split('.');
+        var currentObject = context;
+        var i, length, variable;
+        
+        for (i = 0, length = variables.length; i < length; ++i) {
+            variable = variables[i];
+            currentObject = currentObject[variable];
+            if (currentObject === undefined || currentObject === null) return '';
+        }
+        return currentObject;
+    });
+};
+
+var re = /x/;
+console.log(re);
+
+function empty(obj) {return typeof obj=="undefined"||obj==null||obj==""?true:false}
+function getRandText(text) {return Array.isArray(text) ? text[Math.floor(Math.random() * text.length + 1)-1] : text}
+
+function showMessage(text, timeout, flag) {
+    if(flag || sessionStorage.getItem('waifu-text') === '' || sessionStorage.getItem('waifu-text') === null){
+        if(Array.isArray(text)) text = text[Math.floor(Math.random() * text.length + 1)-1];
+        if (live2d_settings.showF12Message) console.log('[Message]', text.replace(/<[^<>]+>/g,''));
+        
+        if(flag) sessionStorage.setItem('waifu-text', text);
+        
+        $('.waifu-tips').stop();
+        $('.waifu-tips').html(text).fadeTo(200, 1);
+        if (timeout === undefined) timeout = 5000;
+        hideMessage(timeout);
+    }
+}
+
+function hideMessage(timeout) {
+    $('.waifu-tips').stop().css('opacity',1);
+    if (timeout === undefined) timeout = 5000;
+    window.setTimeout(function() {sessionStorage.removeItem('waifu-text')}, timeout);
+    $('.waifu-tips').delay(timeout).fadeTo(200, 0);
+}
+
+function initModel(waifuPath, type) {
+    /* console welcome message */
+    eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('8.d(" ");8.d("\\U,.\\y\\5.\\1\\1\\1\\1/\\1,\\u\\2 \\H\\n\\1\\1\\1\\1\\1\\b \', !-\\r\\j-i\\1/\\1/\\g\\n\\1\\1\\1 \\1 \\a\\4\\f\'\\1\\1\\1 L/\\a\\4\\5\\2\\n\\1\\1 \\1 /\\1 \\a,\\1 /|\\1 ,\\1 ,\\1\\1\\1 \',\\n\\1\\1\\1\\q \\1/ /-\\j/\\1\\h\\E \\9 \\5!\\1 i\\n\\1\\1\\1 \\3 \\6 7\\q\\4\\c\\1 \\3\'\\s-\\c\\2!\\t|\\1 |\\n\\1\\1\\1\\1 !,/7 \'0\'\\1\\1 \\X\\w| \\1 |\\1\\1\\1\\n\\1\\1\\1\\1 |.\\x\\"\\1\\l\\1\\1 ,,,, / |./ \\1 |\\n\\1\\1\\1\\1 \\3\'| i\\z.\\2,,A\\l,.\\B / \\1.i \\1|\\n\\1\\1\\1\\1\\1 \\3\'| | / C\\D/\\3\'\\5,\\1\\9.\\1|\\n\\1\\1\\1\\1\\1\\1 | |/i \\m|/\\1 i\\1,.\\6 |\\F\\1|\\n\\1\\1\\1\\1\\1\\1.|/ /\\1\\h\\G \\1 \\6!\\1\\1\\b\\1|\\n\\1\\1\\1 \\1 \\1 k\\5>\\2\\9 \\1 o,.\\6\\2 \\1 /\\2!\\n\\1\\1\\1\\1\\1\\1 !\'\\m//\\4\\I\\g\', \\b \\4\'7\'\\J\'\\n\\1\\1\\1\\1\\1\\1 \\3\'\\K|M,p,\\O\\3|\\P\\n\\1\\1\\1\\1\\1 \\1\\1\\1\\c-,/\\1|p./\\n\\1\\1\\1\\1\\1 \\1\\1\\1\'\\f\'\\1\\1!o,.:\\Q \\R\\S\\T v"+e.V+" / W "+e.N);8.d(" ");',60,60,'|u3000|uff64|uff9a|uff40|u30fd|uff8d||console|uff8a|uff0f|uff3c|uff84|log|live2d_settings|uff70|u00b4|uff49||u2010||u3000_|u3008||_|___|uff72|u2500|uff67|u30cf|u30fc||u30bd|u4ece|u30d8|uff1e|__|u30a4|k_|uff17_|u3000L_|u3000i|uff1a|u3009|uff34|uff70r|u30fdL__||___i|l2dVerDate|u30f3|u30ce|nLive2D|u770b|u677f|u5a18|u304f__|l2dVersion|FGHRSH|u00b40i'.split('|'),0,{}));
+    
+    /* 判断 JQuery */
+    if (typeof($.ajax) != 'function') typeof(jQuery.ajax) == 'function' ? window.$ = jQuery : console.log('[Error] JQuery is not defined.');
+    
+    /* 加载看板娘样式 */
+    live2d_settings.waifuSize = live2d_settings.waifuSize.split('x');
+    live2d_settings.waifuTipsSize = live2d_settings.waifuTipsSize.split('x');
+    live2d_settings.waifuEdgeSide = live2d_settings.waifuEdgeSide.split(':');
+    
+    $("#live2d").attr("width",live2d_settings.waifuSize[0]);
+    $("#live2d").attr("height",live2d_settings.waifuSize[1]);
+    $(".waifu-tips").width(live2d_settings.waifuTipsSize[0]);
+    $(".waifu-tips").height(live2d_settings.waifuTipsSize[1]);
+    $(".waifu-tips").css("top",live2d_settings.waifuToolTop);
+    $(".waifu-tips").css("font-size",live2d_settings.waifuFontSize);
+    $(".waifu-tool").css("font-size",live2d_settings.waifuToolFont);
+    $(".waifu-tool span").css("line-height",live2d_settings.waifuToolLine);
+    
+    if (live2d_settings.waifuEdgeSide[0] == 'left') $(".waifu").css("left",live2d_settings.waifuEdgeSide[1]+'px');
+    else if (live2d_settings.waifuEdgeSide[0] == 'right') $(".waifu").css("right",live2d_settings.waifuEdgeSide[1]+'px');
+    
+    window.waifuResize = function() { $(window).width() <= Number(live2d_settings.waifuMinWidth.replace('px','')) ? $(".waifu").hide() : $(".waifu").show(); };
+    if (live2d_settings.waifuMinWidth != 'disable') { waifuResize(); $(window).resize(function() {waifuResize()}); }
+    
+    try {
+        if (live2d_settings.waifuDraggable == 'axis-x') $(".waifu").draggable({ axis: "x", revert: live2d_settings.waifuDraggableRevert });
+        else if (live2d_settings.waifuDraggable == 'unlimited') $(".waifu").draggable({ revert: live2d_settings.waifuDraggableRevert });
+        else $(".waifu").css("transition", 'all .3s ease-in-out');
+    } catch(err) { console.log('[Error] JQuery UI is not defined.') }
+    
+    live2d_settings.homePageUrl = live2d_settings.homePageUrl == 'auto' ? window.location.protocol+'//'+window.location.hostname+'/' : live2d_settings.homePageUrl;
+    if (window.location.protocol == 'file:' && live2d_settings.modelAPI.substr(0,2) == '//') live2d_settings.modelAPI = 'http:'+live2d_settings.modelAPI;
+    
+    $('.waifu-tool .fui-home').click(function (){
+        //window.location = 'https://www.fghrsh.net/';
+        window.location = live2d_settings.homePageUrl;
+    });
+    
+    $('.waifu-tool .fui-info-circle').click(function (){
+        //window.open('https://imjad.cn/archives/lab/add-dynamic-poster-girl-with-live2d-to-your-blog-02');
+        window.open(live2d_settings.aboutPageUrl);
+    });
+    
+    if (typeof(waifuPath) == "object") loadTipsMessage(waifuPath); else {
+        $.ajax({
+            cache: true,
+            url: waifuPath == '' ? live2d_settings.tipsMessage : (waifuPath.substr(waifuPath.length-15)=='waifu-tips.json'?waifuPath:waifuPath+'waifu-tips.json'),
+            dataType: "json",
+            success: function (result){ loadTipsMessage(result); }
+        });
+    }
+    
+    if (!live2d_settings.showToolMenu) $('.waifu-tool').hide();
+    if (!live2d_settings.canCloseLive2d) $('.waifu-tool .fui-cross').hide();
+    if (!live2d_settings.canSwitchModel) $('.waifu-tool .fui-eye').hide();
+    if (!live2d_settings.canSwitchTextures) $('.waifu-tool .fui-user').hide();
+    if (!live2d_settings.canSwitchHitokoto) $('.waifu-tool .fui-chat').hide();
+    if (!live2d_settings.canTakeScreenshot) $('.waifu-tool .fui-photo').hide();
+    if (!live2d_settings.canTurnToHomePage) $('.waifu-tool .fui-home').hide();
+    if (!live2d_settings.canTurnToAboutPage) $('.waifu-tool .fui-info-circle').hide();
+
+    if (waifuPath === undefined) waifuPath = '';
+    var modelId = localStorage.getItem('modelId');
+    var modelTexturesId = localStorage.getItem('modelTexturesId');
+    
+    if (!live2d_settings.modelStorage || modelId == null) {
+        var modelId = live2d_settings.modelId;
+        var modelTexturesId = live2d_settings.modelTexturesId;
+    } loadModel(modelId, modelTexturesId);
+}
+
+function loadModel(modelId, modelTexturesId=0) {
+    if (live2d_settings.modelStorage) {
+        localStorage.setItem('modelId', modelId);
+        localStorage.setItem('modelTexturesId', modelTexturesId);
+    } else {
+        sessionStorage.setItem('modelId', modelId);
+        sessionStorage.setItem('modelTexturesId', modelTexturesId);
+    } loadlive2d('live2d', live2d_settings.modelAPI+'get/?id='+modelId+'-'+modelTexturesId, (live2d_settings.showF12Status ? console.log('[Status]','live2d','模型',modelId+'-'+modelTexturesId,'加载完成'):null));
+}
+
+function loadTipsMessage(result) {
+    window.waifu_tips = result;
+    
+    $.each(result.mouseover, function (index, tips){
+        $(document).on("mouseover", tips.selector, function (){
+            var text = getRandText(tips.text);
+            text = text.render({text: $(this).text()});
+            showMessage(text, 3000);
+        });
+    });
+    $.each(result.click, function (index, tips){
+        $(document).on("click", tips.selector, function (){
+            var text = getRandText(tips.text);
+            text = text.render({text: $(this).text()});
+            showMessage(text, 3000, true);
+        });
+    });
+    $.each(result.seasons, function (index, tips){
+        var now = new Date();
+        var after = tips.date.split('-')[0];
+        var before = tips.date.split('-')[1] || after;
+        
+        if((after.split('/')[0] <= now.getMonth()+1 && now.getMonth()+1 <= before.split('/')[0]) && 
+           (after.split('/')[1] <= now.getDate() && now.getDate() <= before.split('/')[1])){
+            var text = getRandText(tips.text);
+            text = text.render({year: now.getFullYear()});
+            showMessage(text, 6000, true);
+        }
+    });
+    
+    if (live2d_settings.showF12OpenMsg) {
+        re.toString = function() {
+            showMessage(getRandText(result.waifu.console_open_msg), 5000, true);
+            return '';
+        };
+    }
+    
+    if (live2d_settings.showCopyMessage) {
+        $(document).on('copy', function() {
+            showMessage(getRandText(result.waifu.copy_message), 5000, true);
+        });
+    }
+    
+    $('.waifu-tool .fui-photo').click(function(){
+        showMessage(getRandText(result.waifu.screenshot_message), 5000, true);
+        window.Live2D.captureName = live2d_settings.screenshotCaptureName;
+        window.Live2D.captureFrame = true;
+    });
+    
+    $('.waifu-tool .fui-cross').click(function(){
+        sessionStorage.setItem('waifu-dsiplay', 'none');
+        showMessage(getRandText(result.waifu.hidden_message), 1300, true);
+        window.setTimeout(function() {$('.waifu').hide();}, 1300);
+    });
+    
+    window.showWelcomeMessage = function(result) {
+        var text;
+        if (window.location.href == live2d_settings.homePageUrl) {
+            var now = (new Date()).getHours();
+            if (now > 23 || now <= 5) text = getRandText(result.waifu.hour_tips['t23-5']);
+            else if (now > 5 && now <= 7) text = getRandText(result.waifu.hour_tips['t5-7']);
+            else if (now > 7 && now <= 11) text = getRandText(result.waifu.hour_tips['t7-11']);
+            else if (now > 11 && now <= 14) text = getRandText(result.waifu.hour_tips['t11-14']);
+            else if (now > 14 && now <= 17) text = getRandText(result.waifu.hour_tips['t14-17']);
+            else if (now > 17 && now <= 19) text = getRandText(result.waifu.hour_tips['t17-19']);
+            else if (now > 19 && now <= 21) text = getRandText(result.waifu.hour_tips['t19-21']);
+            else if (now > 21 && now <= 23) text = getRandText(result.waifu.hour_tips['t21-23']);
+            else text = getRandText(result.waifu.hour_tips.default);
+        } else {
+            var referrer_message = result.waifu.referrer_message;
+            if (document.referrer !== '') {
+                var referrer = document.createElement('a');
+                referrer.href = document.referrer;
+                var domain = referrer.hostname.split('.')[1];
+                if (window.location.hostname == referrer.hostname)
+                    text = referrer_message.localhost[0] + document.title.split(referrer_message.localhost[2])[0] + referrer_message.localhost[1];
+                else if (domain == 'baidu')
+                    text = referrer_message.baidu[0] + referrer.search.split('&wd=')[1].split('&')[0] + referrer_message.baidu[1];
+                else if (domain == 'so')
+                    text = referrer_message.so[0] + referrer.search.split('&q=')[1].split('&')[0] + referrer_message.so[1];
+                else if (domain == 'google')
+                    text = referrer_message.google[0] + document.title.split(referrer_message.google[2])[0] + referrer_message.google[1];
+                else {
+                    $.each(result.waifu.referrer_hostname, function(i,val) {if (i==referrer.hostname) referrer.hostname = getRandText(val)});
+                    text = referrer_message.default[0] + referrer.hostname + referrer_message.default[1];
+                }
+            } else text = referrer_message.none[0] + document.title.split(referrer_message.none[2])[0] + referrer_message.none[1];
+        }
+        showMessage(text, 6000);
+    }; if (live2d_settings.showWelcomeMessage) showWelcomeMessage(result);
+    
+    var waifu_tips = result.waifu;
+    
+    function loadOtherModel() {
+        var modelId = modelStorageGetItem('modelId');
+        var modelRandMode = live2d_settings.modelRandMode;
+        
+        $.ajax({
+            cache: modelRandMode == 'switch' ? true : false,
+            url: live2d_settings.modelAPI+modelRandMode+'/?id='+modelId,
+            dataType: "json",
+            success: function(result) {
+                loadModel(result.model['id']);
+                var message = result.model['message'];
+                $.each(waifu_tips.model_message, function(i,val) {if (i==result.model['id']) message = getRandText(val)});
+                showMessage(message, 3000, true);
+            }
+        });
+    }
+    
+    function loadRandTextures() {
+        var modelId = modelStorageGetItem('modelId');
+        var modelTexturesId = modelStorageGetItem('modelTexturesId');
+        var modelTexturesRandMode = live2d_settings.modelTexturesRandMode;
+        
+        $.ajax({
+            cache: modelTexturesRandMode == 'switch' ? true : false,
+            url: live2d_settings.modelAPI+modelTexturesRandMode+'_textures/?id='+modelId+'-'+modelTexturesId,
+            dataType: "json",
+            success: function(result) {
+                if (result.textures['id'] == 1 && (modelTexturesId == 1 || modelTexturesId == 0))
+                    showMessage(waifu_tips.load_rand_textures[0], 3000, true);
+                else showMessage(waifu_tips.load_rand_textures[1], 3000, true);
+                loadModel(modelId, result.textures['id']);
+            }
+        });
+    }
+    
+    function modelStorageGetItem(key) { return live2d_settings.modelStorage ? localStorage.getItem(key) : sessionStorage.getItem(key); }
+    
+    /* 检测用户活动状态，并在空闲时显示一言 */
+    if (live2d_settings.showHitokoto) {
+        window.getActed = false; window.hitokotoTimer = 0; window.hitokotoInterval = false;
+        $(document).mousemove(function(e){getActed = true;}).keydown(function(){getActed = true;});
+        setInterval(function(){ if (!getActed) ifActed(); else elseActed(); }, 1000);
+    }
+    
+    function ifActed() {
+        if (!hitokotoInterval) {
+            hitokotoInterval = true;
+            hitokotoTimer = window.setInterval(showHitokotoActed, 30000);
+        }
+    }
+    
+    function elseActed() {
+        getActed = hitokotoInterval = false;
+        window.clearInterval(hitokotoTimer);
+    }
+    
+    function showHitokotoActed() {
+        if ($(document)[0].visibilityState == 'visible') showHitokoto();
+    }
+    
+    function showHitokoto() {
+    	switch(live2d_settings.hitokotoAPI) {
+    	    case 'lwl12.com':
+    	        $.getJSON('https://api.lwl12.com/hitokoto/v1?encode=realjson',function(result){
+        	        if (!empty(result.source)) {
+                        var text = waifu_tips.hitokoto_api_message['lwl12.com'][0];
+                        if (!empty(result.author)) text += waifu_tips.hitokoto_api_message['lwl12.com'][1];
+                        text = text.render({source: result.source, creator: result.author});
+                        window.setTimeout(function() {showMessage(text+waifu_tips.hitokoto_api_message['lwl12.com'][2], 3000, true);}, 5000);
+                    } showMessage(result.text, 5000, true);
+                });break;
+    	    case 'fghrsh.net':
+    	        $.getJSON('https://api.fghrsh.net/hitokoto/rand/?encode=jsc&uid=3335',function(result){
+            	    if (!empty(result.source)) {
+                        var text = waifu_tips.hitokoto_api_message['fghrsh.net'][0];
+                        text = text.render({source: result.source, date: result.date});
+                        window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
+                        showMessage(result.hitokoto, 5000, true);
+            	    }
+                });break;
+            case 'jinrishici.com':
+                $.ajax({
+                    url: 'https://v2.jinrishici.com/one.json',
+                    xhrFields: {withCredentials: true},
+                    success: function (result, status) {
+                        if (!empty(result.data.origin.title)) {
+                            var text = waifu_tips.hitokoto_api_message['jinrishici.com'][0];
+                            text = text.render({title: result.data.origin.title, dynasty: result.data.origin.dynasty, author:result.data.origin.author});
+                            window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
+                        } showMessage(result.data.content, 5000, true);
+                    }
+                });break;
+    	    default:
+    	        $.getJSON('https://v1.hitokoto.cn',function(result){
+            	    if (!empty(result.from)) {
+                        var text = waifu_tips.hitokoto_api_message['hitokoto.cn'][0];
+                        text = text.render({source: result.from, creator: result.creator});
+                        window.setTimeout(function() {showMessage(text, 3000, true);}, 5000);
+            	    }
+                    showMessage(result.hitokoto, 5000, true);
+                });
+    	}
+    }
+    
+    $('.waifu-tool .fui-eye').click(function (){loadOtherModel()});
+    $('.waifu-tool .fui-user').click(function (){loadRandTextures()});
+    $('.waifu-tool .fui-chat').click(function (){showHitokoto()});
+}

docs/waifu_plugin/waifu-tips.json

@@ -0,0 +1,116 @@
+{
+    "waifu": {
+        "console_open_msg": ["哈哈，你打开了控制台，是想要看看我的秘密吗？"],
+        "copy_message": ["你都复制了些什么呀，转载要记得加上出处哦"],
+        "screenshot_message": ["照好了嘛，是不是很可爱呢？"],
+        "hidden_message": ["我们还能再见面的吧…"],
+        "load_rand_textures": ["我还没有其他衣服呢", "我的新衣服好看嘛"],
+        "hour_tips": {
+            "t0-5": ["快睡觉去吧，年纪轻轻小心猝死哦"],
+            "t5-7": ["早上好！一日之计在于晨，美好的一天就要开始了"],
+            "t7-11": ["上午好！工作顺利嘛，不要久坐，多起来走动走动哦！"],
+            "t11-14": ["中午了，工作了一个上午，现在是午餐时间！"],
+            "t14-17": ["午后很容易犯困呢，今天的运动目标完成了吗？"],
+            "t17-19": ["傍晚了！窗外夕阳的景色很美丽呢，最美不过夕阳红~"],
+            "t19-21": ["晚上好，今天过得怎么样？"],
+            "t21-23": ["已经这么晚了呀，早点休息吧，晚安~"],
+            "t23-24": ["你是夜猫子呀？这么晚还不睡觉，明天起的来嘛"],
+            "default": ["嗨~ 快来逗我玩吧！"]
+        },
+        "referrer_message": {
+            "localhost": ["欢迎使用<span style=\"color:rgba(245, 20, 20, 0.62);\">『ChatGPT", "』</span>", " - "],
+            "baidu": ["Hello! 来自 百度搜索 的朋友<br>你是搜索 <span style=\"color:rgba(245, 20, 20, 0.62);\">", "</span> 找到的我吗？"],
+            "so": ["Hello! 来自 360搜索 的朋友<br>你是搜索 <span style=\"color:rgba(245, 20, 20, 0.62);\">", "</span> 找到的我吗？"],
+            "google": ["Hello! 来自 谷歌搜索 的朋友<br>欢迎使用<span style=\"color:rgba(245, 20, 20, 0.62);\">『ChatGPT", "』</span>", " - "],
+            "default": ["Hello! 来自 <span style=\"color:rgba(245, 20, 20, 0.62);\">", "</span> 的朋友"],
+            "none": ["欢迎使用<span style=\"color:rgba(245, 20, 20, 0.62);\">『ChatGPT", "』</span>", " - "]
+        },
+        "referrer_hostname": {
+            "example.com": ["示例网站"],
+            "www.fghrsh.net": ["FGHRSH 的博客"]
+        },
+        "model_message": {
+            "1": ["来自 Potion Maker 的 Pio 酱 ~"],
+            "2": ["来自 Potion Maker 的 Tia 酱 ~"]  
+        },
+        "hitokoto_api_message": {
+            "lwl12.com": ["这句一言来自 <span style=\"color:#0099cc;\">『{source}』</span>", "，是 <span style=\"color:#0099cc;\">{creator}</span> 投稿的", "。"],
+            "fghrsh.net": ["这句一言出处是 <span style=\"color:#0099cc;\">『{source}』</span>，是 <span style=\"color:#0099cc;\">FGHRSH</span> 在 {date} 收藏的！"],
+            "jinrishici.com": ["这句诗词出自 <span style=\"color:#0099cc;\">《{title}》</span>，是 {dynasty}诗人 {author} 创作的！"],
+            "hitokoto.cn": ["这句一言来自 <span style=\"color:#0099cc;\">『{source}』</span>，是 <span style=\"color:#0099cc;\">{creator}</span> 在 hitokoto.cn 投稿的。"]
+        }
+    },
+    "mouseover": [
+        { "selector": ".container a[href^='http']", "text": ["要看看 <span style=\"color:#0099cc;\">{text}</span> 么？"] },
+        { "selector": ".fui-home", "text": ["点击前往首页，想回到上一页可以使用浏览器的后退功能哦"] },
+        { "selector": ".fui-chat", "text": ["一言一语，一颦一笑。一字一句，一颗赛艇。"] },
+        { "selector": ".fui-eye", "text": ["嗯··· 要切换 看板娘 吗？"] },
+        { "selector": ".fui-user", "text": ["喜欢换装 Play 吗？"] },
+        { "selector": ".fui-photo", "text": ["要拍张纪念照片吗？"] },
+        { "selector": ".fui-info-circle", "text": ["这里有关于我的信息呢"] },
+        { "selector": ".fui-cross", "text": ["你不喜欢我了吗..."] },
+        { "selector": "#tor_show", "text": ["翻页比较麻烦吗，点击可以显示这篇文章的目录呢"] },
+        { "selector": "#comment_go", "text": ["想要去评论些什么吗？"] },
+        { "selector": "#night_mode", "text": ["深夜时要爱护眼睛呀"] },
+        { "selector": "#qrcode", "text": ["手机扫一下就能继续看，很方便呢"] },
+        { "selector": ".comment_reply", "text": ["要吐槽些什么呢"] },
+        { "selector": "#back-to-top", "text": ["回到开始的地方吧"] },
+        { "selector": "#author", "text": ["该怎么称呼你呢"] },
+        { "selector": "#mail", "text": ["留下你的邮箱，不然就是无头像人士了"] },
+        { "selector": "#url", "text": ["你的家在哪里呢，好让我去参观参观"] },
+        { "selector": "#textarea", "text": ["认真填写哦，垃圾评论是禁止事项"] },
+        { "selector": ".OwO-logo", "text": ["要插入一个表情吗"] },
+        { "selector": "#csubmit", "text": ["要[提交]^(Commit)了吗，首次评论需要审核，请耐心等待~"] },
+        { "selector": ".ImageBox", "text": ["点击图片可以放大呢"] },
+        { "selector": "input[name=s]", "text": ["找不到想看的内容？搜索看看吧"] },
+        { "selector": ".previous", "text": ["去上一页看看吧"] },
+        { "selector": ".next", "text": ["去下一页看看吧"] },
+        { "selector": ".dropdown-toggle", "text": ["这里是菜单"] },
+        { "selector": "c-player a.play-icon", "text": ["想要听点音乐吗"] },
+        { "selector": "c-player div.time", "text": ["在这里可以调整<span style=\"color:#0099cc;\">播放进度</span>呢"] },
+        { "selector": "c-player div.volume", "text": ["在这里可以调整<span style=\"color:#0099cc;\">音量</span>呢"] },
+        { "selector": "c-player div.list-button", "text": ["<span style=\"color:#0099cc;\">播放列表</span>里都有什么呢"] },
+        { "selector": "c-player div.lyric-button", "text": ["有<span style=\"color:#0099cc;\">歌词</span>的话就能跟着一起唱呢"] },
+        { "selector": ".waifu #live2d", "text": [
+            "别玩了，快去学习！",
+            "偶尔放松下眼睛吧。",
+            "看什么看(*^▽^*)",
+            "焦虑时，吃顿大餐心情就好啦^_^",
+            "你这个年纪，怎么睡得着觉的你^_^",
+            "修改ADD_WAIFU=False，我就不再打扰你了~",
+            "经常去github看看我们的更新吧，也许有好玩的新功能呢。",
+            "试试本地大模型吧，有的也很强大的哦。",
+            "很多强大的函数插件隐藏在下拉菜单中呢。",
+            "红色的插件，使用之前需要把文件上传进去哦。",
+            "想添加功能按钮吗？读读readme很容易就学会啦。",
+            "敏感或机密的信息，不可以问chatGPT的哦！",
+            "chatGPT究竟是划时代的创新，还是扼杀创造力的毒药呢？"
+        ] }
+    ],
+    "click": [
+        {
+            "selector": ".waifu #live2d",
+            "text": [
+                "是…是不小心碰到了吧",
+                "萝莉控是什么呀",
+                "你看到我的小熊了吗",
+                "再摸的话我可要报警了！⌇●﹏●⌇",
+                "110吗，这里有个变态一直在摸我(ó﹏ò｡)"
+            ]
+        }
+    ],
+    "seasons": [
+        { "date": "01/01", "text": ["<span style=\"color:#0099cc;\">元旦</span>了呢，新的一年又开始了，今年是{year}年~"] },
+        { "date": "02/14", "text": ["又是一年<span style=\"color:#0099cc;\">情人节</span>，{year}年找到对象了嘛~"] },
+        { "date": "03/08", "text": ["今天是<span style=\"color:#0099cc;\">妇女节</span>！"] },
+        { "date": "03/12", "text": ["今天是<span style=\"color:#0099cc;\">植树节</span>，要保护环境呀"] },
+        { "date": "04/01", "text": ["悄悄告诉你一个秘密~<span style=\"background-color:#34495e;\">今天是愚人节，不要被骗了哦~</span>"] },
+        { "date": "05/01", "text": ["今天是<span style=\"color:#0099cc;\">五一劳动节</span>，计划好假期去哪里了吗~"] },
+        { "date": "06/01", "text": ["<span style=\"color:#0099cc;\">儿童节</span>了呢，快活的时光总是短暂，要是永远长不大该多好啊…"] },
+        { "date": "09/03", "text": ["<span style=\"color:#0099cc;\">中国人民抗日战争胜利纪念日</span>，铭记历史、缅怀先烈、珍爱和平、开创未来。"] },
+        { "date": "09/10", "text": ["<span style=\"color:#0099cc;\">教师节</span>，在学校要给老师问声好呀~"] },
+        { "date": "10/01", "text": ["<span style=\"color:#0099cc;\">国庆节</span>，新中国已经成立69年了呢"] },
+        { "date": "11/05-11/12", "text": ["今年的<span style=\"color:#0099cc;\">双十一</span>是和谁一起过的呢~"] },
+        { "date": "12/20-12/31", "text": ["这几天是<span style=\"color:#0099cc;\">圣诞节</span>，主人肯定又去剁手买买买了~"] }
+    ]
+}