readme update

* 适配 google gemini 优化为从用户input中提取文件

* Update README.md (#1477)

* Update README.md

* Update README.md

* Update requirements.txt (#1480)

* welcome glm4 from 智谱!

* Update README.md (#1484)

* Update README.md (#1485)

* update zhipu

* Fix translation task name in core_functional.py

* zhipuai version problem

---------

Co-authored-by: binary-husky <96192199+binary-husky@users.noreply.github.com>
Co-authored-by: binary-husky <qingxu.fu@outlook.com>

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -34,7 +34,7 @@ body:
         - Others | 非最新版
     validations:
       required: true
-      
+
   - type: dropdown
     id: os
     attributes:
@@ -47,7 +47,7 @@ body:
         - Docker
     validations:
       required: true
-      
+
   - type: textarea
     id: describe
     attributes:
@@ -55,7 +55,7 @@ body:
       description: Describe the bug | 简述
     validations:
       required: true
-      
+
   - type: textarea
     id: screenshot
     attributes:
@@ -63,15 +63,9 @@ body:
       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

@@ -21,8 +21,3 @@ body:
     attributes:
       label: Feature Request | 功能请求
       description: Feature Request | 功能请求
-
-
-
-
-

.github/workflows/build-with-all-capacity-beta.yml

@@ -0,0 +1,44 @@
+# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
+name: build-with-all-capacity-beta
+
+on:
+  push:
+    branches:
+      - 'master'
+
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta
+
+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+AllCapacityBeta
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}

.github/workflows/stale.yml

@@ -15,7 +15,7 @@ jobs:
     permissions:
       issues: write
       pull-requests: read
-    
+
     steps:
       - uses: actions/stale@v8
         with:

.gitignore

@@ -152,3 +152,4 @@ request_llms/moss
 media
 flagged
 request_llms/ChatGLM-6b-onnx-u8s8
+.pre-commit-config.yaml

Dockerfile

@@ -18,7 +18,6 @@ WORKDIR /gpt
 
 # 安装大部分依赖，利用Docker缓存加速以后的构建 （以下三行，可以删除）
 COPY requirements.txt ./
-COPY ./docs/gradio-3.32.6-py3-none-any.whl ./docs/gradio-3.32.6-py3-none-any.whl
 RUN pip3 install -r requirements.txt
 
 

README.md

@@ -1,8 +1,8 @@
-> **Caution**
-> 
-> 2023.11.12: 某些依赖包尚不兼容python 3.12，推荐python 3.11。
-> 
-> 2023.11.7: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目开源免费，近期发现有人蔑视开源协议并利用本项目违规圈钱，请提高警惕，谨防上当受骗。
+> [!IMPORTANT]  
+> 2024.1.18: 更新3.70版本，支持Mermaid绘图库（让大模型绘制脑图）  
+> 2024.1.17: 恭迎GLM4，全力支持Qwen、GLM、DeepseekCoder等国内中文大语言基座模型！  
+> 2024.1.17: 某些依赖包尚不兼容python 3.12，推荐python 3.11。  
+> 2024.1.17: 安装依赖时，请选择`requirements.txt`中**指定的版本**。 安装命令：`pip install -r requirements.txt`。本项目完全开源免费，您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
 
 <br>
 
@@ -42,13 +42,11 @@ If you like this project, please give it a Star.
 Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanese.md) | [한국어](docs/README.Korean.md) | [Русский](docs/README.Russian.md) | [Français](docs/README.French.md). All translations have been provided by the project itself. To translate this project to arbitrary language with GPT, read and run [`multi_language.py`](multi_language.py) (experimental).
 <br>
 
-
-> 1.请注意只有 **高亮** 标识的插件（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[自译解报告](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)`self_analysis.md`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。常见问题请查阅wiki。
+> [!NOTE]
+> 1.本项目中每个文件的功能都在[自译解报告](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告)`self_analysis.md`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。常见问题请查阅wiki。
 >    [![常规安装方法](https://img.shields.io/static/v1?label=&message=常规安装方法&color=gray)](#installation)  [![一键安装脚本](https://img.shields.io/static/v1?label=&message=一键安装脚本&color=gray)](https://github.com/binary-husky/gpt_academic/releases)  [![配置说明](https://img.shields.io/static/v1?label=&message=配置说明&color=gray)](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明) [![wiki](https://img.shields.io/static/v1?label=&message=wiki&color=gray)]([https://github.com/binary-husky/gpt_academic/wiki/项目配置说明](https://github.com/binary-husky/gpt_academic/wiki))
-> 
-> 3.本项目兼容并鼓励尝试国产大语言模型ChatGLM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,azure-key3,api2d-key4"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交即可生效。
+>
+> 2.本项目兼容并鼓励尝试国内中文大语言基座模型如通义千问，智谱GLM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,azure-key3,api2d-key4"`。需要临时更换`API_KEY`时，在输入区输入临时的`API_KEY`然后回车键提交即可生效。
 
 <br><br>
 
@@ -56,7 +54,7 @@ Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanes
 
 功能（⭐= 近期新增功能） | 描述
 --- | ---
-⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B) | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, 通义千问[Qwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)，[智谱API](https://open.bigmodel.cn/)，DALLE3, [DeepseekCoder](https://coder.deepseek.com/)
+⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B) | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, 通义千问[Qwen](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)，[智谱GLM4](https://open.bigmodel.cn/)，DALLE3, [DeepseekCoder](https://coder.deepseek.com/)
 润色、翻译、代码解释 | 一键润色、翻译、查找论文语法错误、解释代码
 [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[插件](https://github.com/binary-husky/gpt_academic/tree/master/crazy_functions)，插件支持[热更新](https://github.com/binary-husky/gpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
@@ -65,7 +63,7 @@ Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanes
 Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [插件] 一键翻译或润色latex论文
 批量注释生成 | [插件] 一键批量生成函数注释
 Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [插件] 看到上面5种语言的[README](https://github.com/binary-husky/gpt_academic/blob/master/docs/README_EN.md)了吗？就是出自他的手笔
-chat分析报告生成 | [插件] 运行后自动生成总结汇报
+⭐支持mermaid图像渲染 | 支持让GPT生成[流程图](https://www.bilibili.com/video/BV18c41147H9/)、状态转移图、甘特图、饼状图、GitGraph等等（3.7版本）
 [PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [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) | [插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
 Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼写纠错+输出对照PDF
@@ -77,7 +75,6 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
 ⭐AutoGen多智能体插件 | [插件] 借助微软AutoGen，探索多Agent的智能涌现可能！
 启动暗色[主题](https://github.com/binary-husky/gpt_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 | 同时被GPT3.5、GPT4、[清华ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)、[复旦MOSS](https://github.com/OpenLMLab/MOSS)伺候的感觉一定会很不错吧？
-⭐ChatGLM2微调模型 | 支持加载ChatGLM2微调模型，提供ChatGLM2微调辅助插件
 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)和[盘古α](https://openi.org.cn/pangu/)
 ⭐[void-terminal](https://github.com/binary-husky/void-terminal) pip包 | 脱离GUI，在Python中直接调用本项目的所有函数插件（开发中）
 ⭐虚空终端插件 | [插件] 能够使用自然语言直接调度本项目其他插件
@@ -111,7 +108,7 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
 <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）
+- 多种大语言模型混合调用（ChatGLM + OpenAI-GPT3.5 + GPT4）
 <div align="center">
 <img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
 </div>
@@ -119,7 +116,7 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
 <br><br>
 
 # Installation
-### 安装方法I：直接运行 (Windows, Linux or MacOS) 
+### 安装方法I：直接运行 (Windows, Linux or MacOS)
 
 1. 下载项目
 
@@ -152,11 +149,11 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
 <details><summary>如果需要支持清华ChatGLM2/复旦MOSS/RWKV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ChatGLM2/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）：
+【可选步骤】如果需要支持清华ChatGLM3/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ChatGLM2。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_llms/requirements_chatglm.txt  
+# 【可选步骤I】支持清华ChatGLM3。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_llms/requirements_chatglm.txt
 
 # 【可选步骤II】支持复旦MOSS
 python -m pip install -r request_llms/requirements_moss.txt
@@ -197,7 +194,7 @@ pip install peft
     docker-compose up
     ```
 
-1. 仅ChatGPT+文心一言+spark等在线模型（推荐大多数人选择）
+1. 仅ChatGPT + GLM4 + 文心一言+spark等在线模型（推荐大多数人选择）
 [![basic](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
 [![basiclatex](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
 [![basicaudio](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
@@ -209,7 +206,7 @@ pip install peft
 
 P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以直接使用方案4或者方案0获取Latex功能。
 
-2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + 通义千问（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
+2. ChatGPT + GLM3 + MOSS + LLAMA2 + 通义千问（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
 [![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
 
     ``` sh
@@ -243,8 +240,8 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
 ```python
 "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", 
-    
+    "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": "",
 },
@@ -308,9 +305,9 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 <img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
 </div>
 
-8. OpenAI音频解析与总结
+8. 基于mermaid的流图、脑图绘制
 <div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
+<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/c518b82f-bd53-46e2-baf5-ad1b081c1da4" width="500" >
 </div>
 
 9. Latex全文校对纠错
@@ -327,8 +324,8 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 
 
 ### II：版本:
-
-- version 3.70（todo）: 优化AutoGen插件主题并设计一系列衍生插件
+- version 3.80(TODO): 优化AutoGen插件主题并设计一系列衍生插件
+- version 3.70: 引入Mermaid绘图，实现GPT画脑图等功能   
 - version 3.60: 引入AutoGen作为新一代插件的基石
 - version 3.57: 支持GLM3，星火v3，文心一言v4，修复本地模型的并发BUG
 - version 3.56: 支持动态追加基础功能按钮，新汇报PDF汇总页面
@@ -370,8 +367,8 @@ GPT Academic开发者QQ群：`610599535`
 
 1. `master` 分支: 主分支，稳定版
 2. `frontier` 分支: 开发分支，测试版
-3. 如何接入其他大模型：[接入其他大模型](request_llms/README.md)
-
+3. 如何[接入其他大模型](request_llms/README.md)
+4. 访问GPT-Academic的[在线服务并支持我们](https://github.com/binary-husky/gpt_academic/wiki/online)
 
 ### V：参考与学习
 

config.py

@@ -89,11 +89,14 @@ DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
 LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview","gpt-4-vision-preview",
                     "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
-                    "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
                     "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
-                    "chatglm3", "moss", "claude-2"]
-# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random"
-# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]
+                    "gemini-pro", "chatglm3", "claude-2", "zhipuai"]
+# P.S. 其他可用的模型还包括 [
+# "moss", "qwen-turbo", "qwen-plus", "qwen-max"
+# "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", 
+# "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random", "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
+# "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"
+# ]
 
 
 # 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
@@ -103,7 +106,11 @@ MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
 # 选择本地模型变体（只有当AVAIL_LLM_MODELS包含了对应本地模型时，才会起作用）
 # 如果你选择Qwen系列的模型，那么请在下面的QWEN_MODEL_SELECTION中指定具体的模型
 # 也可以是具体的模型路径
-QWEN_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"
+QWEN_LOCAL_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"
+
+
+# 接入通义千问在线大模型 https://dashscope.console.aliyun.com/
+DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
 
 
 # 百度千帆（LLM_MODEL="qianfan"）
@@ -188,7 +195,13 @@ XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
 
 # 接入智谱大模型
 ZHIPUAI_API_KEY = ""
-ZHIPUAI_MODEL = "chatglm_turbo"
+ZHIPUAI_MODEL = "glm-4" # 可选 "glm-3-turbo" "glm-4"
+
+
+# # 火山引擎YUNQUE大模型
+# YUNQUE_SECRET_KEY = ""
+# YUNQUE_ACCESS_KEY = ""
+# YUNQUE_MODEL = ""
 
 
 # Claude API KEY
@@ -199,6 +212,10 @@ ANTHROPIC_API_KEY = ""
 CUSTOM_API_KEY_PATTERN = ""
 
 
+# Google Gemini API-Key
+GEMINI_API_KEY = ''
+
+
 # HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
 HUGGINGFACE_ACCESS_TOKEN = "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"
 
@@ -284,6 +301,12 @@ NUM_CUSTOM_BASIC_BTN = 4
 │   ├── ZHIPUAI_API_KEY
 │   └── ZHIPUAI_MODEL
 │
+├── "qwen-turbo" 等通义千问大模型
+│   └──  DASHSCOPE_API_KEY
+│
+├── "Gemini"
+│   └──  GEMINI_API_KEY
+│
 └── "newbing" Newbing接口不再稳定，不推荐使用
     ├── NEWBING_STYLE
     └── NEWBING_COOKIES
@@ -300,7 +323,7 @@ NUM_CUSTOM_BASIC_BTN = 4
 ├── "jittorllms_pangualpha"
 ├── "jittorllms_llama"
 ├── "deepseekcoder"
-├── "qwen"
+├── "qwen-local"
 ├──  RWKV的支持见Wiki
 └── "llama2"
 

core_functional.py

@@ -3,30 +3,58 @@
 # 'stop' 颜色对应 theme.py 中的 color_er
 import importlib
 from toolbox import clear_line_break
-
+from textwrap import dedent
 
 def get_core_functions():
     return {
+
         "英语学术润色": {
-            # 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":   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. " +
+            # [1*] 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":   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"Firstly, you should provide the polished paragraph. "
                         r"Secondly, you should list all your modification and explain the reasons to do so in markdown table." + "\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
+            # [2*] 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":   r"",
-            # 按钮颜色 (默认 secondary)
+            # [3] 按钮颜色 (可选参数，默认 secondary)
             "Color":    r"secondary",
-            # 按钮是否可见 (默认 True，即可见)
+            # [4] 按钮是否可见 (可选参数，默认 True，即可见)
             "Visible": True,
-            # 是否在触发时清除历史 (默认 False，即不处理之前的对话历史)
-            "AutoClearHistory": False
+            # [5] 是否在触发时清除历史 (可选参数，默认 False，即不处理之前的对话历史)
+            "AutoClearHistory": False,
+            # [6] 文本预处理 （可选参数，默认 None，举例：写个函数移除所有的换行符）
+            "PreProcess": None,
         },
-        "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"",
+        
+        
+        "总结绘制脑图": {
+            # 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":   r"",
+            # 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":
+                dedent("\n"+r'''
+                    ==============================
+                    使用mermaid flowchart对以上文本进行总结，概括上述段落的内容以及内在逻辑关系，例如：
+
+                    以下是对以上文本的总结，以mermaid flowchart的形式展示：
+                    ```mermaid
+                    flowchart LR
+                        A["节点名1"] --> B("节点名2")
+                        B --> C{"节点名3"}
+                        C --> D["节点名4"]
+                        C --> |"箭头名1"| E["节点名5"]
+                        C --> |"箭头名2"| F["节点名6"]
+                    ```
+
+                    警告：
+                    （1）使用中文
+                    （2）节点名字使用引号包裹，如["Laptop"]
+                    （3）`|` 和 `"`之间不要存在空格
+                    （4）根据情况选择flowchart LR（从左到右）或者flowchart TD（从上到下）
+                '''),
         },
+        
+        
         "查找语法错误": {
             "Prefix":   r"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. "
@@ -46,11 +74,15 @@ def get_core_functions():
             "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": {
+        
+        
+        "学术英中互译": {
             "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. " +
@@ -59,29 +91,36 @@ def get_core_functions():
                         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",
+            "Suffix":   r"",
         },
+        
+        
         "英译中": {
             "Prefix":   r"翻译成地道的中文：" + "\n\n",
             "Suffix":   r"",
-            "Visible": False,
+            "Visible":  False,
         },
+        
+        
         "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，" +
+            "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"",
-            "Visible": False,
+            "Visible":  False,
         },
+        
+        
         "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:",
-            "Visible": False,
+            "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:" + "\n\n",
+            "Visible":  False,
             "Suffix":   r"",
         }
     }
@@ -98,8 +137,14 @@ def handle_core_functionality(additional_fn, inputs, history, chatbot):
         return inputs, history
     else:
         # 预制功能
-        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        if "PreProcess" in core_functional[additional_fn]:
+            if core_functional[additional_fn]["PreProcess"] is not None:
+                inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
         inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
         if core_functional[additional_fn].get("AutoClearHistory", False):
             history = []
         return inputs, history
+
+if __name__ == "__main__":
+    t = get_core_functions()["总结绘制脑图"]
+    print(t["Prefix"] + t["Suffix"])

crazy_functional.py

@@ -37,110 +37,109 @@ def get_crazy_functions():
     from crazy_functions.批量Markdown翻译 import Markdown中译英
     from crazy_functions.虚空终端 import 虚空终端
 
-
     function_plugins = {
         "虚空终端": {
             "Group": "对话|编程|学术|智能体",
             "Color": "stop",
             "AsButton": True,
-            "Function": HotReload(虚空终端)
+            "Function": HotReload(虚空终端),
         },
         "解析整个Python项目": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": True,
             "Info": "解析一个Python项目的所有源文件(.py) | 输入参数为路径",
-            "Function": HotReload(解析一个Python项目)
+            "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）": {
             "Group": "对话",
             "Color": "stop",
             "AsButton": False,
             "Info": "载入对话历史存档 | 输入参数为路径",
-            "Function": HotReload(载入对话历史存档)
+            "Function": HotReload(载入对话历史存档),
         },
         "删除所有本地对话历史记录（谨慎操作）": {
             "Group": "对话",
             "AsButton": False,
             "Info": "删除所有本地对话历史记录，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(删除所有本地对话历史记录)
+            "Function": HotReload(删除所有本地对话历史记录),
         },
         "清除所有缓存文件（谨慎操作）": {
             "Group": "对话",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "清除所有缓存文件，谨慎操作 | 不需要输入参数",
-            "Function": HotReload(清除缓存)
+            "Function": HotReload(清除缓存),
         },
         "批量总结Word文档": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": True,
             "Info": "批量总结word文档 | 输入参数为路径",
-            "Function": HotReload(总结word文档)
+            "Function": HotReload(总结word文档),
         },
         "解析整个Matlab项目": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,
             "Info": "解析一个Matlab项目的所有源文件(.m) | 输入参数为路径",
-            "Function": HotReload(解析一个Matlab项目)
+            "Function": HotReload(解析一个Matlab项目),
         },
         "解析整个C++项目头文件": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个C++项目的所有头文件(.h/.hpp) | 输入参数为路径",
-            "Function": HotReload(解析一个C项目的头文件)
+            "Function": HotReload(解析一个C项目的头文件),
         },
         "解析整个C++项目（.cpp/.hpp/.c/.h）": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个C++项目的所有源文件（.cpp/.hpp/.c/.h）| 输入参数为路径",
-            "Function": HotReload(解析一个C项目)
+            "Function": HotReload(解析一个C项目),
         },
         "解析整个Go项目": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个Go项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Golang项目)
+            "Function": HotReload(解析一个Golang项目),
         },
         "解析整个Rust项目": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个Rust项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Rust项目)
+            "Function": HotReload(解析一个Rust项目),
         },
         "解析整个Java项目": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个Java项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Java项目)
+            "Function": HotReload(解析一个Java项目),
         },
         "解析整个前端项目（js,ts,css等）": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个前端项目的所有源文件（js,ts,css等） | 输入参数为路径",
-            "Function": HotReload(解析一个前端项目)
+            "Function": HotReload(解析一个前端项目),
         },
         "解析整个Lua项目": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个Lua项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个Lua项目)
+            "Function": HotReload(解析一个Lua项目),
         },
         "解析整个CSharp项目": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "解析一个CSharp项目的所有源文件 | 输入参数为路径",
-            "Function": HotReload(解析一个CSharp项目)
+            "Function": HotReload(解析一个CSharp项目),
         },
         "解析Jupyter Notebook文件": {
             "Group": "编程",
@@ -156,103 +155,102 @@ def get_crazy_functions():
             "Color": "stop",
             "AsButton": False,
             "Info": "读取Tex论文并写摘要 | 输入参数为路径",
-            "Function": HotReload(读文章写摘要)
+            "Function": HotReload(读文章写摘要),
         },
         "翻译README或MD": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": True,
             "Info": "将Markdown翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中)
+            "Function": HotReload(Markdown英译中),
         },
         "翻译Markdown或README（支持Github链接）": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,
             "Info": "将Markdown或README翻译为中文 | 输入参数为路径或URL",
-            "Function": HotReload(Markdown英译中)
+            "Function": HotReload(Markdown英译中),
         },
         "批量生成函数注释": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "批量生成函数的注释 | 输入参数为路径",
-            "Function": HotReload(批量生成函数注释)
+            "Function": HotReload(批量生成函数注释),
         },
         "保存当前的对话": {
             "Group": "对话",
             "AsButton": True,
             "Info": "保存当前的对话 | 不需要输入参数",
-            "Function": HotReload(对话历史存档)
+            "Function": HotReload(对话历史存档),
         },
         "[多线程Demo]解析此项目本身（源码自译解）": {
             "Group": "对话|编程",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "多线程解析并翻译此项目的源码 | 不需要输入参数",
-            "Function": HotReload(解析项目本身)
+            "Function": HotReload(解析项目本身),
         },
         "历史上的今天": {
             "Group": "对话",
             "AsButton": True,
             "Info": "查看历史上的今天事件 (这是一个面向开发者的插件Demo) | 不需要输入参数",
-            "Function": HotReload(高阶功能模板函数)
+            "Function": HotReload(高阶功能模板函数),
         },
         "精准翻译PDF论文": {
             "Group": "学术",
             "Color": "stop",
-            "AsButton": True,  
+            "AsButton": True,
             "Info": "精准翻译PDF论文为中文 | 输入参数为路径",
-            "Function": HotReload(批量翻译PDF文档)
+            "Function": HotReload(批量翻译PDF文档),
         },
         "询问多个GPT模型": {
             "Group": "对话",
             "Color": "stop",
             "AsButton": True,
-            "Function": HotReload(同时问询)
+            "Function": HotReload(同时问询),
         },
         "批量总结PDF文档": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "批量总结PDF文档的内容 | 输入参数为路径",
-            "Function": HotReload(批量总结PDF文档)
+            "Function": HotReload(批量总结PDF文档),
         },
         "谷歌学术检索助手（输入谷歌学术搜索页url）": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "使用谷歌学术检索助手搜索指定URL的结果 | 输入参数为谷歌学术搜索页的URL",
-            "Function": HotReload(谷歌检索小助手)
+            "Function": HotReload(谷歌检索小助手),
         },
         "理解PDF文档内容 （模仿ChatPDF）": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "理解PDF文档的内容并进行回答 | 输入参数为路径",
-            "Function": HotReload(理解PDF文档内容标准文件输入)
+            "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）": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "对英文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英文润色)
+            "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）": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "对英文Latex项目全文进行纠错处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex英文纠错)
+            "Function": HotReload(Latex英文纠错),
         },
         "中文Latex项目全文润色（输入路径或上传压缩包）": {
             "Group": "学术",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "对中文Latex项目全文进行润色处理 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Latex中文润色)
+            "Function": HotReload(Latex中文润色),
         },
-
         # 已经被新插件取代
         # "Latex项目全文中译英（输入路径或上传压缩包）": {
         #     "Group": "学术",
@@ -261,7 +259,6 @@ def get_crazy_functions():
         #     "Info": "对Latex项目全文进行中译英处理 | 输入参数为路径或上传压缩包",
         #     "Function": HotReload(Latex中译英)
         # },
-
         # 已经被新插件取代
         # "Latex项目全文英译中（输入路径或上传压缩包）": {
         #     "Group": "学术",
@@ -270,334 +267,409 @@ def get_crazy_functions():
         #     "Info": "对Latex项目全文进行英译中处理 | 输入参数为路径或上传压缩包",
         #     "Function": HotReload(Latex英译中)
         # },
-        
         "批量Markdown中译英（输入路径或上传压缩包）": {
             "Group": "编程",
             "Color": "stop",
             "AsButton": False,  # 加入下拉菜单中
             "Info": "批量将Markdown文件中文翻译为英文 | 输入参数为路径或上传压缩包",
-            "Function": HotReload(Markdown中译英)
+            "Function": HotReload(Markdown中译英),
         },
     }
 
     # -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
     try:
         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）": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                # "Info": "下载arxiv论文并翻译摘要 | 输入参数为arxiv编号如1812.10695",
-                "Function": HotReload(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）": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,  # 加入下拉菜单中
+                    # "Info": "下载arxiv论文并翻译摘要 | 输入参数为arxiv编号如1812.10695",
+                    "Function": HotReload(下载arxiv论文并翻译摘要),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         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（输入问题后点击该插件，需要访问谷歌）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
-                "Function": HotReload(连接网络回答问题)
+
+        function_plugins.update(
+            {
+                "连接网络回答问题（输入问题后点击该插件，需要访问谷歌）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,  # 加入下拉菜单中
+                    # "Info": "连接网络回答问题（需要访问谷歌）| 输入参数是一个问题",
+                    "Function": HotReload(连接网络回答问题),
+                }
             }
-        })
+        )
         from crazy_functions.联网的ChatGPT_bing版 import 连接bing搜索回答问题
-        function_plugins.update({
-            "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,  # 加入下拉菜单中
-                "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
-                "Function": HotReload(连接bing搜索回答问题)
+
+        function_plugins.update(
+            {
+                "连接网络回答问题（中文Bing版，输入问题后点击该插件）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,  # 加入下拉菜单中
+                    "Info": "连接网络回答问题（需要访问中文Bing）| 输入参数是一个问题",
+                    "Function": HotReload(连接bing搜索回答问题),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         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）": {
-                "Group": "编程",
-                "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)
-            },
-        })
+
+        function_plugins.update(
+            {
+                "解析项目源代码（手动指定和筛选源代码文件类型）": {
+                    "Group": "编程",
+                    "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),
+                },
+            }
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         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）": {
-                "Group": "对话",
-                "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)
-            },
-        })
+
+        function_plugins.update(
+            {
+                "询问多个GPT模型（手动指定询问哪些模型）": {
+                    "Group": "对话",
+                    "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&gpt-4",  # 高级参数输入区的显示提示
+                    "Function": HotReload(同时问询_指定模型),
+                },
+            }
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.图片生成 import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
-        function_plugins.update({
-            "图片生成_DALLE2 （先切换模型到openai或api2d）": {
-                "Group": "对话",
-                "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1024x1024（默认），支持 256x256, 512x512, 1024x1024",  # 高级参数输入区的显示提示
-                "Info": "使用DALLE2生成图片 | 输入参数字符串，提供图像的内容",
-                "Function": HotReload(图片生成_DALLE2)
-            },
-        })
-        function_plugins.update({
-            "图片生成_DALLE3 （先切换模型到openai或api2d）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
-                "ArgsReminder": "在这里输入自定义参数「分辨率-质量(可选)-风格(可选)」, 参数示例「1024x1024-hd-vivid」 || 分辨率支持 「1024x1024」(默认) /「1792x1024」/「1024x1792」 || 质量支持 「-standard」(默认) /「-hd」 || 风格支持 「-vivid」(默认) /「-natural」", # 高级参数输入区的显示提示
-                "Info": "使用DALLE3生成图片 | 输入参数字符串，提供图像的内容",
-                "Function": HotReload(图片生成_DALLE3)
-            },
-        })
-        function_plugins.update({
-            "图片修改_DALLE2 （先切换模型到openai或api2d）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": False,  # 调用时，唤起高级参数输入区（默认False）
-                # "Info": "使用DALLE2修改图片 | 输入参数字符串，提供图像的内容",
-                "Function": HotReload(图片修改_DALLE2)
-            },
-        })
+
+        function_plugins.update(
+            {
+                "图片生成_DALLE2 （先切换模型到gpt-*）": {
+                    "Group": "对话",
+                    "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1024x1024（默认），支持 256x256, 512x512, 1024x1024",  # 高级参数输入区的显示提示
+                    "Info": "使用DALLE2生成图片 | 输入参数字符串，提供图像的内容",
+                    "Function": HotReload(图片生成_DALLE2),
+                },
+            }
+        )
+        function_plugins.update(
+            {
+                "图片生成_DALLE3 （先切换模型到gpt-*）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,  # 调用时，唤起高级参数输入区（默认False）
+                    "ArgsReminder": "在这里输入自定义参数「分辨率-质量(可选)-风格(可选)」, 参数示例「1024x1024-hd-vivid」 || 分辨率支持 「1024x1024」(默认) /「1792x1024」/「1024x1792」 || 质量支持 「-standard」(默认) /「-hd」 || 风格支持 「-vivid」(默认) /「-natural」",  # 高级参数输入区的显示提示
+                    "Info": "使用DALLE3生成图片 | 输入参数字符串，提供图像的内容",
+                    "Function": HotReload(图片生成_DALLE3),
+                },
+            }
+        )
+        function_plugins.update(
+            {
+                "图片修改_DALLE2 （先切换模型到gpt-*）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": False,  # 调用时，唤起高级参数输入区（默认False）
+                    # "Info": "使用DALLE2修改图片 | 输入参数字符串，提供图像的内容",
+                    "Function": HotReload(图片修改_DALLE2),
+                },
+            }
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         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）": {
-                "Group": "对话",
-                "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）。",
-                "Info": "批量总结音频或视频 | 输入参数为路径",
-                "Function": HotReload(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）": {
+                    "Group": "对话",
+                    "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）。",
+                    "Info": "批量总结音频或视频 | 输入参数为路径",
+                    "Function": HotReload(总结音视频),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     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）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "Info": "按照自然语言描述生成一个动画 | 输入参数是一段话",
-                "Function": HotReload(动画生成)
+
+        function_plugins.update(
+            {
+                "数学动画生成（Manim）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "Info": "按照自然语言描述生成一个动画 | 输入参数是一段话",
+                    "Function": HotReload(动画生成),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        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（指定翻译成何种语言）": {
-                "Group": "编程",
-                "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)
+
+        function_plugins.update(
+            {
+                "Markdown翻译（指定翻译成何种语言）": {
+                    "Group": "编程",
+                    "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(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.知识库问答 import 知识库文件注入
-        function_plugins.update({
-            "构建知识库（先上传文件素材,再运行此插件）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "此处待注入的知识库名称id, 默认为default。文件进入知识库后可长期保存。可以通过再次调用本插件的方式，向知识库追加更多文档。",
-                "Function": HotReload(知识库文件注入)
+
+        function_plugins.update(
+            {
+                "构建知识库（先上传文件素材,再运行此插件）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": "此处待注入的知识库名称id, 默认为default。文件进入知识库后可长期保存。可以通过再次调用本插件的方式，向知识库追加更多文档。",
+                    "Function": HotReload(知识库文件注入),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.知识库问答 import 读取知识库作答
-        function_plugins.update({
-            "知识库文件注入（构建知识库后,再运行此插件）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "待提取的知识库名称id, 默认为default, 您需要构建知识库后再运行此插件。",
-                "Function": HotReload(读取知识库作答)
+
+        function_plugins.update(
+            {
+                "知识库文件注入（构建知识库后,再运行此插件）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": "待提取的知识库名称id, 默认为default, 您需要构建知识库后再运行此插件。",
+                    "Function": HotReload(读取知识库作答),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.交互功能函数模板 import 交互功能模板函数
-        function_plugins.update({
-            "交互功能模板Demo函数（查找wallhaven.cc的壁纸）": {
-                "Group": "对话",
-                "Color": "stop",
-                "AsButton": False,
-                "Function": HotReload(交互功能模板函数)
+
+        function_plugins.update(
+            {
+                "交互功能模板Demo函数（查找wallhaven.cc的壁纸）": {
+                    "Group": "对话",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "Function": HotReload(交互功能模板函数),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
-        function_plugins.update({
-            "Latex英文纠错+高亮修正位置 [需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
-                "Function": HotReload(Latex英文纠错加PDF对比)
+
+        function_plugins.update(
+            {
+                "Latex英文纠错+高亮修正位置 [需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": "如果有必要, 请在此处追加更细致的矫错指令（使用英文）。",
+                    "Function": HotReload(Latex英文纠错加PDF对比),
+                }
             }
-        })
+        )
         from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
-        function_plugins.update({
-            "Arxiv论文精细翻译（输入arxivID）[需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder":
-                    "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 " +
-                    "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: " +
-                    'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
-                "Function": HotReload(Latex翻译中文并重新编译PDF)
+
+        function_plugins.update(
+            {
+                "Arxiv论文精细翻译（输入arxivID）[需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                    + "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                    + 'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "Info": "Arixv论文精细翻译 | 输入参数arxiv论文的ID，比如1812.10695",
+                    "Function": HotReload(Latex翻译中文并重新编译PDF),
+                }
             }
-        })
-        function_plugins.update({
-            "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "AdvancedArgs": True,
-                "ArgsReminder":
-                    "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 " +
-                    "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: " +
-                    'If the term "agent" is used in this section, it should be translated to "智能体". ',
-                "Info": "本地Latex论文精细翻译 | 输入参数是路径",
-                "Function": HotReload(Latex翻译中文并重新编译PDF)
+        )
+        function_plugins.update(
+            {
+                "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "AdvancedArgs": True,
+                    "ArgsReminder": "如果有必要, 请在此处给出自定义翻译命令, 解决部分词汇翻译不准确的问题。 "
+                    + "例如当单词'agent'翻译不准确时, 请尝试把以下指令复制到高级参数区: "
+                    + 'If the term "agent" is used in this section, it should be translated to "智能体". ',
+                    "Info": "本地Latex论文精细翻译 | 输入参数是路径",
+                    "Function": HotReload(Latex翻译中文并重新编译PDF),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from toolbox import get_conf
-        ENABLE_AUDIO = get_conf('ENABLE_AUDIO')
+
+        ENABLE_AUDIO = get_conf("ENABLE_AUDIO")
         if ENABLE_AUDIO:
             from crazy_functions.语音助手 import 语音助手
-            function_plugins.update({
-                "实时语音对话": {
-                    "Group": "对话",
-                    "Color": "stop",
-                    "AsButton": True,
-                    "Info": "这是一个时刻聆听着的语音对话助手 | 没有输入参数",
-                    "Function": HotReload(语音助手)
+
+            function_plugins.update(
+                {
+                    "实时语音对话": {
+                        "Group": "对话",
+                        "Color": "stop",
+                        "AsButton": True,
+                        "Info": "这是一个时刻聆听着的语音对话助手 | 没有输入参数",
+                        "Function": HotReload(语音助手),
+                    }
                 }
-            })
+            )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.批量翻译PDF文档_NOUGAT import 批量翻译PDF文档
-        function_plugins.update({
-            "精准翻译PDF文档（NOUGAT）": {
-                "Group": "学术",
-                "Color": "stop",
-                "AsButton": False,
-                "Function": HotReload(批量翻译PDF文档)
+
+        function_plugins.update(
+            {
+                "精准翻译PDF文档（NOUGAT）": {
+                    "Group": "学术",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "Function": HotReload(批量翻译PDF文档),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.函数动态生成 import 函数动态生成
-        function_plugins.update({
-            "动态代码解释器（CodeInterpreter）": {
-                "Group": "智能体",
-                "Color": "stop",
-                "AsButton": False,
-                "Function": HotReload(函数动态生成)
+
+        function_plugins.update(
+            {
+                "动态代码解释器（CodeInterpreter）": {
+                    "Group": "智能体",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "Function": HotReload(函数动态生成),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
 
     try:
         from crazy_functions.多智能体 import 多智能体终端
-        function_plugins.update({
-            "AutoGen多智能体终端（仅供测试）": {
-                "Group": "智能体",
-                "Color": "stop",
-                "AsButton": False,
-                "Function": HotReload(多智能体终端)
+
+        function_plugins.update(
+            {
+                "AutoGen多智能体终端（仅供测试）": {
+                    "Group": "智能体",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "Function": HotReload(多智能体终端),
+                }
             }
-        })
+        )
     except:
         print(trimmed_format_exc())
-        print('Load function plugin failed')
+        print("Load function plugin failed")
+
+    try:
+        from crazy_functions.互动小游戏 import 随机小游戏
+
+        function_plugins.update(
+            {
+                "随机互动小游戏（仅供测试）": {
+                    "Group": "智能体",
+                    "Color": "stop",
+                    "AsButton": False,
+                    "Function": HotReload(随机小游戏),
+                }
+            }
+        )
+    except:
+        print(trimmed_format_exc())
+        print("Load function plugin failed")
 
     # try:
-    #     from crazy_functions.互动小游戏 import 随机小游戏
+    #     from crazy_functions.高级功能函数模板 import 测试图表渲染
     #     function_plugins.update({
-    #         "随机小游戏": {
+    #         "绘制逻辑关系（测试图表渲染）": {
     #             "Group": "智能体",
     #             "Color": "stop",
     #             "AsButton": True,
-    #             "Function": HotReload(随机小游戏)
+    #             "Function": HotReload(测试图表渲染)
     #         }
     #     })
     # except:
@@ -618,8 +690,6 @@ def get_crazy_functions():
     # except:
     #     print('Load function plugin failed')
 
-
-
     """
     设置默认值:
     - 默认 Group = 对话
@@ -629,12 +699,12 @@ def get_crazy_functions():
     """
     for name, function_meta in function_plugins.items():
         if "Group" not in function_meta:
-            function_plugins[name]["Group"] = '对话'
+            function_plugins[name]["Group"] = "对话"
         if "AsButton" not in function_meta:
             function_plugins[name]["AsButton"] = True
         if "AdvancedArgs" not in function_meta:
             function_plugins[name]["AdvancedArgs"] = False
         if "Color" not in function_meta:
-            function_plugins[name]["Color"] = 'secondary'
+            function_plugins[name]["Color"] = "secondary"
 
     return function_plugins

crazy_functions/Latex全文润色.py

@@ -26,8 +26,8 @@ class PaperFileGroup():
                 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)
+                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

crazy_functions/Latex全文翻译.py

@@ -26,8 +26,8 @@ class PaperFileGroup():
                 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)
+                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

crazy_functions/Latex输出PDF结果.py

@@ -5,7 +5,7 @@ import glob, os, requests, time
 pj = os.path.join
 ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
 
-# =================================== 工具函数 ===============================================
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 工具函数 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 # 专业词汇声明  = 'If the term "agent" is used in this section, it should be translated to "智能体". '
 def switch_prompt(pfg, mode, more_requirement):
     """
@@ -142,7 +142,7 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
     from toolbox import extract_archive
     extract_archive(file_path=dst, dest_dir=extract_dst)
     return extract_dst, arxiv_id
-# ========================================= 插件主程序1 =====================================================    
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=    
 
 
 @CatchException
@@ -218,7 +218,7 @@ def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, histo
     # <-------------- we are done ------------->
     return success
 
-# ========================================= 插件主程序2 =====================================================    
+# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序2 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=    
 
 @CatchException
 def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):

crazy_functions/crazy_utils.py

@@ -139,6 +139,8 @@ def can_multi_process(llm):
     if llm.startswith('gpt-'): return True
     if llm.startswith('api2d-'): return True
     if llm.startswith('azure-'): return True
+    if llm.startswith('spark'): return True
+    if llm.startswith('zhipuai'): return True
     return False
 
 def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
@@ -312,95 +314,6 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
     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):
     """
@@ -553,6 +466,9 @@ def read_and_clean_pdf_text(fp):
                     return True
                 else:
                     return False
+            # 对于某些PDF会有第一个段落就以小写字母开头,为了避免索引错误将其更改为大写
+            if starts_with_lowercase_word(meta_txt[0]):
+                meta_txt[0] = meta_txt[0].capitalize()
             for _ in range(100):
                 for index, block_txt in enumerate(meta_txt):
                     if starts_with_lowercase_word(block_txt):
@@ -631,7 +547,6 @@ def get_files_from_everything(txt, type): # type='.md'
 
 
 
-    
 @Singleton
 class nougat_interface():
     def __init__(self):

crazy_functions/game_fns/game_ascii_art.py

@@ -0,0 +1,42 @@
+from toolbox import CatchException, update_ui, update_ui_lastest_msg
+from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
+import random
+
+
+class MiniGame_ASCII_Art(GptAcademicGameBaseState):
+    def step(self, prompt, chatbot, history):
+        if self.step_cnt == 0:  
+            chatbot.append(["我画你猜（动物）", "请稍等..."])
+        else:
+            if prompt.strip() == 'exit':
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                return
+            chatbot.append([prompt, ""])
+        yield from update_ui(chatbot=chatbot, history=history)
+
+        if self.step_cnt == 0:
+            self.lock_plugin(chatbot)
+            self.cur_task = 'draw'
+
+        if self.cur_task == 'draw':
+            avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
+            self.obj = random.choice(avail_obj)
+            inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + \
+                f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
+            raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
+            self.cur_task = 'identify user guess'
+            res = get_code_block(raw_res)
+            history += ['', f'the answer is {self.obj}', inputs, res]
+            yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
+
+        elif self.cur_task == 'identify user guess':
+            if is_same_thing(self.obj, prompt, self.llm_kwargs):
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
+            else:
+                self.cur_task = 'identify user guess'
+                yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)

crazy_functions/game_fns/game_interactive_story.py

@@ -0,0 +1,212 @@
+prompts_hs = """ 请以“{headstart}”为开头，编写一个小说的第一幕。
+
+- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
+- 出现人物时，给出人物的名字。
+- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
+- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
+- 字数要求：第一幕的字数少于300字，且少于2个段落。
+"""
+
+prompts_interact = """ 小说的前文回顾：
+「
+{previously_on_story}
+」
+
+你是一个作家，根据以上的情节，给出4种不同的后续剧情发展方向，每个发展方向都精明扼要地用一句话说明。稍后，我将在这4个选择中，挑选一种剧情发展。
+
+输出格式例如：
+1. 后续剧情发展1
+2. 后续剧情发展2
+3. 后续剧情发展3
+4. 后续剧情发展4
+"""
+
+
+prompts_resume = """小说的前文回顾：
+「
+{previously_on_story}
+」
+
+你是一个作家，我们正在互相讨论，确定后续剧情的发展。
+在以下的剧情发展中，
+「
+{choice}
+」
+我认为更合理的是：{user_choice}。
+请在前文的基础上（不要重复前文），围绕我选定的剧情情节，编写小说的下一幕。
+
+- 禁止杜撰不符合我选择的剧情。
+- 尽量短，不要包含太多情节，因为你接下来将会与用户互动续写下面的情节，要留出足够的互动空间。
+- 不要重复前文。
+- 出现人物时，给出人物的名字。
+- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
+- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
+- 小说的下一幕字数少于300字，且少于2个段落。
+"""
+
+
+prompts_terminate = """小说的前文回顾：
+「
+{previously_on_story}
+」
+
+你是一个作家，我们正在互相讨论，确定后续剧情的发展。
+现在，故事该结束了，我认为最合理的故事结局是：{user_choice}。
+
+请在前文的基础上（不要重复前文），编写小说的最后一幕。
+
+- 不要重复前文。
+- 出现人物时，给出人物的名字。
+- 积极地运用环境描写、人物描写等手法，让读者能够感受到你的故事世界。
+- 积极地运用修辞手法，比如比喻、拟人、排比、对偶、夸张等等。
+- 字数要求：最后一幕的字数少于1000字。
+"""
+
+
+from toolbox import CatchException, update_ui, update_ui_lastest_msg
+from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseState
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
+import random
+
+
+class MiniGame_ResumeStory(GptAcademicGameBaseState):
+    story_headstart = [
+        '先行者知道，他现在是全宇宙中唯一的一个人了。',
+        '深夜，一个年轻人穿过天安门广场向纪念堂走去。在二十二世纪编年史中，计算机把他的代号定为M102。',
+        '他知道，这最后一课要提前讲了。又一阵剧痛从肝部袭来，几乎使他晕厥过去。',
+        '在距地球五万光年的远方，在银河系的中心，一场延续了两万年的星际战争已接近尾声。那里的太空中渐渐隐现出一个方形区域，仿佛灿烂的群星的背景被剪出一个方口。',
+        '伊依一行三人乘坐一艘游艇在南太平洋上做吟诗航行，他们的目的地是南极，如果几天后能顺利到达那里，他们将钻出地壳去看诗云。',
+        '很多人生来就会莫名其妙地迷上一样东西，仿佛他的出生就是要和这东西约会似的，正是这样，圆圆迷上了肥皂泡。'
+    ]
+
+
+    def begin_game_step_0(self, prompt, chatbot, history):
+        # init game at step 0
+        self.headstart = random.choice(self.story_headstart)
+        self.story = []
+        chatbot.append(["互动写故事", f"这次的故事开头是：{self.headstart}"])
+        self.sys_prompt_ = '你是一个想象力丰富的杰出作家。正在与你的朋友互动，一起写故事，因此你每次写的故事段落应少于300字（结局除外）。'
+        
+        
+    def generate_story_image(self, story_paragraph):
+        try:
+            from crazy_functions.图片生成 import gen_image
+            prompt_ = predict_no_ui_long_connection(inputs=story_paragraph, llm_kwargs=self.llm_kwargs, history=[], sys_prompt='你需要根据用户给出的小说段落，进行简短的环境描写。要求：80字以内。')
+            image_url, image_path = gen_image(self.llm_kwargs, prompt_, '512x512', model="dall-e-2", quality='standard', style='natural')
+            return f'<br/><div align="center"><img src="file={image_path}"></div>'
+        except:
+            return ''
+        
+    def step(self, prompt, chatbot, history):
+        
+        """
+        首先，处理游戏初始化等特殊情况
+        """
+        if self.step_cnt == 0:  
+            self.begin_game_step_0(prompt, chatbot, history)
+            self.lock_plugin(chatbot)
+            self.cur_task = 'head_start'
+        else:
+            if prompt.strip() == 'exit' or prompt.strip() == '结束剧情':
+                # should we terminate game here?
+                self.delete_game = True
+                yield from update_ui_lastest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
+                return
+            if '剧情收尾' in prompt:
+                self.cur_task = 'story_terminate'
+            # # well, game resumes
+            # chatbot.append([prompt, ""])
+        # update ui, don't keep the user waiting
+        yield from update_ui(chatbot=chatbot, history=history)
+
+
+        """
+        处理游戏的主体逻辑
+        """
+        if self.cur_task == 'head_start':
+            """
+            这是游戏的第一步
+            """
+            inputs_ = prompts_hs.format(headstart=self.headstart)
+            history_ = []
+            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, '故事开头', self.llm_kwargs, 
+                chatbot, history_, self.sys_prompt_
+            )
+            self.story.append(story_paragraph)
+            # # 配图
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+
+            # # 构建后续剧情引导
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
+            history_ = []
+            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, '请在以下几种故事走向中，选择一种（当然，您也可以选择给出其他故事走向）：', self.llm_kwargs, 
+                chatbot,
+                history_,
+                self.sys_prompt_
+            )
+            self.cur_task = 'user_choice'
+
+
+        elif self.cur_task == 'user_choice':
+            """
+            根据用户的提示，确定故事的下一步
+            """
+            if '请在以下几种故事走向中，选择一种' in chatbot[-1][0]: chatbot.pop(-1)
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_resume.format(previously_on_story=previously_on_story, choice=self.next_choices, user_choice=prompt)
+            history_ = []
+            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, f'下一段故事（您的选择是：{prompt}）。', self.llm_kwargs, 
+                chatbot, history_, self.sys_prompt_
+            )
+            self.story.append(story_paragraph)
+            # # 配图
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+
+            # # 构建后续剧情引导
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_interact.format(previously_on_story=previously_on_story)
+            history_ = []
+            self.next_choices = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, 
+                '请在以下几种故事走向中，选择一种。当然，您也可以给出您心中的其他故事走向。另外，如果您希望剧情立即收尾，请输入剧情走向，并以“剧情收尾”四个字提示程序。', self.llm_kwargs, 
+                chatbot, 
+                history_, 
+                self.sys_prompt_
+            )
+            self.cur_task = 'user_choice'
+
+
+        elif self.cur_task == 'story_terminate':
+            """
+            根据用户的提示，确定故事的结局
+            """
+            previously_on_story = ""
+            for s in self.story:
+                previously_on_story += s + '\n'
+            inputs_ = prompts_terminate.format(previously_on_story=previously_on_story, user_choice=prompt)
+            history_ = []
+            story_paragraph = yield from request_gpt_model_in_new_thread_with_ui_alive(
+                inputs_, f'故事收尾（您的选择是：{prompt}）。', self.llm_kwargs, 
+                chatbot, history_, self.sys_prompt_
+            )
+            # # 配图
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
+            yield from update_ui_lastest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
+
+            # terminate game
+            self.delete_game = True
+            return

crazy_functions/ipc_fns/mp.py

@@ -0,0 +1,37 @@
+import platform 
+import pickle
+import multiprocessing
+
+def run_in_subprocess_wrapper_func(v_args):
+    func, args, kwargs, return_dict, exception_dict = pickle.loads(v_args)
+    import sys
+    try:
+        result = func(*args, **kwargs)
+        return_dict['result'] = result
+    except Exception as e:
+        exc_info = sys.exc_info()
+        exception_dict['exception'] = exc_info
+
+def run_in_subprocess_with_timeout(func, timeout=60):
+    if platform.system() == 'Linux':
+        def wrapper(*args, **kwargs):
+            return_dict = multiprocessing.Manager().dict()
+            exception_dict = multiprocessing.Manager().dict()
+            v_args = pickle.dumps((func, args, kwargs, return_dict, exception_dict))
+            process = multiprocessing.Process(target=run_in_subprocess_wrapper_func, args=(v_args,))
+            process.start()
+            process.join(timeout)
+            if process.is_alive():
+                process.terminate()
+                raise TimeoutError(f'功能单元{str(func)}未能在规定时间内完成任务')
+            process.close()
+            if 'exception' in exception_dict:
+                # ooops, the subprocess ran into an exception
+                exc_info = exception_dict['exception']
+                raise exc_info[1].with_traceback(exc_info[2])
+            if 'result' in return_dict.keys():
+                # If the subprocess ran successfully, return the result
+                return return_dict['result']
+        return wrapper
+    else:
+        return func

crazy_functions/latex_fns/latex_actions.py

@@ -175,7 +175,6 @@ class LatexPaperFileGroup():
         self.sp_file_contents = []
         self.sp_file_index = []
         self.sp_file_tag = []
-
         # count_token
         from request_llms.bridge_all import model_info
         enc = model_info["gpt-3.5-turbo"]['tokenizer']
@@ -192,13 +191,12 @@ class LatexPaperFileGroup():
                 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)
+                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                segments = breakdown_text_to_satisfy_token_limit(file_content, 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))]
@@ -404,7 +402,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
             result_pdf = pj(work_folder_modified, f'merge_diff.pdf')    # get pdf path
             promote_file_to_downloadzone(result_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
         if modified_pdf_success:
-            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 即将退出 ...', chatbot, history)    # 刷新Gradio前端界面
+            yield from update_ui_lastest_msg(f'转化PDF编译已经成功, 正在尝试生成对比PDF, 请稍候 ...', chatbot, history)    # 刷新Gradio前端界面
             result_pdf = pj(work_folder_modified, f'{main_file_modified}.pdf') # get pdf path
             origin_pdf = pj(work_folder_original, f'{main_file_original}.pdf') # get pdf path
             if os.path.exists(pj(work_folder, '..', 'translation')):

crazy_functions/latex_fns/latex_toolbox.py

@@ -1,15 +1,18 @@
 import os, shutil
 import re
 import numpy as np
+
 PRESERVE = 0
 TRANSFORM = 1
 
 pj = os.path.join
 
-class LinkedListNode():
+
+class LinkedListNode:
     """
     Linked List Node
     """
+
     def __init__(self, string, preserve=True) -> None:
         self.string = string
         self.preserve = preserve
@@ -18,41 +21,47 @@ class LinkedListNode():
         # self.begin_line = 0
         # self.begin_char = 0
 
+
 def convert_to_linklist(text, mask):
     root = LinkedListNode("", preserve=True)
     current_node = root
     for c, m, i in zip(text, mask, range(len(text))):
-        if (m==PRESERVE and current_node.preserve) \
-            or (m==TRANSFORM and not current_node.preserve):
+        if (m == PRESERVE and current_node.preserve) or (
+            m == TRANSFORM and not current_node.preserve
+        ):
             # add
             current_node.string += c
         else:
-            current_node.next = LinkedListNode(c, preserve=(m==PRESERVE))
+            current_node.next = LinkedListNode(c, preserve=(m == PRESERVE))
             current_node = current_node.next
     return root
 
+
 def post_process(root):
     # 修复括号
     node = root
     while True:
         string = node.string
-        if node.preserve: 
+        if node.preserve:
             node = node.next
-            if node is None: break
+            if node is None:
+                break
             continue
+
         def break_check(string):
-            str_stack = [""] # (lv, index)
+            str_stack = [""]  # (lv, index)
             for i, c in enumerate(string):
-                if c == '{':
-                    str_stack.append('{')
-                elif c == '}':
+                if c == "{":
+                    str_stack.append("{")
+                elif c == "}":
                     if len(str_stack) == 1:
-                        print('stack fix')
+                        print("stack fix")
                         return i
                     str_stack.pop(-1)
                 else:
                     str_stack[-1] += c
             return -1
+
         bp = break_check(string)
 
         if bp == -1:
@@ -69,51 +78,66 @@ def post_process(root):
             node.next = q
 
         node = node.next
-        if node is None: break
+        if node is None:
+            break
 
     # 屏蔽空行和太短的句子
     node = root
     while True:
-        if len(node.string.strip('\n').strip(''))==0: node.preserve = True
-        if len(node.string.strip('\n').strip(''))<42: node.preserve = True
+        if len(node.string.strip("\n").strip("")) == 0:
+            node.preserve = True
+        if len(node.string.strip("\n").strip("")) < 42:
+            node.preserve = True
         node = node.next
-        if node is None: break
+        if node is None:
+            break
     node = root
     while True:
         if node.next and node.preserve and node.next.preserve:
             node.string += node.next.string
             node.next = node.next.next
         node = node.next
-        if node is None: break
+        if node is None:
+            break
 
     # 将前后断行符脱离
     node = root
     prev_node = None
     while True:
         if not node.preserve:
-            lstriped_ = node.string.lstrip().lstrip('\n')
-            if (prev_node is not None) and (prev_node.preserve) and (len(lstriped_)!=len(node.string)):
-                prev_node.string += node.string[:-len(lstriped_)]
+            lstriped_ = node.string.lstrip().lstrip("\n")
+            if (
+                (prev_node is not None)
+                and (prev_node.preserve)
+                and (len(lstriped_) != len(node.string))
+            ):
+                prev_node.string += node.string[: -len(lstriped_)]
                 node.string = lstriped_
-            rstriped_ = node.string.rstrip().rstrip('\n')
-            if (node.next is not None) and (node.next.preserve) and (len(rstriped_)!=len(node.string)):
-                node.next.string = node.string[len(rstriped_):] + node.next.string
+            rstriped_ = node.string.rstrip().rstrip("\n")
+            if (
+                (node.next is not None)
+                and (node.next.preserve)
+                and (len(rstriped_) != len(node.string))
+            ):
+                node.next.string = node.string[len(rstriped_) :] + node.next.string
                 node.string = rstriped_
-        # =====
+        # =-=-=
         prev_node = node
         node = node.next
-        if node is None: break
+        if node is None:
+            break
 
     # 标注节点的行数范围
     node = root
     n_line = 0
     expansion = 2
     while True:
-        n_l = node.string.count('\n')
-        node.range = [n_line-expansion, n_line+n_l+expansion]   # 失败时，扭转的范围
-        n_line = n_line+n_l
+        n_l = node.string.count("\n")
+        node.range = [n_line - expansion, n_line + n_l + expansion]  # 失败时，扭转的范围
+        n_line = n_line + n_l
         node = node.next
-        if node is None: break
+        if node is None:
+            break
     return root
 
 
@@ -128,97 +152,125 @@ def set_forbidden_text(text, mask, pattern, flags=0):
     """
     Add a preserve text area in this paper
     e.g. with pattern = r"\\begin\{algorithm\}(.*?)\\end\{algorithm\}"
-    you can mask out (mask = PRESERVE so that text become untouchable for GPT) 
+    you can mask out (mask = PRESERVE so that text become untouchable for GPT)
     everything between "\begin{equation}" and "\end{equation}"
     """
-    if isinstance(pattern, list): pattern = '|'.join(pattern)
+    if isinstance(pattern, list):
+        pattern = "|".join(pattern)
     pattern_compile = re.compile(pattern, flags)
     for res in pattern_compile.finditer(text):
-        mask[res.span()[0]:res.span()[1]] = PRESERVE
+        mask[res.span()[0] : res.span()[1]] = PRESERVE
     return text, mask
 
+
 def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
     """
     Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch compelete text area. 
+    count the number of the braces so as to catch compelete text area.
     e.g.
-    \begin{abstract} blablablablablabla. \end{abstract} 
+    \begin{abstract} blablablablablabla. \end{abstract}
     """
-    if isinstance(pattern, list): pattern = '|'.join(pattern)
+    if isinstance(pattern, list):
+        pattern = "|".join(pattern)
     pattern_compile = re.compile(pattern, flags)
     for res in pattern_compile.finditer(text):
         if not forbid_wrapper:
-            mask[res.span()[0]:res.span()[1]] = TRANSFORM
+            mask[res.span()[0] : res.span()[1]] = TRANSFORM
         else:
-            mask[res.regs[0][0]: res.regs[1][0]] = PRESERVE   # '\\begin{abstract}'
-            mask[res.regs[1][0]: res.regs[1][1]] = TRANSFORM   # abstract
-            mask[res.regs[1][1]: res.regs[0][1]] = PRESERVE   # abstract
+            mask[res.regs[0][0] : res.regs[1][0]] = PRESERVE  # '\\begin{abstract}'
+            mask[res.regs[1][0] : res.regs[1][1]] = TRANSFORM  # abstract
+            mask[res.regs[1][1] : res.regs[0][1]] = PRESERVE  # abstract
     return text, mask
 
+
 def set_forbidden_text_careful_brace(text, mask, pattern, flags=0):
     """
     Add a preserve text area in this paper (text become untouchable for GPT).
-    count the number of the braces so as to catch compelete text area. 
+    count the number of the braces so as to catch compelete text area.
     e.g.
-    \caption{blablablablabla\texbf{blablabla}blablabla.} 
+    \caption{blablablablabla\texbf{blablabla}blablabla.}
     """
     pattern_compile = re.compile(pattern, flags)
     for res in pattern_compile.finditer(text):
         brace_level = -1
         p = begin = end = res.regs[0][0]
-        for _ in range(1024*16):
-            if text[p] == '}' and brace_level == 0: break
-            elif text[p] == '}':  brace_level -= 1
-            elif text[p] == '{':  brace_level += 1
+        for _ in range(1024 * 16):
+            if text[p] == "}" and brace_level == 0:
+                break
+            elif text[p] == "}":
+                brace_level -= 1
+            elif text[p] == "{":
+                brace_level += 1
             p += 1
-        end = p+1
+        end = p + 1
         mask[begin:end] = PRESERVE
     return text, mask
 
-def reverse_forbidden_text_careful_brace(text, mask, pattern, flags=0, forbid_wrapper=True):
+
+def reverse_forbidden_text_careful_brace(
+    text, mask, pattern, flags=0, forbid_wrapper=True
+):
     """
     Move area out of preserve area (make text editable for GPT)
-    count the number of the braces so as to catch compelete text area. 
+    count the number of the braces so as to catch compelete text area.
     e.g.
-    \caption{blablablablabla\texbf{blablabla}blablabla.} 
+    \caption{blablablablabla\texbf{blablabla}blablabla.}
     """
     pattern_compile = re.compile(pattern, flags)
     for res in pattern_compile.finditer(text):
         brace_level = 0
         p = begin = end = res.regs[1][0]
-        for _ in range(1024*16):
-            if text[p] == '}' and brace_level == 0: break
-            elif text[p] == '}':  brace_level -= 1
-            elif text[p] == '{':  brace_level += 1
+        for _ in range(1024 * 16):
+            if text[p] == "}" and brace_level == 0:
+                break
+            elif text[p] == "}":
+                brace_level -= 1
+            elif text[p] == "{":
+                brace_level += 1
             p += 1
         end = p
         mask[begin:end] = TRANSFORM
         if forbid_wrapper:
-            mask[res.regs[0][0]:begin] = PRESERVE
-            mask[end:res.regs[0][1]] = PRESERVE
+            mask[res.regs[0][0] : begin] = PRESERVE
+            mask[end : res.regs[0][1]] = PRESERVE
     return text, mask
 
+
 def set_forbidden_text_begin_end(text, mask, pattern, flags=0, limit_n_lines=42):
     """
     Find all \begin{} ... \end{} text block that with less than limit_n_lines lines.
     Add it to preserve area
     """
     pattern_compile = re.compile(pattern, flags)
+
     def search_with_line_limit(text, mask):
         for res in pattern_compile.finditer(text):
             cmd = res.group(1)  # begin{what}
-            this = res.group(2) # content between begin and end
-            this_mask = mask[res.regs[2][0]:res.regs[2][1]]
-            white_list = ['document', 'abstract', 'lemma', 'definition', 'sproof', 
-                          'em', 'emph', 'textit', 'textbf', 'itemize', 'enumerate']
-            if (cmd in white_list) or this.count('\n') >= limit_n_lines: # use a magical number 42
+            this = res.group(2)  # content between begin and end
+            this_mask = mask[res.regs[2][0] : res.regs[2][1]]
+            white_list = [
+                "document",
+                "abstract",
+                "lemma",
+                "definition",
+                "sproof",
+                "em",
+                "emph",
+                "textit",
+                "textbf",
+                "itemize",
+                "enumerate",
+            ]
+            if (cmd in white_list) or this.count(
+                "\n"
+            ) >= limit_n_lines:  # use a magical number 42
                 this, this_mask = search_with_line_limit(this, this_mask)
-                mask[res.regs[2][0]:res.regs[2][1]] = this_mask
+                mask[res.regs[2][0] : res.regs[2][1]] = this_mask
             else:
-                mask[res.regs[0][0]:res.regs[0][1]] = PRESERVE
+                mask[res.regs[0][0] : res.regs[0][1]] = PRESERVE
         return text, mask
-    return search_with_line_limit(text, mask) 
 
+    return search_with_line_limit(text, mask)
 
 
 """
@@ -227,6 +279,7 @@ Latex Merge File
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 """
 
+
 def find_main_tex_file(file_manifest, mode):
     """
     在多Tex文档中，寻找主文件，必须包含documentclass，返回找到的第一个。
@@ -234,27 +287,36 @@ def find_main_tex_file(file_manifest, mode):
     """
     canidates = []
     for texf in file_manifest:
-        if os.path.basename(texf).startswith('merge'):
+        if os.path.basename(texf).startswith("merge"):
             continue
-        with open(texf, 'r', encoding='utf8', errors='ignore') as f:
+        with open(texf, "r", encoding="utf8", errors="ignore") as f:
             file_content = f.read()
-        if r'\documentclass' in file_content:
+        if r"\documentclass" in file_content:
             canidates.append(texf)
         else:
             continue
 
     if len(canidates) == 0:
-        raise RuntimeError('无法找到一个主Tex文件（包含documentclass关键字）')
+        raise RuntimeError("无法找到一个主Tex文件（包含documentclass关键字）")
     elif len(canidates) == 1:
         return canidates[0]
-    else: # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
+    else:  # if len(canidates) >= 2 通过一些Latex模板中常见（但通常不会出现在正文）的单词，对不同latex源文件扣分，取评分最高者返回
         canidates_score = []
         # 给出一些判定模板文档的词作为扣分项
-        unexpected_words = ['\LaTeX', 'manuscript', 'Guidelines', 'font', 'citations', 'rejected', 'blind review', 'reviewers']
-        expected_words = ['\input', '\ref', '\cite']
+        unexpected_words = [
+            "\\LaTeX",
+            "manuscript",
+            "Guidelines",
+            "font",
+            "citations",
+            "rejected",
+            "blind review",
+            "reviewers",
+        ]
+        expected_words = ["\\input", "\\ref", "\\cite"]
         for texf in canidates:
             canidates_score.append(0)
-            with open(texf, 'r', encoding='utf8', errors='ignore') as f:
+            with open(texf, "r", encoding="utf8", errors="ignore") as f:
                 file_content = f.read()
                 file_content = rm_comments(file_content)
             for uw in unexpected_words:
@@ -263,9 +325,10 @@ def find_main_tex_file(file_manifest, mode):
             for uw in expected_words:
                 if uw in file_content:
                     canidates_score[-1] += 1
-        select = np.argmax(canidates_score) # 取评分最高者返回
+        select = np.argmax(canidates_score)  # 取评分最高者返回
         return canidates[select]
-    
+
+
 def rm_comments(main_file):
     new_file_remove_comment_lines = []
     for l in main_file.splitlines():
@@ -274,30 +337,39 @@ def rm_comments(main_file):
             pass
         else:
             new_file_remove_comment_lines.append(l)
-    main_file = '\n'.join(new_file_remove_comment_lines)
+    main_file = "\n".join(new_file_remove_comment_lines)
     # main_file = re.sub(r"\\include{(.*?)}", r"\\input{\1}", main_file)  # 将 \include 命令转换为 \input 命令
-    main_file = re.sub(r'(?<!\\)%.*', '', main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串
+    main_file = re.sub(r"(?<!\\)%.*", "", main_file)  # 使用正则表达式查找半行注释, 并替换为空字符串
     return main_file
 
+
 def find_tex_file_ignore_case(fp):
     dir_name = os.path.dirname(fp)
     base_name = os.path.basename(fp)
     # 如果输入的文件路径是正确的
-    if os.path.isfile(pj(dir_name, base_name)): return pj(dir_name, base_name)
+    if os.path.isfile(pj(dir_name, base_name)):
+        return pj(dir_name, base_name)
     # 如果不正确，试着加上.tex后缀试试
-    if not base_name.endswith('.tex'): base_name+='.tex'
-    if os.path.isfile(pj(dir_name, base_name)): return pj(dir_name, base_name)
+    if not base_name.endswith(".tex"):
+        base_name += ".tex"
+    if os.path.isfile(pj(dir_name, base_name)):
+        return pj(dir_name, base_name)
     # 如果还找不到，解除大小写限制，再试一次
     import glob
-    for f in glob.glob(dir_name+'/*.tex'):
+
+    for f in glob.glob(dir_name + "/*.tex"):
         base_name_s = os.path.basename(fp)
         base_name_f = os.path.basename(f)
-        if base_name_s.lower() == base_name_f.lower(): return f
+        if base_name_s.lower() == base_name_f.lower():
+            return f
         # 试着加上.tex后缀试试
-        if not base_name_s.endswith('.tex'): base_name_s+='.tex'
-        if base_name_s.lower() == base_name_f.lower(): return f
+        if not base_name_s.endswith(".tex"):
+            base_name_s += ".tex"
+        if base_name_s.lower() == base_name_f.lower():
+            return f
     return None
 
+
 def merge_tex_files_(project_foler, main_file, mode):
     """
     Merge Tex project recrusively
@@ -309,18 +381,18 @@ def merge_tex_files_(project_foler, main_file, mode):
         fp_ = find_tex_file_ignore_case(fp)
         if fp_:
             try:
-                with open(fp_, 'r', encoding='utf-8', errors='replace') as fx: c = fx.read()
+                with open(fp_, "r", encoding="utf-8", errors="replace") as fx:
+                    c = fx.read()
             except:
                 c = f"\n\nWarning from GPT-Academic: LaTex source file is missing!\n\n"
         else:
-            raise RuntimeError(f'找不到{fp}，Tex源文件缺失！')
+            raise RuntimeError(f"找不到{fp}，Tex源文件缺失！")
         c = merge_tex_files_(project_foler, c, mode)
-        main_file = main_file[:s.span()[0]] + c + main_file[s.span()[1]:]
+        main_file = main_file[: s.span()[0]] + c + main_file[s.span()[1] :]
     return main_file
 
 
 def find_title_and_abs(main_file):
-
     def extract_abstract_1(text):
         pattern = r"\\abstract\{(.*?)\}"
         match = re.search(pattern, text, re.DOTALL)
@@ -362,21 +434,30 @@ def merge_tex_files(project_foler, main_file, mode):
     main_file = merge_tex_files_(project_foler, main_file, mode)
     main_file = rm_comments(main_file)
 
-    if mode == 'translate_zh':
+    if mode == "translate_zh":
         # find paper documentclass
-        pattern = re.compile(r'\\documentclass.*\n')
+        pattern = re.compile(r"\\documentclass.*\n")
         match = pattern.search(main_file)
         assert match is not None, "Cannot find documentclass statement!"
         position = match.end()
-        add_ctex = '\\usepackage{ctex}\n'
-        add_url = '\\usepackage{url}\n' if '{url}' not in main_file else ''
+        add_ctex = "\\usepackage{ctex}\n"
+        add_url = "\\usepackage{url}\n" if "{url}" not in main_file else ""
         main_file = main_file[:position] + add_ctex + add_url + main_file[position:]
         # fontset=windows
         import platform
-        main_file = re.sub(r"\\documentclass\[(.*?)\]{(.*?)}", r"\\documentclass[\1,fontset=windows,UTF8]{\2}",main_file)
-        main_file = re.sub(r"\\documentclass{(.*?)}", r"\\documentclass[fontset=windows,UTF8]{\1}",main_file)
+
+        main_file = re.sub(
+            r"\\documentclass\[(.*?)\]{(.*?)}",
+            r"\\documentclass[\1,fontset=windows,UTF8]{\2}",
+            main_file,
+        )
+        main_file = re.sub(
+            r"\\documentclass{(.*?)}",
+            r"\\documentclass[fontset=windows,UTF8]{\1}",
+            main_file,
+        )
         # find paper abstract
-        pattern_opt1 = re.compile(r'\\begin\{abstract\}.*\n')
+        pattern_opt1 = re.compile(r"\\begin\{abstract\}.*\n")
         pattern_opt2 = re.compile(r"\\abstract\{(.*?)\}", flags=re.DOTALL)
         match_opt1 = pattern_opt1.search(main_file)
         match_opt2 = pattern_opt2.search(main_file)
@@ -385,7 +466,9 @@ def merge_tex_files(project_foler, main_file, mode):
             main_file = insert_abstract(main_file)
         match_opt1 = pattern_opt1.search(main_file)
         match_opt2 = pattern_opt2.search(main_file)
-        assert (match_opt1 is not None) or (match_opt2 is not None), "Cannot find paper abstract section!"
+        assert (match_opt1 is not None) or (
+            match_opt2 is not None
+        ), "Cannot find paper abstract section!"
     return main_file
 
 
@@ -395,6 +478,7 @@ The GPT-Academic program cannot find abstract section in this paper.
 \end{abstract}
 """
 
+
 def insert_abstract(tex_content):
     if "\\maketitle" in tex_content:
         # find the position of "\maketitle"
@@ -402,7 +486,13 @@ def insert_abstract(tex_content):
         # find the nearest ending line
         end_line_index = tex_content.find("\n", find_index)
         # insert "abs_str" on the next line
-        modified_tex = tex_content[:end_line_index+1] + '\n\n' + insert_missing_abs_str + '\n\n' + tex_content[end_line_index+1:]
+        modified_tex = (
+            tex_content[: end_line_index + 1]
+            + "\n\n"
+            + insert_missing_abs_str
+            + "\n\n"
+            + tex_content[end_line_index + 1 :]
+        )
         return modified_tex
     elif r"\begin{document}" in tex_content:
         # find the position of "\maketitle"
@@ -410,29 +500,39 @@ def insert_abstract(tex_content):
         # find the nearest ending line
         end_line_index = tex_content.find("\n", find_index)
         # insert "abs_str" on the next line
-        modified_tex = tex_content[:end_line_index+1] + '\n\n' + insert_missing_abs_str + '\n\n' + tex_content[end_line_index+1:]
+        modified_tex = (
+            tex_content[: end_line_index + 1]
+            + "\n\n"
+            + insert_missing_abs_str
+            + "\n\n"
+            + tex_content[end_line_index + 1 :]
+        )
         return modified_tex
     else:
         return tex_content
 
+
 """
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 Post process
 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 """
+
+
 def mod_inbraket(match):
     """
-    为啥chatgpt会把cite里面的逗号换成中文逗号呀 
+    为啥chatgpt会把cite里面的逗号换成中文逗号呀
     """
     # get the matched string
     cmd = match.group(1)
     str_to_modify = match.group(2)
     # modify the matched string
-    str_to_modify = str_to_modify.replace('：', ':')    # 前面是中文冒号，后面是英文冒号
-    str_to_modify = str_to_modify.replace('，', ',')    # 前面是中文逗号，后面是英文逗号
+    str_to_modify = str_to_modify.replace("：", ":")  # 前面是中文冒号，后面是英文冒号
+    str_to_modify = str_to_modify.replace("，", ",")  # 前面是中文逗号，后面是英文逗号
     # str_to_modify = 'BOOM'
     return "\\" + cmd + "{" + str_to_modify + "}"
 
+
 def fix_content(final_tex, node_string):
     """
     Fix common GPT errors to increase success rate
@@ -443,10 +543,10 @@ def fix_content(final_tex, node_string):
     final_tex = re.sub(r"\\([a-z]{2,10})\{([^\}]*?)\}", mod_inbraket, string=final_tex)
 
     if "Traceback" in final_tex and "[Local Message]" in final_tex:
-        final_tex = node_string # 出问题了，还原原文
-    if node_string.count('\\begin') != final_tex.count('\\begin'):
-        final_tex = node_string # 出问题了，还原原文
-    if node_string.count('\_') > 0 and node_string.count('\_') > final_tex.count('\_'):
+        final_tex = node_string  # 出问题了，还原原文
+    if node_string.count("\\begin") != final_tex.count("\\begin"):
+        final_tex = node_string  # 出问题了，还原原文
+    if node_string.count("\_") > 0 and node_string.count("\_") > final_tex.count("\_"):
         # walk and replace any _ without \
         final_tex = re.sub(r"(?<!\\)_", "\\_", final_tex)
 
@@ -454,24 +554,32 @@ def fix_content(final_tex, node_string):
         # this function count the number of { and }
         brace_level = 0
         for c in string:
-            if c == "{": brace_level += 1
-            elif c == "}": brace_level -= 1
+            if c == "{":
+                brace_level += 1
+            elif c == "}":
+                brace_level -= 1
         return brace_level
+
     def join_most(tex_t, tex_o):
         # this function join translated string and original string when something goes wrong
         p_t = 0
         p_o = 0
+
         def find_next(string, chars, begin):
             p = begin
             while p < len(string):
-                if string[p] in chars: return p, string[p]
+                if string[p] in chars:
+                    return p, string[p]
                 p += 1
             return None, None
+
         while True:
-            res1, char = find_next(tex_o, ['{','}'], p_o)
-            if res1 is None: break
+            res1, char = find_next(tex_o, ["{", "}"], p_o)
+            if res1 is None:
+                break
             res2, char = find_next(tex_t, [char], p_t)
-            if res2 is None: break
+            if res2 is None:
+                break
             p_o = res1 + 1
             p_t = res2 + 1
         return tex_t[:p_t] + tex_o[p_o:]
@@ -480,10 +588,14 @@ def fix_content(final_tex, node_string):
         # 出问题了，还原部分原文，保证括号正确
         final_tex = join_most(final_tex, node_string)
     return final_tex
-    
+
+
 def compile_latex_with_timeout(command, cwd, timeout=60):
     import subprocess
-    process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd)
+
+    process = subprocess.Popen(
+        command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=cwd
+    )
     try:
         stdout, stderr = process.communicate(timeout=timeout)
     except subprocess.TimeoutExpired:
@@ -493,43 +605,52 @@ def compile_latex_with_timeout(command, cwd, timeout=60):
         return False
     return True
 
+
 def run_in_subprocess_wrapper_func(func, args, kwargs, return_dict, exception_dict):
     import sys
+
     try:
         result = func(*args, **kwargs)
-        return_dict['result'] = result
+        return_dict["result"] = result
     except Exception as e:
         exc_info = sys.exc_info()
-        exception_dict['exception'] = exc_info
+        exception_dict["exception"] = exc_info
+
 
 def run_in_subprocess(func):
     import multiprocessing
+
     def wrapper(*args, **kwargs):
         return_dict = multiprocessing.Manager().dict()
         exception_dict = multiprocessing.Manager().dict()
-        process = multiprocessing.Process(target=run_in_subprocess_wrapper_func, 
-                                            args=(func, args, kwargs, return_dict, exception_dict))
+        process = multiprocessing.Process(
+            target=run_in_subprocess_wrapper_func,
+            args=(func, args, kwargs, return_dict, exception_dict),
+        )
         process.start()
         process.join()
         process.close()
-        if 'exception' in exception_dict:
+        if "exception" in exception_dict:
             # ooops, the subprocess ran into an exception
-            exc_info = exception_dict['exception']
+            exc_info = exception_dict["exception"]
             raise exc_info[1].with_traceback(exc_info[2])
-        if 'result' in return_dict.keys():
+        if "result" in return_dict.keys():
             # If the subprocess ran successfully, return the result
-            return return_dict['result']
+            return return_dict["result"]
+
     return wrapper
 
+
 def _merge_pdfs(pdf1_path, pdf2_path, output_path):
-    import PyPDF2   # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
+    import PyPDF2  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
+
     Percent = 0.95
     # raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
     # Open the first PDF file
-    with open(pdf1_path, 'rb') as pdf1_file:
+    with open(pdf1_path, "rb") as pdf1_file:
         pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
         # Open the second PDF file
-        with open(pdf2_path, 'rb') as pdf2_file:
+        with open(pdf2_path, "rb") as pdf2_file:
             pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
             # Create a new PDF file to store the merged pages
             output_writer = PyPDF2.PdfFileWriter()
@@ -549,14 +670,25 @@ def _merge_pdfs(pdf1_path, pdf2_path, output_path):
                     page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
                 # Create a new empty page with double width
                 new_page = PyPDF2.PageObject.createBlankPage(
-                    width = int(int(page1.mediaBox.getWidth()) + int(page2.mediaBox.getWidth()) * Percent),
-                    height = max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight())
+                    width=int(
+                        int(page1.mediaBox.getWidth())
+                        + int(page2.mediaBox.getWidth()) * Percent
+                    ),
+                    height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
                 )
                 new_page.mergeTranslatedPage(page1, 0, 0)
-                new_page.mergeTranslatedPage(page2, int(int(page1.mediaBox.getWidth())-int(page2.mediaBox.getWidth())* (1-Percent)), 0)
+                new_page.mergeTranslatedPage(
+                    page2,
+                    int(
+                        int(page1.mediaBox.getWidth())
+                        - int(page2.mediaBox.getWidth()) * (1 - Percent)
+                    ),
+                    0,
+                )
                 output_writer.addPage(new_page)
             # Save the merged PDF file
-            with open(output_path, 'wb') as output_file:
+            with open(output_path, "wb") as output_file:
                 output_writer.write(output_file)
 
-merge_pdfs = run_in_subprocess(_merge_pdfs) # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
+
+merge_pdfs = run_in_subprocess(_merge_pdfs)  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放

crazy_functions/pdf_fns/breakdown_txt.py

@@ -0,0 +1,125 @@
+from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
+
+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 maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage):
+    """ 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
+    当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出
+    """
+    _min = int(5e4)
+    _max = int(1e5)
+    # print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage))
+    if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0:
+        remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage
+        remain_txt_to_cut_storage = ""
+    if len(remain_txt_to_cut) > _max:
+        remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage
+        remain_txt_to_cut = remain_txt_to_cut[:_max]
+    return remain_txt_to_cut, remain_txt_to_cut_storage
+
+
+def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False):
+    """ 文本切分
+    """
+    res = []
+    total_len = len(txt_tocut)
+    fin_len = 0
+    remain_txt_to_cut = txt_tocut
+    remain_txt_to_cut_storage = ""
+    # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage
+    remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
+    
+    while True:
+        if get_token_fn(remain_txt_to_cut) <= limit:
+            # 如果剩余文本的token数小于限制，那么就不用切了
+            res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut)
+            break
+        else:
+            # 如果剩余文本的token数大于限制，那么就切
+            lines = remain_txt_to_cut.split('\n')
+
+            # 估计一个切分点
+            estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines)
+            estimated_line_cut = int(estimated_line_cut)
+
+            # 开始查找合适切分点的偏移（cnt）
+            cnt = 0
+            for cnt in reversed(range(estimated_line_cut)):
+                if must_break_at_empty_line:
+                    # 首先尝试用双空行（\n\n）作为切分点
+                    if lines[cnt] != "":
+                        continue
+                prev = "\n".join(lines[:cnt])
+                post = "\n".join(lines[cnt:])
+                if get_token_fn(prev) < limit:
+                    break
+
+            if cnt == 0:
+                # 如果没有找到合适的切分点
+                if break_anyway:
+                    # 是否允许暴力切分
+                    prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn)
+                else:
+                    # 不允许直接报错
+                    raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}")
+
+            # 追加列表
+            res.append(prev); fin_len+=len(prev)
+            # 准备下一次迭代
+            remain_txt_to_cut = post
+            remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
+            process = fin_len/total_len
+            print(f'正在文本切分 {int(process*100)}%')
+            if len(remain_txt_to_cut.strip()) == 0:
+                break
+    return res
+
+
+def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"):
+    """ 使用多种方式尝试切分文本，以满足 token 限制
+    """
+    from request_llms.bridge_all import model_info
+    enc = model_info[llm_model]['tokenizer']
+    def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
+    try:
+        # 第1次尝试，将双空行（\n\n）作为切分点
+        return cut(limit, get_token_fn, 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(limit, get_token_fn, 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(limit, get_token_fn, 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(limit, get_token_fn, 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，随便切一下吧
+                    return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True)
+
+breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60)
+
+if __name__ == '__main__':
+    from crazy_functions.crazy_utils import read_and_clean_pdf_text
+    file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf")
+
+    from request_llms.bridge_all import model_info
+    for i in range(5):
+        file_content += file_content
+
+    print(len(file_content))
+    TOKEN_LIMIT_PER_FRAGMENT = 2500
+    res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
+

crazy_functions/pdf_fns/parse_pdf.py

@@ -74,7 +74,7 @@ def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chat
 
 def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG):
     from crazy_functions.pdf_fns.report_gen_html import construct_html
-    from crazy_functions.crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
     from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
     from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 
@@ -116,7 +116,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
             # find a smooth token limit to achieve even seperation
             count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
             token_limit_smooth = raw_token_num // count + count
-            return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth)
+            return breakdown_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model'])
 
     for section in article_dict.get('sections'):
         if len(section['text']) == 0: continue

crazy_functions/互动小游戏.py

@@ -3,47 +3,28 @@ from crazy_functions.multi_stage.multi_stage_utils import GptAcademicGameBaseSta
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.game_fns.game_utils import get_code_block, is_same_thing
-import random
-
-
-class MiniGame_ASCII_Art(GptAcademicGameBaseState):
-    
-    def step(self, prompt, chatbot, history):
-        if self.step_cnt == 0:  
-            chatbot.append(["我画你猜（动物）", "请稍等..."])
-        else:
-            if prompt.strip() == 'exit':
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
-                return
-            chatbot.append([prompt, ""])
-        yield from update_ui(chatbot=chatbot, history=history)
-
-        if self.step_cnt == 0:
-            self.lock_plugin(chatbot)
-            self.cur_task = 'draw'
-
-        if self.cur_task == 'draw':
-            avail_obj = ["狗","猫","鸟","鱼","老鼠","蛇"]
-            self.obj = random.choice(avail_obj)
-            inputs = "I want to play a game called Guess the ASCII art. You can draw the ASCII art and I will try to guess it. " + f"This time you draw a {self.obj}. Note that you must not indicate what you have draw in the text, and you should only produce the ASCII art wrapped by ```. "
-            raw_res = predict_no_ui_long_connection(inputs=inputs, llm_kwargs=self.llm_kwargs, history=[], sys_prompt="")
-            self.cur_task = 'identify user guess'
-            res = get_code_block(raw_res)
-            history += ['', f'the answer is {self.obj}', inputs, res]
-            yield from update_ui_lastest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
-
-        elif self.cur_task == 'identify user guess':
-            if is_same_thing(self.obj, prompt, self.llm_kwargs):
-                self.delete_game = True
-                yield from update_ui_lastest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
-            else:
-                self.cur_task = 'identify user guess'
-                yield from update_ui_lastest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
-            
 
 @CatchException
 def 随机小游戏(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    from crazy_functions.game_fns.game_interactive_story import MiniGame_ResumeStory
+    # 清空历史
+    history = []
+    # 选择游戏
+    cls = MiniGame_ResumeStory
+    # 如果之前已经初始化了游戏实例，则继续该实例；否则重新初始化
+    state = cls.sync_state(chatbot, 
+                           llm_kwargs, 
+                           cls, 
+                           plugin_name='MiniGame_ResumeStory',
+                           callback_fn='crazy_functions.互动小游戏->随机小游戏',
+                           lock_plugin=True
+                           )
+    yield from state.continue_game(prompt, chatbot, history)
+
+
+@CatchException
+def 随机小游戏1(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    from crazy_functions.game_fns.game_ascii_art import MiniGame_ASCII_Art
     # 清空历史
     history = []
     # 选择游戏
@@ -53,7 +34,7 @@ def 随机小游戏(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_
                            llm_kwargs, 
                            cls, 
                            plugin_name='MiniGame_ASCII_Art',
-                           callback_fn='crazy_functions.互动小游戏->随机小游戏',
+                           callback_fn='crazy_functions.互动小游戏->随机小游戏1',
                            lock_plugin=True
                            )
     yield from state.continue_game(prompt, chatbot, history)

crazy_functions/图片生成.py

@@ -104,7 +104,11 @@ def 图片生成_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
     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(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
+    if prompt.strip() == "":
+        chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
+        return
+    chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文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", '1024x1024')
@@ -121,7 +125,11 @@ def 图片生成_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
 @CatchException
 def 图片生成_DALLE3(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
     history = []    # 清空历史,以免输入溢出
-    chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*或者api2d-*。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
+    if prompt.strip() == "":
+        chatbot.append((prompt, "[Local Message] 图像生成提示为空白，请在“输入区”输入图像生成提示。"))
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 界面更新
+        return
+    chatbot.append(("您正在调用“图像生成”插件。", "[Local Message] 生成图像, 请先把模型切换至gpt-*。如果中文Prompt效果不理想, 请尝试英文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_arg = plugin_kwargs.get("advanced_arg", '1024x1024-standard-vivid').lower()

crazy_functions/总结word文档.py

@@ -29,17 +29,12 @@ def 解析docx(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot
             except:
                 raise RuntimeError('请先将.doc文档转换为.docx文档。')
 
-        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 crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
         from request_llms.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
-        )
+        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
         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}```'

crazy_functions/批量Markdown翻译.py

@@ -28,8 +28,8 @@ class PaperFileGroup():
                 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)
+                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

crazy_functions/批量总结PDF文档.py

@@ -20,14 +20,9 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
         
         TOKEN_LIMIT_PER_FRAGMENT = 2500
 
-        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-        from request_llms.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)
+        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content,  limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+        page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
         # 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]
         

crazy_functions/批量翻译PDF文档_多线程.py

@@ -91,14 +91,9 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
         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_llms.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)
+        from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+        paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+        page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=page_one, limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
 
         # 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]

crazy_functions/理解PDF文档内容.py

@@ -18,14 +18,9 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     
     TOKEN_LIMIT_PER_FRAGMENT = 2500
 
-    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    from request_llms.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)
+    from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+    paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
+    page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=str(page_one), limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
     # 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]
     
@@ -45,7 +40,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     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]}"
+        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

crazy_functions/解析JupyterNotebook.py

@@ -12,13 +12,6 @@ class PaperFileGroup():
         self.sp_file_index = []
         self.sp_file_tag = []
 
-        # count_token
-        from request_llms.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
@@ -29,9 +22,8 @@ class PaperFileGroup():
                 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)
+                from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
+                segments = breakdown_text_to_satisfy_token_limit(file_content, max_token_limit)
                 for j, segment in enumerate(segments):
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)

crazy_functions/高级功能函数模板.py

@@ -26,4 +26,46 @@ def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
         )
         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
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
+
+
+
+
+PROMPT = """
+请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例：
+```mermaid
+graph TD
+    P(编程) --> L1(Python)
+    P(编程) --> L2(C)
+    P(编程) --> L3(C++)
+    P(编程) --> L4(Javascipt)
+    P(编程) --> L5(PHP)
+```
+"""
+@CatchException
+def 测试图表渲染(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，用于灵活调整复杂功能的各种参数
+    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？", "一个测试mermaid绘制图表的功能，您可以在输入框中输入一些关键词，然后使用mermaid+llm绘制图表。"))
+    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 txt == "": txt = "空白的输入栏" # 调皮一下
+    
+    i_say_show_user = f'请绘制有关“{txt}”的逻辑关系图。'
+    i_say = PROMPT.format(subject=txt)
+    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=""
+    )
+    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

@@ -129,7 +129,7 @@ services:
     runtime: nvidia
     devices:
       - /dev/nvidia0:/dev/nvidia0
-      
+
     # 与宿主的网络融合
     network_mode: "host"
     command: >
@@ -163,7 +163,7 @@ services:
     runtime: nvidia
     devices:
       - /dev/nvidia0:/dev/nvidia0
-      
+
     # 与宿主的网络融合
     network_mode: "host"
 
@@ -229,4 +229,3 @@ services:
     # 不使用代理网络拉取最新代码
     command: >
       bash -c "python3 -u main.py"
-

docs/Dockerfile+ChatGLM

@@ -1,2 +1 @@
 # 此Dockerfile不再维护，请前往docs/GithubAction+ChatGLM+Moss
-

docs/Dockerfile+JittorLLM

@@ -1 +1 @@
-# 此Dockerfile不再维护，请前往docs/GithubAction+JittorLLMs
+# 此Dockerfile不再维护，请前往docs/GithubAction+JittorLLMs

docs/GithubAction+AllCapacityBeta

@@ -0,0 +1,53 @@
+# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacity  --network=host --build-arg http_proxy=http://localhost:10881 --build-arg https_proxy=http://localhost:10881 .
+# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacityBeta  --network=host .
+# docker run -it --net=host gpt-academic-all-capacity  bash
+
+# 从NVIDIA源，从而支持显卡（检查宿主的nvidia-smi中的cuda版本必须>=11.3）
+FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
+
+# use python3 as the system default python
+WORKDIR /gpt
+RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
+
+# # 非必要步骤，更换pip源 （以下三行，可以删除）
+# 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
+
+# 下载pytorch
+RUN python3 -m pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
+# 准备pip依赖
+RUN python3 -m pip install openai numpy arxiv rich
+RUN python3 -m pip install colorama Markdown pygments pymupdf
+RUN python3 -m pip install python-docx moviepy pdfminer
+RUN python3 -m pip install zh_langchain==0.2.1 pypinyin
+RUN python3 -m pip install rarfile py7zr
+RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
+# 下载分支
+WORKDIR /gpt
+RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
+WORKDIR /gpt/gpt_academic
+RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
+
+RUN python3 -m pip install -r requirements.txt
+RUN python3 -m pip install -r request_llms/requirements_moss.txt
+RUN python3 -m pip install -r request_llms/requirements_qwen.txt
+RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
+RUN python3 -m pip install -r request_llms/requirements_newbing.txt
+RUN python3 -m pip install nougat-ocr
+
+# 预热Tiktoken模块
+RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+# 安装知识库插件的额外依赖
+RUN apt-get update && apt-get install libgl1 -y
+RUN pip3 install transformers protobuf langchain sentence-transformers  faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
+RUN pip3 install unstructured[all-docs] --upgrade
+RUN python3  -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
+RUN rm -rf /usr/local/lib/python3.8/dist-packages/tests
+
+
+# COPY .cache /root/.cache
+# COPY config_private.py config_private.py
+# 启动
+CMD ["python3", "-u", "main.py"]

docs/GithubAction+NoLocal+Latex

@@ -15,7 +15,7 @@ WORKDIR /gpt
 
 RUN pip3 install openai numpy arxiv rich
 RUN pip3 install colorama Markdown pygments pymupdf
-RUN pip3 install python-docx pdfminer 
+RUN pip3 install python-docx pdfminer
 RUN pip3 install nougat-ocr
 
 # 装载项目文件

docs/GithubAction+NoLocal+Vectordb

@@ -17,10 +17,10 @@ RUN apt-get update && apt-get install libgl1 -y
 RUN pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
 RUN pip3 install transformers protobuf langchain sentence-transformers  faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
 RUN pip3 install unstructured[all-docs] --upgrade
+RUN python3  -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
 
 # 可选步骤，用于预热模块
 RUN python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()'
-RUN python3  -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
 
 # 启动
 CMD ["python3", "-u", "main.py"]

docs/README.Arabic.md

@@ -2,9 +2,9 @@
 
 
 > **ملحوظة**
-> 
+>
 > تمت ترجمة هذا الملف README باستخدام GPT (بواسطة المكون الإضافي لهذا المشروع) وقد لا تكون الترجمة 100٪ موثوقة، يُرجى التمييز بعناية بنتائج الترجمة.
-> 
+>
 > 2023.11.7: عند تثبيت التبعيات، يُرجى اختيار الإصدار المُحدد في `requirements.txt`. الأمر للتثبيت: `pip install -r requirements.txt`.
 
 # <div align=center><img src="logo.png" width="40"> GPT الأكاديمي</div>
@@ -12,14 +12,14 @@
 **إذا كنت تحب هذا المشروع، فيُرجى إعطاؤه Star. لترجمة هذا المشروع إلى لغة عشوائية باستخدام GPT، قم بقراءة وتشغيل [`multi_language.py`](multi_language.py) (تجريبي).
 
 > **ملحوظة**
-> 
+>
 > 1. يُرجى ملاحظة أنها الإضافات (الأزرار) المميزة فقط التي تدعم قراءة الملفات، وبعض الإضافات توجد في قائمة منسدلة في منطقة الإضافات. بالإضافة إلى ذلك، نرحب بأي Pull Request جديد بأعلى أولوية لأي إضافة جديدة.
-> 
+>
 > 2. تُوضّح كل من الملفات في هذا المشروع وظيفتها بالتفصيل في [تقرير الفهم الذاتي `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). يمكنك في أي وقت أن تنقر على إضافة وظيفة ذات صلة لاستدعاء GPT وإعادة إنشاء تقرير الفهم الذاتي للمشروع. للأسئلة الشائعة [`الويكي`](https://github.com/binary-husky/gpt_academic/wiki). [طرق التثبيت العادية](#installation) |  [نصب بنقرة واحدة](https://github.com/binary-husky/gpt_academic/releases) | [تعليمات التكوين](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
-> 
+>
 > 3. يتم توافق هذا المشروع مع ودعم توصيات اللغة البيجائية الأكبر شمولًا وشجاعة لمثل ChatGLM. يمكنك توفير العديد من مفاتيح Api المشتركة في تكوين الملف، مثل `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. عند تبديل مؤقت لـ `API_KEY`، قم بإدخال `API_KEY` المؤقت في منطقة الإدخال ثم اضغط على زر "إدخال" لجعله ساري المفعول.
 
- 
+
 
 <div align="center">
 
@@ -46,7 +46,7 @@
 ⭐إضغط على وكيل "شارلوت الذكي" | [وظائف] استكمال الذكاء للكأس الأول للذكاء المكتسب من مايكروسوفت، اكتشاف وتطوير عالمي العميل
 تبديل الواجهة المُظلمة | يمكنك التبديل إلى الواجهة المظلمة بإضافة ```/?__theme=dark``` إلى نهاية عنوان URL في المتصفح
 دعم المزيد من نماذج LLM | دعم لجميع GPT3.5 وGPT4 و[ChatGLM2 في جامعة ثوه في لين](https://github.com/THUDM/ChatGLM2-6B) و[MOSS في جامعة فودان](https://github.com/OpenLMLab/MOSS)
-⭐تحوي انطباعة "ChatGLM2" | يدعم استيراد "ChatGLM2" ويوفر إضافة المساعدة في تعديله 
+⭐تحوي انطباعة "ChatGLM2" | يدعم استيراد "ChatGLM2" ويوفر إضافة المساعدة في تعديله
 دعم المزيد من نماذج "LLM"، دعم [نشر الحديس](https://huggingface.co/spaces/qingxu98/gpt-academic) | انضم إلى واجهة "Newbing" (Bing الجديدة)،نقدم نماذج Jittorllms الجديدة تؤيدهم [LLaMA](https://github.com/facebookresearch/llama) و   [盘古α](https://openi.org.cn/pangu/)
 ⭐حزمة "void-terminal" للشبكة (pip) | قم بطلب كافة وظائف إضافة هذا المشروع في python بدون واجهة رسومية (قيد التطوير)
 ⭐PCI-Express لإعلام (PCI) | [وظائف] باللغة الطبيعية، قم بتنفيذ المِهام الأخرى في المشروع
@@ -200,8 +200,8 @@ docker-compose up
 ```
 "ترجمة سوبر الإنجليزية إلى العربية": {
     # البادئة، ستتم إضافتها قبل إدخالاتك. مثلاً، لوصف ما تريده مثل ترجمة أو شرح كود أو تلوين وهلم جرا
-    "بادئة": "يرجى ترجمة النص التالي إلى العربية ثم استخدم جدول Markdown لشرح المصطلحات المختصة المذكورة في النص:\n\n", 
-    
+    "بادئة": "يرجى ترجمة النص التالي إلى العربية ثم استخدم جدول Markdown لشرح المصطلحات المختصة المذكورة في النص:\n\n",
+
     # اللاحقة، سيتم إضافتها بعد إدخالاتك. يمكن استخدامها لوضع علامات اقتباس حول إدخالك.
     "لاحقة": "",
 },
@@ -341,4 +341,3 @@ https://github.com/oobabooga/one-click-installers
 # المزيد:
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.English.md

@@ -18,11 +18,11 @@ To translate this project to arbitrary language with GPT, read and run [`multi_l
 > 1.Please note that only plugins (buttons) highlighted in **bold** support reading files, and some plugins are located in the **dropdown menu** in the plugin area. Additionally, we welcome and process any new plugins with the **highest priority** through PRs.
 >
 > 2.The functionalities of each file in this project are described in detail in the [self-analysis report `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). As the version iterates, you can also click on the relevant function plugin at any time to call GPT to regenerate the project's self-analysis report. Common questions are in the [`wiki`](https://github.com/binary-husky/gpt_academic/wiki). [Regular installation method](#installation) | [One-click installation script](https://github.com/binary-husky/gpt_academic/releases) | [Configuration instructions](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
-> 
+>
 > 3.This project is compatible with and encourages the use of domestic large-scale language models such as ChatGLM. Multiple api-keys can be used together. You can fill in the configuration file with `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"` to temporarily switch `API_KEY` during input, enter the temporary `API_KEY`, and then press enter to apply it.
 
 
- 
+
 
 <div align="center">
 
@@ -126,7 +126,7 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
 【Optional Step】If you need to support THU ChatGLM2 or Fudan MOSS as backends, you need to install additional dependencies (Prerequisites: Familiar with Python + Familiar with Pytorch + Sufficient computer configuration):
 ```sh
 # 【Optional Step I】Support THU ChatGLM2. Note: If you encounter the "Call ChatGLM fail unable to load ChatGLM parameters" error, refer to the following: 1. The default installation above is for torch+cpu version. To use cuda, 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. 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_llms/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt
 
 # 【Optional Step II】Support Fudan MOSS
 python -m pip install -r request_llms/requirements_moss.txt
@@ -204,8 +204,8 @@ For example:
 ```
 "Super Translation": {
     # Prefix: will be added before your input. For example, used to describe your request, such as translation, code explanation, proofreading, etc.
-    "Prefix": "Please translate the following paragraph into Chinese and then explain each proprietary term in the text using a markdown table:\n\n", 
-    
+    "Prefix": "Please translate the following paragraph into Chinese and then explain each proprietary term in the text using a markdown table:\n\n",
+
     # Suffix: will be added after your input. For example, used to wrap your input in quotation marks along with the prefix.
     "Suffix": "",
 },
@@ -355,4 +355,3 @@ https://github.com/oobabooga/one-click-installers
 # More:
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.French.md

@@ -2,9 +2,9 @@
 
 
 > **Remarque**
-> 
+>
 > Ce README a été traduit par GPT (implémenté par le plugin de ce projet) et n'est pas fiable à 100 %. Veuillez examiner attentivement les résultats de la traduction.
-> 
+>
 > 7 novembre 2023 : Lors de l'installation des dépendances, veuillez choisir les versions **spécifiées** dans le fichier `requirements.txt`. Commande d'installation : `pip install -r requirements.txt`.
 
 
@@ -12,7 +12,7 @@
 
 **Si vous aimez ce projet, merci de lui donner une étoile ; si vous avez inventé des raccourcis ou des plugins utiles, n'hésitez pas à envoyer des demandes d'extraction !**
 
-Si vous aimez ce projet, veuillez lui donner une étoile. 
+Si vous aimez ce projet, veuillez lui donner une étoile.
 Pour traduire ce projet dans une langue arbitraire avec GPT, lisez et exécutez [`multi_language.py`](multi_language.py) (expérimental).
 
 > **Remarque**
@@ -22,7 +22,7 @@ Pour traduire ce projet dans une langue arbitraire avec GPT, lisez et exécutez
 > 2. Les fonctionnalités de chaque fichier de ce projet sont spécifiées en détail dans [le rapport d'auto-analyse `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic个项目自译解报告). Vous pouvez également cliquer à tout moment sur les plugins de fonctions correspondants pour appeler GPT et générer un rapport d'auto-analyse du projet. Questions fréquemment posées [wiki](https://github.com/binary-husky/gpt_academic/wiki). [Méthode d'installation standard](#installation) | [Script d'installation en un clic](https://github.com/binary-husky/gpt_academic/releases) | [Instructions de configuration](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)..
 >
 > 3. Ce projet est compatible avec et recommande l'expérimentation de grands modèles de langage chinois tels que ChatGLM, etc. Prend en charge plusieurs clés API, vous pouvez les remplir dans le fichier de configuration comme `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Pour changer temporairement la clé API, entrez la clé API temporaire dans la zone de saisie, puis appuyez sur Entrée pour soumettre et activer celle-ci.
- 
+
 
 <div align="center">
 
@@ -128,7 +128,7 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
 [Optional Steps] If you need to support Tsinghua ChatGLM2/Fudan MOSS as backends, you need to install additional dependencies (Prerequisites: Familiar with Python + Have used PyTorch + Sufficient computer configuration):
 ```sh
 # [Optional Step I] Support Tsinghua ChatGLM2. Comment on this note: If you encounter the error "Call ChatGLM generated an error and cannot load the parameters of ChatGLM", refer to the following: 1: The default installation is the 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 computer configuration, you can modify the model precision in request_llm/bridge_chatglm.py. 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_llms/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt
 
 # [Optional Step II] Support Fudan MOSS
 python -m pip install -r request_llms/requirements_moss.txt
@@ -201,7 +201,7 @@ Par exemple:
 "Traduction avancée de l'anglais vers le français": {
     # Préfixe, ajouté avant votre saisie. Par exemple, utilisez-le pour décrire votre demande, telle que la traduction, l'explication du code, l'amélioration, etc.
     "Prefix": "Veuillez traduire le contenu suivant en français, puis expliquer chaque terme propre à la langue anglaise utilisé dans le texte à l'aide d'un tableau markdown : \n\n",
-    
+
     # Suffixe, ajouté après votre saisie. Par exemple, en utilisant le préfixe, vous pouvez entourer votre contenu par des guillemets.
     "Suffix": "",
 },
@@ -354,4 +354,3 @@ https://github.com/oobabooga/one-click-installers
 # Plus：
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.German.md

@@ -2,9 +2,9 @@
 
 
 > **Hinweis**
-> 
-> Dieses README wurde mithilfe der GPT-Übersetzung (durch das Plugin dieses Projekts) erstellt und ist nicht zu 100 % zuverlässig. Bitte überprüfen Sie die Übersetzungsergebnisse sorgfältig. 
-> 
+>
+> Dieses README wurde mithilfe der GPT-Übersetzung (durch das Plugin dieses Projekts) erstellt und ist nicht zu 100 % zuverlässig. Bitte überprüfen Sie die Übersetzungsergebnisse sorgfältig.
+>
 > 7. November 2023: Beim Installieren der Abhängigkeiten bitte nur die in der `requirements.txt` **angegebenen Versionen** auswählen. Installationsbefehl: `pip install -r requirements.txt`.
 
 
@@ -12,19 +12,19 @@
 
 **Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Star. Wenn Sie praktische Tastenkombinationen oder Plugins entwickelt haben, sind Pull-Anfragen willkommen!**
 
-Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Star. 
+Wenn Ihnen dieses Projekt gefällt, geben Sie ihm bitte einen Star.
 Um dieses Projekt mit GPT in eine beliebige Sprache zu übersetzen, lesen Sie [`multi_language.py`](multi_language.py) (experimentell).
 
 > **Hinweis**
 >
 > 1. Beachten Sie bitte, dass nur die mit **hervorgehobenen** Plugins (Schaltflächen) Dateien lesen können. Einige Plugins befinden sich im **Drop-down-Menü** des Plugin-Bereichs. Außerdem freuen wir uns über jede neue Plugin-PR mit **höchster Priorität**.
-> 
+>
 > 2. Die Funktionen jeder Datei in diesem Projekt sind im [Selbstanalysebericht `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT-Academic-Selbstanalysebericht) ausführlich erläutert. Sie können jederzeit auf die relevanten Funktions-Plugins klicken und GPT aufrufen, um den Selbstanalysebericht des Projekts neu zu generieren. Häufig gestellte Fragen finden Sie im [`Wiki`](https://github.com/binary-husky/gpt_academic/wiki). [Standardinstallationsmethode](#installation) | [Ein-Klick-Installationsskript](https://github.com/binary-husky/gpt_academic/releases) | [Konfigurationsanleitung](https://github.com/binary-husky/gpt_academic/wiki/Projekt-Konfigurationsanleitung).
-> 
+>
 > 3. Dieses Projekt ist kompatibel mit und unterstützt auch die Verwendung von inländischen Sprachmodellen wie ChatGLM. Die gleichzeitige Verwendung mehrerer API-Schlüssel ist möglich, indem Sie sie in der Konfigurationsdatei wie folgt angeben: `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Wenn Sie den `API_KEY` vorübergehend ändern möchten, geben Sie vorübergehend den temporären `API_KEY` im Eingabebereich ein und drücken Sie die Eingabetaste, um die Änderung wirksam werden zu lassen.
 
 
- 
+
 
 <div align="center">
 
@@ -93,7 +93,7 @@ Weitere Funktionen anzeigen (z. B. Bildgenerierung) …… | Siehe das Ende dies
 </div>
 
 # Installation
-### Installation Method I: Run directly (Windows, Linux or MacOS) 
+### Installation Method I: Run directly (Windows, Linux or MacOS)
 
 1. Download the project
 ```sh
@@ -128,7 +128,7 @@ python -m pip install -r requirements.txt # This step is the same as installing
 [Optional] If you need to support Tsinghua ChatGLM2/Fudan MOSS as the backend, you need to install additional dependencies (Prerequisites: Familiar with Python + Have used PyTorch + Strong computer configuration):
 ```sh
 # [Optional Step I] Support Tsinghua ChatGLM2. Tsinghua ChatGLM note: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters normally", refer to the following: 1: The default installation above is torch+cpu version. To use cuda, you need to uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient computer configuration, you can modify the model accuracy in request_llm/bridge_chatglm.py. 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_llms/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt
 
 # [Optional Step II] Support Fudan MOSS
 python -m pip install -r request_llms/requirements_moss.txt
@@ -207,8 +207,8 @@ Beispiel:
 ```
 "Übersetzung von Englisch nach Chinesisch": {
     # Präfix, wird vor Ihrer Eingabe hinzugefügt. Zum Beispiel, um Ihre Anforderungen zu beschreiben, z.B. Übersetzen, Code erklären, verbessern usw.
-    "Präfix": "Bitte übersetzen Sie den folgenden Abschnitt ins Chinesische und erklären Sie dann jedes Fachwort in einer Markdown-Tabelle:\n\n", 
-    
+    "Präfix": "Bitte übersetzen Sie den folgenden Abschnitt ins Chinesische und erklären Sie dann jedes Fachwort in einer Markdown-Tabelle:\n\n",
+
     # Suffix, wird nach Ihrer Eingabe hinzugefügt. Zum Beispiel, um Ihre Eingabe in Anführungszeichen zu setzen.
     "Suffix": "",
 },
@@ -361,4 +361,3 @@ https://github.com/oobabooga/one-click-installers
 # Weitere:
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.Italian.md

@@ -12,7 +12,7 @@
 
 **Se ti piace questo progetto, per favore dagli una stella; se hai idee o plugin utili, fai una pull request!**
 
-Se ti piace questo progetto, dagli una stella. 
+Se ti piace questo progetto, dagli una stella.
 Per tradurre questo progetto in qualsiasi lingua con GPT, leggi ed esegui [`multi_language.py`](multi_language.py) (sperimentale).
 
 > **Nota**
@@ -20,11 +20,11 @@ Per tradurre questo progetto in qualsiasi lingua con GPT, leggi ed esegui [`mult
 > 1. Fai attenzione che solo i plugin (pulsanti) **evidenziati** supportano la lettura dei file, alcuni plugin si trovano nel **menu a tendina** nell'area dei plugin. Inoltre, accogliamo e gestiamo con **massima priorità** qualsiasi nuovo plugin attraverso pull request.
 >
 > 2. Le funzioni di ogni file in questo progetto sono descritte in dettaglio nel [rapporto di traduzione automatica del progetto `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). Con l'iterazione della versione, puoi anche fare clic sui plugin delle funzioni rilevanti in qualsiasi momento per richiamare GPT e rigenerare il rapporto di auto-analisi del progetto. Domande frequenti [`wiki`](https://github.com/binary-husky/gpt_academic/wiki) | [Metodo di installazione standard](#installazione) | [Script di installazione one-click](https://github.com/binary-husky/gpt_academic/releases) | [Configurazione](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
-> 
+>
 > 3. Questo progetto è compatibile e incoraggia l'uso di modelli di linguaggio di grandi dimensioni nazionali, come ChatGLM. Supporto per la coesistenza di più chiavi API, puoi compilare nel file di configurazione come `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Quando è necessario sostituire temporaneamente `API_KEY`, inserisci temporaneamente `API_KEY` nell'area di input e premi Invio per confermare.
 
 
- 
+
 
 <div align="center">
 
@@ -128,7 +128,7 @@ python -m pip install -r requirements.txt # Questo passaggio è identico alla pr
 [Optional] Se desideri utilizzare ChatGLM2 di Tsinghua/Fudan MOSS come backend, è necessario installare ulteriori dipendenze (Requisiti: conoscenza di Python + esperienza con Pytorch + hardware potente):
 ```sh
 # [Optional Step I] Supporto per ChatGLM2 di Tsinghua. Note di ChatGLM di Tsinghua: Se si verifica l'errore "Call ChatGLM fail non può caricare i parametri di ChatGLM", fare riferimento a quanto segue: 1: L'installazione predefinita è la versione torch+cpu, per usare cuda è necessario disinstallare torch ed installare nuovamente la versione con torch+cuda; 2: Se il modello non può essere caricato a causa di una configurazione insufficiente, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, sostituendo AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) con AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llms/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt
 
 # [Optional Step II] Supporto per Fudan MOSS
 python -m pip install -r request_llms/requirements_moss.txt
@@ -206,8 +206,8 @@ Ad esempio,
 ```
 "Traduzione avanzata Cinese-Inglese": {
      # Prefisso, sarà aggiunto prima del tuo input. Ad esempio, utilizzato per descrivere la tua richiesta, come traduzione, spiegazione del codice, rifinitura, ecc.
-     "Prefisso": "Si prega di tradurre il seguente testo in cinese e fornire spiegazione per i termini tecnici utilizzati, utilizzando una tabella in markdown uno per uno:\n\n", 
-     
+     "Prefisso": "Si prega di tradurre il seguente testo in cinese e fornire spiegazione per i termini tecnici utilizzati, utilizzando una tabella in markdown uno per uno:\n\n",
+
      # Suffisso, sarà aggiunto dopo il tuo input. Ad esempio, in combinazione con il prefisso, puoi circondare il tuo input con virgolette.
      "Suffisso": "",
 },
@@ -224,7 +224,7 @@ La scrittura di plugin per questo progetto è facile e richiede solo conoscenze
 # Aggiornamenti
 ### I: Aggiornamenti
 
-1. Funzionalità di salvataggio della conversazione. Chiamare `Salva la conversazione corrente` nell'area del plugin per salvare la conversazione corrente come un file html leggibile e ripristinabile. 
+1. Funzionalità di salvataggio della conversazione. Chiamare `Salva la conversazione corrente` nell'area del plugin per salvare la conversazione corrente come un file html leggibile e ripristinabile.
 Inoltre, nella stessa area del plugin (menu a tendina) chiamare `Carica la cronologia della conversazione` per ripristinare una conversazione precedente.
 Suggerimento: fare clic su `Carica la cronologia della conversazione` senza specificare un file per visualizzare la tua cronologia di archiviazione HTML.
 <div align="center">
@@ -358,4 +358,3 @@ https://github.com/oobabooga/one-click-installers
 # Altre risorse:
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.Japanese.md

@@ -2,9 +2,9 @@
 
 
 > **注意**
-> 
+>
 > 此READMEはGPTによる翻訳で生成されました（このプロジェクトのプラグインによって実装されています）、翻訳結果は100%正確ではないため、注意してください。
-> 
+>
 > 2023年11月7日: 依存関係をインストールする際は、`requirements.txt`で**指定されたバージョン**を選択してください。 インストールコマンド: `pip install -r requirements.txt`。
 
 
@@ -18,11 +18,11 @@ GPTを使用してこのプロジェクトを任意の言語に翻訳するに
 > 1. **強調された** プラグイン（ボタン）のみがファイルを読み込むことができることに注意してください。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にあります。また、新しいプラグインのPRを歓迎し、最優先で対応します。
 >
 > 2. このプロジェクトの各ファイルの機能は、[自己分析レポート`self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E5%A0%82)で詳しく説明されています。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クリックして、プロジェクトの自己分析レポートをGPTで再生成することもできます。よくある質問については、[`wiki`](https://github.com/binary-husky/gpt_academic/wiki)をご覧ください。[標準的なインストール方法](#installation) | [ワンクリックインストールスクリプト](https://github.com/binary-husky/gpt_academic/releases) | [構成の説明](https://github.com/binary-husky/gpt_academic/wiki/Project-Configuration-Explain)。
-> 
+>
 > 3. このプロジェクトは、[ChatGLM](https://www.chatglm.dev/)などの中国製の大規模言語モデルも互換性があり、試してみることを推奨しています。複数のAPIキーを共存させることができ、設定ファイルに`API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`のように記入できます。`API_KEY`を一時的に変更する必要がある場合は、入力エリアに一時的な`API_KEY`を入力し、Enterキーを押して提出すると有効になります。
 
 
- 
+
 
 <div align="center">
 
@@ -189,7 +189,7 @@ Python環境に詳しくないWindowsユーザーは、[リリース](https://gi
 "超级英译中"： {
     # プレフィックス、入力の前に追加されます。例えば、要求を記述するために使用されます。翻訳、コードの解説、校正など
     "プレフィックス"： "下記の内容を中国語に翻訳し、専門用語を一つずつマークダウンテーブルで解説してください：\n\n"、
-    
+
     # サフィックス、入力の後に追加されます。プレフィックスと一緒に使用して、入力内容を引用符で囲むことができます。
     "サフィックス"： ""、
 }、
@@ -342,4 +342,3 @@ https://github.com/oobabooga/one-click-installers
 # その他：
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.Korean.md

@@ -27,7 +27,7 @@ GPT를 사용하여 이 프로젝트를 임의의 언어로 번역하려면 [`mu
 
 
 
- 
+
 
 <div align="center">
 
@@ -130,7 +130,7 @@ python -m pip install -r requirements.txt # This step is the same as the pip ins
 [Optional Step] If you need support for Tsinghua ChatGLM2/Fudan MOSS as the backend, you need to install additional dependencies (Prerequisites: Familiar with Python + Have used Pytorch + Sufficient computer configuration):
 ```sh
 # [Optional Step I] Support for Tsinghua ChatGLM2. Note for Tsinghua ChatGLM: If you encounter the error "Call ChatGLM fail cannot load ChatGLM parameters", refer to the following: 1: The default installation above is torch+cpu version. To use cuda, uninstall torch and reinstall torch+cuda; 2: If you cannot load the model due to insufficient computer configuration, you can modify the model precision in request_llm/bridge_chatglm.py, 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_llms/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt
 
 # [Optional Step II] Support for Fudan MOSS
 python -m pip install -r request_llms/requirements_moss.txt
@@ -208,8 +208,8 @@ Please visit the [cloud server remote deployment wiki](https://github.com/binary
 ```
 "초급영문 번역": {
     # 접두사, 입력 내용 앞에 추가됩니다. 예를 들어 요구 사항을 설명하는 데 사용됩니다. 예를 들어 번역, 코드 설명, 교정 등
-    "Prefix": "다음 내용을 한국어로 번역하고 전문 용어에 대한 설명을 적용한 마크다운 표를 사용하세요:\n\n", 
-    
+    "Prefix": "다음 내용을 한국어로 번역하고 전문 용어에 대한 설명을 적용한 마크다운 표를 사용하세요:\n\n",
+
     # 접미사, 입력 내용 뒤에 추가됩니다. 예를 들어 접두사와 함께 입력 내용을 따옴표로 감쌀 수 있습니다.
     "Suffix": "",
 },
@@ -361,4 +361,3 @@ https://github.com/oobabooga/one-click-installers
 # 더보기：
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.Portuguese.md

@@ -2,9 +2,9 @@
 
 
 > **Nota**
-> 
+>
 > Este README foi traduzido pelo GPT (implementado por um plugin deste projeto) e não é 100% confiável. Por favor, verifique cuidadosamente o resultado da tradução.
-> 
+>
 > 7 de novembro de 2023: Ao instalar as dependências, favor selecionar as **versões especificadas** no `requirements.txt`. Comando de instalação: `pip install -r requirements.txt`.
 
 # <div align=center><img src="logo.png" width="40"> GPT Acadêmico</div>
@@ -15,12 +15,12 @@ Para traduzir este projeto para qualquer idioma utilizando o GPT, leia e execute
 > **Nota**
 >
 > 1. Observe que apenas os plugins (botões) marcados em **destaque** são capazes de ler arquivos, alguns plugins estão localizados no **menu suspenso** do plugin area. Também damos boas-vindas e prioridade máxima a qualquer novo plugin via PR.
-> 
+>
 > 2. As funcionalidades de cada arquivo deste projeto estão detalhadamente explicadas em [autoanálise `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). Com a iteração das versões, você também pode clicar nos plugins de funções relevantes a qualquer momento para chamar o GPT para regerar o relatório de autonálise do projeto. Perguntas frequentes [`wiki`](https://github.com/binary-husky/gpt_academic/wiki) | [Método de instalação convencional](#installation) | [Script de instalação em um clique](https://github.com/binary-husky/gpt_academic/releases) | [Explicação de configuração](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明)。
 >
 > 3. Este projeto é compatível e encoraja o uso de modelos de linguagem chineses, como ChatGLM. Vários api-keys podem ser usados simultaneamente, podendo ser especificados no arquivo de configuração como `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Quando precisar alterar temporariamente o `API_KEY`, insira o `API_KEY` temporário na área de entrada e pressione Enter para que ele seja efetivo.
 
- 
+
 <div align="center">
 
 Funcionalidades (⭐= funcionalidade recentemente adicionada) | Descrição
@@ -89,7 +89,7 @@ Apresentação de mais novas funcionalidades (geração de imagens, etc.) ... |
 </div>
 
 # Instalação
-### Método de instalação I: Executar diretamente (Windows, Linux ou MacOS) 
+### Método de instalação I: Executar diretamente (Windows, Linux ou MacOS)
 
 1. Baixe o projeto
 ```sh
@@ -124,7 +124,7 @@ python -m pip install -r requirements.txt # Este passo é igual ao da instalaç
 [Opcional] Se você quiser suporte para o ChatGLM2 do THU/ MOSS do Fudan, precisará instalar dependências extras (pré-requisitos: familiarizado com o Python + já usou o PyTorch + o computador tem configuração suficiente):
 ```sh
 # [Opcional Passo I] Suporte para ChatGLM2 do THU. Observações sobre o ChatGLM2 do THU: Se você encontrar o erro "Call ChatGLM fail 不能正常加载ChatGLM的参数" (Falha ao chamar o ChatGLM, não é possível carregar os parâmetros do ChatGLM), consulte o seguinte: 1: A versão instalada por padrão é a versão torch+cpu. Se você quiser usar a versão cuda, desinstale o torch e reinstale uma versão com torch+cuda; 2: Se a sua configuração não for suficiente para carregar o modelo, você pode modificar a precisão do modelo em request_llm/bridge_chatglm.py, alterando todas as ocorrências de AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) para AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llms/requirements_chatglm.txt  
+python -m pip install -r request_llms/requirements_chatglm.txt
 
 # [Opcional Passo II] Suporte para MOSS do Fudan
 python -m pip install -r request_llms/requirements_moss.txt
@@ -202,8 +202,8 @@ Por exemplo:
 ```
 "超级英译中": {
     # Prefixo, adicionado antes do seu input. Por exemplo, usado para descrever sua solicitação, como traduzir, explicar o código, revisar, etc.
-    "Prefix": "Por favor, traduza o parágrafo abaixo para o chinês e explique cada termo técnico dentro de uma tabela markdown:\n\n", 
-    
+    "Prefix": "Por favor, traduza o parágrafo abaixo para o chinês e explique cada termo técnico dentro de uma tabela markdown:\n\n",
+
     # Sufixo, adicionado após o seu input. Por exemplo, em conjunto com o prefixo, pode-se colocar seu input entre aspas.
     "Suffix": "",
 },
@@ -355,4 +355,3 @@ https://github.com/oobabooga/instaladores-de-um-clique
 # Mais:
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/README.Russian.md

@@ -2,9 +2,9 @@
 
 
 > **Примечание**
-> 
+>
 > Этот README был переведен с помощью GPT (реализовано с помощью плагина этого проекта) и не может быть полностью надежным, пожалуйста, внимательно проверьте результаты перевода.
-> 
+>
 > 7 ноября 2023 года: При установке зависимостей, пожалуйста, выберите **указанные версии** из `requirements.txt`. Команда установки: `pip install -r requirements.txt`.
 
 
@@ -17,12 +17,12 @@
 >
 > 1. Пожалуйста, обратите внимание, что только плагины (кнопки), выделенные **жирным шрифтом**, поддерживают чтение файлов, некоторые плагины находятся в выпадающем меню **плагинов**. Кроме того, мы с радостью приветствуем и обрабатываем PR для любых новых плагинов с **наивысшим приоритетом**.
 >
-> 2. Функции каждого файла в этом проекте подробно описаны в [отчете о самостоятельном анализе проекта `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). С каждым новым релизом вы также можете в любое время нажать на соответствующий функциональный плагин, вызвать GPT для повторной генерации сводного отчета о самоанализе проекта. Часто задаваемые вопросы [`wiki`](https://github.com/binary-husky/gpt_academic/wiki) | [обычные методы установки](#installation) | [скрипт одношаговой установки](https://github.com/binary-husky/gpt_academic/releases) | [инструкции по настройке](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明). 
+> 2. Функции каждого файла в этом проекте подробно описаны в [отчете о самостоятельном анализе проекта `self_analysis.md`](https://github.com/binary-husky/gpt_academic/wiki/GPT‐Academic项目自译解报告). С каждым новым релизом вы также можете в любое время нажать на соответствующий функциональный плагин, вызвать GPT для повторной генерации сводного отчета о самоанализе проекта. Часто задаваемые вопросы [`wiki`](https://github.com/binary-husky/gpt_academic/wiki) | [обычные методы установки](#installation) | [скрипт одношаговой установки](https://github.com/binary-husky/gpt_academic/releases) | [инструкции по настройке](https://github.com/binary-husky/gpt_academic/wiki/项目配置说明).
 >
 > 3. Этот проект совместим и настоятельно рекомендуется использование китайской NLP-модели ChatGLM и других моделей больших языков производства Китая. Поддерживает одновременное использование нескольких ключей API, которые можно указать в конфигурационном файле, например, `API_KEY="openai-key1,openai-key2,azure-key3,api2d-key4"`. Если нужно временно заменить `API_KEY`, введите временный `API_KEY` в окне ввода и нажмите Enter для его подтверждения.
 
 
- 
+
 
 <div align="center">
 
@@ -204,8 +204,8 @@ docker-compose up
 ```
 "Супер-англо-русский перевод": {
     # Префикс, который будет добавлен перед вашим вводом. Например, используется для описания вашего запроса, например, перевода, объяснения кода, редактирования и т.д.
-    "Префикс": "Пожалуйста, переведите следующий абзац на русский язык, а затем покажите каждый термин на экране с помощью таблицы Markdown:\n\n", 
-    
+    "Префикс": "Пожалуйста, переведите следующий абзац на русский язык, а затем покажите каждый термин на экране с помощью таблицы Markdown:\n\n",
+
     # Суффикс, который будет добавлен после вашего ввода. Например, можно использовать с префиксом, чтобы заключить ваш ввод в кавычки.
     "Суффикс": "",
 },
@@ -335,7 +335,7 @@ GPT Academic Группа QQ разработчиков: `610599535`
 ```
 В коде использовались многие функции, представленные в других отличных проектах, поэтому их порядок не имеет значения:
 
-# ChatGLM2-6B от Тиньхуа: 
+# ChatGLM2-6B от Тиньхуа:
 https://github.com/THUDM/ChatGLM2-6B
 
 # Линейные модели с ограниченной памятью от Тиньхуа:
@@ -358,4 +358,3 @@ https://github.com/oobabooga/one-click-installers
 # Больше:
 https://github.com/gradio-app/gradio
 https://github.com/fghrsh/live2d_demo
-

docs/WithFastapi.md

@@ -17,18 +17,18 @@ nano config.py
 
     - # 如果需要在二级路径下运行
     - # CUSTOM_PATH = get_conf('CUSTOM_PATH')
-    - # if 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: 
+    - # 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 != "/": 
+    + if CUSTOM_PATH != "/":
     +     from toolbox import run_gradio_in_subpath
     +     run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
-    + else: 
+    + else:
     +     demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
 
 if __name__ == "__main__":

docs/test_markdown_format.py

@@ -7,13 +7,27 @@ sample = """
 """
 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>'
+    pattern = r"\^(\d+)\^"  # 匹配^数字^
+    pattern2 = r"\[(\d+)\]"  # 匹配^数字^
+
+    def sub(m):
+        return "\\[" + 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
 
 
@@ -28,37 +42,39 @@ def close_up_code_segment_during_stream(gpt_reply):
         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:
+    if "```" not in gpt_reply:
         return gpt_reply
-    if gpt_reply.endswith('```'):
+    if gpt_reply.endswith("```"):
         return gpt_reply
 
     # 排除了以上两个情况，我们
-    segments = gpt_reply.split('```')
+    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```'
+        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>'
+    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
-    
+        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,
+        "mdx_math": {
+            "enable_dollar_delimiter": True,
+            "use_gitlab_delimiters": False,
         },
     }
     find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
@@ -72,19 +88,19 @@ def markdown_convertion(txt):
 
     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>"
+        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>"
+            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('&', ' ')
+        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:
@@ -94,37 +110,58 @@ def markdown_convertion(txt):
         """
         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>')
+        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
+    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)
+        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)
+        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)
+        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
+        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
+        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("""
+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/translate_english.json

@@ -2863,7 +2863,7 @@
     "加载API_KEY": "Loading API_KEY",
     "协助您编写代码": "Assist you in writing code",
     "我可以为您提供以下服务": "I can provide you with the following services",
-    "排队中请稍后 ...": "Please wait in line ...",
+    "排队中请稍候 ...": "Please wait in line ...",
     "建议您使用英文提示词": "It is recommended to use English prompts",
     "不能支撑AutoGen运行": "Cannot support AutoGen operation",
     "帮助您解决编程问题": "Help you solve programming problems",

docs/translate_japanese.json

@@ -2106,4 +2106,4 @@
     "改变输入参数的顺序与结构": "入力パラメータの順序と構造を変更する",
     "正在精细切分latex文件": "LaTeXファイルを細かく分割しています",
     "读取文件": "ファイルを読み込んでいます"
-}
+}

docs/translate_std.json

@@ -98,4 +98,4 @@
     "图片生成_DALLE2": "ImageGeneration_DALLE2",
     "图片生成_DALLE3": "ImageGeneration_DALLE3",
     "图片修改_DALLE2": "ImageModification_DALLE2"
-}
+}

docs/use_audio.md

@@ -61,4 +61,3 @@ VI 两种音频监听模式切换时，需要刷新页面才有效。
 VII 非localhost运行+非https情况下无法打开录音功能的坑：https://blog.csdn.net/weixin_39461487/article/details/109594434
 
 ## 5.点击函数插件区“实时音频采集” 或者其他音频交互功能
-

docs/waifu_plugin/autoload.js

@@ -8,8 +8,8 @@ try {
             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['waifuSize']            = '210x187';
+            live2d_settings['waifuTipsSize']        = '187x52';
             live2d_settings['canSwitchModel']       = true;
             live2d_settings['canSwitchTextures']    = true;
             live2d_settings['canSwitchHitokoto']    = false;

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

@@ -123,4 +123,4 @@
 <glyph unicode="&#xe65e;" d="M512 748.8l211.2 179.2 300.8-198.4-204.8-166.4-307.2 185.6zM1024 396.8l-300.8-198.4-211.2 172.8 300.8 185.6 211.2-160zM300.8 198.4l-300.8 198.4 204.8 166.4 307.2-192-211.2-172.8zM0 729.6l300.8 198.4 211.2-179.2-300.8-192-211.2 172.8zM512 332.8l211.2-179.2 89.6 57.6v-64l-300.8-179.2-300.8 179.2v64l89.6-51.2 211.2 172.8z" />
 <glyph unicode="&#xe65f;" d="M864 249.6c-38.4 0-64 32-64 64v256c0 38.4 32 64 64 64 38.4 0 64-32 64-64v-256c0-32-25.6-64-64-64zM697.6 102.4h-38.4v-108.8c0-38.4-25.6-64-57.6-64s-57.6 25.6-57.6 64v108.8h-70.4v-108.8c0-38.4-25.6-64-57.6-64s-57.6 25.6-57.6 64v108.8h-32c-19.2 0-38.4 19.2-38.4 44.8v428.8h448v-422.4c0-32-12.8-51.2-38.4-51.2zM736 633.6h-448c0 89.6 32 153.6 76.8 192l-70.4 83.2c-6.4 12.8-6.4 25.6 0 38.4 12.8 12.8 25.6 12.8 38.4 0l83.2-96c32 12.8 64 19.2 96 19.2s70.4-6.4 96-19.2l83.2 96c12.8 12.8 25.6 12.8 38.4 0s12.8-32 0-38.4l-70.4-83.2c44.8-32 76.8-102.4 76.8-192zM441.6 761.6c-12.8 0-25.6-12.8-25.6-32s12.8-32 25.6-32 25.6 12.8 25.6 32-12.8 32-25.6 32zM582.4 761.6c-12.8 0-25.6-12.8-25.6-32s12.8-32 25.6-32 25.6 19.2 25.6 32-12.8 32-25.6 32zM160 249.6c-38.4 0-64 32-64 64v256c0 38.4 25.6 64 64 64s64-32 64-64v-256c0-32-25.6-64-64-64z" />
 <glyph unicode="&#xe660;" d="M921.6 211.2c-32-153.6-115.2-211.2-147.2-249.6-32-25.6-121.6-25.6-153.6-6.4-38.4 25.6-134.4 25.6-166.4 0-44.8-32-115.2-19.2-128-12.8-256 179.2-352 716.8 12.8 774.4 64 12.8 134.4-32 134.4-32 51.2-25.6 70.4-12.8 115.2 6.4 96 44.8 243.2 44.8 313.6-76.8-147.2-96-153.6-294.4 19.2-403.2zM716.8 960c12.8-70.4-64-224-204.8-230.4-12.8 38.4 32 217.6 204.8 230.4z" />
-</font></defs></svg>
+</font></defs></svg>

docs/waifu_plugin/source

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

docs/waifu_plugin/waifu-tips.js

@@ -5,11 +5,11 @@ window.live2d_settings = Array(); /*
     　　　 　 ／｀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|　 |  
+    　　　 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.　|           
+    　　　　　 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
@@ -77,11 +77,11 @@ String.prototype.render = function(context) {
 
     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];
@@ -101,9 +101,9 @@ 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();
         $('.waifu-tips').html(text).fadeTo(200, 1);
         if (timeout === undefined) timeout = 5000;
@@ -121,15 +121,15 @@ function hideMessage(timeout) {
 function initModel(waifuPath, type) {
     /* console welcome message */
     eval(function(p,a,c,k,e,r){e=function(c){return(c<a?'':e(parseInt(c/a)))+((c=c%a)>35?String.fromCharCode(c+29):c.toString(36))};if(!''.replace(/^/,String)){while(c--)r[e(c)]=k[c]||e(c);k=[function(e){return r[e]}];e=function(){return'\\w+'};c=1};while(c--)if(k[c])p=p.replace(new RegExp('\\b'+e(c)+'\\b','g'),k[c]);return p}('8.d(" ");8.d("\\U,.\\y\\5.\\1\\1\\1\\1/\\1,\\u\\2 \\H\\n\\1\\1\\1\\1\\1\\b \', !-\\r\\j-i\\1/\\1/\\g\\n\\1\\1\\1 \\1 \\a\\4\\f\'\\1\\1\\1 L/\\a\\4\\5\\2\\n\\1\\1 \\1 /\\1 \\a,\\1 /|\\1 ,\\1 ,\\1\\1\\1 \',\\n\\1\\1\\1\\q \\1/ /-\\j/\\1\\h\\E \\9 \\5!\\1 i\\n\\1\\1\\1 \\3 \\6 7\\q\\4\\c\\1 \\3\'\\s-\\c\\2!\\t|\\1 |\\n\\1\\1\\1\\1 !,/7 \'0\'\\1\\1 \\X\\w| \\1 |\\1\\1\\1\\n\\1\\1\\1\\1 |.\\x\\"\\1\\l\\1\\1 ,,,, / |./ \\1 |\\n\\1\\1\\1\\1 \\3\'| i\\z.\\2,,A\\l,.\\B / \\1.i \\1|\\n\\1\\1\\1\\1\\1 \\3\'| | / C\\D/\\3\'\\5,\\1\\9.\\1|\\n\\1\\1\\1\\1\\1\\1 | |/i \\m|/\\1 i\\1,.\\6 |\\F\\1|\\n\\1\\1\\1\\1\\1\\1.|/ /\\1\\h\\G \\1 \\6!\\1\\1\\b\\1|\\n\\1\\1\\1 \\1 \\1 k\\5>\\2\\9 \\1 o,.\\6\\2 \\1 /\\2!\\n\\1\\1\\1\\1\\1\\1 !\'\\m//\\4\\I\\g\', \\b \\4\'7\'\\J\'\\n\\1\\1\\1\\1\\1\\1 \\3\'\\K|M,p,\\O\\3|\\P\\n\\1\\1\\1\\1\\1 \\1\\1\\1\\c-,/\\1|p./\\n\\1\\1\\1\\1\\1 \\1\\1\\1\'\\f\'\\1\\1!o,.:\\Q \\R\\S\\T v"+e.V+" / W "+e.N);8.d(" ");',60,60,'|u3000|uff64|uff9a|uff40|u30fd|uff8d||console|uff8a|uff0f|uff3c|uff84|log|live2d_settings|uff70|u00b4|uff49||u2010||u3000_|u3008||_|___|uff72|u2500|uff67|u30cf|u30fc||u30bd|u4ece|u30d8|uff1e|__|u30a4|k_|uff17_|u3000L_|u3000i|uff1a|u3009|uff34|uff70r|u30fdL__||___i|l2dVerDate|u30f3|u30ce|nLive2D|u770b|u677f|u5a18|u304f__|l2dVersion|FGHRSH|u00b40i'.split('|'),0,{}));
-    
+
     /* 判断 JQuery */
     if (typeof($.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]);
@@ -138,32 +138,32 @@ function initModel(waifuPath, type) {
     $(".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,
@@ -172,7 +172,7 @@ function initModel(waifuPath, type) {
             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();
@@ -185,7 +185,7 @@ function initModel(waifuPath, type) {
     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;
@@ -204,7 +204,7 @@ function loadModel(modelId, modelTexturesId=0) {
 
 function loadTipsMessage(result) {
     window.waifu_tips = result;
-    
+
     $.each(result.mouseover, function (index, tips){
         $(document).on("mouseover", tips.selector, function (){
             var text = getRandText(tips.text);
@@ -223,50 +223,50 @@ function loadTipsMessage(result) {
         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]) && 
+
+        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) {
         showMessage('欢迎使用GPT-Academic', 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,
@@ -279,12 +279,12 @@ function loadTipsMessage(result) {
             }
         });
     }
-    
+
     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,
@@ -297,32 +297,32 @@ function loadTipsMessage(result) {
             }
         });
     }
-    
+
     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':
@@ -366,7 +366,7 @@ function loadTipsMessage(result) {
                 });
     	}
     }
-    
+
     $('.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

@@ -31,7 +31,7 @@
         },
         "model_message": {
             "1": ["来自 Potion Maker 的 Pio 酱 ~"],
-            "2": ["来自 Potion Maker 的 Tia 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", "。"],
@@ -111,4 +111,4 @@
         { "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~"] }
     ]
-}
+}

docs/waifu_plugin/waifu.css

@@ -287,4 +287,4 @@
 }
 .fui-user:before {
   content: "\e631";
-}
+}

main.py

@@ -1,14 +1,25 @@
 import os; os.environ['no_proxy'] = '*' # 避免代理网络产生意外污染
-import pickle
-import base64
+
+help_menu_description = \
+"""Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), 
+感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors).
+</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki), 
+如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues).
+</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交
+</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮
+</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮
+</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端
+</br></br>如何保存对话: 点击保存当前的对话按钮
+</br></br>如何语音对话: 请阅读Wiki
+</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
 
 def main():
     import gradio as gr
-    if gr.__version__ not in ['3.32.6']: 
+    if gr.__version__ not in ['3.32.6', '3.32.7']: 
         raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
     from request_llms.bridge_all import predict
     from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
-    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
+    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址
     proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
     CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
     ENABLE_AUDIO, AUTO_CLEAR_TXT, PATH_LOGGING, AVAIL_THEMES, THEME = get_conf('ENABLE_AUDIO', 'AUTO_CLEAR_TXT', 'PATH_LOGGING', 'AVAIL_THEMES', 'THEME')
@@ -18,21 +29,11 @@ def main():
     # 如果WEB_PORT是-1, 则随机选取WEB端口
     PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
     from check_proxy import get_current_version
-    from themes.theme import adjust_theme, advanced_css, theme_declaration, load_dynamic_theme
-
+    from themes.theme import adjust_theme, advanced_css, theme_declaration
+    from themes.theme import js_code_for_css_changing, js_code_for_darkmode_init, js_code_for_toggle_darkmode, js_code_for_persistent_cookie_init
+    from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, init_cookie
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
-    description =  "Github源代码开源和更新[地址🚀](https://github.com/binary-husky/gpt_academic), "
-    description += "感谢热情的[开发者们❤️](https://github.com/binary-husky/gpt_academic/graphs/contributors)."
-    description += "</br></br>常见问题请查阅[项目Wiki](https://github.com/binary-husky/gpt_academic/wiki), "
-    description += "如遇到Bug请前往[Bug反馈](https://github.com/binary-husky/gpt_academic/issues)."
-    description += "</br></br>普通对话使用说明: 1. 输入问题; 2. 点击提交"
-    description += "</br></br>基础功能区使用说明: 1. 输入文本; 2. 点击任意基础功能区按钮"
-    description += "</br></br>函数插件区使用说明: 1. 输入路径/问题, 或者上传文件; 2. 点击任意函数插件区按钮"
-    description += "</br></br>虚空终端使用说明: 点击虚空终端, 然后根据提示输入指令, 再次点击虚空终端"
-    description += "</br></br>如何保存对话: 点击保存当前的对话按钮"
-    description += "</br></br>如何语音对话: 请阅读Wiki"
-    description += "</br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"
-
+    
     # 问询记录, python 版本建议3.9+（越新越好）
     import logging, uuid
     os.makedirs(PATH_LOGGING, exist_ok=True)
@@ -138,17 +139,17 @@ def main():
                             with gr.Row():
                                 switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary").style(size="sm")
                     with gr.Row():
-                        with gr.Accordion("点击展开“文件上传区”。上传本地文件/压缩包供函数插件调用。", open=False) as area_file_up:
+                        with gr.Accordion("点击展开“文件下载区”。", open=False) as area_file_up:
                             file_upload = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload")
 
 
-        with gr.Floating(init_x="0%", init_y="0%", visible=True, width=None, drag="forbidden"):
+        with gr.Floating(init_x="0%", init_y="0%", visible=True, width=None, drag="forbidden", elem_id="tooltip"):
             with gr.Row():
                 with gr.Tab("上传文件", elem_id="interact-panel"):
                     gr.Markdown("请上传本地文件/压缩包供“函数插件区”功能调用。请注意: 上传文件后会自动把输入区修改为相应路径。")
                     file_upload_2 = gr.Files(label="任何文件, 推荐上传压缩文件(zip, tar)", file_count="multiple", elem_id="elem_upload_float")
     
-                with gr.Tab("更换模型 & Prompt", elem_id="interact-panel"):
+                with gr.Tab("更换模型", elem_id="interact-panel"):
                     md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
                     top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
                     temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
@@ -160,18 +161,11 @@ def main():
                     checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "浮动输入区", "输入清除键", "插件参数区"], 
                                                   value=["基础功能区", "函数插件区"], label="显示/隐藏功能区", elem_id='cbs').style(container=False)
                     checkboxes_2 = gr.CheckboxGroup(["自定义菜单"], 
-                                                  value=[], label="显示/隐藏自定义菜单", elem_id='cbs').style(container=False)
+                                                  value=[], label="显示/隐藏自定义菜单", elem_id='cbsc').style(container=False)
                     dark_mode_btn = gr.Button("切换界面明暗 ☀", variant="secondary").style(size="sm")
-                    dark_mode_btn.click(None, None, None, _js="""() => {
-                            if (document.querySelectorAll('.dark').length) {
-                                document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
-                            } else {
-                                document.querySelector('body').classList.add('dark');
-                            }
-                        }""",
-                    )
+                    dark_mode_btn.click(None, None, None, _js=js_code_for_toggle_darkmode)
                 with gr.Tab("帮助", elem_id="interact-panel"):
-                    gr.Markdown(description)
+                    gr.Markdown(help_menu_description)
 
         with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_input_secondary:
             with gr.Accordion("浮动输入区", open=True, elem_id="input-panel2"):
@@ -186,16 +180,6 @@ def main():
                         stopBtn2 = gr.Button("停止", variant="secondary"); stopBtn2.style(size="sm")
                         clearBtn2 = gr.Button("清除", variant="secondary", visible=False); clearBtn2.style(size="sm")
 
-        def to_cookie_str(d):
-            # Pickle the dictionary and encode it as a string
-            pickled_dict = pickle.dumps(d)
-            cookie_value = base64.b64encode(pickled_dict).decode('utf-8')
-            return cookie_value
-        
-        def from_cookie_str(c):
-            # Decode the base64-encoded string and unpickle it into a dictionary
-            pickled_dict = base64.b64decode(c.encode('utf-8'))
-            return pickle.loads(pickled_dict)
 
         with gr.Floating(init_x="20%", init_y="50%", visible=False, width="40%", drag="top") as area_customize:
             with gr.Accordion("自定义菜单", open=True, elem_id="edit-panel"):
@@ -227,11 +211,11 @@ def main():
                             else:
                                 ret.update({predefined_btns[basic_btn_dropdown_]: gr.update(visible=True, value=basic_fn_title)})
                             ret.update({cookies: cookies_})
-                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)    # persistent cookie to dict
+                            try: persistent_cookie_ = from_cookie_str(persistent_cookie_)   # persistent cookie to dict
                             except: persistent_cookie_ = {}
-                            persistent_cookie_["custom_bnt"] = customize_fn_overwrite_   # dict update new value
-                            persistent_cookie_ = to_cookie_str(persistent_cookie_)         # persistent cookie to dict
-                            ret.update({persistent_cookie: persistent_cookie_})                             # write persistent cookie
+                            persistent_cookie_["custom_bnt"] = customize_fn_overwrite_      # dict update new value
+                            persistent_cookie_ = to_cookie_str(persistent_cookie_)          # persistent cookie to dict
+                            ret.update({persistent_cookie: persistent_cookie_})             # write persistent cookie
                             return ret
                         
                         def reflesh_btn(persistent_cookie_, cookies_):
@@ -252,10 +236,11 @@ def main():
                                 else: ret.update({predefined_btns[k]: gr.update(visible=True, value=v['Title'])})
                             return ret
                         
-                        basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies],[cookies, *customize_btns.values(), *predefined_btns.values()])
+                        basic_fn_load.click(reflesh_btn, [persistent_cookie, cookies], [cookies, *customize_btns.values(), *predefined_btns.values()])
                         h = basic_fn_confirm.click(assign_btn, [persistent_cookie, cookies, basic_btn_dropdown, basic_fn_title, basic_fn_prefix, basic_fn_suffix], 
                                                    [persistent_cookie, cookies, *customize_btns.values(), *predefined_btns.values()])
-                        h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""") # save persistent cookie
+                        # save persistent cookie
+                        h.then(None, [persistent_cookie], None, _js="""(persistent_cookie)=>{setCookie("persistent_cookie", persistent_cookie, 5);}""")
 
         # 功能区显示开关与功能区的互动
         def fn_area_visibility(a):
@@ -305,8 +290,8 @@ def main():
             click_handle = btn.click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(btn.value)], outputs=output_combo)
             cancel_handles.append(click_handle)
         # 文件上传区，接收文件后与chatbot的互动
-        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
-        file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies])
+        file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None,   _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
+        file_upload_2.upload(on_file_uploaded, [file_upload_2, chatbot, txt, txt2, checkboxes, cookies], [chatbot, txt, txt2, cookies]).then(None, None, None, _js=r"()=>{toast_push('上传完毕 ...'); cancel_loading_status();}")
         # 函数插件-固定按钮区
         for k in plugins:
             if not plugins[k].get("AsButton", True): continue
@@ -342,18 +327,7 @@ def main():
             None,
             [secret_css],
             None,
-            _js="""(css) => {
-                var existingStyles = document.querySelectorAll("style[data-loaded-css]");
-                for (var i = 0; i < existingStyles.length; i++) {
-                    var style = existingStyles[i];
-                    style.parentNode.removeChild(style);
-                }
-                var styleElement = document.createElement('style');
-                styleElement.setAttribute('data-loaded-css', css);
-                styleElement.innerHTML = css;
-                document.head.appendChild(styleElement);
-            }
-            """
+            _js=js_code_for_css_changing
         )
         # 随变按钮的回调函数注册
         def route(request: gr.Request, k, *args, **kwargs):
@@ -385,27 +359,10 @@ def main():
                 rad.feed(cookies['uuid'].hex, audio)
             audio_mic.stream(deal_audio, inputs=[audio_mic, cookies])
 
-        def init_cookie(cookies, chatbot):
-            # 为每一位访问的用户赋予一个独一无二的uuid编码
-            cookies.update({'uuid': uuid.uuid4()})
-            return cookies
+
         demo.load(init_cookie, inputs=[cookies, chatbot], outputs=[cookies])
-        darkmode_js = """(dark) => {
-            dark = dark == "True";
-            if (document.querySelectorAll('.dark').length) {
-                if (!dark){
-                    document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
-                }
-            } else {
-                if (dark){
-                    document.querySelector('body').classList.add('dark');
-                }
-            }
-        }"""
-        load_cookie_js = """(persistent_cookie) => {
-            return getCookie("persistent_cookie");
-        }"""
-        demo.load(None, inputs=None, outputs=[persistent_cookie], _js=load_cookie_js)
+        darkmode_js = js_code_for_darkmode_init
+        demo.load(None, inputs=None, outputs=[persistent_cookie], _js=js_code_for_persistent_cookie_init)
         demo.load(None, inputs=[dark_mode], outputs=None, _js=darkmode_js)    # 配置暗色主题或亮色主题
         demo.load(None, inputs=[gr.Textbox(LAYOUT, visible=False)], outputs=None, _js='(LAYOUT)=>{GptAcademicJavaScriptInit(LAYOUT);}')
         
@@ -418,7 +375,7 @@ def main():
 
         def auto_updates(): time.sleep(0); auto_update()
         def open_browser(): time.sleep(2); webbrowser.open_new_tab(f"http://localhost:{PORT}")
-        def warm_up_mods(): time.sleep(4); warm_up_modules()
+        def warm_up_mods(): time.sleep(6); warm_up_modules()
         
         threading.Thread(target=auto_updates, name="self-upgrade", daemon=True).start() # 查看自动更新
         threading.Thread(target=open_browser, name="open-browser", daemon=True).start() # 打开浏览器页面

multi_language.py

@@ -352,9 +352,9 @@ def step_1_core_key_translate():
         chinese_core_keys_norepeat_mapping.update({k:cached_translation[k]})
     chinese_core_keys_norepeat_mapping = dict(sorted(chinese_core_keys_norepeat_mapping.items(), key=lambda x: -len(x[0])))
 
-    # ===============================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     # copy
-    # ===============================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     def copy_source_code():
 
         from toolbox import get_conf
@@ -367,9 +367,9 @@ def step_1_core_key_translate():
         shutil.copytree('./', backup_dir, ignore=lambda x, y: blacklist)
     copy_source_code()
 
-    # ===============================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     # primary key replace
-    # ===============================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     directory_path = f'./multi-language/{LANG}/'
     for root, dirs, files in os.walk(directory_path):
         for file in files:
@@ -389,9 +389,9 @@ def step_1_core_key_translate():
 
 def step_2_core_key_translate():
 
-    # =================================================================================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
     # step2 
-    # =================================================================================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
 
     def load_string(strings, string_input):
         string_ = string_input.strip().strip(',').strip().strip('.').strip()
@@ -492,9 +492,9 @@ def step_2_core_key_translate():
     cached_translation.update(read_map_from_json(language=LANG_STD))
     cached_translation = dict(sorted(cached_translation.items(), key=lambda x: -len(x[0])))
 
-    # ===============================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     # literal key replace
-    # ===============================================
+    # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     directory_path = f'./multi-language/{LANG}/'
     for root, dirs, files in os.walk(directory_path):
         for file in files:

request_llms/README.md

@@ -32,4 +32,4 @@ P.S. 如果您按照以下步骤成功接入了新的大模型，欢迎发Pull R
 
 5. 测试通过后，在`request_llms/bridge_all.py`中做最后的修改，把你的模型完全接入到框架中（聪慧如您，只需要看一眼该文件就明白怎么修改了）
 
-6. 修改`LLM_MODEL`配置，然后运行`python main.py`，测试最后的效果
+6. 修改`LLM_MODEL`配置，然后运行`python main.py`，测试最后的效果

request_llms/bridge_all.py

@@ -28,6 +28,9 @@ from .bridge_chatglm3 import predict as chatglm3_ui
 from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
 from .bridge_qianfan import predict as qianfan_ui
 
+from .bridge_google_gemini import predict as genai_ui
+from .bridge_google_gemini import predict_no_ui_long_connection  as genai_noui
+
 colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
 
 class LazyloadTiktoken(object):
@@ -246,6 +249,22 @@ model_info = {
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "gemini-pro": {
+        "fn_with_ui": genai_ui,
+        "fn_without_ui": genai_noui,
+        "endpoint": None,
+        "max_token": 1024 * 32,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "gemini-pro-vision": {
+        "fn_with_ui": genai_ui,
+        "fn_without_ui": genai_noui,
+        "endpoint": None,
+        "max_token": 1024 * 32,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
 }
 
 # -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-
@@ -431,14 +450,14 @@ if "chatglm_onnx" in AVAIL_LLM_MODELS:
         })
     except:
         print(trimmed_format_exc())
-if "qwen" in AVAIL_LLM_MODELS:
+if "qwen-local" in AVAIL_LLM_MODELS:
     try:
-        from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
-        from .bridge_qwen import predict as qwen_ui
+        from .bridge_qwen_local import predict_no_ui_long_connection as qwen_local_noui
+        from .bridge_qwen_local import predict as qwen_local_ui
         model_info.update({
-            "qwen": {
-                "fn_with_ui": qwen_ui,
-                "fn_without_ui": qwen_noui,
+            "qwen-local": {
+                "fn_with_ui": qwen_local_ui,
+                "fn_without_ui": qwen_local_noui,
                 "endpoint": None,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
@@ -447,16 +466,32 @@ if "qwen" in AVAIL_LLM_MODELS:
         })
     except:
         print(trimmed_format_exc())
-if "chatgpt_website" in AVAIL_LLM_MODELS:   # 接入一些逆向工程https://github.com/acheong08/ChatGPT-to-API/
+if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-max" in AVAIL_LLM_MODELS:   # zhipuai
     try:
-        from .bridge_chatgpt_website import predict_no_ui_long_connection as chatgpt_website_noui
-        from .bridge_chatgpt_website import predict as chatgpt_website_ui
+        from .bridge_qwen import predict_no_ui_long_connection as qwen_noui
+        from .bridge_qwen import predict as qwen_ui
         model_info.update({
-            "chatgpt_website": {
-                "fn_with_ui": chatgpt_website_ui,
-                "fn_without_ui": chatgpt_website_noui,
-                "endpoint": openai_endpoint,
-                "max_token": 4096,
+            "qwen-turbo": {
+                "fn_with_ui": qwen_ui,
+                "fn_without_ui": qwen_noui,
+                "endpoint": None,
+                "max_token": 6144,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "qwen-plus": {
+                "fn_with_ui": qwen_ui,
+                "fn_without_ui": qwen_noui,
+                "endpoint": None,
+                "max_token": 30720,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            },
+            "qwen-max": {
+                "fn_with_ui": qwen_ui,
+                "fn_without_ui": qwen_noui,
+                "endpoint": None,
+                "max_token": 28672,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
             }
@@ -559,6 +594,23 @@ if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
         })
     except:
         print(trimmed_format_exc())
+# if "skylark" in AVAIL_LLM_MODELS:
+#     try:
+#         from .bridge_skylark2 import predict_no_ui_long_connection as skylark_noui
+#         from .bridge_skylark2 import predict as skylark_ui
+#         model_info.update({
+#             "skylark": {
+#                 "fn_with_ui": skylark_ui,
+#                 "fn_without_ui": skylark_noui,
+#                 "endpoint": None,
+#                 "max_token": 4096,
+#                 "tokenizer": tokenizer_gpt35,
+#                 "token_cnt": get_token_num_gpt35,
+#             }
+#         })
+#     except:
+#         print(trimmed_format_exc())
+
 
 # <-- 用于定义和切换多个azure模型 -->
 AZURE_CFG_ARRAY = get_conf("AZURE_CFG_ARRAY")

request_llms/bridge_chatgpt.py

@@ -51,7 +51,8 @@ def decode_chunk(chunk):
         chunkjson = json.loads(chunk_decoded[6:])
         has_choices = 'choices' in chunkjson
         if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
-        if has_choices and choice_valid: has_content = "content" in chunkjson['choices'][0]["delta"]
+        if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"])
+        if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
         if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
     except: 
         pass
@@ -101,20 +102,25 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
     result = ''
     json_data = None
     while True:
-        try: chunk = next(stream_response).decode()
+        try: chunk = next(stream_response)
         except StopIteration: 
             break
         except requests.exceptions.ConnectionError:
-            chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。
-        if len(chunk)==0: continue
-        if not chunk.startswith('data:'): 
-            error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
+        if len(chunk_decoded)==0: continue
+        if not chunk_decoded.startswith('data:'): 
+            error_msg = get_full_error(chunk, stream_response).decode()
             if "reduce the length" in error_msg:
                 raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
             else:
                 raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
-        if ('data: [DONE]' in chunk): break # api2d 正常完成
-        json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
+        if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
+        # 提前读取一些信息 （用于判断异常）
+        if has_choices and not choice_valid:
+            # 一些垃圾第三方接口的出现这样的错误
+            continue
+        json_data = chunkjson['choices'][0]
         delta = json_data["delta"]
         if len(delta) == 0: break
         if "role" in delta: continue
@@ -238,6 +244,9 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     if has_choices and not choice_valid:
                         # 一些垃圾第三方接口的出现这样的错误
                         continue
+                    if ('data: [DONE]' not in chunk_decoded) and len(chunk_decoded) > 0 and (chunkjson is None):
+                        # 传递进来一些奇怪的东西
+                        raise ValueError(f'无法读取以下数据，请检查配置。\n\n{chunk_decoded}')
                     # 前者是API2D的结束条件，后者是OPENAI的结束条件
                     if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了

request_llms/bridge_google_gemini.py

@@ -0,0 +1,114 @@
+# encoding: utf-8
+# @Time   : 2023/12/21
+# @Author : Spike
+# @Descr   :
+import json
+import re
+import os
+import time
+from request_llms.com_google import GoogleChatInit
+from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc
+
+proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
+timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
+                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
+
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
+                                  console_slience=False):
+    # 检查API_KEY
+    if get_conf("GEMINI_API_KEY") == "":
+        raise ValueError(f"请配置 GEMINI_API_KEY。")
+
+    genai = GoogleChatInit()
+    watch_dog_patience = 5  # 看门狗的耐心, 设置5秒即可
+    gpt_replying_buffer = ''
+    stream_response = genai.generate_chat(inputs, llm_kwargs, history, sys_prompt)
+    for response in stream_response:
+        results = response.decode()
+        match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
+        error_match = re.search(r'\"message\":\s*\"(.*?)\"', results, flags=re.DOTALL)
+        if match:
+            try:
+                paraphrase = json.loads('{"text": "%s"}' % match.group(1))
+            except:
+                raise ValueError(f"解析GEMINI消息出错。")
+            buffer = paraphrase['text']
+            gpt_replying_buffer += buffer
+            if len(observe_window) >= 1:
+                observe_window[0] = gpt_replying_buffer
+            if len(observe_window) >= 2:
+                if (time.time() - observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+        if error_match:
+            raise RuntimeError(f'{gpt_replying_buffer} 对话错误')
+    return gpt_replying_buffer
+
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
+    # 检查API_KEY
+    if get_conf("GEMINI_API_KEY") == "":
+        yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
+        return
+
+    # 适配润色区域
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    if "vision" in llm_kwargs["llm_model"]:
+        have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
+        def make_media_input(inputs, image_paths): 
+            for image_path in image_paths:
+                inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
+            return inputs
+        if have_recent_file:
+            inputs = make_media_input(inputs, image_paths)
+
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history)
+    genai = GoogleChatInit()
+    retry = 0
+    while True:
+        try:
+            stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt)
+            break
+        except Exception as e:
+            retry += 1
+            chatbot[-1] = ((chatbot[-1][0], trimmed_format_exc()))
+            yield from update_ui(chatbot=chatbot, history=history, msg="请求失败")  # 刷新界面
+            return
+    gpt_replying_buffer = ""
+    gpt_security_policy = ""
+    history.extend([inputs, ''])
+    for response in stream_response:
+        results = response.decode("utf-8")    # 被这个解码给耍了。。
+        gpt_security_policy += results
+        match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
+        error_match = re.search(r'\"message\":\s*\"(.*)\"', results, flags=re.DOTALL)
+        if match:
+            try:
+                paraphrase = json.loads('{"text": "%s"}' % match.group(1))
+            except:
+                raise ValueError(f"解析GEMINI消息出错。")
+            gpt_replying_buffer += paraphrase['text']    # 使用 json 解析库进行处理
+            chatbot[-1] = (inputs, gpt_replying_buffer)
+            history[-1] = gpt_replying_buffer
+            yield from update_ui(chatbot=chatbot, history=history)
+        if error_match:
+            history = history[-2]  # 错误的不纳入对话
+            chatbot[-1] = (inputs, gpt_replying_buffer + f"对话错误，请查看message\n\n```\n{error_match.group(1)}\n```")
+            yield from update_ui(chatbot=chatbot, history=history)
+            raise RuntimeError('对话错误')
+    if not gpt_replying_buffer:
+        history = history[-2]  # 错误的不纳入对话
+        chatbot[-1] = (inputs, gpt_replying_buffer + f"触发了Google的安全访问策略，没有回答\n\n```\n{gpt_security_policy}\n```")
+        yield from update_ui(chatbot=chatbot, history=history)
+
+
+
+if __name__ == '__main__':
+    import sys
+    llm_kwargs = {'llm_model': 'gemini-pro'}
+    result = predict('Write long a story about a magic backpack.', llm_kwargs, llm_kwargs, [])
+    for i in result:
+        print(i)

request_llms/bridge_newbingfree.py

@@ -1,16 +1,17 @@
 """
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 第一部分：来自EdgeGPT.py
 https://github.com/acheong08/EdgeGPT
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 """
 from .edge_gpt_free import Chatbot as NewbingChatbot
+
 load_message = "等待NewBing响应。"
 
 """
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 第二部分：子进程Worker（调用主体）
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 """
 import time
 import json
@@ -22,19 +23,30 @@ import threading
 from toolbox import update_ui, get_conf, trimmed_format_exc
 from multiprocessing import Process, Pipe
 
+
 def preprocess_newbing_out(s):
-    pattern = 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 += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
+    pattern = 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 += (
+            "\n\n```reference\n"
+            + "\n".join([r for r in result.split("\n") if r.startswith("[")])
+            + "\n```\n"
+        )
     return result
 
+
 def preprocess_newbing_out_simple(result):
-    if '[1]' in result:
-        result += '\n\n```reference\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
+    if "[1]" in result:
+        result += (
+            "\n\n```reference\n"
+            + "\n".join([r for r in result.split("\n") if r.startswith("[")])
+            + "\n```\n"
+        )
     return result
 
+
 class NewBingHandle(Process):
     def __init__(self):
         super().__init__(daemon=True)
@@ -46,11 +58,12 @@ class NewBingHandle(Process):
         self.check_dependency()
         self.start()
         self.threadLock = threading.Lock()
-        
+
     def check_dependency(self):
         try:
             self.success = False
             import certifi, httpx, rich
+
             self.info = "依赖检测通过，等待NewBing响应。注意目前不能多人同时调用NewBing接口（有线程锁），否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时，会自动使用已配置的代理。"
             self.success = True
         except:
@@ -62,18 +75,19 @@ class NewBingHandle(Process):
 
     async def async_run(self):
         # 读取配置
-        NEWBING_STYLE = get_conf('NEWBING_STYLE')
+        NEWBING_STYLE = get_conf("NEWBING_STYLE")
         from request_llms.bridge_all import model_info
-        endpoint = model_info['newbing']['endpoint']
+
+        endpoint = model_info["newbing"]["endpoint"]
         while True:
             # 等待
             kwargs = self.child.recv()
-            question=kwargs['query']
-            history=kwargs['history']
-            system_prompt=kwargs['system_prompt']
+            question = kwargs["query"]
+            history = kwargs["history"]
+            system_prompt = kwargs["system_prompt"]
 
             # 是否重置
-            if len(self.local_history) > 0 and len(history)==0:
+            if len(self.local_history) > 0 and len(history) == 0:
                 await self.newbing_model.reset()
                 self.local_history = []
 
@@ -81,34 +95,33 @@ class NewBingHandle(Process):
             prompt = ""
             if system_prompt not in self.local_history:
                 self.local_history.append(system_prompt)
-                prompt += system_prompt + '\n'
+                prompt += system_prompt + "\n"
 
             # 追加历史
             for ab in history:
                 a, b = ab
                 if a not in self.local_history:
                     self.local_history.append(a)
-                    prompt += a + '\n'
+                    prompt += a + "\n"
 
             # 问题
             prompt += question
             self.local_history.append(question)
-            print('question:', prompt)
+            print("question:", prompt)
             # 提交
             async for final, response in self.newbing_model.ask_stream(
                 prompt=question,
-                conversation_style=NEWBING_STYLE,     # ["creative", "balanced", "precise"]
-                wss_link=endpoint,                    # "wss://sydney.bing.com/sydney/ChatHub"
+                conversation_style=NEWBING_STYLE,  # ["creative", "balanced", "precise"]
+                wss_link=endpoint,  # "wss://sydney.bing.com/sydney/ChatHub"
             ):
                 if not final:
                     print(response)
                     self.child.send(str(response))
                 else:
-                    print('-------- receive final ---------')
-                    self.child.send('[Finish]')
+                    print("-------- receive final ---------")
+                    self.child.send("[Finish]")
                     # self.local_history.append(response)
 
-    
     def run(self):
         """
         这个函数运行在子进程
@@ -118,32 +131,37 @@ class NewBingHandle(Process):
         self.local_history = []
         if (self.newbing_model is None) or (not self.success):
             # 代理设置
-            proxies, NEWBING_COOKIES = get_conf('proxies', 'NEWBING_COOKIES')
-            if proxies is None: 
+            proxies, NEWBING_COOKIES = get_conf("proxies", "NEWBING_COOKIES")
+            if proxies is None:
                 self.proxies_https = None
-            else: 
-                self.proxies_https = proxies['https']
+            else:
+                self.proxies_https = proxies["https"]
 
             if (NEWBING_COOKIES is not None) and len(NEWBING_COOKIES) > 100:
                 try:
                     cookies = json.loads(NEWBING_COOKIES)
                 except:
                     self.success = False
-                    tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                    self.child.send(f'[Local Message] NEWBING_COOKIES未填写或有格式错误。')
-                    self.child.send('[Fail]'); self.child.send('[Finish]')
+                    tb_str = "\n```\n" + trimmed_format_exc() + "\n```\n"
+                    self.child.send(f"[Local Message] NEWBING_COOKIES未填写或有格式错误。")
+                    self.child.send("[Fail]")
+                    self.child.send("[Finish]")
                     raise RuntimeError(f"NEWBING_COOKIES未填写或有格式错误。")
             else:
                 cookies = None
 
             try:
-                self.newbing_model = NewbingChatbot(proxy=self.proxies_https, cookies=cookies)
+                self.newbing_model = NewbingChatbot(
+                    proxy=self.proxies_https, cookies=cookies
+                )
             except:
                 self.success = False
-                tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                self.child.send(f'[Local Message] 不能加载Newbing组件，请注意Newbing组件已不再维护。{tb_str}')
-                self.child.send('[Fail]')
-                self.child.send('[Finish]')
+                tb_str = "\n```\n" + trimmed_format_exc() + "\n```\n"
+                self.child.send(
+                    f"[Local Message] 不能加载Newbing组件，请注意Newbing组件已不再维护。{tb_str}"
+                )
+                self.child.send("[Fail]")
+                self.child.send("[Finish]")
                 raise RuntimeError(f"不能加载Newbing组件，请注意Newbing组件已不再维护。")
 
         self.success = True
@@ -151,66 +169,100 @@ class NewBingHandle(Process):
             # 进入任务等待状态
             asyncio.run(self.async_run())
         except Exception:
-            tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-            self.child.send(f'[Local Message] Newbing 请求失败，报错信息如下. 如果是与网络相关的问题，建议更换代理协议（推荐http）或代理节点 {tb_str}.')
-            self.child.send('[Fail]')
-            self.child.send('[Finish]')
-        
+            tb_str = "\n```\n" + trimmed_format_exc() + "\n```\n"
+            self.child.send(
+                f"[Local Message] Newbing 请求失败，报错信息如下. 如果是与网络相关的问题，建议更换代理协议（推荐http）或代理节点 {tb_str}."
+            )
+            self.child.send("[Fail]")
+            self.child.send("[Finish]")
+
     def stream_chat(self, **kwargs):
         """
         这个函数运行在主进程
         """
-        self.threadLock.acquire()   # 获取线程锁
-        self.parent.send(kwargs)    # 请求子进程
+        self.threadLock.acquire()  # 获取线程锁
+        self.parent.send(kwargs)  # 请求子进程
         while True:
-            res = self.parent.recv()                            # 等待newbing回复的片段
-            if res == '[Finish]': break                         # 结束
-            elif res == '[Fail]': self.success = False; break   # 失败
-            else: yield res                                     # newbing回复的片段
-        self.threadLock.release()   # 释放线程锁
+            res = self.parent.recv()  # 等待newbing回复的片段
+            if res == "[Finish]":
+                break  # 结束
+            elif res == "[Fail]":
+                self.success = False
+                break  # 失败
+            else:
+                yield res  # newbing回复的片段
+        self.threadLock.release()  # 释放线程锁
 
 
 """
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 第三部分：主进程统一调用函数接口
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 """
 global newbingfree_handle
 newbingfree_handle = None
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+
+def predict_no_ui_long_connection(
+    inputs,
+    llm_kwargs,
+    history=[],
+    sys_prompt="",
+    observe_window=[],
+    console_slience=False,
+):
     """
-        多线程方法
-        函数的说明请见 request_llms/bridge_all.py
+    多线程方法
+    函数的说明请见 request_llms/bridge_all.py
     """
     global newbingfree_handle
     if (newbingfree_handle is None) or (not newbingfree_handle.success):
         newbingfree_handle = NewBingHandle()
-        if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + newbingfree_handle.info
-        if not newbingfree_handle.success: 
+        if len(observe_window) >= 1:
+            observe_window[0] = load_message + "\n\n" + newbingfree_handle.info
+        if not newbingfree_handle.success:
             error = newbingfree_handle.info
             newbingfree_handle = None
             raise RuntimeError(error)
 
     # 没有 sys_prompt 接口，因此把prompt加入 history
     history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
+    for i in range(len(history) // 2):
+        history_feedin.append([history[2 * i], history[2 * i + 1]])
 
-    watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
+    watch_dog_patience = 5  # 看门狗 (watchdog) 的耐心, 设置5秒即可
     response = ""
-    if len(observe_window) >= 1: observe_window[0] = "[Local Message] 等待NewBing响应中 ..."
-    for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
-        if len(observe_window) >= 1:  observe_window[0] = preprocess_newbing_out_simple(response)
-        if len(observe_window) >= 2:  
-            if (time.time()-observe_window[1]) > watch_dog_patience:
+    if len(observe_window) >= 1:
+        observe_window[0] = "[Local Message] 等待NewBing响应中 ..."
+    for response in newbingfree_handle.stream_chat(
+        query=inputs,
+        history=history_feedin,
+        system_prompt=sys_prompt,
+        max_length=llm_kwargs["max_length"],
+        top_p=llm_kwargs["top_p"],
+        temperature=llm_kwargs["temperature"],
+    ):
+        if len(observe_window) >= 1:
+            observe_window[0] = preprocess_newbing_out_simple(response)
+        if len(observe_window) >= 2:
+            if (time.time() - observe_window[1]) > watch_dog_patience:
                 raise RuntimeError("程序终止。")
     return preprocess_newbing_out_simple(response)
 
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+
+def predict(
+    inputs,
+    llm_kwargs,
+    plugin_kwargs,
+    chatbot,
+    history=[],
+    system_prompt="",
+    stream=True,
+    additional_fn=None,
+):
     """
-        单线程方法
-        函数的说明请见 request_llms/bridge_all.py
+    单线程方法
+    函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, "[Local Message] 等待NewBing响应中 ..."))
 
@@ -219,27 +271,41 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         newbingfree_handle = NewBingHandle()
         chatbot[-1] = (inputs, load_message + "\n\n" + newbingfree_handle.info)
         yield from update_ui(chatbot=chatbot, history=[])
-        if not newbingfree_handle.success: 
+        if not newbingfree_handle.success:
             newbingfree_handle = None
             return
 
     if additional_fn is not None:
         from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+        inputs, history = handle_core_functionality(
+            additional_fn, inputs, history, chatbot
+        )
 
     history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]] )
+    for i in range(len(history) // 2):
+        history_feedin.append([history[2 * i], history[2 * i + 1]])
 
     chatbot[-1] = (inputs, "[Local Message] 等待NewBing响应中 ...")
     response = "[Local Message] 等待NewBing响应中 ..."
-    yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    for response in newbingfree_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
+    yield from update_ui(
+        chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。"
+    )
+    for response in newbingfree_handle.stream_chat(
+        query=inputs,
+        history=history_feedin,
+        system_prompt=system_prompt,
+        max_length=llm_kwargs["max_length"],
+        top_p=llm_kwargs["top_p"],
+        temperature=llm_kwargs["temperature"],
+    ):
         chatbot[-1] = (inputs, preprocess_newbing_out(response))
-        yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    if response == "[Local Message] 等待NewBing响应中 ...": response = "[Local Message] NewBing响应异常，请刷新界面重试 ..."
+        yield from update_ui(
+            chatbot=chatbot, history=history, msg="NewBing响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。"
+        )
+    if response == "[Local Message] 等待NewBing响应中 ...":
+        response = "[Local Message] NewBing响应异常，请刷新界面重试 ..."
     history.extend([inputs, response])
-    logging.info(f'[raw_input] {inputs}')
-    logging.info(f'[response] {response}')
+    logging.info(f"[raw_input] {inputs}")
+    logging.info(f"[response] {response}")
     yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")
-

request_llms/bridge_qwen.py

@@ -1,59 +1,62 @@
-model_name = "Qwen"
-cmd_to_install = "`pip install -r request_llms/requirements_qwen.txt`"
+import time
+import os
+from toolbox import update_ui, get_conf, update_ui_lastest_msg
+from toolbox import check_packages, report_exception
 
-from toolbox import ProxyNetworkActivate, get_conf
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+model_name = 'Qwen'
 
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+    """
+        ⭐多线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
 
+    from .com_qwenapi import QwenRequestInstance
+    sri = QwenRequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
+        if len(observe_window) >= 1:
+            observe_window[0] = response
+        if len(observe_window) >= 2:
+            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+    return response
 
-# ------------------------------------------------------------------------------------------------------------------------
-# 🔌💻 Local Model
-# ------------------------------------------------------------------------------------------------------------------------
-class GetQwenLMHandle(LocalLLMHandle):
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+        ⭐单线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history)
 
-    def load_model_info(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        self.model_name = model_name
-        self.cmd_to_install = cmd_to_install
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        check_packages(["dashscope"])
+    except:
+        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade dashscope```。",
+                                         chatbot=chatbot, history=history, delay=0)
+        return
 
-    def load_model_and_tokenizer(self):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        # from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
-        from transformers import AutoModelForCausalLM, AutoTokenizer
-        from transformers.generation import GenerationConfig
-        with ProxyNetworkActivate('Download_LLM'):
-            model_id = get_conf('QWEN_MODEL_SELECTION')
-            self._tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, resume_download=True)
-            # use fp16
-            model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True).eval()
-            model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True)  # 可指定不同的生成长度、top_p等相关超参
-            self._model = model
+    # 检查DASHSCOPE_API_KEY
+    if get_conf("DASHSCOPE_API_KEY") == "":
+        yield from update_ui_lastest_msg(f"请配置 DASHSCOPE_API_KEY。",
+                                         chatbot=chatbot, history=history, delay=0)
+        return
 
-        return self._model, self._tokenizer
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
-    def llm_stream_generator(self, **kwargs):
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        def adaptor(kwargs):
-            query = kwargs['query']
-            max_length = kwargs['max_length']
-            top_p = kwargs['top_p']
-            temperature = kwargs['temperature']
-            history = kwargs['history']
-            return query, max_length, top_p, temperature, history
+    # 开始接收回复
+    from .com_qwenapi import QwenRequestInstance
+    sri = QwenRequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
+        chatbot[-1] = (inputs, response)
+        yield from update_ui(chatbot=chatbot, history=history)
 
-        query, max_length, top_p, temperature, history = adaptor(kwargs)
-
-        for response in self._model.chat_stream(self._tokenizer, query, history=history):
-            yield response
-        
-    def try_to_import_special_deps(self, **kwargs):
-        # import something that will raise error if the user does not install requirement_*.txt
-        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
-        import importlib
-        importlib.import_module('modelscope')
-
-
-# ------------------------------------------------------------------------------------------------------------------------
-# 🔌💻 GPT-Academic Interface
-# ------------------------------------------------------------------------------------------------------------------------
-predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetQwenLMHandle, model_name)
+    # 总结输出
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
+        response = f"[Local Message] {model_name}响应异常 ..."
+    history.extend([inputs, response])
+    yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_qwen_local.py

@@ -0,0 +1,59 @@
+model_name = "Qwen_Local"
+cmd_to_install = "`pip install -r request_llms/requirements_qwen_local.txt`"
+
+from toolbox import ProxyNetworkActivate, get_conf
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+class GetQwenLMHandle(LocalLLMHandle):
+
+    def load_model_info(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        self.model_name = model_name
+        self.cmd_to_install = cmd_to_install
+
+    def load_model_and_tokenizer(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        # from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        from transformers.generation import GenerationConfig
+        with ProxyNetworkActivate('Download_LLM'):
+            model_id = get_conf('QWEN_LOCAL_MODEL_SELECTION')
+            self._tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, resume_download=True)
+            # use fp16
+            model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True).eval()
+            model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True)  # 可指定不同的生成长度、top_p等相关超参
+            self._model = model
+
+        return self._model, self._tokenizer
+
+    def llm_stream_generator(self, **kwargs):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        def adaptor(kwargs):
+            query = kwargs['query']
+            max_length = kwargs['max_length']
+            top_p = kwargs['top_p']
+            temperature = kwargs['temperature']
+            history = kwargs['history']
+            return query, max_length, top_p, temperature, history
+
+        query, max_length, top_p, temperature, history = adaptor(kwargs)
+
+        for response in self._model.chat_stream(self._tokenizer, query, history=history):
+            yield response
+        
+    def try_to_import_special_deps(self, **kwargs):
+        # import something that will raise error if the user does not install requirement_*.txt
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
+        import importlib
+        importlib.import_module('modelscope')
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 GPT-Academic Interface
+# ------------------------------------------------------------------------------------------------------------------------
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetQwenLMHandle, model_name)

request_llms/bridge_skylark2.py

@@ -0,0 +1,67 @@
+import time
+from toolbox import update_ui, get_conf, update_ui_lastest_msg
+from toolbox import check_packages, report_exception
+
+model_name = '云雀大模型'
+
+def validate_key():
+    YUNQUE_SECRET_KEY = get_conf("YUNQUE_SECRET_KEY")
+    if YUNQUE_SECRET_KEY == '': return False
+    return True
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+    """
+        ⭐ 多线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
+
+    if validate_key() is False:
+        raise RuntimeError('请配置YUNQUE_SECRET_KEY')
+
+    from .com_skylark2api import YUNQUERequestInstance
+    sri = YUNQUERequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
+        if len(observe_window) >= 1:
+            observe_window[0] = response
+        if len(observe_window) >= 2:
+            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+    return response
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+        ⭐ 单线程方法
+        函数的说明请见 request_llms/bridge_all.py
+    """
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history)
+
+    # 尝试导入依赖，如果缺少依赖，则给出安装建议
+    try:
+        check_packages(["zhipuai"])
+    except:
+        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
+                                         chatbot=chatbot, history=history, delay=0)
+        return
+
+    if validate_key() is False:
+        yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置HUOSHAN_API_KEY", chatbot=chatbot, history=history, delay=0)
+        return
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    # 开始接收回复
+    from .com_skylark2api import YUNQUERequestInstance
+    sri = YUNQUERequestInstance()
+    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
+        chatbot[-1] = (inputs, response)
+        yield from update_ui(chatbot=chatbot, history=history)
+
+    # 总结输出
+    if response == f"[Local Message] 等待{model_name}响应中 ...":
+        response = f"[Local Message] {model_name}响应异常 ..."
+    history.extend([inputs, response])
+    yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_spark.py

@@ -26,7 +26,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 
     from .com_sparkapi import SparkRequestInstance
     sri = SparkRequestInstance()
-    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
+    for response in sri.generate(inputs, llm_kwargs, history, sys_prompt, use_image_api=False):
         if len(observe_window) >= 1:
             observe_window[0] = response
         if len(observe_window) >= 2:
@@ -52,7 +52,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     # 开始接收回复    
     from .com_sparkapi import SparkRequestInstance
     sri = SparkRequestInstance()
-    for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
+    for response in sri.generate(inputs, llm_kwargs, history, system_prompt, use_image_api=True):
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 

request_llms/bridge_stackclaude.py

@@ -7,14 +7,15 @@ import logging
 import time
 from toolbox import get_conf
 import asyncio
+
 load_message = "正在加载Claude组件，请稍候..."
 
 try:
     """
-    ========================================================================
+    =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     第一部分：Slack API Client
     https://github.com/yokonsan/claude-in-slack-api
-    ========================================================================
+    =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
     """
 
     from slack_sdk.errors import SlackApiError
@@ -23,20 +24,23 @@ try:
     class SlackClient(AsyncWebClient):
         """SlackClient类用于与Slack API进行交互，实现消息发送、接收等功能。
 
-            属性：
-            - CHANNEL_ID：str类型，表示频道ID。
+        属性：
+        - CHANNEL_ID：str类型，表示频道ID。
 
-            方法：
-            - open_channel()：异步方法。通过调用conversations_open方法打开一个频道，并将返回的频道ID保存在属性CHANNEL_ID中。
-            - chat(text: str)：异步方法。向已打开的频道发送一条文本消息。
-            - get_slack_messages()：异步方法。获取已打开频道的最新消息并返回消息列表，目前不支持历史消息查询。
-            - get_reply()：异步方法。循环监听已打开频道的消息，如果收到"Typing…_"结尾的消息说明Claude还在继续输出，否则结束循环。
+        方法：
+        - open_channel()：异步方法。通过调用conversations_open方法打开一个频道，并将返回的频道ID保存在属性CHANNEL_ID中。
+        - chat(text: str)：异步方法。向已打开的频道发送一条文本消息。
+        - get_slack_messages()：异步方法。获取已打开频道的最新消息并返回消息列表，目前不支持历史消息查询。
+        - get_reply()：异步方法。循环监听已打开频道的消息，如果收到"Typing…_"结尾的消息说明Claude还在继续输出，否则结束循环。
 
         """
+
         CHANNEL_ID = None
 
         async def open_channel(self):
-            response = await self.conversations_open(users=get_conf('SLACK_CLAUDE_BOT_ID'))
+            response = await self.conversations_open(
+                users=get_conf("SLACK_CLAUDE_BOT_ID")
+            )
             self.CHANNEL_ID = response["channel"]["id"]
 
         async def chat(self, text):
@@ -49,33 +53,39 @@ try:
         async def get_slack_messages(self):
             try:
                 # TODO：暂时不支持历史消息，因为在同一个频道里存在多人使用时历史消息渗透问题
-                resp = await self.conversations_history(channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1)
-                msg = [msg for msg in resp["messages"]
-                    if msg.get("user") == get_conf('SLACK_CLAUDE_BOT_ID')]
+                resp = await self.conversations_history(
+                    channel=self.CHANNEL_ID, oldest=self.LAST_TS, limit=1
+                )
+                msg = [
+                    msg
+                    for msg in resp["messages"]
+                    if msg.get("user") == get_conf("SLACK_CLAUDE_BOT_ID")
+                ]
                 return msg
             except (SlackApiError, KeyError) as e:
                 raise RuntimeError(f"获取Slack消息失败。")
-        
+
         async def get_reply(self):
             while True:
                 slack_msgs = await self.get_slack_messages()
                 if len(slack_msgs) == 0:
                     await asyncio.sleep(0.5)
                     continue
-                
+
                 msg = slack_msgs[-1]
                 if msg["text"].endswith("Typing…_"):
                     yield False, msg["text"]
                 else:
                     yield True, msg["text"]
                     break
+
 except:
     pass
 
 """
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 第二部分：子进程Worker（调用主体）
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 """
 
 
@@ -88,7 +98,7 @@ class ClaudeHandle(Process):
         self.success = True
         self.local_history = []
         self.check_dependency()
-        if self.success: 
+        if self.success:
             self.start()
             self.threadLock = threading.Lock()
 
@@ -96,6 +106,7 @@ class ClaudeHandle(Process):
         try:
             self.success = False
             import slack_sdk
+
             self.info = "依赖检测通过，等待Claude响应。注意目前不能多人同时调用Claude接口（有线程锁），否则将导致每个人的Claude问询历史互相渗透。调用Claude时，会自动使用已配置的代理。"
             self.success = True
         except:
@@ -103,40 +114,44 @@ class ClaudeHandle(Process):
             self.success = False
 
     def ready(self):
-        return self.claude_model is not None    
-    
+        return self.claude_model is not None
+
     async def async_run(self):
         await self.claude_model.open_channel()
         while True:
             # 等待
             kwargs = self.child.recv()
-            question = kwargs['query']
-            history = kwargs['history']
+            question = kwargs["query"]
+            history = kwargs["history"]
 
             # 开始问问题
             prompt = ""
 
             # 问题
             prompt += question
-            print('question:', prompt)
+            print("question:", prompt)
 
             # 提交
             await self.claude_model.chat(prompt)
-            
+
             # 获取回复
-            async for final, response in self.claude_model.get_reply():                
+            async for final, response in self.claude_model.get_reply():
                 if not final:
                     print(response)
                     self.child.send(str(response))
                 else:
                     # 防止丢失最后一条消息
                     slack_msgs = await self.claude_model.get_slack_messages()
-                    last_msg = slack_msgs[-1]["text"] if slack_msgs and len(slack_msgs) > 0 else ""
+                    last_msg = (
+                        slack_msgs[-1]["text"]
+                        if slack_msgs and len(slack_msgs) > 0
+                        else ""
+                    )
                     if last_msg:
                         self.child.send(last_msg)
-                    print('-------- receive final ---------')
-                    self.child.send('[Finish]')
-                    
+                    print("-------- receive final ---------")
+                    self.child.send("[Finish]")
+
     def run(self):
         """
         这个函数运行在子进程
@@ -146,22 +161,24 @@ class ClaudeHandle(Process):
         self.local_history = []
         if (self.claude_model is None) or (not self.success):
             # 代理设置
-            proxies = get_conf('proxies')
+            proxies = get_conf("proxies")
             if proxies is None:
                 self.proxies_https = None
             else:
-                self.proxies_https = proxies['https']
+                self.proxies_https = proxies["https"]
 
             try:
-                SLACK_CLAUDE_USER_TOKEN = get_conf('SLACK_CLAUDE_USER_TOKEN')
-                self.claude_model = SlackClient(token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https)
-                print('Claude组件初始化成功。')
+                SLACK_CLAUDE_USER_TOKEN = get_conf("SLACK_CLAUDE_USER_TOKEN")
+                self.claude_model = SlackClient(
+                    token=SLACK_CLAUDE_USER_TOKEN, proxy=self.proxies_https
+                )
+                print("Claude组件初始化成功。")
             except:
                 self.success = False
-                tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-                self.child.send(f'[Local Message] 不能加载Claude组件。{tb_str}')
-                self.child.send('[Fail]')
-                self.child.send('[Finish]')
+                tb_str = "\n```\n" + trimmed_format_exc() + "\n```\n"
+                self.child.send(f"[Local Message] 不能加载Claude组件。{tb_str}")
+                self.child.send("[Fail]")
+                self.child.send("[Finish]")
                 raise RuntimeError(f"不能加载Claude组件。")
 
         self.success = True
@@ -169,42 +186,49 @@ class ClaudeHandle(Process):
             # 进入任务等待状态
             asyncio.run(self.async_run())
         except Exception:
-            tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
-            self.child.send(f'[Local Message] Claude失败 {tb_str}.')
-            self.child.send('[Fail]')
-            self.child.send('[Finish]')
+            tb_str = "\n```\n" + trimmed_format_exc() + "\n```\n"
+            self.child.send(f"[Local Message] Claude失败 {tb_str}.")
+            self.child.send("[Fail]")
+            self.child.send("[Finish]")
 
     def stream_chat(self, **kwargs):
         """
         这个函数运行在主进程
         """
         self.threadLock.acquire()
-        self.parent.send(kwargs)    # 发送请求到子进程
+        self.parent.send(kwargs)  # 发送请求到子进程
         while True:
-            res = self.parent.recv()    # 等待Claude回复的片段
-            if res == '[Finish]':
-                break       # 结束
-            elif res == '[Fail]':
+            res = self.parent.recv()  # 等待Claude回复的片段
+            if res == "[Finish]":
+                break  # 结束
+            elif res == "[Fail]":
                 self.success = False
                 break
             else:
-                yield res   # Claude回复的片段
+                yield res  # Claude回复的片段
         self.threadLock.release()
 
 
 """
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 第三部分：主进程统一调用函数接口
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 """
 global claude_handle
 claude_handle = None
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+def predict_no_ui_long_connection(
+    inputs,
+    llm_kwargs,
+    history=[],
+    sys_prompt="",
+    observe_window=None,
+    console_slience=False,
+):
     """
-        多线程方法
-        函数的说明请见 request_llms/bridge_all.py
+    多线程方法
+    函数的说明请见 request_llms/bridge_all.py
     """
     global claude_handle
     if (claude_handle is None) or (not claude_handle.success):
@@ -217,24 +241,40 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
 
     # 没有 sys_prompt 接口，因此把prompt加入 history
     history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]])
+    for i in range(len(history) // 2):
+        history_feedin.append([history[2 * i], history[2 * i + 1]])
 
     watch_dog_patience = 5  # 看门狗 (watchdog) 的耐心, 设置5秒即可
     response = ""
     observe_window[0] = "[Local Message] 等待Claude响应中 ..."
-    for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
+    for response in claude_handle.stream_chat(
+        query=inputs,
+        history=history_feedin,
+        system_prompt=sys_prompt,
+        max_length=llm_kwargs["max_length"],
+        top_p=llm_kwargs["top_p"],
+        temperature=llm_kwargs["temperature"],
+    ):
         observe_window[0] = preprocess_newbing_out_simple(response)
         if len(observe_window) >= 2:
-            if (time.time()-observe_window[1]) > watch_dog_patience:
+            if (time.time() - observe_window[1]) > watch_dog_patience:
                 raise RuntimeError("程序终止。")
     return preprocess_newbing_out_simple(response)
 
 
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
+def predict(
+    inputs,
+    llm_kwargs,
+    plugin_kwargs,
+    chatbot,
+    history=[],
+    system_prompt="",
+    stream=True,
+    additional_fn=None,
+):
     """
-        单线程方法
-        函数的说明请见 request_llms/bridge_all.py
+    单线程方法
+    函数的说明请见 request_llms/bridge_all.py
     """
     chatbot.append((inputs, "[Local Message] 等待Claude响应中 ..."))
 
@@ -249,21 +289,30 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
 
     if additional_fn is not None:
         from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+        inputs, history = handle_core_functionality(
+            additional_fn, inputs, history, chatbot
+        )
 
     history_feedin = []
-    for i in range(len(history)//2):
-        history_feedin.append([history[2*i], history[2*i+1]])
+    for i in range(len(history) // 2):
+        history_feedin.append([history[2 * i], history[2 * i + 1]])
 
     chatbot[-1] = (inputs, "[Local Message] 等待Claude响应中 ...")
     response = "[Local Message] 等待Claude响应中 ..."
-    yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
-    for response in claude_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt):
+    yield from update_ui(
+        chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。"
+    )
+    for response in claude_handle.stream_chat(
+        query=inputs, history=history_feedin, system_prompt=system_prompt
+    ):
         chatbot[-1] = (inputs, preprocess_newbing_out(response))
-        yield from update_ui(chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。")
+        yield from update_ui(
+            chatbot=chatbot, history=history, msg="Claude响应缓慢，尚未完成全部响应，请耐心完成后再提交新问题。"
+        )
     if response == "[Local Message] 等待Claude响应中 ...":
         response = "[Local Message] Claude响应异常，请刷新界面重试 ..."
     history.extend([inputs, response])
-    logging.info(f'[raw_input] {inputs}')
-    logging.info(f'[response] {response}')
+    logging.info(f"[raw_input] {inputs}")
+    logging.info(f"[response] {response}")
     yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应，请提交新问题。")

request_llms/bridge_zhipu.py

@@ -42,7 +42,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     try:
         check_packages(["zhipuai"])
     except:
-        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
+        yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install zhipuai==1.0.7```。",
                                          chatbot=chatbot, history=history, delay=0)
         return
     

request_llms/com_google.py

@@ -0,0 +1,229 @@
+# encoding: utf-8
+# @Time   : 2023/12/25
+# @Author : Spike
+# @Descr   :
+import json
+import os
+import re
+import requests
+from typing import List, Dict, Tuple
+from toolbox import get_conf, encode_image, get_pictures_list
+
+proxies, TIMEOUT_SECONDS = get_conf("proxies", "TIMEOUT_SECONDS")
+
+"""
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
+第五部分 一些文件处理方法
+files_filter_handler 根据type过滤文件
+input_encode_handler 提取input中的文件，并解析
+file_manifest_filter_html 根据type过滤文件, 并解析为html or md 文本
+link_mtime_to_md 文件增加本地时间参数，避免下载到缓存文件
+html_view_blank 超链接
+html_local_file 本地文件取相对路径
+to_markdown_tabs 文件list 转换为 md tab
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
+"""
+
+
+def files_filter_handler(file_list):
+    new_list = []
+    filter_ = [
+        "png",
+        "jpg",
+        "jpeg",
+        "bmp",
+        "svg",
+        "webp",
+        "ico",
+        "tif",
+        "tiff",
+        "raw",
+        "eps",
+    ]
+    for file in file_list:
+        file = str(file).replace("file=", "")
+        if os.path.exists(file):
+            if str(os.path.basename(file)).split(".")[-1] in filter_:
+                new_list.append(file)
+    return new_list
+
+
+def input_encode_handler(inputs, llm_kwargs):
+    if llm_kwargs["most_recent_uploaded"].get("path"):
+        image_paths = get_pictures_list(llm_kwargs["most_recent_uploaded"]["path"])
+    md_encode = []
+    for md_path in image_paths:
+        type_ = os.path.splitext(md_path)[1].replace(".", "")
+        type_ = "jpeg" if type_ == "jpg" else type_
+        md_encode.append({"data": encode_image(md_path), "type": type_})
+    return inputs, md_encode
+
+
+def file_manifest_filter_html(file_list, filter_: list = None, md_type=False):
+    new_list = []
+    if not filter_:
+        filter_ = [
+            "png",
+            "jpg",
+            "jpeg",
+            "bmp",
+            "svg",
+            "webp",
+            "ico",
+            "tif",
+            "tiff",
+            "raw",
+            "eps",
+        ]
+    for file in file_list:
+        if str(os.path.basename(file)).split(".")[-1] in filter_:
+            new_list.append(html_local_img(file, md=md_type))
+        elif os.path.exists(file):
+            new_list.append(link_mtime_to_md(file))
+        else:
+            new_list.append(file)
+    return new_list
+
+
+def link_mtime_to_md(file):
+    link_local = html_local_file(file)
+    link_name = os.path.basename(file)
+    a = f"[{link_name}]({link_local}?{os.path.getmtime(file)})"
+    return a
+
+
+def html_local_file(file):
+    base_path = os.path.dirname(__file__)  # 项目目录
+    if os.path.exists(str(file)):
+        file = f'file={file.replace(base_path, ".")}'
+    return file
+
+
+def html_local_img(__file, layout="left", max_width=None, max_height=None, md=True):
+    style = ""
+    if max_width is not None:
+        style += f"max-width: {max_width};"
+    if max_height is not None:
+        style += f"max-height: {max_height};"
+    __file = html_local_file(__file)
+    a = f'<div align="{layout}"><img src="{__file}" style="{style}"></div>'
+    if md:
+        a = f"![{__file}]({__file})"
+    return a
+
+
+def to_markdown_tabs(head: list, tabs: list, alignment=":---:", column=False):
+    """
+    Args:
+        head: 表头：[]
+        tabs: 表值：[[列1], [列2], [列3], [列4]]
+        alignment: :--- 左对齐， :---: 居中对齐， ---: 右对齐
+        column: True to keep data in columns, False to keep data in rows (default).
+    Returns:
+        A string representation of the markdown table.
+    """
+    if column:
+        transposed_tabs = list(map(list, zip(*tabs)))
+    else:
+        transposed_tabs = tabs
+    # Find the maximum length among the columns
+    max_len = max(len(column) for column in transposed_tabs)
+
+    tab_format = "| %s "
+    tabs_list = "".join([tab_format % i for i in head]) + "|\n"
+    tabs_list += "".join([tab_format % alignment for i in head]) + "|\n"
+
+    for i in range(max_len):
+        row_data = [tab[i] if i < len(tab) else "" for tab in transposed_tabs]
+        row_data = file_manifest_filter_html(row_data, filter_=None)
+        tabs_list += "".join([tab_format % i for i in row_data]) + "|\n"
+
+    return tabs_list
+
+
+class GoogleChatInit:
+    def __init__(self):
+        self.url_gemini = "https://generativelanguage.googleapis.com/v1beta/models/%m:streamGenerateContent?key=%k"
+
+    def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
+        headers, payload = self.generate_message_payload(
+            inputs, llm_kwargs, history, system_prompt
+        )
+        response = requests.post(
+            url=self.url_gemini,
+            headers=headers,
+            data=json.dumps(payload),
+            stream=True,
+            proxies=proxies,
+            timeout=TIMEOUT_SECONDS,
+        )
+        return response.iter_lines()
+
+    def __conversation_user(self, user_input, llm_kwargs):
+        what_i_have_asked = {"role": "user", "parts": []}
+        if "vision" not in self.url_gemini:
+            input_ = user_input
+            encode_img = []
+        else:
+            input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
+        what_i_have_asked["parts"].append({"text": input_})
+        if encode_img:
+            for data in encode_img:
+                what_i_have_asked["parts"].append(
+                    {
+                        "inline_data": {
+                            "mime_type": f"image/{data['type']}",
+                            "data": data["data"],
+                        }
+                    }
+                )
+        return what_i_have_asked
+
+    def __conversation_history(self, history, llm_kwargs):
+        messages = []
+        conversation_cnt = len(history) // 2
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
+                what_gpt_answer = {
+                    "role": "model",
+                    "parts": [{"text": history[index + 1]}],
+                }
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+        return messages
+
+    def generate_message_payload(
+        self, inputs, llm_kwargs, history, system_prompt
+    ) -> Tuple[Dict, Dict]:
+        messages = [
+            # {"role": "system", "parts": [{"text": system_prompt}]},  # gemini 不允许对话轮次为偶数，所以这个没有用，看后续支持吧。。。
+            # {"role": "user", "parts": [{"text": ""}]},
+            # {"role": "model", "parts": [{"text": ""}]}
+        ]
+        self.url_gemini = self.url_gemini.replace(
+            "%m", llm_kwargs["llm_model"]
+        ).replace("%k", get_conf("GEMINI_API_KEY"))
+        header = {"Content-Type": "application/json"}
+        if "vision" not in self.url_gemini:  # 不是vision 才处理history
+            messages.extend(
+                self.__conversation_history(history, llm_kwargs)
+            )  # 处理 history
+        messages.append(self.__conversation_user(inputs, llm_kwargs))  # 处理用户对话
+        payload = {
+            "contents": messages,
+            "generationConfig": {
+                # "maxOutputTokens": 800,
+                "stopSequences": str(llm_kwargs.get("stop", "")).split(" "),
+                "temperature": llm_kwargs.get("temperature", 1),
+                "topP": llm_kwargs.get("top_p", 0.8),
+                "topK": 10,
+            },
+        }
+        return header, payload
+
+
+if __name__ == "__main__":
+    google = GoogleChatInit()
+    # print(gootle.generate_message_payload('你好呀', {},  ['123123', '3123123'], ''))
+    # gootle.input_encode_handle('123123[123123](./123123), ![53425](./asfafa/fff.jpg)')

request_llms/com_qwenapi.py

@@ -0,0 +1,94 @@
+from http import HTTPStatus
+from toolbox import get_conf
+import threading
+import logging
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error.'
+
+class QwenRequestInstance():
+    def __init__(self):
+        import dashscope
+        self.time_to_yield_event = threading.Event()
+        self.time_to_exit_event = threading.Event()
+        self.result_buf = ""
+
+        def validate_key():
+            DASHSCOPE_API_KEY = get_conf("DASHSCOPE_API_KEY")
+            if DASHSCOPE_API_KEY == '': return False
+            return True
+
+        if not validate_key():
+            raise RuntimeError('请配置 DASHSCOPE_API_KEY')
+        dashscope.api_key = get_conf("DASHSCOPE_API_KEY")
+
+
+    def generate(self, inputs, llm_kwargs, history, system_prompt):
+        # import _thread as thread
+        from dashscope import Generation
+        QWEN_MODEL = {
+            'qwen-turbo': Generation.Models.qwen_turbo,
+            'qwen-plus': Generation.Models.qwen_plus,
+            'qwen-max': Generation.Models.qwen_max,
+        }[llm_kwargs['llm_model']]
+        top_p = llm_kwargs.get('top_p', 0.8)
+        if top_p == 0: top_p += 1e-5
+        if top_p == 1: top_p -= 1e-5
+
+        self.result_buf = ""
+        responses = Generation.call(
+            model=QWEN_MODEL,
+            messages=generate_message_payload(inputs, llm_kwargs, history, system_prompt),
+            top_p=top_p,
+            temperature=llm_kwargs.get('temperature', 1.0),
+            result_format='message',
+            stream=True,
+            incremental_output=True
+        )
+
+        for response in responses:
+            if response.status_code == HTTPStatus.OK:
+                if response.output.choices[0].finish_reason == 'stop':
+                    yield self.result_buf
+                    break
+                elif response.output.choices[0].finish_reason == 'length':
+                    self.result_buf += "[Local Message] 生成长度过长，后续输出被截断"
+                    yield self.result_buf
+                    break
+                else:
+                    self.result_buf += response.output.choices[0].message.content
+                    yield self.result_buf
+            else:
+                self.result_buf += f"[Local Message] 请求错误：状态码：{response.status_code}，错误码:{response.code}，消息：{response.message}"
+                yield self.result_buf
+                break
+        logging.info(f'[raw_input] {inputs}')
+        logging.info(f'[response] {self.result_buf}')
+        return self.result_buf
+
+
+def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
+    conversation_cnt = len(history) // 2
+    if system_prompt == '': system_prompt = 'Hello!'
+    messages = [{"role": "user", "content": system_prompt}, {"role": "assistant", "content": "Certainly!"}]
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "":
+                    continue
+                if what_gpt_answer["content"] == timeout_bot_msg:
+                    continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+    return messages

request_llms/com_skylark2api.py

@@ -0,0 +1,95 @@
+from toolbox import get_conf
+import threading
+import logging
+import os
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error.'
+#os.environ['VOLC_ACCESSKEY'] = ''
+#os.environ['VOLC_SECRETKEY'] = ''
+
+class YUNQUERequestInstance():
+    def __init__(self):
+
+        self.time_to_yield_event = threading.Event()
+        self.time_to_exit_event = threading.Event()
+
+        self.result_buf = ""
+
+    def generate(self, inputs, llm_kwargs, history, system_prompt):
+        # import _thread as thread
+        from volcengine.maas import MaasService, MaasException
+
+        maas = MaasService('maas-api.ml-platform-cn-beijing.volces.com', 'cn-beijing')
+
+        YUNQUE_SECRET_KEY, YUNQUE_ACCESS_KEY,YUNQUE_MODEL = get_conf("YUNQUE_SECRET_KEY", "YUNQUE_ACCESS_KEY","YUNQUE_MODEL")
+        maas.set_ak(YUNQUE_ACCESS_KEY) #填写 VOLC_ACCESSKEY
+        maas.set_sk(YUNQUE_SECRET_KEY) #填写 'VOLC_SECRETKEY'
+
+        self.result_buf = ""
+
+        req = {
+        "model": {
+            "name": YUNQUE_MODEL,
+            "version": "1.0", # use default version if not specified.
+        },
+        "parameters": {
+            "max_new_tokens": 4000,  # 输出文本的最大tokens限制
+            "min_new_tokens": 1,  # 输出文本的最小tokens限制
+            "temperature": llm_kwargs['temperature'],  # 用于控制生成文本的随机性和创造性，Temperature值越大随机性越大，取值范围0~1
+            "top_p": llm_kwargs['top_p'],  # 用于控制输出tokens的多样性，TopP值越大输出的tokens类型越丰富，取值范围0~1
+            "top_k": 0,  # 选择预测值最大的k个token进行采样，取值范围0-1000，0表示不生效
+            "max_prompt_tokens": 4000,  # 最大输入 token 数，如果给出的 prompt 的 token 长度超过此限制，取最后 max_prompt_tokens 个 token 输入模型。
+        },
+            "messages": self.generate_message_payload(inputs, llm_kwargs, history, system_prompt)
+        }
+
+        response = maas.stream_chat(req)
+
+        for resp in response:
+            self.result_buf += resp.choice.message.content
+            yield self.result_buf
+        '''
+        for event in response.events():
+            if event.event == "add":
+                self.result_buf += event.data
+                yield self.result_buf
+            elif event.event == "error" or event.event == "interrupted":
+                raise RuntimeError("Unknown error:" + event.data)
+            elif event.event == "finish":
+                yield self.result_buf
+                break
+            else:
+                raise RuntimeError("Unknown error:" + str(event))
+
+        logging.info(f'[raw_input] {inputs}')
+        logging.info(f'[response] {self.result_buf}')
+        '''
+        return self.result_buf
+
+    def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
+        from volcengine.maas import ChatRole
+        conversation_cnt = len(history) // 2
+        messages = [{"role": ChatRole.USER, "content": system_prompt},
+                    {"role": ChatRole.ASSISTANT, "content": "Certainly!"}]
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = {}
+                what_i_have_asked["role"] = ChatRole.USER
+                what_i_have_asked["content"] = history[index]
+                what_gpt_answer = {}
+                what_gpt_answer["role"] = ChatRole.ASSISTANT
+                what_gpt_answer["content"] = history[index + 1]
+                if what_i_have_asked["content"] != "":
+                    if what_gpt_answer["content"] == "":
+                        continue
+                    if what_gpt_answer["content"] == timeout_bot_msg:
+                        continue
+                    messages.append(what_i_have_asked)
+                    messages.append(what_gpt_answer)
+                else:
+                    messages[-1]['content'] = what_gpt_answer['content']
+        what_i_ask_now = {}
+        what_i_ask_now["role"] = ChatRole.USER
+        what_i_ask_now["content"] = inputs
+        messages.append(what_i_ask_now)
+        return messages

request_llms/com_sparkapi.py

@@ -72,12 +72,12 @@ class SparkRequestInstance():
 
         self.result_buf = ""
 
-    def generate(self, inputs, llm_kwargs, history, system_prompt):
+    def generate(self, inputs, llm_kwargs, history, system_prompt, use_image_api=False):
         llm_kwargs = llm_kwargs
         history = history
         system_prompt = system_prompt
         import _thread as thread
-        thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt))
+        thread.start_new_thread(self.create_blocking_request, (inputs, llm_kwargs, history, system_prompt, use_image_api))
         while True:
             self.time_to_yield_event.wait(timeout=1)
             if self.time_to_yield_event.is_set():
@@ -86,7 +86,7 @@ class SparkRequestInstance():
                 return self.result_buf
 
 
-    def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt):
+    def create_blocking_request(self, inputs, llm_kwargs, history, system_prompt, use_image_api):
         if llm_kwargs['llm_model'] == 'sparkv2':
             gpt_url = self.gpt_url_v2
         elif llm_kwargs['llm_model'] == 'sparkv3':
@@ -94,10 +94,12 @@ class SparkRequestInstance():
         else:
             gpt_url = self.gpt_url
         file_manifest = []
-        if llm_kwargs.get('most_recent_uploaded'):
+        if use_image_api and llm_kwargs.get('most_recent_uploaded'):
             if llm_kwargs['most_recent_uploaded'].get('path'):
                 file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path'])
-                gpt_url = self.gpt_url_img
+                if len(file_manifest) > 0:
+                    print('正在使用讯飞图片理解API')
+                    gpt_url = self.gpt_url_img
         wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
         websocket.enableTrace(False)
         wsUrl = wsParam.create_url()

request_llms/com_zhipuapi.py

@@ -26,6 +26,8 @@ class ZhipuRequestInstance():
         )
         for event in response.events():
             if event.event == "add":
+                # if self.result_buf == "" and event.data.startswith(" "):
+                #     event.data = event.data.lstrip(" ") # 每次智谱为啥都要带个空格开头呢？
                 self.result_buf += event.data
                 yield self.result_buf
             elif event.event == "error" or event.event == "interrupted":
@@ -35,7 +37,8 @@ class ZhipuRequestInstance():
                 break
             else:
                 raise RuntimeError("Unknown error:" + str(event))
-            
+        if self.result_buf == "":
+            yield "智谱没有返回任何数据, 请检查ZHIPUAI_API_KEY和ZHIPUAI_MODEL是否填写正确."
         logging.info(f'[raw_input] {inputs}')
         logging.info(f'[response] {self.result_buf}')
         return self.result_buf

request_llms/edge_gpt_free.py

@@ -1,8 +1,8 @@
 """
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 第一部分：来自EdgeGPT.py
 https://github.com/acheong08/EdgeGPT
-========================================================================
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
 """
 """
 Main.py
@@ -196,9 +196,9 @@ class _ChatHubRequest:
         self,
         prompt: str,
         conversation_style: CONVERSATION_STYLE_TYPE,
-        options = None,
-        webpage_context = None,
-        search_result = False,
+        options=None,
+        webpage_context=None,
+        search_result=False,
     ) -> None:
         """
         Updates request object
@@ -294,9 +294,9 @@ class _Conversation:
 
     def __init__(
         self,
-        proxy = None,
-        async_mode = False,
-        cookies = None,
+        proxy=None,
+        async_mode=False,
+        cookies=None,
     ) -> None:
         if async_mode:
             return
@@ -350,8 +350,8 @@ class _Conversation:
 
     @staticmethod
     async def create(
-        proxy = None,
-        cookies = None,
+        proxy=None,
+        cookies=None,
     ):
         self = _Conversation(async_mode=True)
         self.struct = {
@@ -418,8 +418,8 @@ class _ChatHub:
     def __init__(
         self,
         conversation: _Conversation,
-        proxy = None,
-        cookies = None,
+        proxy=None,
+        cookies=None,
     ) -> None:
         self.session = None
         self.wss = None
@@ -441,7 +441,7 @@ class _ChatHub:
         conversation_style: CONVERSATION_STYLE_TYPE = None,
         raw: bool = False,
         options: dict = None,
-        webpage_context = None,
+        webpage_context=None,
         search_result: bool = False,
     ) -> Generator[str, None, None]:
         """
@@ -452,10 +452,12 @@ class _ChatHub:
             ws_cookies = []
             for cookie in self.cookies:
                 ws_cookies.append(f"{cookie['name']}={cookie['value']}")
-            req_header.update({
-                'Cookie': ';'.join(ws_cookies),
-            })
-            
+            req_header.update(
+                {
+                    "Cookie": ";".join(ws_cookies),
+                }
+            )
+
         timeout = aiohttp.ClientTimeout(total=30)
         self.session = aiohttp.ClientSession(timeout=timeout)
 
@@ -521,9 +523,9 @@ class _ChatHub:
             msg = await self.wss.receive()
             try:
                 objects = msg.data.split(DELIMITER)
-            except :
+            except:
                 continue
-            
+
             for obj in objects:
                 if obj is None or not obj:
                     continue
@@ -624,8 +626,8 @@ class Chatbot:
 
     def __init__(
         self,
-        proxy = None,
-        cookies = None,
+        proxy=None,
+        cookies=None,
     ) -> None:
         self.proxy = proxy
         self.chat_hub: _ChatHub = _ChatHub(
@@ -636,8 +638,8 @@ class Chatbot:
 
     @staticmethod
     async def create(
-        proxy = None,
-        cookies = None,
+        proxy=None,
+        cookies=None,
     ):
         self = Chatbot.__new__(Chatbot)
         self.proxy = proxy
@@ -654,7 +656,7 @@ class Chatbot:
         wss_link: str = "wss://sydney.bing.com/sydney/ChatHub",
         conversation_style: CONVERSATION_STYLE_TYPE = None,
         options: dict = None,
-        webpage_context = None,
+        webpage_context=None,
         search_result: bool = False,
     ) -> dict:
         """
@@ -680,7 +682,7 @@ class Chatbot:
         conversation_style: CONVERSATION_STYLE_TYPE = None,
         raw: bool = False,
         options: dict = None,
-        webpage_context = None,
+        webpage_context=None,
         search_result: bool = False,
     ) -> Generator[str, None, None]:
         """

request_llms/local_llm_class.py

@@ -183,11 +183,11 @@ class LocalLLMHandle(Process):
     def stream_chat(self, **kwargs):
         # ⭐run in main process
         if self.get_state() == "`准备就绪`":
-            yield "`正在等待线程锁，排队中请稍后 ...`"
+            yield "`正在等待线程锁，排队中请稍候 ...`"
 
         with self.threadLock:
             if self.parent.poll():
-                yield "`排队中请稍后 ...`"
+                yield "`排队中请稍候 ...`"
                 self.clear_pending_messages()
             self.parent.send(kwargs)
             std_out = ""

request_llms/requirements_chatglm.txt

@@ -2,4 +2,4 @@ protobuf
 cpm_kernels
 torch>=1.10
 mdtex2html
-sentencepiece
+sentencepiece

request_llms/requirements_chatglm_onnx.txt

@@ -6,5 +6,3 @@ sentencepiece
 numpy
 onnxruntime
 sentencepiece
-streamlit
-streamlit-chat

request_llms/requirements_jittorllms.txt

@@ -3,4 +3,4 @@ jtorch >= 0.1.3
 torch
 torchvision
 pandas
-jieba
+jieba

request_llms/requirements_moss.txt

@@ -5,5 +5,3 @@ accelerate
 matplotlib
 huggingface_hub
 triton
-streamlit
-

request_llms/requirements_qwen.txt

@@ -1,4 +1 @@
-modelscope
-transformers_stream_generator
-auto-gptq
-optimum
+dashscope

request_llms/requirements_qwen_local.txt

@@ -0,0 +1,5 @@
+modelscope
+transformers_stream_generator
+auto-gptq
+optimum
+urllib3<2

request_llms/requirements_slackclaude.txt

@@ -1 +1 @@
-slack-sdk==3.21.3
+slack-sdk==3.21.3

requirements.txt

@@ -1,11 +1,14 @@
-./docs/gradio-3.32.6-py3-none-any.whl
+https://fastly.jsdelivr.net/gh/binary-husky/gradio-fix@gpt-academic/release/gradio-3.32.7-py3-none-any.whl
 pypdf2==2.12.1
+zhipuai<2
 tiktoken>=0.3.3
 requests[socks]
 pydantic==1.10.11
+protobuf==3.18
 transformers>=4.27.1
 scipdf_parser>=0.52
 python-markdown-math
+pymdown-extensions
 websocket-client
 beautifulsoup4
 prompt_toolkit

shared_utils/advanced_markdown_format.py

@@ -0,0 +1,287 @@
+import markdown
+import re
+import os
+import math
+from textwrap import dedent
+from functools import lru_cache
+from pymdownx.superfences import fence_div_format, fence_code_format
+from latex2mathml.converter import convert as tex2mathml
+from shared_utils.config_loader import get_conf as get_conf
+
+pj = os.path.join
+default_user_name = 'default_user'
+
+markdown_extension_configs = {
+    'mdx_math': {
+        'enable_dollar_delimiter': True,
+        'use_gitlab_delimiters': False,
+    },
+}
+
+code_highlight_configs = {
+    "pymdownx.superfences": {
+        'css_class': 'codehilite',
+        "custom_fences": [
+            {
+                'name': 'mermaid',
+                'class': 'mermaid',
+                'format': fence_code_format
+            }
+        ]
+    },
+    "pymdownx.highlight": {
+        'css_class': 'codehilite',
+        'guess_lang': True,
+        # 'auto_title': True,
+        # 'linenums': True
+    }
+}
+
+def text_divide_paragraph(text):
+    """
+    将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
+    """
+    pre = '<div class="markdown-body">'
+    suf = '</div>'
+    if text.startswith(pre) and text.endswith(suf):
+        return text
+
+    if '```' in text:
+        # careful input
+        return text
+    elif '</div>' in text:
+        # careful input
+        return text
+    else:
+        # whatever input
+        lines = text.split("\n")
+        for i, line in enumerate(lines):
+            lines[i] = lines[i].replace(" ", "&nbsp;")
+        text = "</br>".join(lines)
+        return pre + text + suf
+
+
+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$包裹begin命令时多余<script>）
+    """
+    content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">',
+                                '<script type="math/tex; mode=display">')
+    content = content.replace('</script>\n</script>', '</script>')
+    return content
+
+
+def is_equation(txt):
+    """
+    判定是否为公式 | 测试1 写出洛伦兹定律，使用tex格式公式 测试2 给出柯西不等式，使用latex格式 测试3 写出麦克斯韦方程组
+    """
+    if '```' in txt and '```reference' not in txt: return False
+    if '$' not in txt and '\\[' not in txt: return False
+    mathpatterns = {
+        r'(?<!\\|\$)(\$)([^\$]+)(\$)': {'allow_multi_lines': False},                       #  $...$
+        r'(?<!\\)(\$\$)([^\$]+)(\$\$)': {'allow_multi_lines': True},                       # $$...$$
+        r'(?<!\\)(\\\[)(.+?)(\\\])': {'allow_multi_lines': False},                         # \[...\]
+        # r'(?<!\\)(\\\()(.+?)(\\\))': {'allow_multi_lines': False},                       # \(...\)
+        # r'(?<!\\)(\\begin{([a-z]+?\*?)})(.+?)(\\end{\2})': {'allow_multi_lines': True},  # \begin...\end
+        # r'(?<!\\)(\$`)([^`]+)(`\$)': {'allow_multi_lines': False},                       # $`...`$
+    }
+    matches = []
+    for pattern, property in mathpatterns.items():
+        flags = re.ASCII | re.DOTALL if property['allow_multi_lines'] else re.ASCII
+        matches.extend(re.findall(pattern, txt, flags))
+    if len(matches) == 0: return False
+    contain_any_eq = False
+    illegal_pattern = re.compile(r'[^\x00-\x7F]|echo')
+    for match in matches:
+        if len(match) != 3: return False
+        eq_canidate = match[1]
+        if illegal_pattern.search(eq_canidate):
+            return False
+        else:
+            contain_any_eq = True
+    return contain_any_eq
+
+
+def fix_markdown_indent(txt):
+    # fix markdown indent
+    if (' - ' not in txt) or ('. ' not in txt):
+        # do not need to fix, fast escape
+        return txt
+    # walk through the lines and fix non-standard indentation
+    lines = txt.split("\n")
+    pattern = re.compile(r'^\s+-')
+    activated = False
+    for i, line in enumerate(lines):
+        if line.startswith('- ') or line.startswith('1. '):
+            activated = True
+        if activated and pattern.match(line):
+            stripped_string = line.lstrip()
+            num_spaces = len(line) - len(stripped_string)
+            if (num_spaces % 4) == 3:
+                num_spaces_should_be = math.ceil(num_spaces / 4) * 4
+                lines[i] = ' ' * num_spaces_should_be + stripped_string
+    return '\n'.join(lines)
+
+
+FENCED_BLOCK_RE = re.compile(
+    dedent(r'''
+        (?P<fence>^[ \t]*(?:~{3,}|`{3,}))[ ]*                      # opening fence
+        ((\{(?P<attrs>[^\}\n]*)\})|                              # (optional {attrs} or
+        (\.?(?P<lang>[\w#.+-]*)[ ]*)?                            # optional (.)lang
+        (hl_lines=(?P<quot>"|')(?P<hl_lines>.*?)(?P=quot)[ ]*)?) # optional hl_lines)
+        \n                                                       # newline (end of opening fence)
+        (?P<code>.*?)(?<=\n)                                     # the code block
+        (?P=fence)[ ]*$                                          # closing fence
+    '''),
+    re.MULTILINE | re.DOTALL | re.VERBOSE
+)
+
+
+def get_line_range(re_match_obj, txt):
+    start_pos, end_pos = re_match_obj.regs[0]
+    num_newlines_before = txt[:start_pos+1].count('\n')
+    line_start = num_newlines_before
+    line_end = num_newlines_before + txt[start_pos:end_pos].count('\n')+1
+    return line_start, line_end
+
+
+def fix_code_segment_indent(txt):
+    lines = []
+    change_any = False
+    txt_tmp = txt
+    while True:
+        re_match_obj = FENCED_BLOCK_RE.search(txt_tmp)
+        if not re_match_obj: break
+        if len(lines) == 0: lines = txt.split("\n")
+        
+        # 清空 txt_tmp 对应的位置方便下次搜索
+        start_pos, end_pos = re_match_obj.regs[0]
+        txt_tmp = txt_tmp[:start_pos] + ' '*(end_pos-start_pos) + txt_tmp[end_pos:]
+        line_start, line_end = get_line_range(re_match_obj, txt)
+        
+        # 获取公共缩进
+        shared_indent_cnt = 1e5
+        for i in range(line_start, line_end):
+            stripped_string = lines[i].lstrip()
+            num_spaces = len(lines[i]) - len(stripped_string)
+            if num_spaces < shared_indent_cnt:
+                shared_indent_cnt = num_spaces
+
+        # 修复缩进
+        if (shared_indent_cnt < 1e5) and (shared_indent_cnt % 4) == 3:
+            num_spaces_should_be = math.ceil(shared_indent_cnt / 4) * 4
+            for i in range(line_start, line_end):
+                add_n = num_spaces_should_be - shared_indent_cnt
+                lines[i] = ' ' * add_n + lines[i]
+            if not change_any: # 遇到第一个
+                change_any = True
+
+    if change_any:
+        return '\n'.join(lines)
+    else:
+        return txt
+    
+    
+@lru_cache(maxsize=128) # 使用 lru缓存 加快转换速度
+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
+
+    find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
+
+    txt = fix_markdown_indent(txt)
+    # txt = fix_code_segment_indent(txt)
+    if is_equation(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=['sane_lists', 'tables', 'mdx_math', 'pymdownx.superfences', 'pymdownx.highlight'],
+                                            extension_configs={**markdown_extension_configs, **code_highlight_configs})
+        convert_stage_1 = markdown_bug_hunt(convert_stage_1)
+        # 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=['sane_lists', 'tables', 'pymdownx.superfences', 'pymdownx.highlight'], extension_configs=code_highlight_configs) + suf
+
+
+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:
+        return gpt_reply + '\n```' # 输出代码片段中！
+    else:
+        return gpt_reply
+
+
+def format_io(self, y):
+    """
+    将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
+    """
+    if y is None or y == []:
+        return []
+    i_ask, gpt_reply = y[-1]
+    # 输入部分太自由，预处理一波
+    if i_ask is not None: i_ask = text_divide_paragraph(i_ask)
+    # 当代码输出半截的时候，试着补上后个```
+    if gpt_reply is not None: gpt_reply = close_up_code_segment_during_stream(gpt_reply)
+    # process
+    y[-1] = (
+        None if i_ask is None else markdown.markdown(i_ask, extensions=['pymdownx.superfences', 'tables', 'pymdownx.highlight'], extension_configs=code_highlight_configs),
+        None if gpt_reply is None else markdown_convertion(gpt_reply)
+    )
+    return y

shared_utils/config_loader.py

@@ -0,0 +1,131 @@
+import importlib
+import time
+import os
+from functools import lru_cache
+from colorful import print亮红, print亮绿, print亮蓝
+
+pj = os.path.join
+default_user_name = 'default_user'
+
+def read_env_variable(arg, default_value):
+    """
+    环境变量可以是 `GPT_ACADEMIC_CONFIG`(优先)，也可以直接是`CONFIG`
+    例如在windows cmd中，既可以写：
+        set USE_PROXY=True
+        set API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+        set proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",}
+        set AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"]
+        set AUTHENTICATION=[("username", "password"), ("username2", "password2")]
+    也可以写：
+        set GPT_ACADEMIC_USE_PROXY=True
+        set GPT_ACADEMIC_API_KEY=sk-j7caBpkRoxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+        set GPT_ACADEMIC_proxies={"http":"http://127.0.0.1:10085", "https":"http://127.0.0.1:10085",}
+        set GPT_ACADEMIC_AVAIL_LLM_MODELS=["gpt-3.5-turbo", "chatglm"]
+        set GPT_ACADEMIC_AUTHENTICATION=[("username", "password"), ("username2", "password2")]
+    """
+    arg_with_prefix = "GPT_ACADEMIC_" + arg
+    if arg_with_prefix in os.environ:
+        env_arg = os.environ[arg_with_prefix]
+    elif arg in os.environ:
+        env_arg = os.environ[arg]
+    else:
+        raise KeyError
+    print(f"[ENV_VAR] 尝试加载{arg}，默认值：{default_value} --> 修正值：{env_arg}")
+    try:
+        if isinstance(default_value, bool):
+            env_arg = env_arg.strip()
+            if env_arg == 'True': r = True
+            elif env_arg == 'False': r = False
+            else: print('Enter True or False, but have:', env_arg); r = default_value
+        elif isinstance(default_value, int):
+            r = int(env_arg)
+        elif isinstance(default_value, float):
+            r = float(env_arg)
+        elif isinstance(default_value, str):
+            r = env_arg.strip()
+        elif isinstance(default_value, dict):
+            r = eval(env_arg)
+        elif isinstance(default_value, list):
+            r = eval(env_arg)
+        elif default_value is None:
+            assert arg == "proxies"
+            r = eval(env_arg)
+        else:
+            print亮红(f"[ENV_VAR] 环境变量{arg}不支持通过环境变量设置! ")
+            raise KeyError
+    except:
+        print亮红(f"[ENV_VAR] 环境变量{arg}加载失败! ")
+        raise KeyError(f"[ENV_VAR] 环境变量{arg}加载失败! ")
+
+    print亮绿(f"[ENV_VAR] 成功读取环境变量{arg}")
+    return r
+
+
+@lru_cache(maxsize=128)
+def read_single_conf_with_lru_cache(arg):
+    from shared_utils.key_pattern_manager import is_any_api_key
+    try:
+        # 优先级1. 获取环境变量作为配置
+        default_ref = getattr(importlib.import_module('config'), arg) # 读取默认值作为数据类型转换的参考
+        r = read_env_variable(arg, default_ref)
+    except:
+        try:
+            # 优先级2. 获取config_private中的配置
+            r = getattr(importlib.import_module('config_private'), arg)
+        except:
+            # 优先级3. 获取config中的配置
+            r = getattr(importlib.import_module('config'), arg)
+
+    # 在读取API_KEY时，检查一下是不是忘了改config
+    if arg == 'API_URL_REDIRECT':
+        oai_rd = r.get("https://api.openai.com/v1/chat/completions", None) # API_URL_REDIRECT填写格式是错误的，请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`
+        if oai_rd and not oai_rd.endswith('/completions'):
+            print亮红("\n\n[API_URL_REDIRECT] API_URL_REDIRECT填错了。请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`。如果您确信自己没填错，无视此消息即可。")
+            time.sleep(5)
+    if arg == 'API_KEY':
+        print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和Azure的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,azure-key3\"")
+        print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
+        if is_any_api_key(r):
+            print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
+        else:
+            print亮红("[API_KEY] 您的 API_KEY 不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行。")
+    if arg == 'proxies':
+        if not read_single_conf_with_lru_cache('USE_PROXY'): r = None # 检查USE_PROXY，防止proxies单独起作用
+        if r is None:
+            print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
+        else:
+            print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
+            assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
+    return r
+
+
+@lru_cache(maxsize=128)
+def get_conf(*args):
+    """
+    本项目的所有配置都集中在config.py中。 修改配置有三种方法，您只需要选择其中一种即可：
+        - 直接修改config.py
+        - 创建并修改config_private.py
+        - 修改环境变量（修改docker-compose.yml等价于修改容器内部的环境变量）
+
+    注意：如果您使用docker-compose部署，请修改docker-compose（等价于修改容器内部的环境变量）
+    """
+    res = []
+    for arg in args:
+        r = read_single_conf_with_lru_cache(arg)
+        res.append(r)
+    if len(res) == 1: return res[0]
+    return res
+
+
+def set_conf(key, value):
+    from toolbox import read_single_conf_with_lru_cache
+    read_single_conf_with_lru_cache.cache_clear()
+    get_conf.cache_clear()
+    os.environ[key] = str(value)
+    altered = get_conf(key)
+    return altered
+
+
+def set_multi_conf(dic):
+    for k, v in dic.items(): set_conf(k, v)
+    return

shared_utils/connect_void_terminal.py

@@ -0,0 +1,91 @@
+import os
+
+"""
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
+接驳void-terminal:
+    - set_conf:                     在运行过程中动态地修改配置
+    - set_multi_conf:               在运行过程中动态地修改多个配置
+    - get_plugin_handle:            获取插件的句柄
+    - get_plugin_default_kwargs:    获取插件的默认参数
+    - get_chat_handle:              获取简单聊天的句柄
+    - get_chat_default_kwargs:      获取简单聊天的默认参数
+=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-
+"""
+
+
+def get_plugin_handle(plugin_name):
+    """
+    e.g. plugin_name = 'crazy_functions.批量Markdown翻译->Markdown翻译指定语言'
+    """
+    import importlib
+
+    assert (
+        "->" in plugin_name
+    ), "Example of plugin_name: crazy_functions.批量Markdown翻译->Markdown翻译指定语言"
+    module, fn_name = plugin_name.split("->")
+    f_hot_reload = getattr(importlib.import_module(module, fn_name), fn_name)
+    return f_hot_reload
+
+
+def get_chat_handle():
+    """
+    Get chat function
+    """
+    from request_llms.bridge_all import predict_no_ui_long_connection
+
+    return predict_no_ui_long_connection
+
+
+def get_plugin_default_kwargs():
+    """
+    Get Plugin Default Arguments
+    """
+    from toolbox import ChatBotWithCookies, load_chat_cookies
+
+    cookies = load_chat_cookies()
+    llm_kwargs = {
+        "api_key": cookies["api_key"],
+        "llm_model": cookies["llm_model"],
+        "top_p": 1.0,
+        "max_length": None,
+        "temperature": 1.0,
+    }
+    chatbot = ChatBotWithCookies(llm_kwargs)
+
+    # txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port
+    DEFAULT_FN_GROUPS_kwargs = {
+        "main_input": "./README.md",
+        "llm_kwargs": llm_kwargs,
+        "plugin_kwargs": {},
+        "chatbot_with_cookie": chatbot,
+        "history": [],
+        "system_prompt": "You are a good AI.",
+        "web_port": None,
+    }
+    return DEFAULT_FN_GROUPS_kwargs
+
+
+def get_chat_default_kwargs():
+    """
+    Get Chat Default Arguments
+    """
+    from toolbox import load_chat_cookies
+
+    cookies = load_chat_cookies()
+    llm_kwargs = {
+        "api_key": cookies["api_key"],
+        "llm_model": cookies["llm_model"],
+        "top_p": 1.0,
+        "max_length": None,
+        "temperature": 1.0,
+    }
+    default_chat_kwargs = {
+        "inputs": "Hello there, are you ready?",
+        "llm_kwargs": llm_kwargs,
+        "history": [],
+        "sys_prompt": "You are AI assistant",
+        "observe_window": None,
+        "console_slience": False,
+    }
+
+    return default_chat_kwargs

shared_utils/key_pattern_manager.py

@@ -0,0 +1,81 @@
+import re
+import os
+from functools import wraps, lru_cache
+from shared_utils.advanced_markdown_format import format_io
+from shared_utils.config_loader import get_conf as get_conf
+
+
+pj = os.path.join
+default_user_name = 'default_user'
+
+
+def is_openai_api_key(key):
+    CUSTOM_API_KEY_PATTERN = get_conf('CUSTOM_API_KEY_PATTERN')
+    if len(CUSTOM_API_KEY_PATTERN) != 0:
+        API_MATCH_ORIGINAL = re.match(CUSTOM_API_KEY_PATTERN, key)
+    else:
+        API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
+    return bool(API_MATCH_ORIGINAL)
+
+
+def is_azure_api_key(key):
+    API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{32}$", key)
+    return bool(API_MATCH_AZURE)
+
+
+def is_api2d_key(key):
+    API_MATCH_API2D = re.match(r"fk[a-zA-Z0-9]{6}-[a-zA-Z0-9]{32}$", key)
+    return bool(API_MATCH_API2D)
+
+
+def is_any_api_key(key):
+    if ',' in key:
+        keys = key.split(',')
+        for k in keys:
+            if is_any_api_key(k): return True
+        return False
+    else:
+        return is_openai_api_key(key) or is_api2d_key(key) or is_azure_api_key(key)
+
+
+def what_keys(keys):
+    avail_key_list = {'OpenAI Key': 0, "Azure Key": 0, "API2D Key": 0}
+    key_list = keys.split(',')
+
+    for k in key_list:
+        if is_openai_api_key(k):
+            avail_key_list['OpenAI Key'] += 1
+
+    for k in key_list:
+        if is_api2d_key(k):
+            avail_key_list['API2D Key'] += 1
+
+    for k in key_list:
+        if is_azure_api_key(k):
+            avail_key_list['Azure Key'] += 1
+
+    return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个, Azure Key {avail_key_list['Azure Key']} 个, API2D Key {avail_key_list['API2D Key']} 个"
+
+
+def select_api_key(keys, llm_model):
+    import random
+    avail_key_list = []
+    key_list = keys.split(',')
+
+    if llm_model.startswith('gpt-'):
+        for k in key_list:
+            if is_openai_api_key(k): avail_key_list.append(k)
+
+    if llm_model.startswith('api2d-'):
+        for k in key_list:
+            if is_api2d_key(k): avail_key_list.append(k)
+
+    if llm_model.startswith('azure-'):
+        for k in key_list:
+            if is_azure_api_key(k): avail_key_list.append(k)
+
+    if len(avail_key_list) == 0:
+        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（右下角更换模型菜单中可切换openai,azure,claude,api2d等请求源）。")
+
+    api_key = random.choice(avail_key_list) # 随机负载均衡
+    return api_key

tests/test_llms.py

@@ -3,12 +3,14 @@
 # """
 def validate_path():
     import os, sys
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
+
+    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
+
+
+validate_path()  # validate path so you can run from base directory
 if __name__ == "__main__":
     # from request_llms.bridge_newbingfree import predict_no_ui_long_connection
     # from request_llms.bridge_moss import predict_no_ui_long_connection
@@ -18,19 +20,19 @@ if __name__ == "__main__":
     # from request_llms.bridge_internlm import predict_no_ui_long_connection
     # from request_llms.bridge_deepseekcoder import predict_no_ui_long_connection
     # from request_llms.bridge_qwen_7B import predict_no_ui_long_connection
-    from request_llms.bridge_qwen import predict_no_ui_long_connection
+    from request_llms.bridge_qwen_local import predict_no_ui_long_connection
+
     # from request_llms.bridge_spark import predict_no_ui_long_connection
     # from request_llms.bridge_zhipu import predict_no_ui_long_connection
     # from request_llms.bridge_chatglm3 import predict_no_ui_long_connection
 
     llm_kwargs = {
-        'max_length': 4096,
-        'top_p': 1,
-        'temperature': 1,
+        "max_length": 4096,
+        "top_p": 1,
+        "temperature": 1,
     }
 
-    result = predict_no_ui_long_connection( inputs="请问什么是质子？", 
-                                            llm_kwargs=llm_kwargs,
-                                            history=["你好", "我好！"],
-                                            sys_prompt="")
-    print('final result:', result)
+    result = predict_no_ui_long_connection(
+        inputs="请问什么是质子？", llm_kwargs=llm_kwargs, history=["你好", "我好！"], sys_prompt=""
+    )
+    print("final result:", result)

tests/test_markdown.py

@@ -1,44 +1,53 @@
 md = """
-作为您的写作和编程助手，我可以为您提供以下服务：
+You can use the following Python script to rename files matching the pattern '* - 副本.tex' to '* - wushiguang.tex' in a directory:
 
-1. 写作：
-    - 帮助您撰写文章、报告、散文、故事等。
-    - 提供写作建议和技巧。
-    - 协助您进行文案策划和内容创作。
+```python
+import os
 
-2. 编程：
-    - 帮助您解决编程问题，提供编程思路和建议。
-    - 协助您编写代码，包括但不限于 Python、Java、C++ 等。
-    - 为您解释复杂的技术概念，让您更容易理解。
+# Directory containing the files
+directory = 'Tex/'
 
-3. 项目支持：
-    - 协助您规划项目进度和任务分配。
-    - 提供项目管理和协作建议。
-    - 在项目实施过程中提供支持，确保项目顺利进行。
+for filename in os.listdir(directory):
+    if filename.endswith(' - 副本.tex'):
+        new_filename = filename.replace(' - 副本.tex', ' - wushiguang.tex')
+        os.rename(os.path.join(directory, filename), os.path.join(directory, new_filename))
+```
 
-4. 学习辅导：
-    - 帮助您巩固编程基础，提高编程能力。
-    - 提供计算机科学、数据科学、人工智能等相关领域的学习资源和建议。
-    - 解答您在学习过程中遇到的问题，让您更好地掌握知识。
-
-5. 行业动态和趋势分析：
-    - 为您提供业界最新的新闻和技术趋势。
-    - 分析行业动态，帮助您了解市场发展和竞争态势。
-    - 为您制定技术战略提供参考和建议。
-
-请随时告诉我您的需求，我会尽力提供帮助。如果您有任何问题或需要解答的议题，请随时提问。
+Replace 'Tex/' with the actual directory path where your files are located before running the script.
 """
 
+
+md = """
+Following code including wrapper
+
+```mermaid
+graph TD
+    A[Enter Chart Definition] --> B(Preview)
+    B --> C{decide}
+    C --> D[Keep]
+    C --> E[Edit Definition]
+    E --> B
+    D --> F[Save Image and Code]
+    F --> B
+```
+
+"""
 def validate_path():
     import os, sys
-    dir_name = os.path.dirname(__file__)
-    root_dir_assume = os.path.abspath(os.path.dirname(__file__) +  '/..')
+
+    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
+
+
+validate_path()  # validate path so you can run from base directory
 from toolbox import markdown_convertion
 
 html = markdown_convertion(md)
-print(html)
-with open('test.html', 'w', encoding='utf-8') as f:
-    f.write(html)
+# print(html)
+with open("test.html", "w", encoding="utf-8") as f:
+    f.write(html)
+
+
+# TODO: 列出10个经典名著

tests/test_plugins.py

@@ -4,16 +4,28 @@
 
 
 import os, sys
-def validate_path(): dir_name = os.path.dirname(__file__); root_dir_assume = os.path.abspath(dir_name +  '/..'); os.chdir(root_dir_assume); sys.path.append(root_dir_assume)
-validate_path() # 返回项目根路径
+
+
+def validate_path():
+    dir_name = os.path.dirname(__file__)
+    root_dir_assume = os.path.abspath(dir_name + "/..")
+    os.chdir(root_dir_assume)
+    sys.path.append(root_dir_assume)
+
+
+validate_path()  # 返回项目根路径
 
 if __name__ == "__main__":
     from tests.test_utils import plugin_test
+
     # plugin_test(plugin='crazy_functions.函数动态生成->函数动态生成', main_input='交换图像的蓝色通道和红色通道', advanced_arg={"file_path_arg": "./build/ants.jpg"})
 
     # plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2307.07522")
 
-    plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="G:/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix")
+    plugin_test(
+        plugin="crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF",
+        main_input="G:/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix",
+    )
 
     # plugin_test(plugin='crazy_functions.虚空终端->虚空终端', main_input='修改api-key为sk-jhoejriotherjep')
 
@@ -34,7 +46,7 @@ if __name__ == "__main__":
     # plugin_test(plugin='crazy_functions.批量翻译PDF文档_多线程->批量翻译PDF文档', main_input='crazy_functions/test_project/pdf_and_word/aaai.pdf')
 
     # plugin_test(plugin='crazy_functions.谷歌检索小助手->谷歌检索小助手', main_input="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG=")
-    
+
     # plugin_test(plugin='crazy_functions.总结word文档->总结word文档', main_input="crazy_functions/test_project/pdf_and_word")
 
     # plugin_test(plugin='crazy_functions.下载arxiv论文翻译摘要->下载arxiv论文并翻译摘要', main_input="1812.10695")
@@ -53,12 +65,11 @@ if __name__ == "__main__":
     # plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="What is the installation method？")
 
     # plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="远程云服务器部署？")
-    
+
     # plugin_test(plugin='crazy_functions.Latex输出PDF结果->Latex翻译中文并重新编译PDF', main_input="2210.03629")
-    
+
     # advanced_arg = {"advanced_arg":"--llm_to_learn=gpt-3.5-turbo --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、人设进行描写。要求：100字以内，用第二人称。' --system_prompt=''" }
     # plugin_test(plugin='crazy_functions.chatglm微调工具->微调数据集生成', main_input='build/dev.json', advanced_arg=advanced_arg)
 
     # advanced_arg = {"advanced_arg":"--pre_seq_len=128 --learning_rate=2e-2 --num_gpus=1 --json_dataset='t_code.json' --ptuning_directory='/home/hmp/ChatGLM2-6B/ptuning'     " }
     # plugin_test(plugin='crazy_functions.chatglm微调工具->启动微调', main_input='build/dev.json', advanced_arg=advanced_arg)
-

tests/test_utils.py

@@ -9,45 +9,52 @@ from functools import wraps
 import sys
 import os
 
+
 def chat_to_markdown_str(chat):
     result = ""
     for i, cc in enumerate(chat):
-        result += f'\n\n{cc[0]}\n\n{cc[1]}'
-        if i != len(chat)-1:
-            result += '\n\n---'
+        result += f"\n\n{cc[0]}\n\n{cc[1]}"
+        if i != len(chat) - 1:
+            result += "\n\n---"
     return result
 
+
 def silence_stdout(func):
     @wraps(func)
     def wrapper(*args, **kwargs):
         _original_stdout = sys.stdout
-        sys.stdout = open(os.devnull, 'w')
-        sys.stdout.reconfigure(encoding='utf-8')
+        sys.stdout = open(os.devnull, "w")
+        sys.stdout.reconfigure(encoding="utf-8")
         for q in func(*args, **kwargs):
             sys.stdout = _original_stdout
             yield q
-            sys.stdout = open(os.devnull, 'w')
-            sys.stdout.reconfigure(encoding='utf-8')
+            sys.stdout = open(os.devnull, "w")
+            sys.stdout.reconfigure(encoding="utf-8")
         sys.stdout.close()
         sys.stdout = _original_stdout
+
     return wrapper
 
+
 def silence_stdout_fn(func):
     @wraps(func)
     def wrapper(*args, **kwargs):
         _original_stdout = sys.stdout
-        sys.stdout = open(os.devnull, 'w')
-        sys.stdout.reconfigure(encoding='utf-8')
+        sys.stdout = open(os.devnull, "w")
+        sys.stdout.reconfigure(encoding="utf-8")
         result = func(*args, **kwargs)
         sys.stdout.close()
         sys.stdout = _original_stdout
         return result
+
     return wrapper
 
-class VoidTerminal():
+
+class VoidTerminal:
     def __init__(self) -> None:
         pass
-    
+
+
 vt = VoidTerminal()
 vt.get_conf = silence_stdout_fn(get_conf)
 vt.set_conf = silence_stdout_fn(set_conf)
@@ -56,9 +63,27 @@ vt.get_plugin_handle = silence_stdout_fn(get_plugin_handle)
 vt.get_plugin_default_kwargs = silence_stdout_fn(get_plugin_default_kwargs)
 vt.get_chat_handle = silence_stdout_fn(get_chat_handle)
 vt.get_chat_default_kwargs = silence_stdout_fn(get_chat_default_kwargs)
-vt.chat_to_markdown_str = (chat_to_markdown_str)
-proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT, LAYOUT, API_KEY = \
-    vt.get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION', 'CHATBOT_HEIGHT', 'LAYOUT', 'API_KEY')
+vt.chat_to_markdown_str = chat_to_markdown_str
+(
+    proxies,
+    WEB_PORT,
+    LLM_MODEL,
+    CONCURRENT_COUNT,
+    AUTHENTICATION,
+    CHATBOT_HEIGHT,
+    LAYOUT,
+    API_KEY,
+) = vt.get_conf(
+    "proxies",
+    "WEB_PORT",
+    "LLM_MODEL",
+    "CONCURRENT_COUNT",
+    "AUTHENTICATION",
+    "CHATBOT_HEIGHT",
+    "LAYOUT",
+    "API_KEY",
+)
+
 
 def plugin_test(main_input, plugin, advanced_arg=None, debug=True):
     from rich.live import Live
@@ -69,9 +94,9 @@ def plugin_test(main_input, plugin, advanced_arg=None, debug=True):
 
     plugin = vt.get_plugin_handle(plugin)
     plugin_kwargs = vt.get_plugin_default_kwargs()
-    plugin_kwargs['main_input'] = main_input
+    plugin_kwargs["main_input"] = main_input
     if advanced_arg is not None:
-        plugin_kwargs['plugin_kwargs'] = advanced_arg
+        plugin_kwargs["plugin_kwargs"] = advanced_arg
     if debug:
         my_working_plugin = (plugin)(**plugin_kwargs)
     else:
@@ -81,4 +106,4 @@ def plugin_test(main_input, plugin, advanced_arg=None, debug=True):
         for cookies, chat, hist, msg in my_working_plugin:
             md_str = vt.chat_to_markdown_str(chat)
             md = Markdown(md_str)
-            live.update(md, refresh=True)
+            live.update(md, refresh=True)

tests/test_vector_plugins.py

@@ -4,14 +4,25 @@
 
 
 import os, sys
-def validate_path(): dir_name = os.path.dirname(__file__); root_dir_assume = os.path.abspath(dir_name +  '/..'); os.chdir(root_dir_assume); sys.path.append(root_dir_assume)
-validate_path() # 返回项目根路径
+
+
+def validate_path():
+    dir_name = os.path.dirname(__file__)
+    root_dir_assume = os.path.abspath(dir_name + "/..")
+    os.chdir(root_dir_assume)
+    sys.path.append(root_dir_assume)
+
+
+validate_path()  # 返回项目根路径
 
 if __name__ == "__main__":
     from tests.test_utils import plugin_test
 
-    plugin_test(plugin='crazy_functions.知识库问答->知识库文件注入', main_input="./README.md")
+    plugin_test(plugin="crazy_functions.知识库问答->知识库文件注入", main_input="./README.md")
 
-    plugin_test(plugin='crazy_functions.知识库问答->读取知识库作答', main_input="What is the installation method？")
+    plugin_test(
+        plugin="crazy_functions.知识库问答->读取知识库作答",
+        main_input="What is the installation method？",
+    )
 
-    plugin_test(plugin='crazy_functions.知识库问答->读取知识库作答', main_input="远程云服务器部署？")
+    plugin_test(plugin="crazy_functions.知识库问答->读取知识库作答", main_input="远程云服务器部署？")

themes/common.css

@@ -94,6 +94,10 @@
     background-color: var(--block-background-fill) !important;
 }
 
+#cbsc {
+    background-color: var(--block-background-fill) !important;
+}
+
 #interact-panel .form {
     border: hidden
 }
@@ -111,4 +115,4 @@
     border: solid;
     border-width: thin;
     border-top-width: 0;
-}
+}

themes/common.js

@@ -1,9 +1,13 @@
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 1 部分: 工具函数
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
 function gradioApp() {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     const elems = document.getElementsByTagName('gradio-app');
     const elem = elems.length == 0 ? document : elems[0];
     if (elem !== document) {
-        elem.getElementById = function(id) {
+        elem.getElementById = function (id) {
             return document.getElementById(id);
         };
     }
@@ -12,31 +16,168 @@ function gradioApp() {
 
 function setCookie(name, value, days) {
     var expires = "";
-  
+
     if (days) {
-      var date = new Date();
-      date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
-      expires = "; expires=" + date.toUTCString();
+        var date = new Date();
+        date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
+        expires = "; expires=" + date.toUTCString();
     }
-  
+
     document.cookie = name + "=" + value + expires + "; path=/";
 }
 
 function getCookie(name) {
     var decodedCookie = decodeURIComponent(document.cookie);
     var cookies = decodedCookie.split(';');
-  
+
     for (var i = 0; i < cookies.length; i++) {
-      var cookie = cookies[i].trim();
-  
-      if (cookie.indexOf(name + "=") === 0) {
-        return cookie.substring(name.length + 1, cookie.length);
-      }
+        var cookie = cookies[i].trim();
+
+        if (cookie.indexOf(name + "=") === 0) {
+            return cookie.substring(name.length + 1, cookie.length);
+        }
     }
-  
+
     return null;
-  }
-  
+}
+
+let toastCount = 0;
+function toast_push(msg, duration) {
+    duration = isNaN(duration) ? 3000 : duration;
+    const existingToasts = document.querySelectorAll('.toast');
+    existingToasts.forEach(toast => {
+        toast.style.top = `${parseInt(toast.style.top, 10) - 70}px`;
+    });
+    const m = document.createElement('div');
+    m.innerHTML = msg;
+    m.classList.add('toast');
+    m.style.cssText = `font-size: var(--text-md) !important; color: rgb(255, 255, 255); background-color: rgba(0, 0, 0, 0.6); padding: 10px 15px; border-radius: 4px; position: fixed; top: ${50 + toastCount * 70}%; left: 50%; transform: translateX(-50%); width: auto; text-align: center; transition: top 0.3s;`;
+    document.body.appendChild(m);
+    setTimeout(function () {
+        m.style.opacity = '0';
+        setTimeout(function () {
+            document.body.removeChild(m);
+            toastCount--;
+        }, 500);
+    }, duration);
+    toastCount++;
+}
+
+function toast_up(msg) {
+    var m = document.getElementById('toast_up');
+    if (m) {
+        document.body.removeChild(m); // remove the loader from the body
+    }
+    m = document.createElement('div');
+    m.id = 'toast_up';
+    m.innerHTML = msg;
+    m.style.cssText = "font-size: var(--text-md) !important; color: rgb(255, 255, 255); background-color: rgba(0, 0, 100, 0.6); padding: 10px 15px; margin: 0 0 0 -60px; border-radius: 4px; position: fixed; top: 50%; left: 50%; width: auto; text-align: center;";
+    document.body.appendChild(m);
+}
+
+function toast_down() {
+    var m = document.getElementById('toast_up');
+    if (m) {
+        document.body.removeChild(m); // remove the loader from the body
+    }
+}
+
+function begin_loading_status() {
+    // Create the loader div and add styling
+    var loader = document.createElement('div');
+    loader.id = 'Js_File_Loading';
+    var C1 = document.createElement('div');
+    var C2 = document.createElement('div');
+    // var C3 = document.createElement('span');
+    // C3.textContent = '上传中...'
+    // C3.style.position = "fixed";
+    // C3.style.top = "50%";
+    // C3.style.left = "50%";
+    // C3.style.width = "80px";
+    // C3.style.height = "80px";
+    // C3.style.margin = "-40px 0 0 -40px";
+
+    C1.style.position = "fixed";
+    C1.style.top = "50%";
+    C1.style.left = "50%";
+    C1.style.width = "80px";
+    C1.style.height = "80px";
+    C1.style.borderLeft = "12px solid #00f3f300";
+    C1.style.borderRight = "12px solid #00f3f300";
+    C1.style.borderTop = "12px solid #82aaff";
+    C1.style.borderBottom = "12px solid #82aaff"; // Added for effect
+    C1.style.borderRadius = "50%";
+    C1.style.margin = "-40px 0 0 -40px";
+    C1.style.animation = "spinAndPulse 2s linear infinite";
+
+    C2.style.position = "fixed";
+    C2.style.top = "50%";
+    C2.style.left = "50%";
+    C2.style.width = "40px";
+    C2.style.height = "40px";
+    C2.style.borderLeft = "12px solid #00f3f300";
+    C2.style.borderRight = "12px solid #00f3f300";
+    C2.style.borderTop = "12px solid #33c9db";
+    C2.style.borderBottom = "12px solid #33c9db"; // Added for effect
+    C2.style.borderRadius = "50%";
+    C2.style.margin = "-20px 0 0 -20px";
+    C2.style.animation = "spinAndPulse2 2s linear infinite";
+
+    loader.appendChild(C1);
+    loader.appendChild(C2);
+    // loader.appendChild(C3);
+    document.body.appendChild(loader); // Add the loader to the body
+
+    // Set the CSS animation keyframes for spin and pulse to be synchronized
+    var styleSheet = document.createElement('style');
+    styleSheet.id = 'Js_File_Loading_Style';
+    styleSheet.textContent = `
+    @keyframes spinAndPulse {
+        0% { transform: rotate(0deg) scale(1); }
+        25% { transform: rotate(90deg) scale(1.1); }
+        50% { transform: rotate(180deg) scale(1); }
+        75% { transform: rotate(270deg) scale(0.9); }
+        100% { transform: rotate(360deg) scale(1); }
+    }
+
+    @keyframes spinAndPulse2 {
+        0% { transform: rotate(-90deg);}
+        25% { transform: rotate(-180deg);}
+        50% { transform: rotate(-270deg);}
+        75% { transform: rotate(-360deg);}
+        100% { transform: rotate(-450deg);}
+    }
+    `;
+    document.head.appendChild(styleSheet);
+}
+
+
+function cancel_loading_status() {
+    // remove the loader from the body
+    var loadingElement = document.getElementById('Js_File_Loading');
+    if (loadingElement) {
+        document.body.removeChild(loadingElement);
+    }
+    var loadingStyle = document.getElementById('Js_File_Loading_Style');
+    if (loadingStyle) {
+        document.head.removeChild(loadingStyle);
+    }
+    // create new listen event
+    let clearButton = document.querySelectorAll('div[id*="elem_upload"] button[aria-label="Clear"]');
+    for (let button of clearButton) {
+        button.addEventListener('click', function () {
+            setTimeout(function () {
+                register_upload_event();
+            }, 50);
+        });
+    }
+}
+
+
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 2 部分: 复制按钮
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
 function addCopyButton(botElement) {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     // Copy bot button
@@ -45,11 +186,10 @@ function addCopyButton(botElement) {
 
     const messageBtnColumnElement = botElement.querySelector('.message-btn-row');
     if (messageBtnColumnElement) {
-        // Do something if .message-btn-column exists, for example, remove it
-        // messageBtnColumnElement.remove();
+        // if .message-btn-column exists
         return;
     }
-    
+
     var copyButton = document.createElement('button');
     copyButton.classList.add('copy-bot-btn');
     copyButton.setAttribute('aria-label', 'Copy');
@@ -89,6 +229,33 @@ function addCopyButton(botElement) {
     botElement.appendChild(messageBtnColumn);
 }
 
+
+let timeoutID = null;
+let lastInvocationTime = 0;
+let lastArgs = null;
+function do_something_but_not_too_frequently(min_interval, func) {
+    return function(...args) {
+        lastArgs = args;
+        const now = Date.now();
+        if (!lastInvocationTime || (now - lastInvocationTime) >= min_interval) {
+            lastInvocationTime = now;
+            // 现在就执行
+            setTimeout(() => {
+                func.apply(this, lastArgs);
+            }, 0);   
+        } else if (!timeoutID) {
+            // 等一会执行
+            timeoutID = setTimeout(() => {
+                timeoutID = null;
+                lastInvocationTime = Date.now();
+                func.apply(this, lastArgs);
+            }, min_interval - (now - lastInvocationTime));      
+        } else {
+            // 压根不执行
+        }
+    }
+}
+
 function chatbotContentChanged(attempt = 1, force = false) {
     // https://github.com/GaiZhenbiao/ChuanhuChatGPT/tree/main/web_assets/javascript
     for (var i = 0; i < attempt; i++) {
@@ -96,49 +263,72 @@ function chatbotContentChanged(attempt = 1, force = false) {
             gradioApp().querySelectorAll('#gpt-chatbot .message-wrap .message.bot').forEach(addCopyButton);
         }, i === 0 ? 0 : 200);
     }
+
+    const run_mermaid_render = do_something_but_not_too_frequently(1000, function () {
+        const blocks = document.querySelectorAll(`pre.mermaid, diagram-div`);
+        if (blocks.length == 0) { return; }
+        uml("mermaid");
+    });
+    run_mermaid_render();
 }
 
-function chatbotAutoHeight(){
-    // 自动调整高度
-    function update_height(){
-        var { panel_height_target, chatbot_height, chatbot } = get_elements(true);
-        if (panel_height_target!=chatbot_height)
-        {
-            var pixelString = panel_height_target.toString() + 'px';
-            chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString; 
+
+
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 3 部分: chatbot动态高度调整
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+function chatbotAutoHeight() {
+    // 自动调整高度：立即
+    function update_height() {
+        var { height_target, chatbot_height, chatbot } = get_elements(true);
+        if (height_target != chatbot_height) {
+            var pixelString = height_target.toString() + 'px';
+            chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
         }
     }
 
-    function update_height_slow(){
-        var { panel_height_target, chatbot_height, chatbot } = get_elements();
-        if (panel_height_target!=chatbot_height)
-        {
-            new_panel_height = (panel_height_target - chatbot_height)*0.5 + chatbot_height;
-            if (Math.abs(new_panel_height - panel_height_target) < 10){
-                new_panel_height = panel_height_target;
+    // 自动调整高度：缓慢
+    function update_height_slow() {
+        var { height_target, chatbot_height, chatbot } = get_elements();
+        if (height_target != chatbot_height) {
+            // sign = (height_target - chatbot_height)/Math.abs(height_target - chatbot_height);
+            // speed = Math.max(Math.abs(height_target - chatbot_height), 1);
+            new_panel_height = (height_target - chatbot_height) * 0.5 + chatbot_height;
+            if (Math.abs(new_panel_height - height_target) < 10) {
+                new_panel_height = height_target;
             }
-            // console.log(chatbot_height, panel_height_target, new_panel_height);
             var pixelString = new_panel_height.toString() + 'px';
-            chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString; 
+            chatbot.style.maxHeight = pixelString; chatbot.style.height = pixelString;
         }
     }
     monitoring_input_box()
     update_height();
-    setInterval(function() {
-        update_height_slow()
-    }, 50); // 每100毫秒执行一次
+    window.addEventListener('resize', function () { update_height(); });
+    window.addEventListener('scroll', function () { update_height_slow(); });
+    setInterval(function () { update_height_slow() }, 50); // 每50毫秒执行一次
 }
 
-function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
-    chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
-    var chatbotObserver = new MutationObserver(() => {
-        chatbotContentChanged(1);
-    });
-    chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
-    if (LAYOUT === "LEFT-RIGHT") {chatbotAutoHeight();}
+swapped = false;
+function swap_input_area() {
+    // Get the elements to be swapped
+    var element1 = document.querySelector("#input-panel");
+    var element2 = document.querySelector("#basic-panel");
+
+    // Get the parent of the elements
+    var parent = element1.parentNode;
+
+    // Get the next sibling of element2
+    var nextSibling = element2.nextSibling;
+
+    // Swap the elements
+    parent.insertBefore(element2, element1);
+    parent.insertBefore(element1, nextSibling);
+    if (swapped) { swapped = false; }
+    else { swapped = true; }
 }
 
-function get_elements(consider_state_panel=false) {
+function get_elements(consider_state_panel = false) {
     var chatbot = document.querySelector('#gpt-chatbot > div.wrap.svelte-18telvq');
     if (!chatbot) {
         chatbot = document.querySelector('#gpt-chatbot');
@@ -147,23 +337,95 @@ function get_elements(consider_state_panel=false) {
     const panel2 = document.querySelector('#basic-panel').getBoundingClientRect()
     const panel3 = document.querySelector('#plugin-panel').getBoundingClientRect();
     // const panel4 = document.querySelector('#interact-panel').getBoundingClientRect();
-    const panel5 = document.querySelector('#input-panel2').getBoundingClientRect();
     const panel_active = document.querySelector('#state-panel').getBoundingClientRect();
-    if (consider_state_panel || panel_active.height < 25){
+    if (consider_state_panel || panel_active.height < 25) {
         document.state_panel_height = panel_active.height;
     }
     // 25 是chatbot的label高度, 16 是右侧的gap
-    var panel_height_target = panel1.height + panel2.height + panel3.height + 0 + 0 - 25 + 16*2;
+    var height_target = panel1.height + panel2.height + panel3.height + 0 + 0 - 25 + 16 * 2;
     // 禁止动态的state-panel高度影响
-    panel_height_target = panel_height_target + (document.state_panel_height-panel_active.height)
-    var panel_height_target = parseInt(panel_height_target);
+    height_target = height_target + (document.state_panel_height - panel_active.height)
+    var height_target = parseInt(height_target);
     var chatbot_height = chatbot.style.height;
+    // 交换输入区位置，使得输入区始终可用
+    if (!swapped) {
+        if (panel1.top != 0 && (panel1.bottom + panel1.top) / 2 < 0) { swap_input_area(); }
+    }
+    else if (swapped) {
+        if (panel2.top != 0 && panel2.top > 0) { swap_input_area(); }
+    }
+    // 调整高度
+    const err_tor = 5;
+    if (Math.abs(panel1.left - chatbot.getBoundingClientRect().left) < err_tor) {
+        // 是否处于窄屏模式
+        height_target = window.innerHeight * 0.6;
+    } else {
+        // 调整高度
+        const chatbot_height_exceed = 15;
+        const chatbot_height_exceed_m = 10;
+        b_panel = Math.max(panel1.bottom, panel2.bottom, panel3.bottom)
+        if (b_panel >= window.innerHeight - chatbot_height_exceed) {
+            height_target = window.innerHeight - chatbot.getBoundingClientRect().top - chatbot_height_exceed_m;
+        }
+        else if (b_panel < window.innerHeight * 0.75) {
+            height_target = window.innerHeight * 0.8;
+        }
+    }
     var chatbot_height = parseInt(chatbot_height);
-    return { panel_height_target, chatbot_height, chatbot };
+    return { height_target, chatbot_height, chatbot };
 }
 
 
-function add_func_paste(input) {
+
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 4 部分: 粘贴、拖拽文件上传
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+var elem_upload = null;
+var elem_upload_float = null;
+var elem_input_main = null;
+var elem_input_float = null;
+var elem_chatbot = null;
+var elem_upload_component_float = null;
+var elem_upload_component = null;
+var exist_file_msg = '⚠️请先删除上传区（左上方）中的历史文件，再尝试上传。'
+
+function locate_upload_elems() {
+    elem_upload = document.getElementById('elem_upload')
+    elem_upload_float = document.getElementById('elem_upload_float')
+    elem_input_main = document.getElementById('user_input_main')
+    elem_input_float = document.getElementById('user_input_float')
+    elem_chatbot = document.getElementById('gpt-chatbot')
+    elem_upload_component_float = elem_upload_float.querySelector("input[type=file]");
+    elem_upload_component = elem_upload.querySelector("input[type=file]");
+}
+
+async function upload_files(files) {
+    let totalSizeMb = 0
+    elem_upload_component_float = elem_upload_float.querySelector("input[type=file]");
+    if (files && files.length > 0) {
+        // 执行具体的上传逻辑
+        if (elem_upload_component_float) {
+            for (let i = 0; i < files.length; i++) {
+                // 将从文件数组中获取的文件大小(单位为字节)转换为MB，
+                totalSizeMb += files[i].size / 1024 / 1024;
+            }
+            // 检查文件总大小是否超过20MB
+            if (totalSizeMb > 20) {
+                toast_push('⚠️文件夹大于 20MB 🚀上传文件中', 3000);
+            }
+            let event = new Event("change");
+            Object.defineProperty(event, "target", { value: elem_upload_component_float, enumerable: true });
+            Object.defineProperty(event, "currentTarget", { value: elem_upload_component_float, enumerable: true });
+            Object.defineProperty(elem_upload_component_float, "files", { value: files, enumerable: true });
+            elem_upload_component_float.dispatchEvent(event);
+        } else {
+            toast_push(exist_file_msg, 3000);
+        }
+    }
+}
+
+function register_func_paste(input) {
     let paste_files = [];
     if (input) {
         input.addEventListener("paste", async function (e) {
@@ -180,7 +442,7 @@ function add_func_paste(input) {
                 }
                 if (paste_files.length > 0) {
                     // 按照文件列表执行批量上传逻辑
-                    await paste_upload_files(paste_files);
+                    await upload_files(paste_files);
                     paste_files = []
 
                 }
@@ -189,72 +451,110 @@ function add_func_paste(input) {
     }
 }
 
+function register_func_drag(elem) {
+    if (elem) {
+        const dragEvents = ["dragover"];
+        const leaveEvents = ["dragleave", "dragend", "drop"];
 
-async function paste_upload_files(files) {
-    const uploadInputElement = elem_upload_float.querySelector("input[type=file]");
-    let totalSizeMb = 0
-    if (files && files.length > 0) {
-        // 执行具体的上传逻辑
-        if (uploadInputElement) {
-            for (let i = 0; i < files.length; i++) {
-                // 将从文件数组中获取的文件大小(单位为字节)转换为MB，
-                totalSizeMb += files[i].size / 1024 / 1024;
+        const onDrag = function (e) {
+            e.preventDefault();
+            e.stopPropagation();
+            if (elem_upload_float.querySelector("input[type=file]")) {
+                toast_up('⚠️释放以上传文件')
+            } else {
+                toast_up(exist_file_msg)
             }
-            // 检查文件总大小是否超过20MB
-            if (totalSizeMb > 20) {
-                toast_push('⚠️文件夹大于20MB 🚀上传文件中', 2000)
-                // return;  // 如果超过了指定大小, 可以不进行后续上传操作
-            }
-             // 监听change事件， 原生Gradio可以实现
-            // uploadInputElement.addEventListener('change', function(){replace_input_string()});
-            let event = new Event("change");
-            Object.defineProperty(event, "target", {value: uploadInputElement, enumerable: true});
-            Object.defineProperty(event, "currentTarget", {value: uploadInputElement, enumerable: true});
-            Object.defineProperty(uploadInputElement, "files", {value: files, enumerable: true});
-            uploadInputElement.dispatchEvent(event);
-            // toast_push('🎉上传文件成功', 2000)
-        } else {
-            toast_push('⚠️请先删除上传区中的历史文件，再尝试粘贴。', 2000)
-        }
+        };
+
+        const onLeave = function (e) {
+            toast_down();
+            e.preventDefault();
+            e.stopPropagation();
+        };
+
+        dragEvents.forEach(event => {
+            elem.addEventListener(event, onDrag);
+        });
+
+        leaveEvents.forEach(event => {
+            elem.addEventListener(event, onLeave);
+        });
+
+        elem.addEventListener("drop", async function (e) {
+            const files = e.dataTransfer.files;
+            await upload_files(files);
+        });
     }
 }
-//提示信息 封装
-function toast_push(msg, duration) {
-    duration = isNaN(duration) ? 3000 : duration;
-    const m = document.createElement('div');
-    m.innerHTML = msg;
-    m.style.cssText = "font-size:  var(--text-md) !important; color: rgb(255, 255, 255);background-color: rgba(0, 0, 0, 0.6);padding: 10px 15px;margin: 0 0 0 -60px;border-radius: 4px;position: fixed;    top: 50%;left: 50%;width: auto; text-align: center;";
-    document.body.appendChild(m);
-    setTimeout(function () {
-        var d = 0.5;
-        m.style.opacity = '0';
-        setTimeout(function () {
-            document.body.removeChild(m)
-        }, d * 1000);
-    }, duration);
+
+function elem_upload_component_pop_message(elem) {
+    if (elem) {
+        const dragEvents = ["dragover"];
+        const leaveEvents = ["dragleave", "dragend", "drop"];
+        dragEvents.forEach(event => {
+            elem.addEventListener(event, function (e) {
+                e.preventDefault();
+                e.stopPropagation();
+                if (elem_upload_float.querySelector("input[type=file]")) {
+                    toast_up('⚠️释放以上传文件')
+                } else {
+                    toast_up(exist_file_msg)
+                }
+            });
+        });
+        leaveEvents.forEach(event => {
+            elem.addEventListener(event, function (e) {
+                toast_down();
+                e.preventDefault();
+                e.stopPropagation();
+            });
+        });
+        elem.addEventListener("drop", async function (e) {
+            toast_push('正在上传中，请稍等。', 2000);
+            begin_loading_status();
+        });
+    }
 }
 
-var elem_upload = null;
-var elem_upload_float = null;
-var elem_input_main = null;
-var elem_input_float = null;
-
+function register_upload_event() {
+    locate_upload_elems();
+    if (elem_upload_float) {
+        _upload = document.querySelector("#elem_upload_float div.center.boundedheight.flex")
+        elem_upload_component_pop_message(_upload);
+    }
+    if (elem_upload_component_float) {
+        elem_upload_component_float.addEventListener('change', function (event) {
+            toast_push('正在上传中，请稍等。', 2000);
+            begin_loading_status();
+        });
+    }
+    if (elem_upload_component) {
+        elem_upload_component.addEventListener('change', function (event) {
+            toast_push('正在上传中，请稍等。', 2000);
+            begin_loading_status();
+        });
+    } else {
+        toast_push("oppps", 3000);
+    }
+}
 
 function monitoring_input_box() {
-    elem_upload = document.getElementById('elem_upload')
-    elem_upload_float = document.getElementById('elem_upload_float')
-    elem_input_main = document.getElementById('user_input_main')
-    elem_input_float = document.getElementById('user_input_float')
+    register_upload_event();
+
     if (elem_input_main) {
         if (elem_input_main.querySelector("textarea")) {
-            add_func_paste(elem_input_main.querySelector("textarea"))
+            register_func_paste(elem_input_main.querySelector("textarea"))
         }
     }
     if (elem_input_float) {
-        if (elem_input_float.querySelector("textarea")){
-            add_func_paste(elem_input_float.querySelector("textarea"))
+        if (elem_input_float.querySelector("textarea")) {
+            register_func_paste(elem_input_float.querySelector("textarea"))
         }
     }
+    if (elem_chatbot) {
+        register_func_drag(elem_chatbot)
+    }
+
 }
 
 
@@ -263,3 +563,153 @@ window.addEventListener("DOMContentLoaded", function () {
     // const ga = document.getElementsByTagName("gradio-app");
     gradioApp().addEventListener("render", monitoring_input_box);
 });
+
+
+
+
+
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 5 部分: 音频按钮样式变化
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+function audio_fn_init() {
+    let audio_component = document.getElementById('elem_audio');
+    if (audio_component) {
+        let buttonElement = audio_component.querySelector('button');
+        let specificElement = audio_component.querySelector('.hide.sr-only');
+        specificElement.remove();
+
+        buttonElement.childNodes[1].nodeValue = '启动麦克风';
+        buttonElement.addEventListener('click', function (event) {
+            event.stopPropagation();
+            toast_push('您启动了麦克风!下一步请点击“实时语音对话”启动语音对话。');
+        });
+
+        // 查找语音插件按钮
+        let buttons = document.querySelectorAll('button');
+        let audio_button = null;
+        for (let button of buttons) {
+            if (button.textContent.includes('语音')) {
+                audio_button = button;
+                break;
+            }
+        }
+        if (audio_button) {
+            audio_button.addEventListener('click', function () {
+                toast_push('您点击了“实时语音对话”启动语音对话。');
+            });
+            let parent_element = audio_component.parentElement; // 将buttonElement移动到audio_button的内部
+            audio_button.appendChild(audio_component);
+            buttonElement.style.cssText = 'border-color: #00ffe0;border-width: 2px; height: 25px;'
+            parent_element.remove();
+            audio_component.style.cssText = 'width: 250px;right: 0px;display: inline-flex;flex-flow: row-reverse wrap;place-content: stretch space-between;align-items: center;background-color: #ffffff00;';
+        }
+
+    }
+}
+
+function minor_ui_adjustment() {
+    let cbsc_area = document.getElementById('cbsc');
+    cbsc_area.style.paddingTop = '15px';
+    var bar_btn_width = [];
+    // 自动隐藏超出范围的toolbar按钮
+    function auto_hide_toolbar() {
+        var qq = document.getElementById('tooltip');
+        var tab_nav = qq.getElementsByClassName('tab-nav');
+        if (tab_nav.length == 0) { return; }
+        var btn_list = tab_nav[0].getElementsByTagName('button')
+        if (btn_list.length == 0) { return; }
+        // 获取页面宽度
+        var page_width = document.documentElement.clientWidth;
+        // 总是保留的按钮数量
+        const always_preserve = 2;
+        // 获取最后一个按钮的右侧位置
+        var cur_right = btn_list[always_preserve - 1].getBoundingClientRect().right;
+        if (bar_btn_width.length == 0) {
+            // 首次运行，记录每个按钮的宽度
+            for (var i = 0; i < btn_list.length; i++) {
+                bar_btn_width.push(btn_list[i].getBoundingClientRect().width);
+            }
+        }
+        // 处理每一个按钮
+        for (var i = always_preserve; i < btn_list.length; i++) {
+            var element = btn_list[i];
+            var element_right = element.getBoundingClientRect().right;
+            if (element_right != 0) { cur_right = element_right; }
+            if (element.style.display === 'none') {
+                if ((cur_right + bar_btn_width[i]) < (page_width * 0.37)) {
+                    // 恢复显示当前按钮
+                    element.style.display = 'block';
+                    return;
+                } else {
+                    return;
+                }
+            } else {
+                if (cur_right > (page_width * 0.38)) {
+                    // 隐藏当前按钮以及右侧所有按钮
+                    for (var j = i; j < btn_list.length; j++) {
+                        if (btn_list[j].style.display !== 'none') {
+                            btn_list[j].style.display = 'none';
+                        }
+                    }
+                    return;
+                }
+            }
+        }
+    }
+
+    setInterval(function () {
+        auto_hide_toolbar()
+    }, 200); // 每50毫秒执行一次
+}
+
+
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 6 部分: 避免滑动
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+let prevented_offset = 0;
+function limit_scroll_position() {
+    let scrollableDiv = document.querySelector('#gpt-chatbot > div.wrap');
+    scrollableDiv.addEventListener('wheel', function (e) {
+        let preventScroll = false;
+        if (e.deltaX != 0) { prevented_offset = 0; return;}
+        if (this.scrollHeight == this.clientHeight) { prevented_offset = 0; return;}
+        if (e.deltaY < 0) { prevented_offset = 0; return;}
+        if (e.deltaY > 0 && this.scrollHeight - this.clientHeight - this.scrollTop <= 1) { preventScroll = true; }
+
+        if (preventScroll) {
+            prevented_offset += e.deltaY;
+            if (Math.abs(prevented_offset) > 499) {
+                if (prevented_offset > 500) { prevented_offset = 500; }
+                if (prevented_offset < -500) { prevented_offset = -500; }
+                preventScroll = false;
+            }
+        } else {
+            prevented_offset = 0;
+        }
+        if (preventScroll) {
+            e.preventDefault();
+            return;
+        }
+    }, { passive: false }); // Passive event listener option should be false
+}
+
+
+
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+//  第 7 部分: JS初始化函数
+// -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
+
+function GptAcademicJavaScriptInit(LAYOUT = "LEFT-RIGHT") {
+    audio_fn_init();
+    minor_ui_adjustment();
+    chatbotIndicator = gradioApp().querySelector('#gpt-chatbot > div.wrap');
+    var chatbotObserver = new MutationObserver(() => {
+        chatbotContentChanged(1);
+    });
+    chatbotObserver.observe(chatbotIndicator, { attributes: true, childList: true, subtree: true });
+    if (LAYOUT === "LEFT-RIGHT") { chatbotAutoHeight(); }
+    if (LAYOUT === "LEFT-RIGHT") { limit_scroll_position(); }
+    // setInterval(function () { uml("mermaid") }, 5000); // 每50毫秒执行一次
+
+}

themes/contrast.css

@@ -17,7 +17,7 @@
     --button-primary-text-color-hover: #FFFFFF;
     --button-secondary-text-color: #FFFFFF;
     --button-secondary-text-color-hover: #FFFFFF;
-    
+
 
     --border-bottom-right-radius: 0px;
     --border-bottom-left-radius:  0px;
@@ -51,8 +51,8 @@
     --button-primary-border-color-hover: #3cff00;
     --button-secondary-border-color: #3cff00;
     --button-secondary-border-color-hover: #3cff00;
-    
-    
+
+
     --body-background-fill: #000000;
     --background-fill-primary: #000000;
     --background-fill-secondary: #000000;
@@ -103,7 +103,7 @@
     --button-primary-text-color-hover: #FFFFFF;
     --button-secondary-text-color: #FFFFFF;
     --button-secondary-text-color-hover: #FFFFFF;
-    
+
 
 
     --border-bottom-right-radius: 0px;
@@ -138,8 +138,8 @@
     --button-primary-border-color-hover: #3cff00;
     --button-secondary-border-color: #3cff00;
     --button-secondary-border-color-hover: #3cff00;
-    
-    
+
+
     --body-background-fill: #000000;
     --background-fill-primary: #000000;
     --background-fill-secondary: #000000;
@@ -479,4 +479,3 @@
 .dark .codehilite .vi { color: #89DDFF } /* Name.Variable.Instance */
 .dark .codehilite .vm { color: #82AAFF } /* Name.Variable.Magic */
 .dark .codehilite .il { color: #F78C6C } /* Literal.Number.Integer.Long */
-

themes/contrast.py

@@ -1,18 +1,26 @@
 import os
 import gradio as gr
 from toolbox import get_conf
-CODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf('CODE_HIGHLIGHT', 'ADD_WAIFU', 'LAYOUT')
+
+CODE_HIGHLIGHT, ADD_WAIFU, LAYOUT = get_conf("CODE_HIGHLIGHT", "ADD_WAIFU", "LAYOUT")
 theme_dir = os.path.dirname(__file__)
 
-def adjust_theme():
 
+def adjust_theme():
     try:
         color_er = gr.themes.utils.colors.fuchsia
         set_theme = gr.themes.Default(
             primary_hue=gr.themes.utils.colors.orange,
             neutral_hue=gr.themes.utils.colors.gray,
-            font=["Helvetica", "Microsoft YaHei", "ui-sans-serif", "sans-serif", "system-ui"],
-            font_mono=["ui-monospace", "Consolas", "monospace"])
+            font=[
+                "Helvetica",
+                "Microsoft YaHei",
+                "ui-sans-serif",
+                "sans-serif",
+                "system-ui",
+            ],
+            font_mono=["ui-monospace", "Consolas", "monospace"],
+        )
         set_theme.set(
             # Colors
             input_background_fill_dark="*neutral_800",
@@ -59,9 +67,15 @@ def adjust_theme():
             button_cancel_text_color_dark="white",
         )
 
-        with open(os.path.join(theme_dir, 'common.js'), 'r', encoding='utf8') as f: 
-            js = f"<script>{f.read()}</script>"
-        
+        js = ""
+        for jsf in [
+            os.path.join(theme_dir, "common.js"),
+            os.path.join(theme_dir, "mermaid.min.js"),
+            os.path.join(theme_dir, "mermaid_loader.js"),
+        ]:
+            with open(jsf, "r", encoding="utf8") as f:
+                js += f"<script>{f.read()}</script>"
+
         # 添加一个萌萌的看板娘
         if ADD_WAIFU:
             js += """
@@ -69,21 +83,26 @@ def adjust_theme():
                 <script src="file=docs/waifu_plugin/jquery-ui.min.js"></script>
                 <script src="file=docs/waifu_plugin/autoload.js"></script>
             """
-        if not hasattr(gr, 'RawTemplateResponse'):
+        if not hasattr(gr, "RawTemplateResponse"):
             gr.RawTemplateResponse = gr.routes.templates.TemplateResponse
         gradio_original_template_fn = gr.RawTemplateResponse
+
         def gradio_new_template_fn(*args, **kwargs):
             res = gradio_original_template_fn(*args, **kwargs)
-            res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
+            res.body = res.body.replace(b"</html>", f"{js}</html>".encode("utf8"))
             res.init_headers()
             return res
-        gr.routes.templates.TemplateResponse = gradio_new_template_fn   # override gradio template
+
+        gr.routes.templates.TemplateResponse = (
+            gradio_new_template_fn  # override gradio template
+        )
     except:
         set_theme = None
-        print('gradio版本较旧, 不能自定义字体和颜色')
+        print("gradio版本较旧, 不能自定义字体和颜色")
     return set_theme
 
-with open(os.path.join(theme_dir, 'contrast.css'), "r", encoding="utf-8") as f:
+
+with open(os.path.join(theme_dir, "contrast.css"), "r", encoding="utf-8") as f:
     advanced_css = f.read()
-with open(os.path.join(theme_dir, 'common.css'), "r", encoding="utf-8") as f:
+with open(os.path.join(theme_dir, "common.css"), "r", encoding="utf-8") as f:
     advanced_css += f.read()