镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
比较提交
2 次代码提交
frontier
...
hongyi-zha
| 作者 | SHA1 | 提交日期 | |
|---|---|---|---|
|
67a3947661 | ||
|
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5231d02307 |
@@ -1,14 +1,14 @@
|
||||
# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
|
||||
name: build-with-latex-arm
|
||||
name: build-with-all-capacity-beta
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- "master"
|
||||
- 'master'
|
||||
|
||||
env:
|
||||
REGISTRY: ghcr.io
|
||||
IMAGE_NAME: ${{ github.repository }}_with_latex_arm
|
||||
IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta
|
||||
|
||||
jobs:
|
||||
build-and-push-image:
|
||||
@@ -18,17 +18,11 @@ jobs:
|
||||
packages: write
|
||||
|
||||
steps:
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v3
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Log in to the Container registry
|
||||
uses: docker/login-action@v3
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
registry: ${{ env.REGISTRY }}
|
||||
username: ${{ github.actor }}
|
||||
@@ -41,11 +35,10 @@ jobs:
|
||||
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
||||
|
||||
- name: Build and push Docker image
|
||||
uses: docker/build-push-action@v6
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
push: true
|
||||
platforms: linux/arm64
|
||||
file: docs/GithubAction+NoLocal+Latex
|
||||
file: docs/GithubAction+AllCapacityBeta
|
||||
tags: ${{ steps.meta.outputs.tags }}
|
||||
labels: ${{ steps.meta.outputs.labels }}
|
||||
44
.github/workflows/build-with-jittorllms.yml
vendored
普通文件
44
.github/workflows/build-with-jittorllms.yml
vendored
普通文件
@@ -0,0 +1,44 @@
|
||||
# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
|
||||
name: build-with-jittorllms
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- 'master'
|
||||
|
||||
env:
|
||||
REGISTRY: ghcr.io
|
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IMAGE_NAME: ${{ github.repository }}_jittorllms
|
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|
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jobs:
|
||||
build-and-push-image:
|
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runs-on: ubuntu-latest
|
||||
permissions:
|
||||
contents: read
|
||||
packages: write
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v3
|
||||
|
||||
- name: Log in to the Container registry
|
||||
uses: docker/login-action@v2
|
||||
with:
|
||||
registry: ${{ env.REGISTRY }}
|
||||
username: ${{ github.actor }}
|
||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Extract metadata (tags, labels) for Docker
|
||||
id: meta
|
||||
uses: docker/metadata-action@v4
|
||||
with:
|
||||
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
||||
|
||||
- name: Build and push Docker image
|
||||
uses: docker/build-push-action@v4
|
||||
with:
|
||||
context: .
|
||||
push: true
|
||||
file: docs/GithubAction+JittorLLMs
|
||||
tags: ${{ steps.meta.outputs.tags }}
|
||||
labels: ${{ steps.meta.outputs.labels }}
|
||||
56
.github/workflows/conda-pack-windows.yml
vendored
56
.github/workflows/conda-pack-windows.yml
vendored
@@ -1,56 +0,0 @@
|
||||
name: Create Conda Environment Package
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
|
||||
jobs:
|
||||
build:
|
||||
runs-on: windows-latest
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Setup Miniconda
|
||||
uses: conda-incubator/setup-miniconda@v3
|
||||
with:
|
||||
auto-activate-base: true
|
||||
activate-environment: ""
|
||||
|
||||
- name: Create new Conda environment
|
||||
shell: bash -l {0}
|
||||
run: |
|
||||
conda create -n gpt python=3.11 -y
|
||||
conda activate gpt
|
||||
|
||||
- name: Install requirements
|
||||
shell: bash -l {0}
|
||||
run: |
|
||||
conda activate gpt
|
||||
pip install -r requirements.txt
|
||||
|
||||
- name: Install conda-pack
|
||||
shell: bash -l {0}
|
||||
run: |
|
||||
conda activate gpt
|
||||
conda install conda-pack -y
|
||||
|
||||
- name: Pack conda environment
|
||||
shell: bash -l {0}
|
||||
run: |
|
||||
conda activate gpt
|
||||
conda pack -n gpt -o gpt.tar.gz
|
||||
|
||||
- name: Create workspace zip
|
||||
shell: pwsh
|
||||
run: |
|
||||
mkdir workspace
|
||||
Get-ChildItem -Exclude "workspace" | Copy-Item -Destination workspace -Recurse
|
||||
Remove-Item -Path workspace/.git* -Recurse -Force -ErrorAction SilentlyContinue
|
||||
Copy-Item gpt.tar.gz workspace/ -Force
|
||||
|
||||
- name: Upload packed files
|
||||
uses: actions/upload-artifact@v4
|
||||
with:
|
||||
name: gpt-academic-package
|
||||
path: workspace
|
||||
7
.github/workflows/stale.yml
vendored
7
.github/workflows/stale.yml
vendored
@@ -7,7 +7,7 @@
|
||||
name: 'Close stale issues and PRs'
|
||||
on:
|
||||
schedule:
|
||||
- cron: '*/30 * * * *'
|
||||
- cron: '*/5 * * * *'
|
||||
|
||||
jobs:
|
||||
stale:
|
||||
@@ -19,6 +19,7 @@ jobs:
|
||||
steps:
|
||||
- uses: actions/stale@v8
|
||||
with:
|
||||
stale-issue-message: 'This issue is stale because it has been open 100 days with no activity. Remove stale label or comment or this will be closed in 7 days.'
|
||||
stale-issue-message: 'This issue is stale because it has been open 100 days with no activity. Remove stale label or comment or this will be closed in 1 days.'
|
||||
days-before-stale: 100
|
||||
days-before-close: 7
|
||||
days-before-close: 1
|
||||
debug-only: true
|
||||
|
||||
10
.gitignore
vendored
10
.gitignore
vendored
@@ -131,9 +131,6 @@ dmypy.json
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# macOS files
|
||||
.DS_Store
|
||||
|
||||
.vscode
|
||||
.idea
|
||||
|
||||
@@ -156,10 +153,3 @@ media
|
||||
flagged
|
||||
request_llms/ChatGLM-6b-onnx-u8s8
|
||||
.pre-commit-config.yaml
|
||||
test.*
|
||||
temp.*
|
||||
objdump*
|
||||
*.min.*.js
|
||||
TODO
|
||||
experimental_mods
|
||||
search_results
|
||||
|
||||
@@ -12,17 +12,11 @@ RUN echo '[global]' > /etc/pip.conf && \
|
||||
echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
|
||||
|
||||
|
||||
# 语音输出功能（以下两行，第一行更换阿里源，第二行安装ffmpeg，都可以删除）
|
||||
RUN UBUNTU_VERSION=$(awk -F= '/^VERSION_CODENAME=/{print $2}' /etc/os-release); echo "deb https://mirrors.aliyun.com/debian/ $UBUNTU_VERSION main non-free contrib" > /etc/apt/sources.list; apt-get update
|
||||
RUN apt-get install ffmpeg -y
|
||||
RUN apt-get clean
|
||||
|
||||
|
||||
# 进入工作路径（必要）
|
||||
WORKDIR /gpt
|
||||
|
||||
|
||||
# 安装大部分依赖，利用Docker缓存加速以后的构建 （以下两行，可以删除）
|
||||
# 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 ./
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
@@ -34,7 +28,6 @@ RUN pip3 install -r requirements.txt
|
||||
|
||||
# 非必要步骤，用于预热模块（可以删除）
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
RUN python3 -m pip cache purge
|
||||
|
||||
|
||||
# 启动（必要）
|
||||
|
||||
64
README.md
64
README.md
@@ -1,14 +1,8 @@
|
||||
> [!IMPORTANT]
|
||||
> `master主分支`最新动态(2025.3.2): 修复大量代码typo / 联网组件支持Jina的api / 增加deepseek-r1支持
|
||||
> `frontier开发分支`最新动态(2024.12.9): 更新对话时间线功能，优化xelatex论文翻译
|
||||
> `wiki文档`最新动态(2024.12.5): 更新ollama接入指南
|
||||
>
|
||||
> 2025.2.2: 三分钟快速接入最强qwen2.5-max[视频](https://www.bilibili.com/video/BV1LeFuerEG4)
|
||||
> 2025.2.1: 支持自定义字体
|
||||
> 2024.10.10: 突发停电，紧急恢复了提供[whl包](https://drive.google.com/drive/folders/14kR-3V-lIbvGxri4AHc8TpiA1fqsw7SK?usp=sharing)的文件服务器
|
||||
> 2024.5.1: 加入Doc2x翻译PDF论文的功能，[查看详情](https://github.com/binary-husky/gpt_academic/wiki/Doc2x)
|
||||
> 2024.3.11: 全力支持Qwen、GLM、DeepseekCoder等中文大语言模型！ SoVits语音克隆模块，[查看详情](https://www.bilibili.com/video/BV1Rp421S7tF/)
|
||||
> 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`。
|
||||
> 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>
|
||||
|
||||
@@ -73,7 +67,7 @@ Read this in [English](docs/README.English.md) | [日本語](docs/README.Japanes
|
||||
读论文、[翻译](https://www.bilibili.com/video/BV1KT411x7Wn)论文 | [插件] 一键解读latex/pdf论文全文并生成摘要
|
||||
Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [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.English.md)了吗？就是出自他的手笔
|
||||
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
|
||||
[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
|
||||
@@ -93,10 +87,6 @@ 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/96192199/279702205-d81137c3-affd-4cd1-bb5e-b15610389762.gif" width="700" >
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/70ff1ec5-e589-4561-a29e-b831079b37fb.gif" width="700" >
|
||||
</div>
|
||||
|
||||
|
||||
- 所有按钮都通过读取functional.py动态生成，可随意加自定义功能，解放剪贴板
|
||||
<div align="center">
|
||||
@@ -129,20 +119,20 @@ Latex论文一键校对 | [插件] 仿Grammarly对Latex文章进行语法、拼
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A{"安装方法"} --> W1("I 🔑直接运行 (Windows, Linux or MacOS)")
|
||||
W1 --> W11["1 Python pip包管理依赖"]
|
||||
W1 --> W12["2 Anaconda包管理依赖（推荐⭐）"]
|
||||
A{"安装方法"} --> W1("I. 🔑直接运行 (Windows, Linux or MacOS)")
|
||||
W1 --> W11["1. Python pip包管理依赖"]
|
||||
W1 --> W12["2. Anaconda包管理依赖（推荐⭐）"]
|
||||
|
||||
A --> W2["II 🐳使用Docker (Windows, Linux or MacOS)"]
|
||||
A --> W2["II. 🐳使用Docker (Windows, Linux or MacOS)"]
|
||||
|
||||
W2 --> k1["1 部署项目全部能力的大镜像（推荐⭐）"]
|
||||
W2 --> k2["2 仅在线模型（GPT, GLM4等）镜像"]
|
||||
W2 --> k3["3 在线模型 + Latex的大镜像"]
|
||||
W2 --> k1["1. 部署项目全部能力的大镜像（推荐⭐）"]
|
||||
W2 --> k2["2. 仅在线模型（GPT, GLM4等）镜像"]
|
||||
W2 --> k3["3. 在线模型 + Latex的大镜像"]
|
||||
|
||||
A --> W4["IV 🚀其他部署方法"]
|
||||
W4 --> C1["1 Windows/MacOS 一键安装运行脚本（推荐⭐）"]
|
||||
W4 --> C2["2 Huggingface, Sealos远程部署"]
|
||||
W4 --> C4["3 其他 ..."]
|
||||
A --> W4["IV. 🚀其他部署方法"]
|
||||
W4 --> C1["1. Windows/MacOS 一键安装运行脚本（推荐⭐）"]
|
||||
W4 --> C2["2. Huggingface, Sealos远程部署"]
|
||||
W4 --> C4["3. ... 其他 ..."]
|
||||
```
|
||||
|
||||
### 安装方法I：直接运行 (Windows, Linux or MacOS)
|
||||
@@ -175,32 +165,26 @@ flowchart TD
|
||||
```
|
||||
|
||||
|
||||
<details><summary>如果需要支持清华ChatGLM系列/复旦MOSS/RWKV作为后端，请点击展开此处</summary>
|
||||
<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ChatGLM系列/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】支持清华ChatGLM4 注意：此模型至少需要24G显存
|
||||
python -m pip install -r request_llms/requirements_chatglm4.txt
|
||||
# 可使用modelscope下载ChatGLM4模型
|
||||
# pip install modelscope
|
||||
# modelscope download --model ZhipuAI/glm-4-9b-chat --local_dir ./THUDM/glm-4-9b-chat
|
||||
|
||||
# 【可选步骤III】支持复旦MOSS
|
||||
# 【可选步骤II】支持复旦MOSS
|
||||
python -m pip install -r request_llms/requirements_moss.txt
|
||||
git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # 注意执行此行代码时，必须处于项目根路径
|
||||
|
||||
# 【可选步骤IV】支持RWKV Runner
|
||||
# 【可选步骤III】支持RWKV Runner
|
||||
参考wiki：https://github.com/binary-husky/gpt_academic/wiki/%E9%80%82%E9%85%8DRWKV-Runner
|
||||
|
||||
# 【可选步骤V】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
|
||||
# 【可选步骤IV】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
|
||||
|
||||
# 【可选步骤VI】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
|
||||
# 【可选步骤V】支持本地模型INT8,INT4量化（这里所指的模型本身不是量化版本，目前deepseek-coder支持，后面测试后会加入更多模型量化选择）
|
||||
pip install bitsandbyte
|
||||
# windows用户安装bitsandbytes需要使用下面bitsandbytes-windows-webui
|
||||
python -m pip install bitsandbytes --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
|
||||
@@ -269,7 +253,8 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
|
||||
# Advanced Usage
|
||||
### I：自定义新的便捷按钮（学术快捷键）
|
||||
|
||||
现在已可以通过UI中的`界面外观`菜单中的`自定义菜单`添加新的便捷按钮。如果需要在代码中定义，请使用任意文本编辑器打开`core_functional.py`，添加如下条目即可：
|
||||
任意文本编辑器打开`core_functional.py`，添加如下条目，然后重启程序。（如果按钮已存在，那么可以直接修改（前缀、后缀都已支持热修改），无需重启程序即可生效。）
|
||||
例如
|
||||
|
||||
```python
|
||||
"超级英译中": {
|
||||
@@ -428,6 +413,7 @@ timeline LR
|
||||
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](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
|
||||
|
||||
|
||||
149
check_proxy.py
149
check_proxy.py
@@ -1,77 +1,37 @@
|
||||
from loguru import logger
|
||||
|
||||
def check_proxy(proxies, return_ip=False):
|
||||
"""
|
||||
检查代理配置并返回结果。
|
||||
|
||||
Args:
|
||||
proxies (dict): 包含http和https代理配置的字典。
|
||||
return_ip (bool, optional): 是否返回代理的IP地址。默认为False。
|
||||
|
||||
Returns:
|
||||
str or None: 检查的结果信息或代理的IP地址（如果`return_ip`为True）。
|
||||
"""
|
||||
def check_proxy(proxies):
|
||||
import requests
|
||||
proxies_https = proxies['https'] if proxies is not None else '无'
|
||||
ip = None
|
||||
try:
|
||||
response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4) # ⭐ 执行GET请求以获取代理信息
|
||||
response = requests.get("https://ipapi.co/json/", proxies=proxies, timeout=4)
|
||||
data = response.json()
|
||||
if 'country_name' in data:
|
||||
country = data['country_name']
|
||||
result = f"代理配置 {proxies_https}, 代理所在地：{country}"
|
||||
if 'ip' in data:
|
||||
ip = data['ip']
|
||||
elif 'error' in data:
|
||||
alternative, ip = _check_with_backup_source(proxies) # ⭐ 调用备用方法检查代理配置
|
||||
alternative = _check_with_backup_source(proxies)
|
||||
if alternative is None:
|
||||
result = f"代理配置 {proxies_https}, 代理所在地：未知，IP查询频率受限"
|
||||
else:
|
||||
result = f"代理配置 {proxies_https}, 代理所在地：{alternative}"
|
||||
else:
|
||||
result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
|
||||
|
||||
if not return_ip:
|
||||
logger.warning(result)
|
||||
print(result)
|
||||
return result
|
||||
else:
|
||||
return ip
|
||||
except:
|
||||
result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
|
||||
if not return_ip:
|
||||
logger.warning(result)
|
||||
print(result)
|
||||
return result
|
||||
else:
|
||||
return ip
|
||||
|
||||
def _check_with_backup_source(proxies):
|
||||
"""
|
||||
通过备份源检查代理，并获取相应信息。
|
||||
|
||||
Args:
|
||||
proxies (dict): 包含代理信息的字典。
|
||||
|
||||
Returns:
|
||||
tuple: 代理信息(geo)和IP地址(ip)的元组。
|
||||
"""
|
||||
import random, string, requests
|
||||
random_string = ''.join(random.choices(string.ascii_letters + string.digits, k=32))
|
||||
try:
|
||||
res_json = requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json() # ⭐ 执行代理检查和备份源请求
|
||||
return res_json['dns']['geo'], res_json['dns']['ip']
|
||||
except:
|
||||
return None, None
|
||||
try: return requests.get(f"http://{random_string}.edns.ip-api.com/json", proxies=proxies, timeout=4).json()['dns']['geo']
|
||||
except: return None
|
||||
|
||||
def backup_and_download(current_version, remote_version):
|
||||
"""
|
||||
一键更新协议：备份当前版本，下载远程版本并解压缩。
|
||||
|
||||
Args:
|
||||
current_version (str): 当前版本号。
|
||||
remote_version (str): 远程版本号。
|
||||
|
||||
Returns:
|
||||
str: 新版本目录的路径。
|
||||
一键更新协议：备份和下载
|
||||
"""
|
||||
from toolbox import get_conf
|
||||
import shutil
|
||||
@@ -87,8 +47,8 @@ def backup_and_download(current_version, remote_version):
|
||||
shutil.copytree('./', backup_dir, ignore=lambda x, y: ['history'])
|
||||
proxies = get_conf('proxies')
|
||||
try: r = requests.get('https://github.com/binary-husky/chatgpt_academic/archive/refs/heads/master.zip', proxies=proxies, stream=True)
|
||||
except: r = requests.get('https://public.agent-matrix.com/publish/master.zip', proxies=proxies, stream=True)
|
||||
zip_file_path = backup_dir+'/master.zip' # ⭐ 保存备份文件的路径
|
||||
except: r = requests.get('https://public.gpt-academic.top/publish/master.zip', proxies=proxies, stream=True)
|
||||
zip_file_path = backup_dir+'/master.zip'
|
||||
with open(zip_file_path, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
dst_path = new_version_dir
|
||||
@@ -104,17 +64,6 @@ def backup_and_download(current_version, remote_version):
|
||||
def patch_and_restart(path):
|
||||
"""
|
||||
一键更新协议：覆盖和重启
|
||||
|
||||
Args:
|
||||
path (str): 新版本代码所在的路径
|
||||
|
||||
注意事项:
|
||||
如果您的程序没有使用config_private.py私密配置文件，则会将config.py重命名为config_private.py以避免配置丢失。
|
||||
|
||||
更新流程:
|
||||
- 复制最新版本代码到当前目录
|
||||
- 更新pip包依赖
|
||||
- 如果更新失败，则提示手动安装依赖库并重启
|
||||
"""
|
||||
from distutils import dir_util
|
||||
import shutil
|
||||
@@ -122,44 +71,33 @@ def patch_and_restart(path):
|
||||
import sys
|
||||
import time
|
||||
import glob
|
||||
from shared_utils.colorful import log亮黄, log亮绿, log亮红
|
||||
|
||||
from colorful import print亮黄, print亮绿, print亮红
|
||||
# if not using config_private, move origin config.py as config_private.py
|
||||
if not os.path.exists('config_private.py'):
|
||||
log亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
|
||||
print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
|
||||
'另外您可以随时在history子文件夹下找回旧版的程序。')
|
||||
shutil.copyfile('config.py', 'config_private.py')
|
||||
|
||||
path_new_version = glob.glob(path + '/*-master')[0]
|
||||
dir_util.copy_tree(path_new_version, './') # ⭐ 将最新版本代码复制到当前目录
|
||||
|
||||
log亮绿('代码已经更新，即将更新pip包依赖……')
|
||||
for i in reversed(range(5)): time.sleep(1); log亮绿(i)
|
||||
|
||||
dir_util.copy_tree(path_new_version, './')
|
||||
print亮绿('代码已经更新，即将更新pip包依赖……')
|
||||
for i in reversed(range(5)): time.sleep(1); print(i)
|
||||
try:
|
||||
import subprocess
|
||||
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
|
||||
except:
|
||||
log亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
|
||||
log亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
|
||||
log亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
log亮绿(' ------------------------------ -----------------------------------')
|
||||
|
||||
for i in reversed(range(8)): time.sleep(1); log亮绿(i)
|
||||
os.execl(sys.executable, sys.executable, *sys.argv) # 重启程序
|
||||
print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
|
||||
print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
|
||||
print(' ------------------------------ -----------------------------------')
|
||||
for i in reversed(range(8)): time.sleep(1); print(i)
|
||||
os.execl(sys.executable, sys.executable, *sys.argv)
|
||||
|
||||
|
||||
def get_current_version():
|
||||
"""
|
||||
获取当前的版本号。
|
||||
|
||||
Returns:
|
||||
str: 当前的版本号。如果无法获取版本号，则返回空字符串。
|
||||
"""
|
||||
import json
|
||||
try:
|
||||
with open('./version', 'r', encoding='utf8') as f:
|
||||
current_version = json.loads(f.read())['version'] # ⭐ 从读取的json数据中提取版本号
|
||||
current_version = json.loads(f.read())['version']
|
||||
except:
|
||||
current_version = ""
|
||||
return current_version
|
||||
@@ -168,12 +106,6 @@ def get_current_version():
|
||||
def auto_update(raise_error=False):
|
||||
"""
|
||||
一键更新协议：查询版本和用户意见
|
||||
|
||||
Args:
|
||||
raise_error (bool, optional): 是否在出错时抛出错误。默认为 False。
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
try:
|
||||
from toolbox import get_conf
|
||||
@@ -181,7 +113,7 @@ def auto_update(raise_error=False):
|
||||
import json
|
||||
proxies = get_conf('proxies')
|
||||
try: response = requests.get("https://raw.githubusercontent.com/binary-husky/chatgpt_academic/master/version", proxies=proxies, timeout=5)
|
||||
except: response = requests.get("https://public.agent-matrix.com/publish/version", proxies=proxies, timeout=5)
|
||||
except: response = requests.get("https://public.gpt-academic.top/publish/version", proxies=proxies, timeout=5)
|
||||
remote_json_data = json.loads(response.text)
|
||||
remote_version = remote_json_data['version']
|
||||
if remote_json_data["show_feature"]:
|
||||
@@ -192,22 +124,22 @@ def auto_update(raise_error=False):
|
||||
current_version = f.read()
|
||||
current_version = json.loads(current_version)['version']
|
||||
if (remote_version - current_version) >= 0.01-1e-5:
|
||||
from shared_utils.colorful import log亮黄
|
||||
log亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}') # ⭐ 在控制台打印新版本信息
|
||||
logger.info('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
|
||||
from colorful import print亮黄
|
||||
print亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
|
||||
print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
|
||||
user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
|
||||
if user_instruction in ['Y', 'y']:
|
||||
path = backup_and_download(current_version, remote_version) # ⭐ 备份并下载文件
|
||||
path = backup_and_download(current_version, remote_version)
|
||||
try:
|
||||
patch_and_restart(path) # ⭐ 执行覆盖并重启操作
|
||||
patch_and_restart(path)
|
||||
except:
|
||||
msg = '更新失败。'
|
||||
if raise_error:
|
||||
from toolbox import trimmed_format_exc
|
||||
msg += trimmed_format_exc()
|
||||
logger.warning(msg)
|
||||
print(msg)
|
||||
else:
|
||||
logger.info('自动更新程序：已禁用')
|
||||
print('自动更新程序：已禁用')
|
||||
return
|
||||
else:
|
||||
return
|
||||
@@ -216,13 +148,10 @@ def auto_update(raise_error=False):
|
||||
if raise_error:
|
||||
from toolbox import trimmed_format_exc
|
||||
msg += trimmed_format_exc()
|
||||
logger.info(msg)
|
||||
print(msg)
|
||||
|
||||
def warm_up_modules():
|
||||
"""
|
||||
预热模块，加载特定模块并执行预热操作。
|
||||
"""
|
||||
logger.info('正在执行一些模块的预热 ...')
|
||||
print('正在执行一些模块的预热 ...')
|
||||
from toolbox import ProxyNetworkActivate
|
||||
from request_llms.bridge_all import model_info
|
||||
with ProxyNetworkActivate("Warmup_Modules"):
|
||||
@@ -232,17 +161,7 @@ def warm_up_modules():
|
||||
enc.encode("模块预热", disallowed_special=())
|
||||
|
||||
def warm_up_vectordb():
|
||||
"""
|
||||
执行一些模块的预热操作。
|
||||
|
||||
本函数主要用于执行一些模块的预热操作，确保在后续的流程中能够顺利运行。
|
||||
|
||||
⭐ 关键作用：预热模块
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
logger.info('正在执行一些模块的预热 ...')
|
||||
print('正在执行一些模块的预热 ...')
|
||||
from toolbox import ProxyNetworkActivate
|
||||
with ProxyNetworkActivate("Warmup_Modules"):
|
||||
import nltk
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import platform
|
||||
from sys import stdout
|
||||
from loguru import logger
|
||||
|
||||
if platform.system()=="Linux":
|
||||
pass
|
||||
@@ -60,29 +59,3 @@ def sprint亮紫(*kw):
|
||||
return "\033[1;35m"+' '.join(kw)+"\033[0m"
|
||||
def sprint亮靛(*kw):
|
||||
return "\033[1;36m"+' '.join(kw)+"\033[0m"
|
||||
|
||||
def log红(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint红(*kw))
|
||||
def log绿(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint绿(*kw))
|
||||
def log黄(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint黄(*kw))
|
||||
def log蓝(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint蓝(*kw))
|
||||
def log紫(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint紫(*kw))
|
||||
def log靛(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint靛(*kw))
|
||||
|
||||
def log亮红(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮红(*kw))
|
||||
def log亮绿(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮绿(*kw))
|
||||
def log亮黄(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮黄(*kw))
|
||||
def log亮蓝(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮蓝(*kw))
|
||||
def log亮紫(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮紫(*kw))
|
||||
def log亮靛(*kw,**kargs):
|
||||
logger.opt(depth=1).info(sprint亮靛(*kw))
|
||||
228
config.py
228
config.py
@@ -7,16 +7,11 @@
|
||||
Configuration reading priority: environment variable > config_private.py > config.py
|
||||
"""
|
||||
|
||||
# [step 1-1]>> ( 接入OpenAI模型家族 ) API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
|
||||
API_KEY = "在此处填写APIKEY" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
|
||||
# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
|
||||
API_KEY = "此处填API密钥" # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
|
||||
|
||||
# [step 1-2]>> ( 强烈推荐！接入通义家族 & 大模型服务平台百炼 ) 接入通义千问在线大模型，api-key获取地址 https://dashscope.console.aliyun.com/
|
||||
DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY（用于接入qwen-max，dashscope-qwen3-14b，dashscope-deepseek-r1等）
|
||||
|
||||
# [step 1-3]>> ( 接入 deepseek-reasoner, 即 deepseek-r1 ) 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
|
||||
DEEPSEEK_API_KEY = ""
|
||||
|
||||
# [step 2]>> 改为True应用代理。如果使用本地或无地域限制的大模型时，此处不修改；如果直接在海外服务器部署，此处不修改
|
||||
# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改；如果使用本地或无地域限制的大模型时，此处也不需要修改
|
||||
USE_PROXY = False
|
||||
if USE_PROXY:
|
||||
"""
|
||||
@@ -35,56 +30,17 @@ if USE_PROXY:
|
||||
else:
|
||||
proxies = None
|
||||
|
||||
# [step 3]>> 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
|
||||
LLM_MODEL = "gpt-3.5-turbo-16k" # 可选 ↓↓↓
|
||||
AVAIL_LLM_MODELS = ["qwen-max", "o1-mini", "o1-mini-2024-09-12", "o1", "o1-2024-12-17", "o1-preview", "o1-preview-2024-09-12",
|
||||
"gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview",
|
||||
"gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
|
||||
"gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
|
||||
"gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
|
||||
"gemini-1.5-pro", "chatglm3", "chatglm4",
|
||||
"deepseek-chat", "deepseek-coder", "deepseek-reasoner",
|
||||
"volcengine-deepseek-r1-250120", "volcengine-deepseek-v3-241226",
|
||||
"dashscope-deepseek-r1", "dashscope-deepseek-v3",
|
||||
"dashscope-qwen3-14b", "dashscope-qwen3-235b-a22b", "dashscope-qwen3-32b",
|
||||
]
|
||||
|
||||
EMBEDDING_MODEL = "text-embedding-3-small"
|
||||
|
||||
# --- --- --- ---
|
||||
# P.S. 其他可用的模型还包括
|
||||
# AVAIL_LLM_MODELS = [
|
||||
# "glm-4-0520", "glm-4-air", "glm-4-airx", "glm-4-flash",
|
||||
# "qianfan", "deepseekcoder",
|
||||
# "spark", "sparkv2", "sparkv3", "sparkv3.5", "sparkv4",
|
||||
# "qwen-turbo", "qwen-plus", "qwen-local",
|
||||
# "moonshot-v1-128k", "moonshot-v1-32k", "moonshot-v1-8k",
|
||||
# "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-turbo-0125", "gpt-4o-2024-05-13"
|
||||
# "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
|
||||
# "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
|
||||
# "deepseek-chat" ,"deepseek-coder",
|
||||
# "gemini-1.5-flash",
|
||||
# "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
|
||||
# "grok-beta",
|
||||
# ]
|
||||
# --- --- --- ---
|
||||
# 此外，您还可以在接入one-api/vllm/ollama/Openroute时，
|
||||
# 使用"one-api-*","vllm-*","ollama-*","openrouter-*"前缀直接使用非标准方式接入的模型，例如
|
||||
# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)","openrouter-openai/gpt-4o-mini","openrouter-openai/chatgpt-4o-latest"]
|
||||
# --- --- --- ---
|
||||
|
||||
|
||||
# --------------- 以下配置可以优化体验 ---------------
|
||||
# ------------------------------------ 以下配置可以优化体验, 但大部分场合下并不需要修改 ------------------------------------
|
||||
|
||||
# 重新URL重新定向，实现更换API_URL的作用（高危设置! 常规情况下不要修改! 通过修改此设置，您将把您的API-KEY和对话隐私完全暴露给您设定的中间人！）
|
||||
# 格式: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
|
||||
# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions", "http://localhost:11434/api/chat": "在这里填写您ollama的URL"}
|
||||
# 举例: API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "https://reverse-proxy-url/v1/chat/completions"}
|
||||
API_URL_REDIRECT = {}
|
||||
|
||||
|
||||
# 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
|
||||
# 一言以蔽之：免费（5刀）用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
|
||||
DEFAULT_WORKER_NUM = 8
|
||||
DEFAULT_WORKER_NUM = 3
|
||||
|
||||
|
||||
# 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
|
||||
@@ -92,31 +48,6 @@ DEFAULT_WORKER_NUM = 8
|
||||
THEME = "Default"
|
||||
AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]
|
||||
|
||||
FONT = "Theme-Default-Font"
|
||||
AVAIL_FONTS = [
|
||||
"默认值(Theme-Default-Font)",
|
||||
"宋体(SimSun)",
|
||||
"黑体(SimHei)",
|
||||
"楷体(KaiTi)",
|
||||
"仿宋(FangSong)",
|
||||
"华文细黑(STHeiti Light)",
|
||||
"华文楷体(STKaiti)",
|
||||
"华文仿宋(STFangsong)",
|
||||
"华文宋体(STSong)",
|
||||
"华文中宋(STZhongsong)",
|
||||
"华文新魏(STXinwei)",
|
||||
"华文隶书(STLiti)",
|
||||
# 备注：以下字体需要网络支持，您可以自定义任意您喜欢的字体，如下所示，需要满足的格式为 "字体昵称(字体英文真名@字体css下载链接)"
|
||||
"思源宋体(Source Han Serif CN VF@https://chinese-fonts-cdn.deno.dev/packages/syst/dist/SourceHanSerifCN/result.css)",
|
||||
"月星楷(Moon Stars Kai HW@https://chinese-fonts-cdn.deno.dev/packages/moon-stars-kai/dist/MoonStarsKaiHW-Regular/result.css)",
|
||||
"珠圆体(MaokenZhuyuanTi@https://chinese-fonts-cdn.deno.dev/packages/mkzyt/dist/猫啃珠圆体/result.css)",
|
||||
"平方萌萌哒(PING FANG MENG MNEG DA@https://chinese-fonts-cdn.deno.dev/packages/pfmmd/dist/平方萌萌哒/result.css)",
|
||||
"Helvetica",
|
||||
"ui-sans-serif",
|
||||
"sans-serif",
|
||||
"system-ui"
|
||||
]
|
||||
|
||||
|
||||
# 默认的系统提示词（system prompt）
|
||||
INIT_SYS_PROMPT = "Serve me as a writing and programming assistant."
|
||||
@@ -146,10 +77,6 @@ TIMEOUT_SECONDS = 30
|
||||
WEB_PORT = -1
|
||||
|
||||
|
||||
# 是否自动打开浏览器页面
|
||||
AUTO_OPEN_BROWSER = True
|
||||
|
||||
|
||||
# 如果OpenAI不响应（网络卡顿、代理失败、KEY失效），重试的次数限制
|
||||
MAX_RETRY = 2
|
||||
|
||||
@@ -158,6 +85,20 @@ MAX_RETRY = 2
|
||||
DEFAULT_FN_GROUPS = ['对话', '编程', '学术', '智能体']
|
||||
|
||||
|
||||
# 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
|
||||
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",
|
||||
"gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
|
||||
"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"
|
||||
MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
|
||||
|
||||
@@ -168,15 +109,16 @@ MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
|
||||
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"）
|
||||
BAIDU_CLOUD_API_KEY = ''
|
||||
BAIDU_CLOUD_SECRET_KEY = ''
|
||||
BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot' # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat", "ERNIE-Speed-128K", "ERNIE-Speed-8K", "ERNIE-Lite-8K"
|
||||
BAIDU_CLOUD_QIANFAN_MODEL = 'ERNIE-Bot' # 可选 "ERNIE-Bot-4"(文心大模型4.0), "ERNIE-Bot"(文心一言), "ERNIE-Bot-turbo", "BLOOMZ-7B", "Llama-2-70B-Chat", "Llama-2-13B-Chat", "Llama-2-7B-Chat"
|
||||
|
||||
|
||||
# 如果使用ChatGLM3或ChatGLM4本地模型，请把 LLM_MODEL="chatglm3" 或LLM_MODEL="chatglm4"，并在此处指定模型路径
|
||||
CHATGLM_LOCAL_MODEL_PATH = "THUDM/glm-4-9b-chat" # 例如"/home/hmp/ChatGLM3-6B/"
|
||||
|
||||
# 如果使用ChatGLM2微调模型，请把 LLM_MODEL="chatglmft"，并在此处指定模型路径
|
||||
CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b-pt-128-1e-2/checkpoint-100"
|
||||
|
||||
@@ -185,7 +127,6 @@ CHATGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
|
||||
LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
|
||||
LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
|
||||
|
||||
|
||||
# 设置gradio的并行线程数（不需要修改）
|
||||
CONCURRENT_COUNT = 100
|
||||
|
||||
@@ -203,8 +144,7 @@ ADD_WAIFU = False
|
||||
AUTHENTICATION = []
|
||||
|
||||
|
||||
# 如果需要在二级路径下运行（常规情况下，不要修改!!）
|
||||
# （举例 CUSTOM_PATH = "/gpt_academic"，可以让软件运行在 http://ip:port/gpt_academic/ 下。）
|
||||
# 如果需要在二级路径下运行（常规情况下，不要修改!!）（需要配合修改main.py才能生效!）
|
||||
CUSTOM_PATH = "/"
|
||||
|
||||
|
||||
@@ -232,8 +172,14 @@ AZURE_ENGINE = "填入你亲手写的部署名" # 读 docs\use_azure.
|
||||
AZURE_CFG_ARRAY = {}
|
||||
|
||||
|
||||
# 阿里云实时语音识别 配置难度较高
|
||||
# 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
|
||||
# 使用Newbing (不推荐使用，未来将删除)
|
||||
NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
|
||||
NEWBING_COOKIES = """
|
||||
put your new bing cookies here
|
||||
"""
|
||||
|
||||
|
||||
# 阿里云实时语音识别 配置难度较高 仅建议高手用户使用 参考 https://github.com/binary-husky/gpt_academic/blob/master/docs/use_audio.md
|
||||
ENABLE_AUDIO = False
|
||||
ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
|
||||
ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
|
||||
@@ -241,12 +187,6 @@ ALIYUN_ACCESSKEY="" # （无需填写）
|
||||
ALIYUN_SECRET="" # （无需填写）
|
||||
|
||||
|
||||
# GPT-SOVITS 文本转语音服务的运行地址（将语言模型的生成文本朗读出来）
|
||||
TTS_TYPE = "EDGE_TTS" # EDGE_TTS / LOCAL_SOVITS_API / DISABLE
|
||||
GPT_SOVITS_URL = ""
|
||||
EDGE_TTS_VOICE = "zh-CN-XiaoxiaoNeural"
|
||||
|
||||
|
||||
# 接入讯飞星火大模型 https://console.xfyun.cn/services/iat
|
||||
XFYUN_APPID = "00000000"
|
||||
XFYUN_API_SECRET = "bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
|
||||
@@ -255,40 +195,19 @@ XFYUN_API_KEY = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
|
||||
|
||||
# 接入智谱大模型
|
||||
ZHIPUAI_API_KEY = ""
|
||||
ZHIPUAI_MODEL = "" # 此选项已废弃，不再需要填写
|
||||
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
|
||||
ANTHROPIC_API_KEY = ""
|
||||
|
||||
|
||||
# 月之暗面 API KEY
|
||||
MOONSHOT_API_KEY = ""
|
||||
|
||||
|
||||
# 零一万物(Yi Model) API KEY
|
||||
YIMODEL_API_KEY = ""
|
||||
|
||||
|
||||
# 接入火山引擎的在线大模型)，api-key获取地址 https://console.volcengine.com/ark/region:ark+cn-beijing/endpoint
|
||||
ARK_API_KEY = "00000000-0000-0000-0000-000000000000" # 火山引擎 API KEY
|
||||
|
||||
|
||||
# 紫东太初大模型 https://ai-maas.wair.ac.cn
|
||||
TAICHU_API_KEY = ""
|
||||
|
||||
# Grok API KEY
|
||||
GROK_API_KEY = ""
|
||||
|
||||
# Mathpix 拥有执行PDF的OCR功能，但是需要注册账号
|
||||
MATHPIX_APPID = ""
|
||||
MATHPIX_APPKEY = ""
|
||||
|
||||
|
||||
# DOC2X的PDF解析服务，注册账号并获取API KEY: https://doc2x.noedgeai.com/login
|
||||
DOC2X_API_KEY = ""
|
||||
|
||||
|
||||
# 自定义API KEY格式
|
||||
CUSTOM_API_KEY_PATTERN = ""
|
||||
|
||||
@@ -310,10 +229,6 @@ GROBID_URLS = [
|
||||
]
|
||||
|
||||
|
||||
# Searxng互联网检索服务（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
|
||||
SEARXNG_URLS = [ f"https://kaletianlre-beardvs{i}dd.hf.space/" for i in range(1,5) ]
|
||||
|
||||
|
||||
# 是否允许通过自然语言描述修改本页的配置，该功能具有一定的危险性，默认关闭
|
||||
ALLOW_RESET_CONFIG = False
|
||||
|
||||
@@ -322,21 +237,21 @@ ALLOW_RESET_CONFIG = False
|
||||
AUTOGEN_USE_DOCKER = False
|
||||
|
||||
|
||||
# 临时的上传文件夹位置，请尽量不要修改
|
||||
# 临时的上传文件夹位置，请勿修改
|
||||
PATH_PRIVATE_UPLOAD = "private_upload"
|
||||
|
||||
|
||||
# 日志文件夹的位置，请尽量不要修改
|
||||
# 日志文件夹的位置，请勿修改
|
||||
PATH_LOGGING = "gpt_log"
|
||||
|
||||
|
||||
# 存储翻译好的arxiv论文的路径，请尽量不要修改
|
||||
ARXIV_CACHE_DIR = "gpt_log/arxiv_cache"
|
||||
# 除了连接OpenAI之外，还有哪些场合允许使用代理，请勿修改
|
||||
WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
|
||||
"Warmup_Modules", "Nougat_Download", "AutoGen"]
|
||||
|
||||
|
||||
# 除了连接OpenAI之外，还有哪些场合允许使用代理，请尽量不要修改
|
||||
WHEN_TO_USE_PROXY = ["Connect_OpenAI", "Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
|
||||
"Warmup_Modules", "Nougat_Download", "AutoGen", "Connect_OpenAI_Embedding"]
|
||||
# *实验性功能*: 自动检测并屏蔽失效的KEY，请勿使用
|
||||
BLOCK_INVALID_APIKEY = False
|
||||
|
||||
|
||||
# 启用插件热加载
|
||||
@@ -346,28 +261,7 @@ PLUGIN_HOT_RELOAD = False
|
||||
# 自定义按钮的最大数量限制
|
||||
NUM_CUSTOM_BASIC_BTN = 4
|
||||
|
||||
|
||||
# 媒体智能体的服务地址（这是一个huggingface空间，请前往huggingface复制该空间，然后把自己新的空间地址填在这里）
|
||||
DAAS_SERVER_URLS = [ f"https://niuziniu-biligpt{i}.hf.space/stream" for i in range(1,5) ]
|
||||
|
||||
|
||||
# 在互联网搜索组件中，负责将搜索结果整理成干净的Markdown
|
||||
JINA_API_KEY = ""
|
||||
|
||||
|
||||
# 是否自动裁剪上下文长度（是否启动，默认不启动）
|
||||
AUTO_CONTEXT_CLIP_ENABLE = False
|
||||
# 目标裁剪上下文的token长度（如果超过这个长度，则会自动裁剪）
|
||||
AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN = 30*1000
|
||||
# 无条件丢弃x以上的轮数
|
||||
AUTO_CONTEXT_MAX_ROUND = 64
|
||||
# 在裁剪上下文时，倒数第x次对话能“最多”保留的上下文token的比例占 AUTO_CONTEXT_CLIP_TRIGGER_TOKEN_LEN 的多少
|
||||
AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0.12, 0.10, 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, 0.01]
|
||||
|
||||
|
||||
"""
|
||||
--------------- 配置关联关系说明 ---------------
|
||||
|
||||
在线大模型配置关联关系示意图
|
||||
│
|
||||
├── "gpt-3.5-turbo" 等openai模型
|
||||
@@ -391,7 +285,7 @@ AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0
|
||||
│ ├── XFYUN_API_SECRET
|
||||
│ └── XFYUN_API_KEY
|
||||
│
|
||||
├── "claude-3-opus-20240229" 等claude模型
|
||||
├── "claude-1-100k" 等claude模型
|
||||
│ └── ANTHROPIC_API_KEY
|
||||
│
|
||||
├── "stack-claude"
|
||||
@@ -403,11 +297,9 @@ AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0
|
||||
│ ├── BAIDU_CLOUD_API_KEY
|
||||
│ └── BAIDU_CLOUD_SECRET_KEY
|
||||
│
|
||||
├── "glm-4", "glm-3-turbo", "zhipuai" 智谱AI大模型
|
||||
│ └── ZHIPUAI_API_KEY
|
||||
│
|
||||
├── "yi-34b-chat-0205", "yi-34b-chat-200k" 等零一万物(Yi Model)大模型
|
||||
│ └── YIMODEL_API_KEY
|
||||
├── "zhipuai" 智谱AI大模型chatglm_turbo
|
||||
│ ├── ZHIPUAI_API_KEY
|
||||
│ └── ZHIPUAI_MODEL
|
||||
│
|
||||
├── "qwen-turbo" 等通义千问大模型
|
||||
│ └── DASHSCOPE_API_KEY
|
||||
@@ -415,15 +307,13 @@ AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0
|
||||
├── "Gemini"
|
||||
│ └── GEMINI_API_KEY
|
||||
│
|
||||
└── "one-api-...(max_token=...)" 用一种更方便的方式接入one-api多模型管理界面
|
||||
├── AVAIL_LLM_MODELS
|
||||
├── API_KEY
|
||||
└── API_URL_REDIRECT
|
||||
└── "newbing" Newbing接口不再稳定，不推荐使用
|
||||
├── NEWBING_STYLE
|
||||
└── NEWBING_COOKIES
|
||||
|
||||
|
||||
本地大模型示意图
|
||||
│
|
||||
├── "chatglm4"
|
||||
├── "chatglm3"
|
||||
├── "chatglm"
|
||||
├── "chatglm_onnx"
|
||||
@@ -453,9 +343,6 @@ AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0
|
||||
|
||||
插件在线服务配置依赖关系示意图
|
||||
│
|
||||
├── 互联网检索
|
||||
│ └── SEARXNG_URLS
|
||||
│
|
||||
├── 语音功能
|
||||
│ ├── ENABLE_AUDIO
|
||||
│ ├── ALIYUN_TOKEN
|
||||
@@ -464,9 +351,6 @@ AUTO_CONTEXT_MAX_CLIP_RATIO = [0.80, 0.60, 0.45, 0.25, 0.20, 0.18, 0.16, 0.14, 0
|
||||
│ └── ALIYUN_SECRET
|
||||
│
|
||||
└── PDF文档精准解析
|
||||
├── GROBID_URLS
|
||||
├── MATHPIX_APPID
|
||||
└── MATHPIX_APPKEY
|
||||
|
||||
└── GROBID_URLS
|
||||
|
||||
"""
|
||||
|
||||
@@ -17,7 +17,7 @@ def get_core_functions():
|
||||
text_show_english=
|
||||
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 (in English). "
|
||||
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.",
|
||||
text_show_chinese=
|
||||
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，"
|
||||
@@ -33,19 +33,17 @@ def get_core_functions():
|
||||
"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,
|
||||
# [7] 模型选择 （可选参数。如不设置，则使用当前全局模型；如设置，则用指定模型覆盖全局模型。）
|
||||
# "ModelOverride": "gpt-3.5-turbo", # 主要用途：强制点击此基础功能按钮时，使用指定的模型。
|
||||
},
|
||||
|
||||
|
||||
"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": '''"""\n\n''',
|
||||
"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() 函数用于去除多行字符串的缩进
|
||||
dedent("\n\n"+r'''
|
||||
"""
|
||||
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：
|
||||
|
||||
@@ -59,7 +57,7 @@ def get_core_functions():
|
||||
C --> |"箭头名2"| F["节点名6"]
|
||||
```
|
||||
|
||||
注意：
|
||||
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
|
||||
|
||||
@@ -1,69 +1,46 @@
|
||||
from toolbox import HotReload # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
|
||||
from toolbox import trimmed_format_exc
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def get_crazy_functions():
|
||||
from crazy_functions.读文章写摘要 import 读文章写摘要
|
||||
from crazy_functions.生成函数注释 import 批量生成函数注释
|
||||
from crazy_functions.SourceCode_Analyse import 解析项目本身
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个Python项目
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个Matlab项目
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个C项目的头文件
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个C项目
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个Golang项目
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个Rust项目
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个Java项目
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个前端项目
|
||||
from crazy_functions.解析项目源代码 import 解析项目本身
|
||||
from crazy_functions.解析项目源代码 import 解析一个Python项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Matlab项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
|
||||
from crazy_functions.解析项目源代码 import 解析一个C项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Golang项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Rust项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Java项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个前端项目
|
||||
from crazy_functions.高级功能函数模板 import 高阶功能模板函数
|
||||
from crazy_functions.高级功能函数模板 import Demo_Wrap
|
||||
from crazy_functions.Latex_Project_Polish import Latex英文润色
|
||||
from crazy_functions.Latex全文润色 import Latex英文润色
|
||||
from crazy_functions.询问多个大语言模型 import 同时问询
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个Lua项目
|
||||
from crazy_functions.SourceCode_Analyse import 解析一个CSharp项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个Lua项目
|
||||
from crazy_functions.解析项目源代码 import 解析一个CSharp项目
|
||||
from crazy_functions.总结word文档 import 总结word文档
|
||||
from crazy_functions.解析JupyterNotebook import 解析ipynb文件
|
||||
from crazy_functions.Conversation_To_File import 载入对话历史存档
|
||||
from crazy_functions.Conversation_To_File import 对话历史存档
|
||||
from crazy_functions.Conversation_To_File import Conversation_To_File_Wrap
|
||||
from crazy_functions.Conversation_To_File import 删除所有本地对话历史记录
|
||||
from crazy_functions.对话历史存档 import 对话历史存档
|
||||
from crazy_functions.对话历史存档 import 载入对话历史存档
|
||||
from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
|
||||
from crazy_functions.辅助功能 import 清除缓存
|
||||
from crazy_functions.Markdown_Translate import Markdown英译中
|
||||
from crazy_functions.批量Markdown翻译 import Markdown英译中
|
||||
from crazy_functions.批量总结PDF文档 import 批量总结PDF文档
|
||||
from crazy_functions.PDF_Translate import 批量翻译PDF文档
|
||||
from crazy_functions.批量翻译PDF文档_多线程 import 批量翻译PDF文档
|
||||
from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
|
||||
from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
|
||||
from crazy_functions.Latex_Project_Polish import Latex中文润色
|
||||
from crazy_functions.Latex_Project_Polish import Latex英文纠错
|
||||
from crazy_functions.Markdown_Translate import Markdown中译英
|
||||
from crazy_functions.Latex全文润色 import Latex中文润色
|
||||
from crazy_functions.Latex全文润色 import Latex英文纠错
|
||||
from crazy_functions.批量Markdown翻译 import Markdown中译英
|
||||
from crazy_functions.虚空终端 import 虚空终端
|
||||
from crazy_functions.生成多种Mermaid图表 import Mermaid_Gen
|
||||
from crazy_functions.PDF_Translate_Wrap import PDF_Tran
|
||||
from crazy_functions.Latex_Function import Latex英文纠错加PDF对比
|
||||
from crazy_functions.Latex_Function import Latex翻译中文并重新编译PDF
|
||||
from crazy_functions.Latex_Function import PDF翻译中文并重新编译PDF
|
||||
from crazy_functions.Latex_Function_Wrap import Arxiv_Localize
|
||||
from crazy_functions.Latex_Function_Wrap import PDF_Localize
|
||||
from crazy_functions.Internet_GPT import 连接网络回答问题
|
||||
from crazy_functions.Internet_GPT_Wrap import NetworkGPT_Wrap
|
||||
from crazy_functions.Image_Generate import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
|
||||
from crazy_functions.Image_Generate_Wrap import ImageGen_Wrap
|
||||
from crazy_functions.SourceCode_Comment import 注释Python项目
|
||||
from crazy_functions.SourceCode_Comment_Wrap import SourceCodeComment_Wrap
|
||||
from crazy_functions.VideoResource_GPT import 多媒体任务
|
||||
from crazy_functions.生成多种Mermaid图表 import 生成多种Mermaid图表
|
||||
|
||||
function_plugins = {
|
||||
"多媒体智能体": {
|
||||
"Group": "智能体",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info": "【仅测试】多媒体任务",
|
||||
"Function": HotReload(多媒体任务),
|
||||
},
|
||||
"虚空终端": {
|
||||
"Group": "对话|编程|学术|智能体",
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
"Info": "使用自然语言实现您的想法",
|
||||
"Function": HotReload(虚空终端),
|
||||
},
|
||||
"解析整个Python项目": {
|
||||
@@ -73,14 +50,6 @@ def get_crazy_functions():
|
||||
"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),
|
||||
},
|
||||
"注释Python项目": {
|
||||
"Group": "编程",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info": "上传一系列python源文件(或者压缩包), 为这些代码添加docstring | 输入参数为路径",
|
||||
"Function": HotReload(注释Python项目),
|
||||
"Class": SourceCodeComment_Wrap,
|
||||
},
|
||||
"载入对话历史存档（先上传存档或输入路径）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
@@ -101,26 +70,19 @@ def get_crazy_functions():
|
||||
"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),
|
||||
},
|
||||
"生成多种Mermaid图表(从当前对话或路径(.pdf/.md/.docx)中生产图表）": {
|
||||
"生成多种Mermaid图表(从当前对话或文件(.pdf/.md)中生产图表）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info" : "基于当前对话或文件生成多种Mermaid图表,图表类型由模型判断",
|
||||
"Function": None,
|
||||
"Class": Mermaid_Gen
|
||||
},
|
||||
"Arxiv论文翻译": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
"Info": "ArXiv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), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
|
||||
"Class": Arxiv_Localize, # 新一代插件需要注册Class
|
||||
"Info" : "基于当前对话或PDF生成多种Mermaid图表,图表类型由模型判断",
|
||||
"Function": HotReload(生成多种Mermaid图表),
|
||||
"AdvancedArgs": True,
|
||||
"ArgsReminder": "请输入图类型对应的数字,不输入则为模型自行判断:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图,9-思维导图",
|
||||
},
|
||||
"批量总结Word文档": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AsButton": True,
|
||||
"Info": "批量总结word文档 | 输入参数为路径",
|
||||
"Function": HotReload(总结word文档),
|
||||
},
|
||||
@@ -226,42 +188,28 @@ def get_crazy_functions():
|
||||
},
|
||||
"保存当前的对话": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"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), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
|
||||
"Class": Conversation_To_File_Wrap # 新一代插件需要注册Class
|
||||
"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",
|
||||
"Color": "stop",
|
||||
"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),
|
||||
},
|
||||
"查互联网后回答": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": True, # 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),
|
||||
"Class": NetworkGPT_Wrap # 新一代插件需要注册Class
|
||||
},
|
||||
"历史上的今天": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"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": None,
|
||||
"Class": Demo_Wrap, # 新一代插件需要注册Class
|
||||
"Function": HotReload(高阶功能模板函数),
|
||||
},
|
||||
"精准翻译PDF论文": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"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), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
|
||||
"Class": PDF_Tran, # 新一代插件需要注册Class
|
||||
"Function": HotReload(批量翻译PDF文档),
|
||||
},
|
||||
"询问多个GPT模型": {
|
||||
"Group": "对话",
|
||||
@@ -336,84 +284,7 @@ def get_crazy_functions():
|
||||
"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),
|
||||
},
|
||||
"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),
|
||||
},
|
||||
"📚Arxiv论文精细翻译（输入arxivID）[需Latex]": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True,
|
||||
"ArgsReminder": r"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。 "
|
||||
r"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: "
|
||||
r'If the term "agent" is used in this section, it should be translated to "智能体". ',
|
||||
"Info": "ArXiv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), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
|
||||
"Class": Arxiv_Localize, # 新一代插件需要注册Class
|
||||
},
|
||||
"📚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": r"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。 "
|
||||
r"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: "
|
||||
r'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),
|
||||
},
|
||||
"PDF翻译中文并重新编译PDF（上传PDF）[需Latex]": {
|
||||
"Group": "学术",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"AdvancedArgs": True,
|
||||
"ArgsReminder": r"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。 "
|
||||
r"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: "
|
||||
r'If the term "agent" is used in this section, it should be translated to "智能体". ',
|
||||
"Info": "PDF翻译中文，并重新编译PDF | 输入参数为路径",
|
||||
"Function": HotReload(PDF翻译中文并重新编译PDF), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
|
||||
"Class": PDF_Localize # 新一代插件需要注册Class
|
||||
}
|
||||
}
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"🎨图片生成（DALLE2/DALLE3, 使用前切换到GPT系列模型）": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info": "使用 DALLE2/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_DALLE2), # 当注册Class后，Function旧接口仅会在“虚空终端”中起作用
|
||||
"Class": ImageGen_Wrap # 新一代插件需要注册Class
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"🎨图片修改_DALLE2 （使用前请切换模型到GPT系列）": {
|
||||
"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),
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# -=--=- 尚未充分测试的实验性插件 & 需要额外依赖的插件 -=--=-
|
||||
try:
|
||||
@@ -431,12 +302,42 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.SourceCode_Analyse import 解析任意code项目
|
||||
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),
|
||||
}
|
||||
}
|
||||
)
|
||||
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),
|
||||
}
|
||||
}
|
||||
)
|
||||
except:
|
||||
print(trimmed_format_exc())
|
||||
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(
|
||||
{
|
||||
@@ -451,8 +352,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
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
|
||||
@@ -470,10 +371,53 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
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")
|
||||
|
||||
try:
|
||||
from crazy_functions.总结音视频 import 总结音视频
|
||||
@@ -492,8 +436,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.数学动画生成manim import 动画生成
|
||||
@@ -510,11 +454,11 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.Markdown_Translate import Markdown翻译指定语言
|
||||
from crazy_functions.批量Markdown翻译 import Markdown翻译指定语言
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
@@ -529,8 +473,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.知识库问答 import 知识库文件注入
|
||||
@@ -548,8 +492,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.知识库问答 import 读取知识库作答
|
||||
@@ -567,8 +511,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
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
|
||||
@@ -584,9 +528,50 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
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
|
||||
from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
|
||||
|
||||
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),
|
||||
},
|
||||
"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),
|
||||
},
|
||||
"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")
|
||||
|
||||
try:
|
||||
from toolbox import get_conf
|
||||
@@ -607,8 +592,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
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
|
||||
@@ -624,8 +609,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.函数动态生成 import 函数动态生成
|
||||
@@ -641,8 +626,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.多智能体 import 多智能体终端
|
||||
@@ -658,8 +643,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.互动小游戏 import 随机小游戏
|
||||
@@ -675,27 +660,8 @@ def get_crazy_functions():
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
|
||||
try:
|
||||
from crazy_functions.Rag_Interface import Rag问答
|
||||
|
||||
function_plugins.update(
|
||||
{
|
||||
"Rag智能召回": {
|
||||
"Group": "对话",
|
||||
"Color": "stop",
|
||||
"AsButton": False,
|
||||
"Info": "将问答数据记录到向量库中，作为长期参考。",
|
||||
"Function": HotReload(Rag问答),
|
||||
},
|
||||
}
|
||||
)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logger.error("Load function plugin failed")
|
||||
|
||||
print(trimmed_format_exc())
|
||||
print("Load function plugin failed")
|
||||
|
||||
# try:
|
||||
# from crazy_functions.高级功能函数模板 import 测试图表渲染
|
||||
@@ -708,9 +674,22 @@ def get_crazy_functions():
|
||||
# }
|
||||
# })
|
||||
# except:
|
||||
# logger.error(trimmed_format_exc())
|
||||
# print(trimmed_format_exc())
|
||||
# print('Load function plugin failed')
|
||||
|
||||
# try:
|
||||
# from crazy_functions.chatglm微调工具 import 微调数据集生成
|
||||
# function_plugins.update({
|
||||
# "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
|
||||
# "Color": "stop",
|
||||
# "AsButton": False,
|
||||
# "AdvancedArgs": True,
|
||||
# "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --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、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
|
||||
# "Function": HotReload(微调数据集生成)
|
||||
# }
|
||||
# })
|
||||
# except:
|
||||
# print('Load function plugin failed')
|
||||
|
||||
"""
|
||||
设置默认值:
|
||||
@@ -730,26 +709,3 @@ def get_crazy_functions():
|
||||
function_plugins[name]["Color"] = "secondary"
|
||||
|
||||
return function_plugins
|
||||
|
||||
|
||||
|
||||
|
||||
def get_multiplex_button_functions():
|
||||
"""多路复用主提交按钮的功能映射
|
||||
"""
|
||||
return {
|
||||
"常规对话":
|
||||
"",
|
||||
|
||||
"查互联网后回答":
|
||||
"查互联网后回答",
|
||||
|
||||
"多模型对话":
|
||||
"询问多个GPT模型", # 映射到上面的 `询问多个GPT模型` 插件
|
||||
|
||||
"智能召回 RAG":
|
||||
"Rag智能召回", # 映射到上面的 `Rag智能召回` 插件
|
||||
|
||||
"多媒体查询":
|
||||
"多媒体智能体", # 映射到上面的 `多媒体智能体` 插件
|
||||
}
|
||||
|
||||
232
crazy_functions/CodeInterpreter.py
普通文件
232
crazy_functions/CodeInterpreter.py
普通文件
@@ -0,0 +1,232 @@
|
||||
from collections.abc import Callable, Iterable, Mapping
|
||||
from typing import Any
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc
|
||||
from toolbox import promote_file_to_downloadzone, get_log_folder
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import input_clipping, try_install_deps
|
||||
from multiprocessing import Process, Pipe
|
||||
import os
|
||||
import time
|
||||
|
||||
templete = """
|
||||
```python
|
||||
import ... # Put dependencies here, e.g. import numpy as np
|
||||
|
||||
class TerminalFunction(object): # Do not change the name of the class, The name of the class must be `TerminalFunction`
|
||||
|
||||
def run(self, path): # The name of the function must be `run`, it takes only a positional argument.
|
||||
# rewrite the function you have just written here
|
||||
...
|
||||
return generated_file_path
|
||||
```
|
||||
"""
|
||||
|
||||
def inspect_dependency(chatbot, history):
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return True
|
||||
|
||||
def get_code_block(reply):
|
||||
import re
|
||||
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
|
||||
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||
if len(matches) == 1:
|
||||
return matches[0].strip('python') # code block
|
||||
for match in matches:
|
||||
if 'class TerminalFunction' in match:
|
||||
return match.strip('python') # code block
|
||||
raise RuntimeError("GPT is not generating proper code.")
|
||||
|
||||
def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
|
||||
# 输入
|
||||
prompt_compose = [
|
||||
f'Your job:\n'
|
||||
f'1. write a single Python function, which takes a path of a `{file_type}` file as the only argument and returns a `string` containing the result of analysis or the path of generated files. \n',
|
||||
f"2. You should write this function to perform following task: " + txt + "\n",
|
||||
f"3. Wrap the output python function with markdown codeblock."
|
||||
]
|
||||
i_say = "".join(prompt_compose)
|
||||
demo = []
|
||||
|
||||
# 第一步
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo,
|
||||
sys_prompt= r"You are a programmer."
|
||||
)
|
||||
history.extend([i_say, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
# 第二步
|
||||
prompt_compose = [
|
||||
"If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
|
||||
templete
|
||||
]
|
||||
i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt= r"You are a programmer."
|
||||
)
|
||||
code_to_return = gpt_say
|
||||
history.extend([i_say, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
# # 第三步
|
||||
# i_say = "Please list to packages to install to run the code above. Then show me how to use `try_install_deps` function to install them."
|
||||
# i_say += 'For instance. `try_install_deps(["opencv-python", "scipy", "numpy"])`'
|
||||
# installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
# inputs=i_say, inputs_show_user=inputs_show_user,
|
||||
# llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
# sys_prompt= r"You are a programmer."
|
||||
# )
|
||||
# # # 第三步
|
||||
# i_say = "Show me how to use `pip` to install packages to run the code above. "
|
||||
# i_say += 'For instance. `pip install -r opencv-python scipy numpy`'
|
||||
# installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
# inputs=i_say, inputs_show_user=i_say,
|
||||
# llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
# sys_prompt= r"You are a programmer."
|
||||
# )
|
||||
installation_advance = ""
|
||||
|
||||
return code_to_return, installation_advance, txt, file_type, llm_kwargs, chatbot, history
|
||||
|
||||
def make_module(code):
|
||||
module_file = 'gpt_fn_' + gen_time_str().replace('-','_')
|
||||
with open(f'{get_log_folder()}/{module_file}.py', 'w', encoding='utf8') as f:
|
||||
f.write(code)
|
||||
|
||||
def get_class_name(class_string):
|
||||
import re
|
||||
# Use regex to extract the class name
|
||||
class_name = re.search(r'class (\w+)\(', class_string).group(1)
|
||||
return class_name
|
||||
|
||||
class_name = get_class_name(code)
|
||||
return f"{get_log_folder().replace('/', '.')}.{module_file}->{class_name}"
|
||||
|
||||
def init_module_instance(module):
|
||||
import importlib
|
||||
module_, class_ = module.split('->')
|
||||
init_f = getattr(importlib.import_module(module_), class_)
|
||||
return init_f()
|
||||
|
||||
def for_immediate_show_off_when_possible(file_type, fp, chatbot):
|
||||
if file_type in ['png', 'jpg']:
|
||||
image_path = os.path.abspath(fp)
|
||||
chatbot.append(['这是一张图片, 展示如下:',
|
||||
f'本地文件地址: <br/>`{image_path}`<br/>'+
|
||||
f'本地文件预览: <br/><div align="center"><img src="file={image_path}"></div>'
|
||||
])
|
||||
return chatbot
|
||||
|
||||
def subprocess_worker(instance, file_path, return_dict):
|
||||
return_dict['result'] = instance.run(file_path)
|
||||
|
||||
def have_any_recent_upload_files(chatbot):
|
||||
_5min = 5 * 60
|
||||
if not chatbot: return False # chatbot is None
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
if not most_recent_uploaded: return False # most_recent_uploaded is None
|
||||
if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
|
||||
else: return False # most_recent_uploaded is too old
|
||||
|
||||
def get_recent_file_prompt_support(chatbot):
|
||||
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
|
||||
path = most_recent_uploaded['path']
|
||||
return path
|
||||
|
||||
@CatchException
|
||||
def 虚空终端CodeInterpreter(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，暂时没有用武之地
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []; clear_file_downloadzone(chatbot)
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"CodeInterpreter开源版, 此插件处于开发阶段, 建议暂时不要使用, 插件初始化中 ..."
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if have_any_recent_upload_files(chatbot):
|
||||
file_path = get_recent_file_prompt_support(chatbot)
|
||||
else:
|
||||
chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 读取文件
|
||||
if ("recently_uploaded_files" in plugin_kwargs) and (plugin_kwargs["recently_uploaded_files"] == ""): plugin_kwargs.pop("recently_uploaded_files")
|
||||
recently_uploaded_files = plugin_kwargs.get("recently_uploaded_files", None)
|
||||
file_path = recently_uploaded_files[-1]
|
||||
file_type = file_path.split('.')[-1]
|
||||
|
||||
# 粗心检查
|
||||
if is_the_upload_folder(txt):
|
||||
chatbot.append([
|
||||
"...",
|
||||
f"请在输入框内填写需求，然后再次点击该插件（文件路径 {file_path} 已经被记忆）"
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 开始干正事
|
||||
for j in range(5): # 最多重试5次
|
||||
try:
|
||||
code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
|
||||
yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
|
||||
code = get_code_block(code)
|
||||
res = make_module(code)
|
||||
instance = init_module_instance(res)
|
||||
break
|
||||
except Exception as e:
|
||||
chatbot.append([f"第{j}次代码生成尝试，失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 代码生成结束, 开始执行
|
||||
try:
|
||||
import multiprocessing
|
||||
manager = multiprocessing.Manager()
|
||||
return_dict = manager.dict()
|
||||
|
||||
p = multiprocessing.Process(target=subprocess_worker, args=(instance, file_path, return_dict))
|
||||
# only has 10 seconds to run
|
||||
p.start(); p.join(timeout=10)
|
||||
if p.is_alive(): p.terminate(); p.join()
|
||||
p.close()
|
||||
res = return_dict['result']
|
||||
# res = instance.run(file_path)
|
||||
except Exception as e:
|
||||
chatbot.append(["执行失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"])
|
||||
# chatbot.append(["如果是缺乏依赖，请参考以下建议", installation_advance])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 顺利完成，收尾
|
||||
res = str(res)
|
||||
if os.path.exists(res):
|
||||
chatbot.append(["执行成功了，结果是一个有效文件", "结果：" + res])
|
||||
new_file_path = promote_file_to_downloadzone(res, chatbot=chatbot)
|
||||
chatbot = for_immediate_show_off_when_possible(file_type, new_file_path, chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
else:
|
||||
chatbot.append(["执行成功了，结果是一个字符串", "结果：" + res])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
"""
|
||||
测试：
|
||||
裁剪图像，保留下半部分
|
||||
交换图像的蓝色通道和红色通道
|
||||
将图像转为灰度图像
|
||||
将csv文件转excel表格
|
||||
"""
|
||||
@@ -1,56 +0,0 @@
|
||||
|
||||
from toolbox import get_conf, update_ui
|
||||
from crazy_functions.Image_Generate import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
|
||||
|
||||
class ImageGen_Wrap(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
|
||||
第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
|
||||
"""
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="输入图片描述", description="需要生成图像的文本描述，尽量使用英文", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
"model_name":
|
||||
ArgProperty(title="模型", options=["DALLE2", "DALLE3"], default_value="DALLE3", description="无", type="dropdown").model_dump_json(),
|
||||
"resolution":
|
||||
ArgProperty(title="分辨率", options=["256x256(限DALLE2)", "512x512(限DALLE2)", "1024x1024", "1792x1024(限DALLE3)", "1024x1792(限DALLE3)"], default_value="1024x1024", description="无", type="dropdown").model_dump_json(),
|
||||
"quality (仅DALLE3生效)":
|
||||
ArgProperty(title="质量", options=["standard", "hd"], default_value="standard", description="无", type="dropdown").model_dump_json(),
|
||||
"style (仅DALLE3生效)":
|
||||
ArgProperty(title="风格", options=["vivid", "natural"], default_value="vivid", description="无", type="dropdown").model_dump_json(),
|
||||
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
# 分辨率
|
||||
resolution = plugin_kwargs["resolution"].replace("(限DALLE2)", "").replace("(限DALLE3)", "")
|
||||
|
||||
if plugin_kwargs["model_name"] == "DALLE2":
|
||||
plugin_kwargs["advanced_arg"] = resolution
|
||||
yield from 图片生成_DALLE2(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
|
||||
elif plugin_kwargs["model_name"] == "DALLE3":
|
||||
quality = plugin_kwargs["quality (仅DALLE3生效)"]
|
||||
style = plugin_kwargs["style (仅DALLE3生效)"]
|
||||
plugin_kwargs["advanced_arg"] = f"{resolution}-{quality}-{style}"
|
||||
yield from 图片生成_DALLE3(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
|
||||
else:
|
||||
chatbot.append([None, "抱歉，找不到该模型"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
@@ -1,365 +0,0 @@
|
||||
import requests
|
||||
import random
|
||||
import time
|
||||
import re
|
||||
import json
|
||||
from bs4 import BeautifulSoup
|
||||
from functools import lru_cache
|
||||
from itertools import zip_longest
|
||||
from check_proxy import check_proxy
|
||||
from toolbox import CatchException, update_ui, get_conf, update_ui_latest_msg
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
from request_llms.bridge_all import model_info
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.prompts.internet import SearchOptimizerPrompt, SearchAcademicOptimizerPrompt
|
||||
|
||||
def search_optimizer(
|
||||
query,
|
||||
proxies,
|
||||
history,
|
||||
llm_kwargs,
|
||||
optimizer=1,
|
||||
categories="general",
|
||||
searxng_url=None,
|
||||
engines=None,
|
||||
):
|
||||
# ------------- < 第1步：尝试进行搜索优化 > -------------
|
||||
# * 增强优化，会尝试结合历史记录进行搜索优化
|
||||
if optimizer == 2:
|
||||
his = " "
|
||||
if len(history) == 0:
|
||||
pass
|
||||
else:
|
||||
for i, h in enumerate(history):
|
||||
if i % 2 == 0:
|
||||
his += f"Q: {h}\n"
|
||||
else:
|
||||
his += f"A: {h}\n"
|
||||
if categories == "general":
|
||||
sys_prompt = SearchOptimizerPrompt.format(query=query, history=his, num=4)
|
||||
elif categories == "science":
|
||||
sys_prompt = SearchAcademicOptimizerPrompt.format(query=query, history=his, num=4)
|
||||
else:
|
||||
his = " "
|
||||
if categories == "general":
|
||||
sys_prompt = SearchOptimizerPrompt.format(query=query, history=his, num=3)
|
||||
elif categories == "science":
|
||||
sys_prompt = SearchAcademicOptimizerPrompt.format(query=query, history=his, num=3)
|
||||
|
||||
mutable = ["", time.time(), ""]
|
||||
llm_kwargs["temperature"] = 0.8
|
||||
try:
|
||||
query_json = predict_no_ui_long_connection(
|
||||
inputs=query,
|
||||
llm_kwargs=llm_kwargs,
|
||||
history=[],
|
||||
sys_prompt=sys_prompt,
|
||||
observe_window=mutable,
|
||||
)
|
||||
except Exception:
|
||||
query_json = "null"
|
||||
#* 尝试解码优化后的搜索结果
|
||||
query_json = re.sub(r"```json|```", "", query_json)
|
||||
try:
|
||||
queries = json.loads(query_json)
|
||||
except Exception:
|
||||
#* 如果解码失败,降低温度再试一次
|
||||
try:
|
||||
llm_kwargs["temperature"] = 0.4
|
||||
query_json = predict_no_ui_long_connection(
|
||||
inputs=query,
|
||||
llm_kwargs=llm_kwargs,
|
||||
history=[],
|
||||
sys_prompt=sys_prompt,
|
||||
observe_window=mutable,
|
||||
)
|
||||
query_json = re.sub(r"```json|```", "", query_json)
|
||||
queries = json.loads(query_json)
|
||||
except Exception:
|
||||
#* 如果再次失败，直接返回原始问题
|
||||
queries = [query]
|
||||
links = []
|
||||
success = 0
|
||||
Exceptions = ""
|
||||
for q in queries:
|
||||
try:
|
||||
link = searxng_request(q, proxies, categories, searxng_url, engines=engines)
|
||||
if len(link) > 0:
|
||||
links.append(link[:-5])
|
||||
success += 1
|
||||
except Exception:
|
||||
Exceptions = Exception
|
||||
pass
|
||||
if success == 0:
|
||||
raise ValueError(f"在线搜索失败！\n{Exceptions}")
|
||||
# * 清洗搜索结果，依次放入每组第一，第二个搜索结果，并清洗重复的搜索结果
|
||||
seen_links = set()
|
||||
result = []
|
||||
for tuple in zip_longest(*links, fillvalue=None):
|
||||
for item in tuple:
|
||||
if item is not None:
|
||||
link = item["link"]
|
||||
if link not in seen_links:
|
||||
seen_links.add(link)
|
||||
result.append(item)
|
||||
return result
|
||||
|
||||
|
||||
@lru_cache
|
||||
def get_auth_ip():
|
||||
ip = check_proxy(None, return_ip=True)
|
||||
if ip is None:
|
||||
return '114.114.114.' + str(random.randint(1, 10))
|
||||
return ip
|
||||
|
||||
|
||||
def searxng_request(query, proxies, categories='general', searxng_url=None, engines=None):
|
||||
if searxng_url is None:
|
||||
urls = get_conf("SEARXNG_URLS")
|
||||
url = random.choice(urls)
|
||||
else:
|
||||
url = searxng_url
|
||||
|
||||
if engines == "Mixed":
|
||||
engines = None
|
||||
|
||||
if categories == 'general':
|
||||
params = {
|
||||
'q': query, # 搜索查询
|
||||
'format': 'json', # 输出格式为JSON
|
||||
'language': 'zh', # 搜索语言
|
||||
'engines': engines,
|
||||
}
|
||||
elif categories == 'science':
|
||||
params = {
|
||||
'q': query, # 搜索查询
|
||||
'format': 'json', # 输出格式为JSON
|
||||
'language': 'zh', # 搜索语言
|
||||
'categories': 'science'
|
||||
}
|
||||
else:
|
||||
raise ValueError('不支持的检索类型')
|
||||
|
||||
headers = {
|
||||
'Accept-Language': 'zh-CN,zh;q=0.9',
|
||||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
|
||||
'X-Forwarded-For': get_auth_ip(),
|
||||
'X-Real-IP': get_auth_ip()
|
||||
}
|
||||
results = []
|
||||
response = requests.post(url, params=params, headers=headers, proxies=proxies, timeout=30)
|
||||
if response.status_code == 200:
|
||||
json_result = response.json()
|
||||
for result in json_result['results']:
|
||||
item = {
|
||||
"title": result.get("title", ""),
|
||||
"source": result.get("engines", "unknown"),
|
||||
"content": result.get("content", ""),
|
||||
"link": result["url"],
|
||||
}
|
||||
results.append(item)
|
||||
return results
|
||||
else:
|
||||
if response.status_code == 429:
|
||||
raise ValueError("Searxng（在线搜索服务）当前使用人数太多，请稍后。")
|
||||
else:
|
||||
raise ValueError("在线搜索失败，状态码: " + str(response.status_code) + '\t' + response.content.decode('utf-8'))
|
||||
|
||||
|
||||
def scrape_text(url, proxies) -> str:
|
||||
"""Scrape text from a webpage
|
||||
|
||||
Args:
|
||||
url (str): The URL to scrape text from
|
||||
|
||||
Returns:
|
||||
str: The scraped text
|
||||
"""
|
||||
from loguru import logger
|
||||
headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
|
||||
'Content-Type': 'text/plain',
|
||||
}
|
||||
|
||||
# 首先采用Jina进行文本提取
|
||||
if get_conf("JINA_API_KEY"):
|
||||
try: return jina_scrape_text(url)
|
||||
except: logger.debug("Jina API 请求失败，回到旧方法")
|
||||
|
||||
try:
|
||||
response = requests.get(url, headers=headers, proxies=proxies, timeout=8)
|
||||
if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
|
||||
except:
|
||||
return "无法连接到该网页"
|
||||
soup = BeautifulSoup(response.text, "html.parser")
|
||||
for script in soup(["script", "style"]):
|
||||
script.extract()
|
||||
text = soup.get_text()
|
||||
lines = (line.strip() for line in text.splitlines())
|
||||
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
|
||||
text = "\n".join(chunk for chunk in chunks if chunk)
|
||||
return text
|
||||
|
||||
|
||||
def jina_scrape_text(url) -> str:
|
||||
"jina_39727421c8fa4e4fa9bd698e5211feaaDyGeVFESNrRaepWiLT0wmHYJSh-d"
|
||||
headers = {
|
||||
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',
|
||||
'Content-Type': 'text/plain',
|
||||
"X-Retain-Images": "none",
|
||||
"Authorization": f'Bearer {get_conf("JINA_API_KEY")}'
|
||||
}
|
||||
response = requests.get("https://r.jina.ai/" + url, headers=headers, proxies=None, timeout=8)
|
||||
if response.status_code != 200:
|
||||
raise ValueError("Jina API 请求失败，开始尝试旧方法！" + response.text)
|
||||
if response.encoding == "ISO-8859-1": response.encoding = response.apparent_encoding
|
||||
result = response.text
|
||||
result = result.replace("\\[", "[").replace("\\]", "]").replace("\\(", "(").replace("\\)", ")")
|
||||
return response.text
|
||||
|
||||
|
||||
def internet_search_with_analysis_prompt(prompt, analysis_prompt, llm_kwargs, chatbot):
|
||||
from toolbox import get_conf
|
||||
proxies = get_conf('proxies')
|
||||
categories = 'general'
|
||||
searxng_url = None # 使用默认的searxng_url
|
||||
engines = None # 使用默认的搜索引擎
|
||||
yield from update_ui_latest_msg(lastmsg=f"检索中: {prompt} ...", chatbot=chatbot, history=[], delay=1)
|
||||
urls = searxng_request(prompt, proxies, categories, searxng_url, engines=engines)
|
||||
yield from update_ui_latest_msg(lastmsg=f"依次访问搜索到的网站 ...", chatbot=chatbot, history=[], delay=1)
|
||||
if len(urls) == 0:
|
||||
return None
|
||||
max_search_result = 5 # 最多收纳多少个网页的结果
|
||||
history = []
|
||||
for index, url in enumerate(urls[:max_search_result]):
|
||||
yield from update_ui_latest_msg(lastmsg=f"依次访问搜索到的网站: {url['link']} ...", chatbot=chatbot, history=[], delay=1)
|
||||
res = scrape_text(url['link'], proxies)
|
||||
prefix = f"第{index}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
|
||||
history.extend([prefix, res])
|
||||
i_say = f"从以上搜索结果中抽取信息，然后回答问题：{prompt} {analysis_prompt}"
|
||||
i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
|
||||
inputs=i_say,
|
||||
history=history,
|
||||
max_token_limit=8192
|
||||
)
|
||||
gpt_say = predict_no_ui_long_connection(
|
||||
inputs=i_say,
|
||||
llm_kwargs=llm_kwargs,
|
||||
history=history,
|
||||
sys_prompt="请从搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。",
|
||||
console_silence=False,
|
||||
)
|
||||
return gpt_say
|
||||
|
||||
@CatchException
|
||||
def 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
optimizer_history = history[:-8]
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append((f"请结合互联网信息回答以下问题：{txt}", "检索中..."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# ------------- < 第1步：爬取搜索引擎的结果 > -------------
|
||||
from toolbox import get_conf
|
||||
proxies = get_conf('proxies')
|
||||
categories = plugin_kwargs.get('categories', 'general')
|
||||
searxng_url = plugin_kwargs.get('searxng_url', None)
|
||||
engines = plugin_kwargs.get('engine', None)
|
||||
optimizer = plugin_kwargs.get('optimizer', "关闭")
|
||||
if optimizer == "关闭":
|
||||
urls = searxng_request(txt, proxies, categories, searxng_url, engines=engines)
|
||||
else:
|
||||
urls = search_optimizer(txt, proxies, optimizer_history, llm_kwargs, optimizer, categories, searxng_url, engines)
|
||||
history = []
|
||||
if len(urls) == 0:
|
||||
chatbot.append((f"结论：{txt}", "[Local Message] 受到限制，无法从searxng获取信息！请尝试更换搜索引擎。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# ------------- < 第2步：依次访问网页 > -------------
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from textwrap import dedent
|
||||
max_search_result = 5 # 最多收纳多少个网页的结果
|
||||
if optimizer == "开启(增强)":
|
||||
max_search_result = 8
|
||||
template = dedent("""
|
||||
<details>
|
||||
<summary>{TITLE}</summary>
|
||||
<div class="search_result">{URL}</div>
|
||||
<div class="search_result">{CONTENT}</div>
|
||||
</details>
|
||||
""")
|
||||
|
||||
buffer = ""
|
||||
|
||||
# 创建线程池
|
||||
with ThreadPoolExecutor(max_workers=5) as executor:
|
||||
# 提交任务到线程池
|
||||
futures = []
|
||||
for index, url in enumerate(urls[:max_search_result]):
|
||||
future = executor.submit(scrape_text, url['link'], proxies)
|
||||
futures.append((index, future, url))
|
||||
|
||||
# 处理完成的任务
|
||||
for index, future, url in futures:
|
||||
# 开始
|
||||
prefix = f"正在加载 第{index+1}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
|
||||
string_structure = template.format(TITLE=prefix, URL=url['link'], CONTENT="正在加载，请稍后 ......")
|
||||
yield from update_ui_latest_msg(lastmsg=(buffer + string_structure), chatbot=chatbot, history=history, delay=0.1) # 刷新界面
|
||||
|
||||
# 获取结果
|
||||
res = future.result()
|
||||
|
||||
# 显示结果
|
||||
prefix = f"第{index+1}份搜索结果 [源自{url['source'][0]}搜索] （{url['title'][:25]}）："
|
||||
string_structure = template.format(TITLE=prefix, URL=url['link'], CONTENT=res[:1000] + "......")
|
||||
buffer += string_structure
|
||||
|
||||
# 更新历史
|
||||
history.extend([prefix, res])
|
||||
yield from update_ui_latest_msg(lastmsg=buffer, chatbot=chatbot, history=history, delay=0.1) # 刷新界面
|
||||
|
||||
# ------------- < 第3步：ChatGPT综合 > -------------
|
||||
if (optimizer != "开启(增强)"):
|
||||
i_say = f"从以上搜索结果中抽取信息，然后回答问题：{txt}"
|
||||
i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
|
||||
inputs=i_say,
|
||||
history=history,
|
||||
max_token_limit=min(model_info[llm_kwargs['llm_model']]['max_token']*3//4, 8192)
|
||||
)
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的两个搜索结果进行总结，然后回答问题。"
|
||||
)
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say);history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
#* 或者使用搜索优化器，这样可以保证后续问答能读取到有效的历史记录
|
||||
else:
|
||||
i_say = f"从以上搜索结果中抽取与问题：{txt} 相关的信息:"
|
||||
i_say, history = input_clipping( # 裁剪输入，从最长的条目开始裁剪，防止爆token
|
||||
inputs=i_say,
|
||||
history=history,
|
||||
max_token_limit=min(model_info[llm_kwargs['llm_model']]['max_token']*3//4, 8192)
|
||||
)
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt="请从给定的若干条搜索结果中抽取信息，对最相关的三个搜索结果进行总结"
|
||||
)
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history = []
|
||||
history.append(i_say);history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
# ------------- < 第4步：根据综合回答问题 > -------------
|
||||
i_say = f"请根据以上搜索结果回答问题：{txt}"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt="请根据给定的若干条搜索结果回答问题"
|
||||
)
|
||||
chatbot[-1] = (i_say, gpt_say)
|
||||
history.append(i_say);history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
@@ -1,49 +0,0 @@
|
||||
import random
|
||||
from toolbox import get_conf
|
||||
from crazy_functions.Internet_GPT import 连接网络回答问题
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
|
||||
|
||||
class NetworkGPT_Wrap(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
|
||||
第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
第三个参数，名称`allow_cache`，参数`type`声明这是一个下拉菜单，下拉菜单上方显示`title`+`description`，下拉菜单的选项为`options`，`default_value`为下拉菜单默认值；
|
||||
|
||||
"""
|
||||
urls = get_conf("SEARXNG_URLS")
|
||||
url = random.choice(urls)
|
||||
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="输入问题", description="待通过互联网检索的问题，会自动读取输入框内容", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
"categories":
|
||||
ArgProperty(title="搜索分类", options=["网页", "学术论文"], default_value="网页", description="无", type="dropdown").model_dump_json(),
|
||||
"engine":
|
||||
ArgProperty(title="选择搜索引擎", options=["Mixed", "bing", "google", "duckduckgo"], default_value="google", description="无", type="dropdown").model_dump_json(),
|
||||
"optimizer":
|
||||
ArgProperty(title="搜索优化", options=["关闭", "开启", "开启(增强)"], default_value="关闭", description="是否使用搜索增强。注意这可能会消耗较多token", type="dropdown").model_dump_json(),
|
||||
"searxng_url":
|
||||
ArgProperty(title="Searxng服务地址", description="输入Searxng的地址", default_value=url, type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs:dict, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
if plugin_kwargs.get("categories", None) == "网页": plugin_kwargs["categories"] = "general"
|
||||
elif plugin_kwargs.get("categories", None) == "学术论文": plugin_kwargs["categories"] = "science"
|
||||
else: plugin_kwargs["categories"] = "general"
|
||||
yield from 连接网络回答问题(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
|
||||
@@ -1,595 +0,0 @@
|
||||
from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone, check_repeat_upload, map_file_to_sha256
|
||||
from toolbox import CatchException, report_exception, update_ui_latest_msg, zip_result, gen_time_str
|
||||
from functools import partial
|
||||
from loguru import logger
|
||||
|
||||
import glob, os, requests, time, json, tarfile, threading
|
||||
|
||||
pj = os.path.join
|
||||
ARXIV_CACHE_DIR = get_conf("ARXIV_CACHE_DIR")
|
||||
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 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):
|
||||
"""
|
||||
Generate prompts and system prompts based on the mode for proofreading or translating.
|
||||
Args:
|
||||
- pfg: Proofreader or Translator instance.
|
||||
- mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
|
||||
|
||||
Returns:
|
||||
- inputs_array: A list of strings containing prompts for users to respond to.
|
||||
- sys_prompt_array: A list of strings containing prompts for system prompts.
|
||||
"""
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
if mode == 'proofread_en':
|
||||
inputs_array = [r"Below is a section from an academic paper, proofread this section." +
|
||||
r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
|
||||
r"Answer me only with the revised text:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
|
||||
elif mode == 'translate_zh':
|
||||
inputs_array = [
|
||||
r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement +
|
||||
r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
|
||||
r"Answer me only with the translated text:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
|
||||
else:
|
||||
assert False, "未知指令"
|
||||
return inputs_array, sys_prompt_array
|
||||
|
||||
|
||||
def descend_to_extracted_folder_if_exist(project_folder):
|
||||
"""
|
||||
Descend into the extracted folder if it exists, otherwise return the original folder.
|
||||
|
||||
Args:
|
||||
- project_folder: A string specifying the folder path.
|
||||
|
||||
Returns:
|
||||
- A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
|
||||
"""
|
||||
maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
|
||||
if len(maybe_dir) == 0: return project_folder
|
||||
if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
|
||||
return project_folder
|
||||
|
||||
|
||||
def move_project(project_folder, arxiv_id=None):
|
||||
"""
|
||||
Create a new work folder and copy the project folder to it.
|
||||
|
||||
Args:
|
||||
- project_folder: A string specifying the folder path of the project.
|
||||
|
||||
Returns:
|
||||
- A string specifying the path to the new work folder.
|
||||
"""
|
||||
import shutil, time
|
||||
time.sleep(2) # avoid time string conflict
|
||||
if arxiv_id is not None:
|
||||
new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
|
||||
else:
|
||||
new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
|
||||
try:
|
||||
shutil.rmtree(new_workfolder)
|
||||
except:
|
||||
pass
|
||||
|
||||
# align subfolder if there is a folder wrapper
|
||||
items = glob.glob(pj(project_folder, '*'))
|
||||
items = [item for item in items if os.path.basename(item) != '__MACOSX']
|
||||
if len(glob.glob(pj(project_folder, '*.tex'))) == 0 and len(items) == 1:
|
||||
if os.path.isdir(items[0]): project_folder = items[0]
|
||||
|
||||
shutil.copytree(src=project_folder, dst=new_workfolder)
|
||||
return new_workfolder
|
||||
|
||||
|
||||
def arxiv_download(chatbot, history, txt, allow_cache=True):
|
||||
def check_cached_translation_pdf(arxiv_id):
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
|
||||
if not os.path.exists(translation_dir):
|
||||
os.makedirs(translation_dir)
|
||||
target_file = pj(translation_dir, 'translate_zh.pdf')
|
||||
if os.path.exists(target_file):
|
||||
promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
|
||||
target_file_compare = pj(translation_dir, 'comparison.pdf')
|
||||
if os.path.exists(target_file_compare):
|
||||
promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
|
||||
return target_file
|
||||
return False
|
||||
|
||||
def is_float(s):
|
||||
try:
|
||||
float(s)
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
if txt.startswith('https://arxiv.org/pdf/'):
|
||||
arxiv_id = txt.split('/')[-1] # 2402.14207v2.pdf
|
||||
txt = arxiv_id.split('v')[0] # 2402.14207
|
||||
|
||||
if ('.' in txt) and ('/' not in txt) and is_float(txt): # is arxiv ID
|
||||
txt = 'https://arxiv.org/abs/' + txt.strip()
|
||||
if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]): # is arxiv ID
|
||||
txt = 'https://arxiv.org/abs/' + txt[:10]
|
||||
|
||||
if not txt.startswith('https://arxiv.org'):
|
||||
return txt, None # 是本地文件，跳过下载
|
||||
|
||||
# <-------------- inspect format ------------->
|
||||
chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
time.sleep(1) # 刷新界面
|
||||
|
||||
url_ = txt # https://arxiv.org/abs/1707.06690
|
||||
|
||||
if not txt.startswith('https://arxiv.org/abs/'):
|
||||
msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
|
||||
yield from update_ui_latest_msg(msg, chatbot=chatbot, history=history) # 刷新界面
|
||||
return msg, None
|
||||
# <-------------- set format ------------->
|
||||
arxiv_id = url_.split('/abs/')[-1]
|
||||
if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
|
||||
cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
|
||||
if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
|
||||
|
||||
extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
|
||||
dst = pj(translation_dir, arxiv_id + '.tar')
|
||||
os.makedirs(translation_dir, exist_ok=True)
|
||||
# <-------------- download arxiv source file ------------->
|
||||
|
||||
def fix_url_and_download():
|
||||
# for url_tar in [url_.replace('/abs/', '/e-print/'), url_.replace('/abs/', '/src/')]:
|
||||
for url_tar in [url_.replace('/abs/', '/src/'), url_.replace('/abs/', '/e-print/')]:
|
||||
proxies = get_conf('proxies')
|
||||
r = requests.get(url_tar, proxies=proxies)
|
||||
if r.status_code == 200:
|
||||
with open(dst, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
return True
|
||||
return False
|
||||
|
||||
if os.path.exists(dst) and allow_cache:
|
||||
yield from update_ui_latest_msg(f"调用缓存 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
||||
success = True
|
||||
else:
|
||||
yield from update_ui_latest_msg(f"开始下载 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
||||
success = fix_url_and_download()
|
||||
yield from update_ui_latest_msg(f"下载完成 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
if not success:
|
||||
yield from update_ui_latest_msg(f"下载失败 {arxiv_id}", chatbot=chatbot, history=history)
|
||||
raise tarfile.ReadError(f"论文下载失败 {arxiv_id}")
|
||||
|
||||
# <-------------- extract file ------------->
|
||||
from toolbox import extract_archive
|
||||
try:
|
||||
extract_archive(file_path=dst, dest_dir=extract_dst)
|
||||
except tarfile.ReadError:
|
||||
os.remove(dst)
|
||||
raise tarfile.ReadError(f"论文下载失败")
|
||||
return extract_dst, arxiv_id
|
||||
|
||||
|
||||
def pdf2tex_project(pdf_file_path, plugin_kwargs):
|
||||
if plugin_kwargs["method"] == "MATHPIX":
|
||||
# Mathpix API credentials
|
||||
app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
|
||||
headers = {"app_id": app_id, "app_key": app_key}
|
||||
|
||||
# Step 1: Send PDF file for processing
|
||||
options = {
|
||||
"conversion_formats": {"tex.zip": True},
|
||||
"math_inline_delimiters": ["$", "$"],
|
||||
"rm_spaces": True
|
||||
}
|
||||
|
||||
response = requests.post(url="https://api.mathpix.com/v3/pdf",
|
||||
headers=headers,
|
||||
data={"options_json": json.dumps(options)},
|
||||
files={"file": open(pdf_file_path, "rb")})
|
||||
|
||||
if response.ok:
|
||||
pdf_id = response.json()["pdf_id"]
|
||||
logger.info(f"PDF processing initiated. PDF ID: {pdf_id}")
|
||||
|
||||
# Step 2: Check processing status
|
||||
while True:
|
||||
conversion_response = requests.get(f"https://api.mathpix.com/v3/pdf/{pdf_id}", headers=headers)
|
||||
conversion_data = conversion_response.json()
|
||||
|
||||
if conversion_data["status"] == "completed":
|
||||
logger.info("PDF processing completed.")
|
||||
break
|
||||
elif conversion_data["status"] == "error":
|
||||
logger.info("Error occurred during processing.")
|
||||
else:
|
||||
logger.info(f"Processing status: {conversion_data['status']}")
|
||||
time.sleep(5) # wait for a few seconds before checking again
|
||||
|
||||
# Step 3: Save results to local files
|
||||
output_dir = os.path.join(os.path.dirname(pdf_file_path), 'mathpix_output')
|
||||
if not os.path.exists(output_dir):
|
||||
os.makedirs(output_dir)
|
||||
|
||||
url = f"https://api.mathpix.com/v3/pdf/{pdf_id}.tex"
|
||||
response = requests.get(url, headers=headers)
|
||||
file_name_wo_dot = '_'.join(os.path.basename(pdf_file_path).split('.')[:-1])
|
||||
output_name = f"{file_name_wo_dot}.tex.zip"
|
||||
output_path = os.path.join(output_dir, output_name)
|
||||
with open(output_path, "wb") as output_file:
|
||||
output_file.write(response.content)
|
||||
logger.info(f"tex.zip file saved at: {output_path}")
|
||||
|
||||
import zipfile
|
||||
unzip_dir = os.path.join(output_dir, file_name_wo_dot)
|
||||
with zipfile.ZipFile(output_path, 'r') as zip_ref:
|
||||
zip_ref.extractall(unzip_dir)
|
||||
|
||||
return unzip_dir
|
||||
|
||||
else:
|
||||
logger.error(f"Error sending PDF for processing. Status code: {response.status_code}")
|
||||
return None
|
||||
else:
|
||||
from crazy_functions.pdf_fns.parse_pdf_via_doc2x import 解析PDF_DOC2X_转Latex
|
||||
unzip_dir = 解析PDF_DOC2X_转Latex(pdf_file_path)
|
||||
return unzip_dir
|
||||
|
||||
|
||||
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 插件主程序1 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# <-------------- information about this plugin ------------->
|
||||
chatbot.append(["函数插件功能？",
|
||||
"对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
subprocess.Popen(['pdflatex', '-version'])
|
||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
||||
except Exception as e:
|
||||
chatbot.append([f"解析项目: {txt}",
|
||||
f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# <-------------- clear history and read input ------------->
|
||||
history = []
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# <-------------- if is a zip/tar file ------------->
|
||||
project_folder = descend_to_extracted_folder_if_exist(project_folder)
|
||||
|
||||
# <-------------- move latex project away from temp folder ------------->
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
project_folder = move_project(project_folder, arxiv_id=None)
|
||||
|
||||
# <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
|
||||
if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
|
||||
yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot, history, system_prompt, mode='proofread_en',
|
||||
switch_prompt=_switch_prompt_)
|
||||
|
||||
# <-------------- compile PDF ------------->
|
||||
success = yield from 编译Latex(chatbot, history, main_file_original='merge',
|
||||
main_file_modified='merge_proofread_en',
|
||||
work_folder_original=project_folder, work_folder_modified=project_folder,
|
||||
work_folder=project_folder)
|
||||
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history);
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
else:
|
||||
chatbot.append((f"失败了",
|
||||
'虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+Conversation_To_File进行反馈 ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history);
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
# <-------------- we are done ------------->
|
||||
return success
|
||||
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 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, user_request):
|
||||
# <-------------- information about this plugin ------------->
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
|
||||
no_cache = ("--no-cache" in more_req)
|
||||
if no_cache: more_req = more_req.replace("--no-cache", "").strip()
|
||||
|
||||
allow_gptac_cloud_io = ("--allow-cloudio" in more_req) # 从云端下载翻译结果，以及上传翻译结果到云端
|
||||
if allow_gptac_cloud_io: more_req = more_req.replace("--allow-cloudio", "").strip()
|
||||
|
||||
allow_cache = not no_cache
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
subprocess.Popen(['pdflatex', '-version'])
|
||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
||||
except Exception as e:
|
||||
chatbot.append([f"解析项目: {txt}",
|
||||
f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# <-------------- clear history and read input ------------->
|
||||
history = []
|
||||
try:
|
||||
txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
|
||||
except tarfile.ReadError as e:
|
||||
yield from update_ui_latest_msg(
|
||||
"无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。",
|
||||
chatbot=chatbot, history=history)
|
||||
return
|
||||
|
||||
if txt.endswith('.pdf'):
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现已经存在翻译好的PDF文档")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# #################################################################
|
||||
if allow_gptac_cloud_io and arxiv_id:
|
||||
# 访问 GPTAC学术云，查询云端是否存在该论文的翻译版本
|
||||
from crazy_functions.latex_fns.latex_actions import check_gptac_cloud
|
||||
success, downloaded = check_gptac_cloud(arxiv_id, chatbot)
|
||||
if success:
|
||||
chatbot.append([
|
||||
f"检测到GPTAC云端存在翻译版本, 如果不满意翻译结果, 请禁用云端分享, 然后重新执行。",
|
||||
None
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
return
|
||||
#################################################################
|
||||
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# <-------------- if is a zip/tar file ------------->
|
||||
project_folder = descend_to_extracted_folder_if_exist(project_folder)
|
||||
|
||||
# <-------------- move latex project away from temp folder ------------->
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
project_folder = move_project(project_folder, arxiv_id)
|
||||
|
||||
# <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
|
||||
if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
|
||||
yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot, history, system_prompt, mode='translate_zh',
|
||||
switch_prompt=_switch_prompt_)
|
||||
|
||||
# <-------------- compile PDF ------------->
|
||||
success = yield from 编译Latex(chatbot, history, main_file_original='merge',
|
||||
main_file_modified='merge_translate_zh', mode='translate_zh',
|
||||
work_folder_original=project_folder, work_folder_modified=project_folder,
|
||||
work_folder=project_folder)
|
||||
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
if allow_gptac_cloud_io and arxiv_id:
|
||||
# 如果用户允许，我们将翻译好的arxiv论文PDF上传到GPTAC学术云
|
||||
from crazy_functions.latex_fns.latex_actions import upload_to_gptac_cloud_if_user_allow
|
||||
threading.Thread(target=upload_to_gptac_cloud_if_user_allow,
|
||||
args=(chatbot, arxiv_id), daemon=True).start()
|
||||
|
||||
chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
else:
|
||||
chatbot.append((f"失败了",
|
||||
'虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
# <-------------- we are done ------------->
|
||||
return success
|
||||
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 插件主程序3 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
@CatchException
|
||||
def PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
# <-------------- information about this plugin ------------->
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"将PDF转换为Latex项目，翻译为中文后重新编译为PDF。函数插件贡献者: Marroh。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
no_cache = more_req.startswith("--no-cache")
|
||||
if no_cache: more_req.lstrip("--no-cache")
|
||||
allow_cache = not no_cache
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
subprocess.Popen(['pdflatex', '-version'])
|
||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
||||
except Exception as e:
|
||||
chatbot.append([f"解析项目: {txt}",
|
||||
f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# <-------------- clear history and read input ------------->
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无法处理: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.pdf文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if len(file_manifest) != 1:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"不支持同时处理多个pdf文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
if plugin_kwargs.get("method", "") == 'MATHPIX':
|
||||
app_id, app_key = get_conf('MATHPIX_APPID', 'MATHPIX_APPKEY')
|
||||
if len(app_id) == 0 or len(app_key) == 0:
|
||||
report_exception(chatbot, history, a="缺失 MATHPIX_APPID 和 MATHPIX_APPKEY。", b=f"请配置 MATHPIX_APPID 和 MATHPIX_APPKEY")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if plugin_kwargs.get("method", "") == 'DOC2X':
|
||||
app_id, app_key = "", ""
|
||||
DOC2X_API_KEY = get_conf('DOC2X_API_KEY')
|
||||
if len(DOC2X_API_KEY) == 0:
|
||||
report_exception(chatbot, history, a="缺失 DOC2X_API_KEY。", b=f"请配置 DOC2X_API_KEY")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
hash_tag = map_file_to_sha256(file_manifest[0])
|
||||
|
||||
# # <-------------- check repeated pdf ------------->
|
||||
# chatbot.append([f"检查PDF是否被重复上传", "正在检查..."])
|
||||
# yield from update_ui(chatbot=chatbot, history=history)
|
||||
# repeat, project_folder = check_repeat_upload(file_manifest[0], hash_tag)
|
||||
|
||||
# if repeat:
|
||||
# yield from update_ui_latest_msg(f"发现重复上传，请查收结果（压缩包）...", chatbot=chatbot, history=history)
|
||||
# try:
|
||||
# translate_pdf = [f for f in glob.glob(f'{project_folder}/**/merge_translate_zh.pdf', recursive=True)][0]
|
||||
# promote_file_to_downloadzone(translate_pdf, rename_file=None, chatbot=chatbot)
|
||||
# comparison_pdf = [f for f in glob.glob(f'{project_folder}/**/comparison.pdf', recursive=True)][0]
|
||||
# promote_file_to_downloadzone(comparison_pdf, rename_file=None, chatbot=chatbot)
|
||||
# zip_res = zip_result(project_folder)
|
||||
# promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
# return
|
||||
# except:
|
||||
# report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"发现重复上传，但是无法找到相关文件")
|
||||
# yield from update_ui(chatbot=chatbot, history=history)
|
||||
# else:
|
||||
# yield from update_ui_latest_msg(f"未发现重复上传", chatbot=chatbot, history=history)
|
||||
|
||||
# <-------------- convert pdf into tex ------------->
|
||||
chatbot.append([f"解析项目: {txt}", "正在将PDF转换为tex项目，请耐心等待..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
project_folder = pdf2tex_project(file_manifest[0], plugin_kwargs)
|
||||
if project_folder is None:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"PDF转换为tex项目失败")
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
return False
|
||||
|
||||
# <-------------- translate latex file into Chinese ------------->
|
||||
yield from update_ui_latest_msg("正在tex项目将翻译为中文...", chatbot=chatbot, history=history)
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# <-------------- if is a zip/tar file ------------->
|
||||
project_folder = descend_to_extracted_folder_if_exist(project_folder)
|
||||
|
||||
# <-------------- move latex project away from temp folder ------------->
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
project_folder = move_project(project_folder)
|
||||
|
||||
# <-------------- set a hash tag for repeat-checking ------------->
|
||||
with open(pj(project_folder, hash_tag + '.tag'), 'w', encoding='utf8') as f:
|
||||
f.write(hash_tag)
|
||||
f.close()
|
||||
|
||||
|
||||
# <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
|
||||
if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
|
||||
yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot, history, system_prompt, mode='translate_zh',
|
||||
switch_prompt=_switch_prompt_)
|
||||
|
||||
# <-------------- compile PDF ------------->
|
||||
yield from update_ui_latest_msg("正在将翻译好的项目tex项目编译为PDF...", chatbot=chatbot, history=history)
|
||||
success = yield from 编译Latex(chatbot, history, main_file_original='merge',
|
||||
main_file_modified='merge_translate_zh', mode='translate_zh',
|
||||
work_folder_original=project_folder, work_folder_modified=project_folder,
|
||||
work_folder=project_folder)
|
||||
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history);
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
else:
|
||||
chatbot.append((f"失败了",
|
||||
'虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history);
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
# <-------------- we are done ------------->
|
||||
return success
|
||||
@@ -1,85 +0,0 @@
|
||||
|
||||
from crazy_functions.Latex_Function import Latex翻译中文并重新编译PDF, PDF翻译中文并重新编译PDF
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
|
||||
|
||||
class Arxiv_Localize(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
|
||||
第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
第三个参数，名称`allow_cache`，参数`type`声明这是一个下拉菜单，下拉菜单上方显示`title`+`description`，下拉菜单的选项为`options`，`default_value`为下拉菜单默认值；
|
||||
|
||||
"""
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="ArxivID", description="输入Arxiv的ID或者网址", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
"advanced_arg":
|
||||
ArgProperty(title="额外的翻译提示词",
|
||||
description=r"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。 "
|
||||
r"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: "
|
||||
r'If the term "agent" is used in this section, it should be translated to "智能体". ',
|
||||
default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
|
||||
"allow_cache":
|
||||
ArgProperty(title="是否允许从缓存中调取结果", options=["允许缓存", "从头执行"], default_value="允许缓存", description="无", type="dropdown").model_dump_json(),
|
||||
"allow_cloudio":
|
||||
ArgProperty(title="是否允许从GPTAC学术云下载(或者上传)翻译结果(仅针对Arxiv论文)", options=["允许", "禁止"], default_value="禁止", description="共享文献，互助互利", type="dropdown").model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
allow_cache = plugin_kwargs["allow_cache"]
|
||||
allow_cloudio = plugin_kwargs["allow_cloudio"]
|
||||
advanced_arg = plugin_kwargs["advanced_arg"]
|
||||
|
||||
if allow_cache == "从头执行": plugin_kwargs["advanced_arg"] = "--no-cache " + plugin_kwargs["advanced_arg"]
|
||||
|
||||
# 从云端下载翻译结果，以及上传翻译结果到云端；人人为我，我为人人。
|
||||
if allow_cloudio == "允许": plugin_kwargs["advanced_arg"] = "--allow-cloudio " + plugin_kwargs["advanced_arg"]
|
||||
|
||||
yield from Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
|
||||
|
||||
|
||||
class PDF_Localize(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
"""
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="PDF文件路径", description="未指定路径，请上传文件后，再点击该插件", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
"advanced_arg":
|
||||
ArgProperty(title="额外的翻译提示词",
|
||||
description=r"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。 "
|
||||
r"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: "
|
||||
r'If the term "agent" is used in this section, it should be translated to "智能体". ',
|
||||
default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
|
||||
"method":
|
||||
ArgProperty(title="采用哪种方法执行转换", options=["MATHPIX", "DOC2X"], default_value="DOC2X", description="无", type="dropdown").model_dump_json(),
|
||||
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
yield from PDF翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
@@ -1,7 +1,6 @@
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder
|
||||
from toolbox import CatchException, report_exception, write_history_to_file, zip_folder
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class PaperFileGroup():
|
||||
def __init__(self):
|
||||
@@ -34,7 +33,7 @@ class PaperFileGroup():
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
|
||||
|
||||
logger.info('Segmentation: done')
|
||||
print('Segmentation: done')
|
||||
def merge_result(self):
|
||||
self.file_result = ["" for _ in range(len(self.file_paths))]
|
||||
for r, k in zip(self.sp_file_result, self.sp_file_index):
|
||||
@@ -57,7 +56,7 @@ class PaperFileGroup():
|
||||
|
||||
def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='polish'):
|
||||
import time, os, re
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
|
||||
# <-------- 读取Latex文件，删除其中的所有注释 ---------->
|
||||
@@ -82,8 +81,8 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
|
||||
# <-------- 多线程润色开始 ---------->
|
||||
if language == 'en':
|
||||
if mode == 'polish':
|
||||
inputs_array = [r"Below is a section from an academic paper, polish this section to meet the academic standard, " +
|
||||
r"improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" +
|
||||
inputs_array = ["Below is a section from an academic paper, polish this section to meet the academic standard, " +
|
||||
"improve the grammar, clarity and overall readability, do not modify any latex command such as \section, \cite and equations:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
else:
|
||||
inputs_array = [r"Below is a section from an academic paper, proofread this section." +
|
||||
@@ -94,10 +93,10 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
|
||||
sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
|
||||
elif language == 'zh':
|
||||
if mode == 'polish':
|
||||
inputs_array = [r"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
|
||||
inputs_array = [f"以下是一篇学术论文中的一段内容，请将此部分润色以满足学术标准，提高语法、清晰度和整体可读性，不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
else:
|
||||
inputs_array = [r"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
|
||||
inputs_array = [f"以下是一篇学术论文中的一段内容，请对这部分内容进行语法矫正。不要修改任何LaTeX命令，例如\section，\cite和方程式：" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"润色 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array=["你是一位专业的中文学术论文作家。" for _ in range(n_split)]
|
||||
@@ -123,7 +122,7 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
|
||||
pfg.write_result()
|
||||
pfg.zip_result()
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
print(trimmed_format_exc())
|
||||
|
||||
# <-------- 整理结果，退出 ---------->
|
||||
create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"
|
||||
@@ -156,7 +155,6 @@ def Latex英文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -195,7 +193,6 @@ def Latex中文润色(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -232,7 +229,6 @@ def Latex英文纠错(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -1,6 +1,6 @@
|
||||
from toolbox import update_ui, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, write_history_to_file
|
||||
from loguru import logger
|
||||
fast_debug = False
|
||||
|
||||
class PaperFileGroup():
|
||||
def __init__(self):
|
||||
@@ -33,11 +33,11 @@ class PaperFileGroup():
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
|
||||
|
||||
logger.info('Segmentation: done')
|
||||
print('Segmentation: done')
|
||||
|
||||
def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
|
||||
import time, os, re
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
# <-------- 读取Latex文件，删除其中的所有注释 ---------->
|
||||
pfg = PaperFileGroup()
|
||||
313
crazy_functions/Latex输出PDF结果.py
普通文件
313
crazy_functions/Latex输出PDF结果.py
普通文件
@@ -0,0 +1,313 @@
|
||||
from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
|
||||
from functools import partial
|
||||
import glob, os, requests, time, tarfile
|
||||
pj = os.path.join
|
||||
ARXIV_CACHE_DIR = os.path.expanduser(f"~/arxiv_cache/")
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- 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):
|
||||
"""
|
||||
Generate prompts and system prompts based on the mode for proofreading or translating.
|
||||
Args:
|
||||
- pfg: Proofreader or Translator instance.
|
||||
- mode: A string specifying the mode, either 'proofread' or 'translate_zh'.
|
||||
|
||||
Returns:
|
||||
- inputs_array: A list of strings containing prompts for users to respond to.
|
||||
- sys_prompt_array: A list of strings containing prompts for system prompts.
|
||||
"""
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
if mode == 'proofread_en':
|
||||
inputs_array = [r"Below is a section from an academic paper, proofread this section." +
|
||||
r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " + more_requirement +
|
||||
r"Answer me only with the revised text:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
sys_prompt_array = ["You are a professional academic paper writer." for _ in range(n_split)]
|
||||
elif mode == 'translate_zh':
|
||||
inputs_array = [r"Below is a section from an English academic paper, translate it into Chinese. " + more_requirement +
|
||||
r"Do not modify any latex command such as \section, \cite, \begin, \item and equations. " +
|
||||
r"Answer me only with the translated text:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
sys_prompt_array = ["You are a professional translator." for _ in range(n_split)]
|
||||
else:
|
||||
assert False, "未知指令"
|
||||
return inputs_array, sys_prompt_array
|
||||
|
||||
def desend_to_extracted_folder_if_exist(project_folder):
|
||||
"""
|
||||
Descend into the extracted folder if it exists, otherwise return the original folder.
|
||||
|
||||
Args:
|
||||
- project_folder: A string specifying the folder path.
|
||||
|
||||
Returns:
|
||||
- A string specifying the path to the extracted folder, or the original folder if there is no extracted folder.
|
||||
"""
|
||||
maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
|
||||
if len(maybe_dir) == 0: return project_folder
|
||||
if maybe_dir[0].endswith('.extract'): return maybe_dir[0]
|
||||
return project_folder
|
||||
|
||||
def move_project(project_folder, arxiv_id=None):
|
||||
"""
|
||||
Create a new work folder and copy the project folder to it.
|
||||
|
||||
Args:
|
||||
- project_folder: A string specifying the folder path of the project.
|
||||
|
||||
Returns:
|
||||
- A string specifying the path to the new work folder.
|
||||
"""
|
||||
import shutil, time
|
||||
time.sleep(2) # avoid time string conflict
|
||||
if arxiv_id is not None:
|
||||
new_workfolder = pj(ARXIV_CACHE_DIR, arxiv_id, 'workfolder')
|
||||
else:
|
||||
new_workfolder = f'{get_log_folder()}/{gen_time_str()}'
|
||||
try:
|
||||
shutil.rmtree(new_workfolder)
|
||||
except:
|
||||
pass
|
||||
|
||||
# align subfolder if there is a folder wrapper
|
||||
items = glob.glob(pj(project_folder,'*'))
|
||||
items = [item for item in items if os.path.basename(item)!='__MACOSX']
|
||||
if len(glob.glob(pj(project_folder,'*.tex'))) == 0 and len(items) == 1:
|
||||
if os.path.isdir(items[0]): project_folder = items[0]
|
||||
|
||||
shutil.copytree(src=project_folder, dst=new_workfolder)
|
||||
return new_workfolder
|
||||
|
||||
def arxiv_download(chatbot, history, txt, allow_cache=True):
|
||||
def check_cached_translation_pdf(arxiv_id):
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'translation')
|
||||
if not os.path.exists(translation_dir):
|
||||
os.makedirs(translation_dir)
|
||||
target_file = pj(translation_dir, 'translate_zh.pdf')
|
||||
if os.path.exists(target_file):
|
||||
promote_file_to_downloadzone(target_file, rename_file=None, chatbot=chatbot)
|
||||
target_file_compare = pj(translation_dir, 'comparison.pdf')
|
||||
if os.path.exists(target_file_compare):
|
||||
promote_file_to_downloadzone(target_file_compare, rename_file=None, chatbot=chatbot)
|
||||
return target_file
|
||||
return False
|
||||
def is_float(s):
|
||||
try:
|
||||
float(s)
|
||||
return True
|
||||
except ValueError:
|
||||
return False
|
||||
if ('.' in txt) and ('/' not in txt) and is_float(txt): # is arxiv ID
|
||||
txt = 'https://arxiv.org/abs/' + txt.strip()
|
||||
if ('.' in txt) and ('/' not in txt) and is_float(txt[:10]): # is arxiv ID
|
||||
txt = 'https://arxiv.org/abs/' + txt[:10]
|
||||
if not txt.startswith('https://arxiv.org'):
|
||||
return txt, None # 是本地文件，跳过下载
|
||||
|
||||
# <-------------- inspect format ------------->
|
||||
chatbot.append([f"检测到arxiv文档连接", '尝试下载 ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
time.sleep(1) # 刷新界面
|
||||
|
||||
url_ = txt # https://arxiv.org/abs/1707.06690
|
||||
if not txt.startswith('https://arxiv.org/abs/'):
|
||||
msg = f"解析arxiv网址失败, 期望格式例如: https://arxiv.org/abs/1707.06690。实际得到格式: {url_}。"
|
||||
yield from update_ui_lastest_msg(msg, chatbot=chatbot, history=history) # 刷新界面
|
||||
return msg, None
|
||||
# <-------------- set format ------------->
|
||||
arxiv_id = url_.split('/abs/')[-1]
|
||||
if 'v' in arxiv_id: arxiv_id = arxiv_id[:10]
|
||||
cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
|
||||
if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
|
||||
|
||||
url_tar = url_.replace('/abs/', '/e-print/')
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
|
||||
extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
|
||||
os.makedirs(translation_dir, exist_ok=True)
|
||||
|
||||
# <-------------- download arxiv source file ------------->
|
||||
dst = pj(translation_dir, arxiv_id+'.tar')
|
||||
if os.path.exists(dst):
|
||||
yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history) # 刷新界面
|
||||
else:
|
||||
yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history) # 刷新界面
|
||||
proxies = get_conf('proxies')
|
||||
r = requests.get(url_tar, proxies=proxies)
|
||||
with open(dst, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
# <-------------- extract file ------------->
|
||||
yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history) # 刷新界面
|
||||
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 =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
||||
|
||||
|
||||
@CatchException
|
||||
def Latex英文纠错加PDF对比(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
# <-------------- information about this plugin ------------->
|
||||
chatbot.append([ "函数插件功能？",
|
||||
"对整个Latex项目进行纠错, 用latex编译为PDF对修正处做高亮。函数插件贡献者: Binary-Husky。注意事项: 目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。仅在Windows系统进行了测试，其他操作系统表现未知。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
subprocess.Popen(['pdflatex', '-version'])
|
||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
||||
except Exception as e:
|
||||
chatbot.append([ f"解析项目: {txt}",
|
||||
f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- clear history and read input ------------->
|
||||
history = []
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- if is a zip/tar file ------------->
|
||||
project_folder = desend_to_extracted_folder_if_exist(project_folder)
|
||||
|
||||
|
||||
# <-------------- move latex project away from temp folder ------------->
|
||||
project_folder = move_project(project_folder, arxiv_id=None)
|
||||
|
||||
|
||||
# <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
|
||||
if not os.path.exists(project_folder + '/merge_proofread_en.tex'):
|
||||
yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot, history, system_prompt, mode='proofread_en', switch_prompt=_switch_prompt_)
|
||||
|
||||
|
||||
# <-------------- compile PDF ------------->
|
||||
success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_proofread_en',
|
||||
work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
|
||||
|
||||
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
else:
|
||||
chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 也是可读的, 您可以到Github Issue区, 用该压缩包+对话历史存档进行反馈 ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
# <-------------- we are done ------------->
|
||||
return success
|
||||
|
||||
# =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 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, user_request):
|
||||
# <-------------- information about this plugin ------------->
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"对整个Latex项目进行翻译, 生成中文PDF。函数插件贡献者: Binary-Husky。注意事项: 此插件Windows支持最佳，Linux下必须使用Docker安装，详见项目主README.md。目前仅支持GPT3.5/GPT4，其他模型转化效果未知。目前对机器学习类文献转化效果最好，其他类型文献转化效果未知。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
no_cache = more_req.startswith("--no-cache")
|
||||
if no_cache: more_req.lstrip("--no-cache")
|
||||
allow_cache = not no_cache
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
subprocess.Popen(['pdflatex', '-version'])
|
||||
from .latex_fns.latex_actions import Latex精细分解与转化, 编译Latex
|
||||
except Exception as e:
|
||||
chatbot.append([ f"解析项目: {txt}",
|
||||
f"尝试执行Latex指令失败。Latex没有安装, 或者不在环境变量PATH中。安装方法https://tug.org/texlive/。报错信息\n\n```\n\n{trimmed_format_exc()}\n\n```\n\n"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- clear history and read input ------------->
|
||||
history = []
|
||||
try:
|
||||
txt, arxiv_id = yield from arxiv_download(chatbot, history, txt, allow_cache)
|
||||
except tarfile.ReadError as e:
|
||||
yield from update_ui_lastest_msg(
|
||||
"无法自动下载该论文的Latex源码，请前往arxiv打开此论文下载页面，点other Formats，然后download source手动下载latex源码包。接下来调用本地Latex翻译插件即可。",
|
||||
chatbot=chatbot, history=history)
|
||||
return
|
||||
|
||||
if txt.endswith('.pdf'):
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"发现已经存在翻译好的PDF文档")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无法处理: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
# <-------------- if is a zip/tar file ------------->
|
||||
project_folder = desend_to_extracted_folder_if_exist(project_folder)
|
||||
|
||||
|
||||
# <-------------- move latex project away from temp folder ------------->
|
||||
project_folder = move_project(project_folder, arxiv_id)
|
||||
|
||||
|
||||
# <-------------- if merge_translate_zh is already generated, skip gpt req ------------->
|
||||
if not os.path.exists(project_folder + '/merge_translate_zh.tex'):
|
||||
yield from Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot, history, system_prompt, mode='translate_zh', switch_prompt=_switch_prompt_)
|
||||
|
||||
|
||||
# <-------------- compile PDF ------------->
|
||||
success = yield from 编译Latex(chatbot, history, main_file_original='merge', main_file_modified='merge_translate_zh', mode='translate_zh',
|
||||
work_folder_original=project_folder, work_folder_modified=project_folder, work_folder=project_folder)
|
||||
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
chatbot.append((f"成功啦", '请查收结果（压缩包）...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
else:
|
||||
chatbot.append((f"失败了", '虽然PDF生成失败了, 但请查收结果（压缩包）, 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux，请检查系统字体（见Github wiki） ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history); time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
|
||||
# <-------------- we are done ------------->
|
||||
return success
|
||||
@@ -1,83 +0,0 @@
|
||||
from toolbox import CatchException, check_packages, get_conf
|
||||
from toolbox import update_ui, update_ui_latest_msg, disable_auto_promotion
|
||||
from toolbox import trimmed_format_exc_markdown
|
||||
from crazy_functions.crazy_utils import get_files_from_everything
|
||||
from crazy_functions.pdf_fns.parse_pdf import get_avail_grobid_url
|
||||
from crazy_functions.pdf_fns.parse_pdf_via_doc2x import 解析PDF_基于DOC2X
|
||||
from crazy_functions.pdf_fns.parse_pdf_legacy import 解析PDF_简单拆解
|
||||
from crazy_functions.pdf_fns.parse_pdf_grobid import 解析PDF_基于GROBID
|
||||
from shared_utils.colorful import *
|
||||
|
||||
@CatchException
|
||||
def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
disable_auto_promotion(chatbot)
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([None, "插件功能：批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
check_packages(["fitz", "tiktoken", "scipdf"])
|
||||
except:
|
||||
chatbot.append([None, f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken scipdf_parser```。"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf', chatbot=chatbot)
|
||||
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if (not success) and txt == "": txt = '空空如也的输入栏。提示：请先上传文件（把PDF文件拖入对话）。'
|
||||
|
||||
# 如果没找到任何文件
|
||||
if len(file_manifest) == 0:
|
||||
chatbot.append([None, f"找不到任何.pdf拓展名的文件: {txt}"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 开始正式执行任务
|
||||
method = plugin_kwargs.get("pdf_parse_method", None)
|
||||
if method == "DOC2X":
|
||||
# ------- 第一种方法，效果最好，但是需要DOC2X服务 -------
|
||||
DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
|
||||
if len(DOC2X_API_KEY) != 0:
|
||||
try:
|
||||
yield from 解析PDF_基于DOC2X(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request)
|
||||
return
|
||||
except:
|
||||
chatbot.append([None, f"DOC2X服务不可用，请检查报错详细。{trimmed_format_exc_markdown()}"])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
if method == "GROBID":
|
||||
# ------- 第二种方法，效果次优 -------
|
||||
grobid_url = get_avail_grobid_url()
|
||||
if grobid_url is not None:
|
||||
yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
|
||||
return
|
||||
|
||||
if method == "Classic":
|
||||
# ------- 第三种方法，早期代码，效果不理想 -------
|
||||
yield from update_ui_latest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
|
||||
yield from 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
return
|
||||
|
||||
if method is None:
|
||||
# ------- 以上三种方法都试一遍 -------
|
||||
DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
|
||||
if len(DOC2X_API_KEY) != 0:
|
||||
try:
|
||||
yield from 解析PDF_基于DOC2X(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, DOC2X_API_KEY, user_request)
|
||||
return
|
||||
except:
|
||||
chatbot.append([None, f"DOC2X服务不可用，正在尝试GROBID。{trimmed_format_exc_markdown()}"])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
grobid_url = get_avail_grobid_url()
|
||||
if grobid_url is not None:
|
||||
yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
|
||||
return
|
||||
yield from update_ui_latest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
|
||||
yield from 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
return
|
||||
|
||||
@@ -1,33 +0,0 @@
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
from .PDF_Translate import 批量翻译PDF文档
|
||||
|
||||
|
||||
class PDF_Tran(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
"""
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="PDF文件路径", description="未指定路径，请上传文件后，再点击该插件", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
"additional_prompt":
|
||||
ArgProperty(title="额外提示词", description="例如：对专有名词、翻译语气等方面的要求", default_value="", type="string").model_dump_json(), # 高级参数输入区，自动同步
|
||||
"pdf_parse_method":
|
||||
ArgProperty(title="PDF解析方法", options=["DOC2X", "GROBID", "Classic"], description="无", default_value="GROBID", type="dropdown").model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
main_input = plugin_kwargs["main_input"]
|
||||
additional_prompt = plugin_kwargs["additional_prompt"]
|
||||
pdf_parse_method = plugin_kwargs["pdf_parse_method"]
|
||||
yield from 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
@@ -1,153 +0,0 @@
|
||||
import os,glob
|
||||
from typing import List
|
||||
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
|
||||
from toolbox import report_exception
|
||||
from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_latest_msg
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
RAG_WORKER_REGISTER = {}
|
||||
MAX_HISTORY_ROUND = 5
|
||||
MAX_CONTEXT_TOKEN_LIMIT = 4096
|
||||
REMEMBER_PREVIEW = 1000
|
||||
|
||||
@CatchException
|
||||
def handle_document_upload(files: List[str], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker):
|
||||
"""
|
||||
Handles document uploads by extracting text and adding it to the vector store.
|
||||
"""
|
||||
from llama_index.core import Document
|
||||
from crazy_functions.rag_fns.rag_file_support import extract_text, supports_format
|
||||
user_name = chatbot.get_user()
|
||||
checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
|
||||
|
||||
for file_path in files:
|
||||
try:
|
||||
validate_path_safety(file_path, user_name)
|
||||
text = extract_text(file_path)
|
||||
if text is None:
|
||||
chatbot.append(
|
||||
[f"上传文件: {os.path.basename(file_path)}", f"文件解析失败，无法提取文本内容，请更换文件。失败原因可能为：1.文档格式过于复杂；2. 不支持的文件格式，支持的文件格式后缀有:" + ", ".join(supports_format)])
|
||||
else:
|
||||
chatbot.append(
|
||||
[f"上传文件: {os.path.basename(file_path)}", f"上传文件前50个字符为:{text[:50]}。"])
|
||||
document = Document(text=text, metadata={"source": file_path})
|
||||
rag_worker.add_documents_to_vector_store([document])
|
||||
chatbot.append([f"上传文件: {os.path.basename(file_path)}", "文件已成功添加到知识库。"])
|
||||
except Exception as e:
|
||||
report_exception(chatbot, history, a=f"处理文件: {file_path}", b=str(e))
|
||||
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
|
||||
# Main Q&A function with document upload support
|
||||
@CatchException
|
||||
def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
# import vector store lib
|
||||
VECTOR_STORE_TYPE = "Milvus"
|
||||
if VECTOR_STORE_TYPE == "Milvus":
|
||||
try:
|
||||
from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
|
||||
except:
|
||||
VECTOR_STORE_TYPE = "Simple"
|
||||
if VECTOR_STORE_TYPE == "Simple":
|
||||
from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
|
||||
|
||||
# 1. we retrieve rag worker from global context
|
||||
user_name = chatbot.get_user()
|
||||
checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
|
||||
|
||||
if user_name in RAG_WORKER_REGISTER:
|
||||
rag_worker = RAG_WORKER_REGISTER[user_name]
|
||||
else:
|
||||
rag_worker = RAG_WORKER_REGISTER[user_name] = LlamaIndexRagWorker(
|
||||
user_name,
|
||||
llm_kwargs,
|
||||
checkpoint_dir=checkpoint_dir,
|
||||
auto_load_checkpoint=True
|
||||
)
|
||||
|
||||
current_context = f"{VECTOR_STORE_TYPE} @ {checkpoint_dir}"
|
||||
tip = "提示：输入“清空向量数据库”可以清空RAG向量数据库"
|
||||
|
||||
# 2. Handle special commands
|
||||
if os.path.exists(txt) and os.path.isdir(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
# Extract file paths from the user input
|
||||
# Assuming the user inputs file paths separated by commas after the command
|
||||
file_paths = [f for f in glob.glob(f'{project_folder}/**/*', recursive=True)]
|
||||
chatbot.append([txt, f'正在处理上传的文档 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
yield from handle_document_upload(file_paths, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker)
|
||||
return
|
||||
|
||||
elif txt == "清空向量数据库":
|
||||
chatbot.append([txt, f'正在清空 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
rag_worker.purge_vector_store()
|
||||
yield from update_ui_latest_msg('已清空', chatbot, history, delay=0) # 刷新界面
|
||||
return
|
||||
|
||||
# 3. Normal Q&A processing
|
||||
chatbot.append([txt, f'正在召回知识 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 4. Clip history to reduce token consumption
|
||||
txt_origin = txt
|
||||
|
||||
if len(history) > MAX_HISTORY_ROUND * 2:
|
||||
history = history[-(MAX_HISTORY_ROUND * 2):]
|
||||
txt_clip, history, flags = input_clipping(txt, history, max_token_limit=MAX_CONTEXT_TOKEN_LIMIT, return_clip_flags=True)
|
||||
input_is_clipped_flag = (flags["original_input_len"] != flags["clipped_input_len"])
|
||||
|
||||
# 5. If input is clipped, add input to vector store before retrieve
|
||||
if input_is_clipped_flag:
|
||||
yield from update_ui_latest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0) # 刷新界面
|
||||
# Save input to vector store
|
||||
rag_worker.add_text_to_vector_store(txt_origin)
|
||||
yield from update_ui_latest_msg('向量化完成 ...', chatbot, history, delay=0) # 刷新界面
|
||||
|
||||
if len(txt_origin) > REMEMBER_PREVIEW:
|
||||
HALF = REMEMBER_PREVIEW // 2
|
||||
i_say_to_remember = txt[:HALF] + f" ...\n...(省略{len(txt_origin)-REMEMBER_PREVIEW}字)...\n... " + txt[-HALF:]
|
||||
if (flags["original_input_len"] - flags["clipped_input_len"]) > HALF:
|
||||
txt_clip = txt_clip + f" ...\n...(省略{len(txt_origin)-len(txt_clip)-HALF}字)...\n... " + txt[-HALF:]
|
||||
else:
|
||||
i_say_to_remember = i_say = txt_clip
|
||||
else:
|
||||
i_say_to_remember = i_say = txt_clip
|
||||
|
||||
# 6. Search vector store and build prompts
|
||||
nodes = rag_worker.retrieve_from_store_with_query(i_say)
|
||||
prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
|
||||
# 7. Query language model
|
||||
if len(chatbot) != 0:
|
||||
chatbot.pop(-1) # Pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`
|
||||
|
||||
model_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=prompt,
|
||||
inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=history,
|
||||
sys_prompt=system_prompt,
|
||||
retry_times_at_unknown_error=0
|
||||
)
|
||||
|
||||
# 8. Remember Q&A
|
||||
yield from update_ui_latest_msg(
|
||||
model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...',
|
||||
chatbot, history, delay=0.5
|
||||
)
|
||||
rag_worker.remember_qa(i_say_to_remember, model_say)
|
||||
history.extend([i_say, model_say])
|
||||
|
||||
# 9. Final UI Update
|
||||
yield from update_ui_latest_msg(model_say, chatbot, history, delay=0, msg=tip)
|
||||
@@ -1,167 +0,0 @@
|
||||
import pickle, os, random
|
||||
from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_latest_msg
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
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.json_fns.select_tool import structure_output, select_tool
|
||||
from pydantic import BaseModel, Field
|
||||
from loguru import logger
|
||||
from typing import List
|
||||
|
||||
|
||||
SOCIAL_NETWORK_WORKER_REGISTER = {}
|
||||
|
||||
class SocialNetwork():
|
||||
def __init__(self):
|
||||
self.people = []
|
||||
|
||||
class SaveAndLoad():
|
||||
def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
|
||||
self.user_name = user_name
|
||||
self.checkpoint_dir = checkpoint_dir
|
||||
if auto_load_checkpoint:
|
||||
self.social_network = self.load_from_checkpoint(checkpoint_dir)
|
||||
else:
|
||||
self.social_network = SocialNetwork()
|
||||
|
||||
def does_checkpoint_exist(self, checkpoint_dir=None):
|
||||
import os, glob
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if not os.path.exists(checkpoint_dir): return False
|
||||
if len(glob.glob(os.path.join(checkpoint_dir, "social_network.pkl"))) == 0: return False
|
||||
return True
|
||||
|
||||
def save_to_checkpoint(self, checkpoint_dir=None):
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
with open(os.path.join(checkpoint_dir, 'social_network.pkl'), "wb+") as f:
|
||||
pickle.dump(self.social_network, f)
|
||||
return
|
||||
|
||||
def load_from_checkpoint(self, checkpoint_dir=None):
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
|
||||
with open(os.path.join(checkpoint_dir, 'social_network.pkl'), "rb") as f:
|
||||
social_network = pickle.load(f)
|
||||
return social_network
|
||||
else:
|
||||
return SocialNetwork()
|
||||
|
||||
|
||||
class Friend(BaseModel):
|
||||
friend_name: str = Field(description="name of a friend")
|
||||
friend_description: str = Field(description="description of a friend (everything about this friend)")
|
||||
friend_relationship: str = Field(description="The relationship with a friend (e.g. friend, family, colleague)")
|
||||
|
||||
class FriendList(BaseModel):
|
||||
friends_list: List[Friend] = Field(description="The list of friends")
|
||||
|
||||
|
||||
class SocialNetworkWorker(SaveAndLoad):
|
||||
def ai_socail_advice(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
|
||||
pass
|
||||
|
||||
def ai_remove_friend(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
|
||||
pass
|
||||
|
||||
def ai_list_friends(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
|
||||
pass
|
||||
|
||||
def ai_add_multi_friends(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
|
||||
friend, err_msg = structure_output(
|
||||
txt=prompt,
|
||||
prompt="根据提示, 解析多个联系人的身份信息\n\n",
|
||||
err_msg=f"不能理解该联系人",
|
||||
run_gpt_fn=run_gpt_fn,
|
||||
pydantic_cls=FriendList
|
||||
)
|
||||
if friend.friends_list:
|
||||
for f in friend.friends_list:
|
||||
self.add_friend(f)
|
||||
msg = f"成功添加{len(friend.friends_list)}个联系人: {str(friend.friends_list)}"
|
||||
yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=0)
|
||||
|
||||
|
||||
def run(self, txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
prompt = txt
|
||||
run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
|
||||
self.tools_to_select = {
|
||||
"SocialAdvice":{
|
||||
"explain_to_llm": "如果用户希望获取社交指导，调用SocialAdvice生成一些社交建议",
|
||||
"callback": self.ai_socail_advice,
|
||||
},
|
||||
"AddFriends":{
|
||||
"explain_to_llm": "如果用户给出了联系人，调用AddMultiFriends把联系人添加到数据库",
|
||||
"callback": self.ai_add_multi_friends,
|
||||
},
|
||||
"RemoveFriend":{
|
||||
"explain_to_llm": "如果用户希望移除某个联系人，调用RemoveFriend",
|
||||
"callback": self.ai_remove_friend,
|
||||
},
|
||||
"ListFriends":{
|
||||
"explain_to_llm": "如果用户列举联系人，调用ListFriends",
|
||||
"callback": self.ai_list_friends,
|
||||
}
|
||||
}
|
||||
|
||||
try:
|
||||
Explanation = '\n'.join([f'{k}: {v["explain_to_llm"]}' for k, v in self.tools_to_select.items()])
|
||||
class UserSociaIntention(BaseModel):
|
||||
intention_type: str = Field(
|
||||
description=
|
||||
f"The type of user intention. You must choose from {self.tools_to_select.keys()}.\n\n"
|
||||
f"Explanation:\n{Explanation}",
|
||||
default="SocialAdvice"
|
||||
)
|
||||
pydantic_cls_instance, err_msg = select_tool(
|
||||
prompt=txt,
|
||||
run_gpt_fn=run_gpt_fn,
|
||||
pydantic_cls=UserSociaIntention
|
||||
)
|
||||
except Exception as e:
|
||||
yield from update_ui_latest_msg(
|
||||
lastmsg=f"无法理解用户意图 {err_msg}",
|
||||
chatbot=chatbot,
|
||||
history=history,
|
||||
delay=0
|
||||
)
|
||||
return
|
||||
|
||||
intention_type = pydantic_cls_instance.intention_type
|
||||
intention_callback = self.tools_to_select[pydantic_cls_instance.intention_type]['callback']
|
||||
yield from intention_callback(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type)
|
||||
|
||||
|
||||
def add_friend(self, friend):
|
||||
# check whether the friend is already in the social network
|
||||
for f in self.social_network.people:
|
||||
if f.friend_name == friend.friend_name:
|
||||
f.friend_description = friend.friend_description
|
||||
f.friend_relationship = friend.friend_relationship
|
||||
logger.info(f"Repeated friend, update info: {friend}")
|
||||
return
|
||||
logger.info(f"Add a new friend: {friend}")
|
||||
self.social_network.people.append(friend)
|
||||
return
|
||||
|
||||
|
||||
@CatchException
|
||||
def I人助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
# 1. we retrieve worker from global context
|
||||
user_name = chatbot.get_user()
|
||||
checkpoint_dir=get_log_folder(user_name, plugin_name='experimental_rag')
|
||||
if user_name in SOCIAL_NETWORK_WORKER_REGISTER:
|
||||
social_network_worker = SOCIAL_NETWORK_WORKER_REGISTER[user_name]
|
||||
else:
|
||||
social_network_worker = SOCIAL_NETWORK_WORKER_REGISTER[user_name] = SocialNetworkWorker(
|
||||
user_name,
|
||||
llm_kwargs,
|
||||
checkpoint_dir=checkpoint_dir,
|
||||
auto_load_checkpoint=True
|
||||
)
|
||||
|
||||
# 2. save
|
||||
yield from social_network_worker.run(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
social_network_worker.save_to_checkpoint(checkpoint_dir)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
@@ -1,162 +0,0 @@
|
||||
import os, copy, time
|
||||
from toolbox import CatchException, report_exception, update_ui, zip_result, promote_file_to_downloadzone, update_ui_latest_msg, get_conf, generate_file_link
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.agent_fns.python_comment_agent import PythonCodeComment
|
||||
from crazy_functions.diagram_fns.file_tree import FileNode
|
||||
from crazy_functions.agent_fns.watchdog import WatchDog
|
||||
from shared_utils.advanced_markdown_format import markdown_convertion_for_file
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
|
||||
summary_batch_isolation = True
|
||||
inputs_array = []
|
||||
inputs_show_user_array = []
|
||||
history_array = []
|
||||
sys_prompt_array = []
|
||||
|
||||
assert len(file_manifest) <= 512, "源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。"
|
||||
|
||||
# 建立文件树
|
||||
file_tree_struct = FileNode("root", build_manifest=True)
|
||||
for file_path in file_manifest:
|
||||
file_tree_struct.add_file(file_path, file_path)
|
||||
|
||||
# <第一步，逐个文件分析，多线程>
|
||||
lang = "" if not plugin_kwargs["use_chinese"] else " (you must use Chinese)"
|
||||
for index, fp in enumerate(file_manifest):
|
||||
# 读取文件
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
prefix = ""
|
||||
i_say = prefix + f'Please conclude the following source code at {os.path.relpath(fp, project_folder)} with only one sentence{lang}, the code is:\n```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index+1}/{len(file_manifest)}] 请用一句话对下面的程序文件做一个整体概述: {fp}'
|
||||
# 装载请求内容
|
||||
MAX_TOKEN_SINGLE_FILE = 2560
|
||||
i_say, _ = input_clipping(inputs=i_say, history=[], max_token_limit=MAX_TOKEN_SINGLE_FILE)
|
||||
inputs_array.append(i_say)
|
||||
inputs_show_user_array.append(i_say_show_user)
|
||||
history_array.append([])
|
||||
sys_prompt_array.append(f"You are a software architecture analyst analyzing a source code project. Do not dig into details, tell me what the code is doing in general. Your answer must be short, simple and clear{lang}.")
|
||||
# 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到大模型进行分析
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array = inputs_array,
|
||||
inputs_show_user_array = inputs_show_user_array,
|
||||
history_array = history_array,
|
||||
sys_prompt_array = sys_prompt_array,
|
||||
llm_kwargs = llm_kwargs,
|
||||
chatbot = chatbot,
|
||||
show_user_at_complete = True
|
||||
)
|
||||
|
||||
# <第二步，逐个文件分析，生成带注释文件>
|
||||
tasks = ["" for _ in range(len(file_manifest))]
|
||||
def bark_fn(tasks):
|
||||
for i in range(len(tasks)): tasks[i] = "watchdog is dead"
|
||||
wd = WatchDog(timeout=10, bark_fn=lambda: bark_fn(tasks), interval=3, msg="ThreadWatcher timeout")
|
||||
wd.begin_watch()
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
executor = ThreadPoolExecutor(max_workers=get_conf('DEFAULT_WORKER_NUM'))
|
||||
def _task_multi_threading(i_say, gpt_say, fp, file_tree_struct, index):
|
||||
language = 'Chinese' if plugin_kwargs["use_chinese"] else 'English'
|
||||
def observe_window_update(x):
|
||||
if tasks[index] == "watchdog is dead":
|
||||
raise TimeoutError("ThreadWatcher: watchdog is dead")
|
||||
tasks[index] = x
|
||||
pcc = PythonCodeComment(llm_kwargs, plugin_kwargs, language=language, observe_window_update=observe_window_update)
|
||||
pcc.read_file(path=fp, brief=gpt_say)
|
||||
revised_path, revised_content = pcc.begin_comment_source_code(None, None)
|
||||
file_tree_struct.manifest[fp].revised_path = revised_path
|
||||
file_tree_struct.manifest[fp].revised_content = revised_content
|
||||
# <将结果写回源文件>
|
||||
with open(fp, 'w', encoding='utf-8') as f:
|
||||
f.write(file_tree_struct.manifest[fp].revised_content)
|
||||
# <生成对比html>
|
||||
with open("crazy_functions/agent_fns/python_comment_compare.html", 'r', encoding='utf-8') as f:
|
||||
html_template = f.read()
|
||||
warp = lambda x: "```python\n\n" + x + "\n\n```"
|
||||
from themes.theme import load_dynamic_theme
|
||||
_, advanced_css, _, _ = load_dynamic_theme("Default")
|
||||
html_template = html_template.replace("ADVANCED_CSS", advanced_css)
|
||||
html_template = html_template.replace("REPLACE_CODE_FILE_LEFT", pcc.get_markdown_block_in_html(markdown_convertion_for_file(warp(pcc.original_content))))
|
||||
html_template = html_template.replace("REPLACE_CODE_FILE_RIGHT", pcc.get_markdown_block_in_html(markdown_convertion_for_file(warp(revised_content))))
|
||||
compare_html_path = fp + '.compare.html'
|
||||
file_tree_struct.manifest[fp].compare_html = compare_html_path
|
||||
with open(compare_html_path, 'w', encoding='utf-8') as f:
|
||||
f.write(html_template)
|
||||
tasks[index] = ""
|
||||
|
||||
chatbot.append([None, f"正在处理:"])
|
||||
futures = []
|
||||
index = 0
|
||||
for i_say, gpt_say, fp in zip(gpt_response_collection[0::2], gpt_response_collection[1::2], file_manifest):
|
||||
future = executor.submit(_task_multi_threading, i_say, gpt_say, fp, file_tree_struct, index)
|
||||
index += 1
|
||||
futures.append(future)
|
||||
|
||||
# <第三步，等待任务完成>
|
||||
cnt = 0
|
||||
while True:
|
||||
cnt += 1
|
||||
wd.feed()
|
||||
time.sleep(3)
|
||||
worker_done = [h.done() for h in futures]
|
||||
remain = len(worker_done) - sum(worker_done)
|
||||
|
||||
# <展示已经完成的部分>
|
||||
preview_html_list = []
|
||||
for done, fp in zip(worker_done, file_manifest):
|
||||
if not done: continue
|
||||
if hasattr(file_tree_struct.manifest[fp], 'compare_html'):
|
||||
preview_html_list.append(file_tree_struct.manifest[fp].compare_html)
|
||||
else:
|
||||
logger.error(f"文件: {fp} 的注释结果未能成功")
|
||||
file_links = generate_file_link(preview_html_list)
|
||||
|
||||
yield from update_ui_latest_msg(
|
||||
f"当前任务: <br/>{'<br/>'.join(tasks)}.<br/>" +
|
||||
f"剩余源文件数量: {remain}.<br/>" +
|
||||
f"已完成的文件: {sum(worker_done)}.<br/>" +
|
||||
file_links +
|
||||
"<br/>" +
|
||||
''.join(['.']*(cnt % 10 + 1)
|
||||
), chatbot=chatbot, history=history, delay=0)
|
||||
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
|
||||
if all(worker_done):
|
||||
executor.shutdown()
|
||||
break
|
||||
|
||||
# <第四步，压缩结果>
|
||||
zip_res = zip_result(project_folder)
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
# <END>
|
||||
chatbot.append((None, "所有源文件均已处理完毕。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 注释Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
plugin_kwargs["use_chinese"] = plugin_kwargs.get("use_chinese", False)
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
yield from 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
@@ -1,36 +0,0 @@
|
||||
|
||||
from toolbox import get_conf, update_ui
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
from crazy_functions.SourceCode_Comment import 注释Python项目
|
||||
|
||||
class SourceCodeComment_Wrap(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
"""
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="路径", description="程序路径（上传文件后自动填写）", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
"use_chinese":
|
||||
ArgProperty(title="注释语言", options=["英文", "中文"], default_value="英文", description="无", type="dropdown").model_dump_json(),
|
||||
# "use_emoji":
|
||||
# ArgProperty(title="在注释中使用emoji", options=["禁止", "允许"], default_value="禁止", description="无", type="dropdown").model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
if plugin_kwargs["use_chinese"] == "中文":
|
||||
plugin_kwargs["use_chinese"] = True
|
||||
else:
|
||||
plugin_kwargs["use_chinese"] = False
|
||||
|
||||
yield from 注释Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
@@ -1,204 +0,0 @@
|
||||
import requests
|
||||
import random
|
||||
import time
|
||||
import re
|
||||
import json
|
||||
from bs4 import BeautifulSoup
|
||||
from functools import lru_cache
|
||||
from itertools import zip_longest
|
||||
from check_proxy import check_proxy
|
||||
from toolbox import CatchException, update_ui, get_conf, promote_file_to_downloadzone, update_ui_latest_msg, generate_file_link
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
from request_llms.bridge_all import model_info
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.prompts.internet import SearchOptimizerPrompt, SearchAcademicOptimizerPrompt
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
from textwrap import dedent
|
||||
from loguru import logger
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
class Query(BaseModel):
|
||||
search_keyword: str = Field(description="search query for video resource")
|
||||
|
||||
|
||||
class VideoResource(BaseModel):
|
||||
thought: str = Field(description="analysis of the search results based on the user's query")
|
||||
title: str = Field(description="title of the video")
|
||||
author: str = Field(description="author/uploader of the video")
|
||||
bvid: str = Field(description="unique ID of the video")
|
||||
another_failsafe_bvid: str = Field(description="provide another bvid, the other one is not working")
|
||||
|
||||
|
||||
def get_video_resource(search_keyword):
|
||||
from crazy_functions.media_fns.get_media import search_videos
|
||||
|
||||
# Search for videos and return the first result
|
||||
videos = search_videos(
|
||||
search_keyword
|
||||
)
|
||||
|
||||
# Return the first video if results exist, otherwise return None
|
||||
return videos
|
||||
|
||||
def download_video(bvid, user_name, chatbot, history):
|
||||
# from experimental_mods.get_bilibili_resource import download_bilibili
|
||||
from crazy_functions.media_fns.get_media import download_video
|
||||
# pause a while
|
||||
tic_time = 8
|
||||
for i in range(tic_time):
|
||||
yield from update_ui_latest_msg(
|
||||
lastmsg=f"即将下载音频。等待{tic_time-i}秒后自动继续, 点击“停止”键取消此操作。",
|
||||
chatbot=chatbot, history=[], delay=1)
|
||||
|
||||
# download audio
|
||||
chatbot.append((None, "下载音频, 请稍等...")); yield from update_ui(chatbot=chatbot, history=history)
|
||||
downloaded_files = yield from download_video(bvid, only_audio=True, user_name=user_name, chatbot=chatbot, history=history)
|
||||
|
||||
if len(downloaded_files) == 0:
|
||||
# failed to download audio
|
||||
return []
|
||||
|
||||
# preview
|
||||
preview_list = [promote_file_to_downloadzone(fp) for fp in downloaded_files]
|
||||
file_links = generate_file_link(preview_list)
|
||||
yield from update_ui_latest_msg(f"已完成的文件: <br/>" + file_links, chatbot=chatbot, history=history, delay=0)
|
||||
chatbot.append((None, f"即将下载视频。"))
|
||||
|
||||
# pause a while
|
||||
tic_time = 16
|
||||
for i in range(tic_time):
|
||||
yield from update_ui_latest_msg(
|
||||
lastmsg=f"即将下载视频。等待{tic_time-i}秒后自动继续, 点击“停止”键取消此操作。",
|
||||
chatbot=chatbot, history=[], delay=1)
|
||||
|
||||
# download video
|
||||
chatbot.append((None, "下载视频, 请稍等...")); yield from update_ui(chatbot=chatbot, history=history)
|
||||
downloaded_files_part2 = yield from download_video(bvid, only_audio=False, user_name=user_name, chatbot=chatbot, history=history)
|
||||
|
||||
# preview
|
||||
preview_list = [promote_file_to_downloadzone(fp) for fp in downloaded_files_part2]
|
||||
file_links = generate_file_link(preview_list)
|
||||
yield from update_ui_latest_msg(f"已完成的文件: <br/>" + file_links, chatbot=chatbot, history=history, delay=0)
|
||||
|
||||
# return
|
||||
return downloaded_files + downloaded_files_part2
|
||||
|
||||
|
||||
class Strategy(BaseModel):
|
||||
thought: str = Field(description="analysis of the user's wish, for example, can you recall the name of the resource?")
|
||||
which_methods: str = Field(description="Which method to use to find the necessary information? choose from 'method_1' and 'method_2'.")
|
||||
method_1_search_keywords: str = Field(description="Generate keywords to search the internet if you choose method 1, otherwise empty.")
|
||||
method_2_generate_keywords: str = Field(description="Generate keywords for video download engine if you choose method 2, otherwise empty.")
|
||||
|
||||
|
||||
@CatchException
|
||||
def 多媒体任务(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
user_wish: str = txt
|
||||
# query demos:
|
||||
# - "我想找一首歌，里面有句歌词是“turn your face towards the sun”"
|
||||
# - "一首歌，第一句是红豆生南国"
|
||||
# - "一首音乐，中国航天任务专用的那首"
|
||||
# - "戴森球计划在熔岩星球的音乐"
|
||||
# - "hanser的百变什么精"
|
||||
# - "打大圣残躯时的bgm"
|
||||
# - "渊下宫战斗音乐"
|
||||
|
||||
# 搜索
|
||||
chatbot.append((txt, "检索中, 请稍等..."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
if "跳过联网搜索" not in user_wish:
|
||||
# 结构化生成
|
||||
internet_search_keyword = user_wish
|
||||
|
||||
yield from update_ui_latest_msg(lastmsg=f"发起互联网检索: {internet_search_keyword} ...", chatbot=chatbot, history=[], delay=1)
|
||||
from crazy_functions.Internet_GPT import internet_search_with_analysis_prompt
|
||||
result = yield from internet_search_with_analysis_prompt(
|
||||
prompt=internet_search_keyword,
|
||||
analysis_prompt="请根据搜索结果分析，获取用户需要找的资源的名称、作者、出处等信息。",
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot
|
||||
)
|
||||
|
||||
yield from update_ui_latest_msg(lastmsg=f"互联网检索结论: {result} \n\n 正在生成进一步检索方案 ...", chatbot=chatbot, history=[], delay=1)
|
||||
rf_req = dedent(f"""
|
||||
The user wish to get the following resource:
|
||||
{user_wish}
|
||||
Meanwhile, you can access another expert's opinion on the user's wish:
|
||||
{result}
|
||||
Generate search keywords (less than 5 keywords) for video download engine accordingly.
|
||||
""")
|
||||
else:
|
||||
user_wish = user_wish.replace("跳过联网搜索", "").strip()
|
||||
rf_req = dedent(f"""
|
||||
The user wish to get the following resource:
|
||||
{user_wish}
|
||||
Generate research keywords (less than 5 keywords) accordingly.
|
||||
""")
|
||||
gpt_json_io = GptJsonIO(Query)
|
||||
inputs = rf_req + gpt_json_io.format_instructions
|
||||
run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
|
||||
analyze_res = run_gpt_fn(inputs, "")
|
||||
logger.info(analyze_res)
|
||||
query: Query = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
|
||||
video_engine_keywords = query.search_keyword
|
||||
# 关键词展示
|
||||
chatbot.append((None, f"检索关键词已确认: {video_engine_keywords}。筛选中, 请稍等..."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 获取候选资源
|
||||
candidate_dictionary: dict = get_video_resource(video_engine_keywords)
|
||||
candidate_dictionary_as_str = json.dumps(candidate_dictionary, ensure_ascii=False, indent=4)
|
||||
|
||||
# 展示候选资源
|
||||
candidate_display = "\n".join([f"{i+1}. {it['title']}" for i, it in enumerate(candidate_dictionary)])
|
||||
chatbot.append((None, f"候选:\n\n{candidate_display}"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 结构化生成
|
||||
rf_req_2 = dedent(f"""
|
||||
The user wish to get the following resource:
|
||||
{user_wish}
|
||||
|
||||
Select the most relevant and suitable video resource from the following search results:
|
||||
{candidate_dictionary_as_str}
|
||||
|
||||
Note:
|
||||
1. The first several search video results are more likely to satisfy the user's wish.
|
||||
2. The time duration of the video should be less than 10 minutes.
|
||||
3. You should analyze the search results first, before giving your answer.
|
||||
4. Use Chinese if possible.
|
||||
5. Beside the primary video selection, give a backup video resource `bvid`.
|
||||
""")
|
||||
gpt_json_io = GptJsonIO(VideoResource)
|
||||
inputs = rf_req_2 + gpt_json_io.format_instructions
|
||||
run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
|
||||
analyze_res = run_gpt_fn(inputs, "")
|
||||
logger.info(analyze_res)
|
||||
video_resource: VideoResource = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
|
||||
|
||||
# Display
|
||||
chatbot.append(
|
||||
(None,
|
||||
f"分析：{video_resource.thought}" "<br/>"
|
||||
f"选择: `{video_resource.title}`。" "<br/>"
|
||||
f"作者：{video_resource.author}"
|
||||
)
|
||||
)
|
||||
chatbot.append((None, f"下载中, 请稍等..."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if video_resource and video_resource.bvid:
|
||||
logger.info(video_resource)
|
||||
downloaded = yield from download_video(video_resource.bvid, chatbot.get_user(), chatbot, history)
|
||||
if not downloaded:
|
||||
chatbot.append((None, f"下载失败, 尝试备选 ..."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
downloaded = yield from download_video(video_resource.another_failsafe_bvid, chatbot.get_user(), chatbot, history)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def debug(bvid, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
yield from download_video(bvid, chatbot.get_user(), chatbot, history)
|
||||
@@ -1,5 +1,5 @@
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
|
||||
from toolbox import report_exception, get_log_folder, update_ui_latest_msg, Singleton
|
||||
from toolbox import report_exception, get_log_folder, update_ui_lastest_msg, Singleton
|
||||
from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
|
||||
from crazy_functions.agent_fns.general import AutoGenGeneral
|
||||
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
|
||||
from loguru import logger
|
||||
|
||||
class EchoDemo(PluginMultiprocessManager):
|
||||
def subprocess_worker(self, child_conn):
|
||||
@@ -8,7 +7,7 @@ class EchoDemo(PluginMultiprocessManager):
|
||||
while True:
|
||||
msg = self.child_conn.recv() # PipeCom
|
||||
if msg.cmd == "user_input":
|
||||
# wait father user input
|
||||
# wait futher user input
|
||||
self.child_conn.send(PipeCom("show", msg.content))
|
||||
wait_success = self.subprocess_worker_wait_user_feedback(wait_msg="我准备好处理下一个问题了.")
|
||||
if not wait_success:
|
||||
@@ -17,4 +16,4 @@ class EchoDemo(PluginMultiprocessManager):
|
||||
elif msg.cmd == "terminate":
|
||||
self.child_conn.send(PipeCom("done", ""))
|
||||
break
|
||||
logger.info('[debug] subprocess_worker terminated')
|
||||
print('[debug] subprocess_worker terminated')
|
||||
@@ -27,7 +27,7 @@ def gpt_academic_generate_oai_reply(
|
||||
llm_kwargs=llm_config,
|
||||
history=history,
|
||||
sys_prompt=self._oai_system_message[0]['content'],
|
||||
console_silence=True
|
||||
console_slience=True
|
||||
)
|
||||
assumed_done = reply.endswith('\nTERMINATE')
|
||||
return True, reply
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
from toolbox import get_log_folder, update_ui, gen_time_str, get_conf, promote_file_to_downloadzone
|
||||
from crazy_functions.agent_fns.watchdog import WatchDog
|
||||
from loguru import logger
|
||||
import time, os
|
||||
|
||||
class PipeCom:
|
||||
@@ -48,7 +47,7 @@ class PluginMultiprocessManager:
|
||||
def terminate(self):
|
||||
self.p.terminate()
|
||||
self.alive = False
|
||||
logger.info("[debug] instance terminated")
|
||||
print("[debug] instance terminated")
|
||||
|
||||
def subprocess_worker(self, child_conn):
|
||||
# ⭐⭐ run in subprocess
|
||||
|
||||
@@ -1,457 +0,0 @@
|
||||
import datetime
|
||||
import re
|
||||
import os
|
||||
from loguru import logger
|
||||
from textwrap import dedent
|
||||
from toolbox import CatchException, update_ui
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
# TODO: 解决缩进问题
|
||||
|
||||
find_function_end_prompt = '''
|
||||
Below is a page of code that you need to read. This page may not yet complete, you job is to split this page to separate functions, class functions etc.
|
||||
- Provide the line number where the first visible function ends.
|
||||
- Provide the line number where the next visible function begins.
|
||||
- If there are no other functions in this page, you should simply return the line number of the last line.
|
||||
- Only focus on functions declared by `def` keyword. Ignore inline functions. Ignore function calls.
|
||||
|
||||
------------------ Example ------------------
|
||||
INPUT:
|
||||
|
||||
```
|
||||
L0000 |import sys
|
||||
L0001 |import re
|
||||
L0002 |
|
||||
L0003 |def trimmed_format_exc():
|
||||
L0004 | import os
|
||||
L0005 | import traceback
|
||||
L0006 | str = traceback.format_exc()
|
||||
L0007 | current_path = os.getcwd()
|
||||
L0008 | replace_path = "."
|
||||
L0009 | return str.replace(current_path, replace_path)
|
||||
L0010 |
|
||||
L0011 |
|
||||
L0012 |def trimmed_format_exc_markdown():
|
||||
L0013 | ...
|
||||
L0014 | ...
|
||||
```
|
||||
|
||||
OUTPUT:
|
||||
|
||||
```
|
||||
<first_function_end_at>L0009</first_function_end_at>
|
||||
<next_function_begin_from>L0012</next_function_begin_from>
|
||||
```
|
||||
|
||||
------------------ End of Example ------------------
|
||||
|
||||
|
||||
------------------ the real INPUT you need to process NOW ------------------
|
||||
```
|
||||
{THE_TAGGED_CODE}
|
||||
```
|
||||
'''
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
revise_function_prompt = '''
|
||||
You need to read the following code, and revise the source code ({FILE_BASENAME}) according to following instructions:
|
||||
1. You should analyze the purpose of the functions (if there are any).
|
||||
2. You need to add docstring for the provided functions (if there are any).
|
||||
|
||||
Be aware:
|
||||
1. You must NOT modify the indent of code.
|
||||
2. You are NOT authorized to change or translate non-comment code, and you are NOT authorized to add empty lines either, toggle qu.
|
||||
3. Use {LANG} to add comments and docstrings. Do NOT translate Chinese that is already in the code.
|
||||
4. Besides adding a docstring, use the ⭐ symbol to annotate the most core and important line of code within the function, explaining its role.
|
||||
|
||||
------------------ Example ------------------
|
||||
INPUT:
|
||||
```
|
||||
L0000 |
|
||||
L0001 |def zip_result(folder):
|
||||
L0002 | t = gen_time_str()
|
||||
L0003 | zip_folder(folder, get_log_folder(), f"result.zip")
|
||||
L0004 | return os.path.join(get_log_folder(), f"result.zip")
|
||||
L0005 |
|
||||
L0006 |
|
||||
```
|
||||
|
||||
OUTPUT:
|
||||
|
||||
<instruction_1_purpose>
|
||||
This function compresses a given folder, and return the path of the resulting `zip` file.
|
||||
</instruction_1_purpose>
|
||||
<instruction_2_revised_code>
|
||||
```
|
||||
def zip_result(folder):
|
||||
"""
|
||||
Compresses the specified folder into a zip file and stores it in the log folder.
|
||||
|
||||
Args:
|
||||
folder (str): The path to the folder that needs to be compressed.
|
||||
|
||||
Returns:
|
||||
str: The path to the created zip file in the log folder.
|
||||
"""
|
||||
t = gen_time_str()
|
||||
zip_folder(folder, get_log_folder(), f"result.zip") # ⭐ Execute the zipping of folder
|
||||
return os.path.join(get_log_folder(), f"result.zip")
|
||||
```
|
||||
</instruction_2_revised_code>
|
||||
------------------ End of Example ------------------
|
||||
|
||||
|
||||
------------------ the real INPUT you need to process NOW ({FILE_BASENAME}) ------------------
|
||||
```
|
||||
{THE_CODE}
|
||||
```
|
||||
{INDENT_REMINDER}
|
||||
{BRIEF_REMINDER}
|
||||
{HINT_REMINDER}
|
||||
'''
|
||||
|
||||
|
||||
revise_function_prompt_chinese = '''
|
||||
您需要阅读以下代码，并根据以下说明修订源代码({FILE_BASENAME}):
|
||||
1. 如果源代码中包含函数的话, 你应该分析给定函数实现了什么功能
|
||||
2. 如果源代码中包含函数的话, 你需要为函数添加docstring, docstring必须使用中文
|
||||
|
||||
请注意：
|
||||
1. 你不得修改代码的缩进
|
||||
2. 你无权更改或翻译代码中的非注释部分，也不允许添加空行
|
||||
3. 使用 {LANG} 添加注释和文档字符串。不要翻译代码中已有的中文
|
||||
4. 除了添加docstring之外, 使用⭐符号给该函数中最核心、最重要的一行代码添加注释，并说明其作用
|
||||
|
||||
------------------ 示例 ------------------
|
||||
INPUT:
|
||||
```
|
||||
L0000 |
|
||||
L0001 |def zip_result(folder):
|
||||
L0002 | t = gen_time_str()
|
||||
L0003 | zip_folder(folder, get_log_folder(), f"result.zip")
|
||||
L0004 | return os.path.join(get_log_folder(), f"result.zip")
|
||||
L0005 |
|
||||
L0006 |
|
||||
```
|
||||
|
||||
OUTPUT:
|
||||
|
||||
<instruction_1_purpose>
|
||||
该函数用于压缩指定文件夹，并返回生成的`zip`文件的路径。
|
||||
</instruction_1_purpose>
|
||||
<instruction_2_revised_code>
|
||||
```
|
||||
def zip_result(folder):
|
||||
"""
|
||||
该函数将指定的文件夹压缩成ZIP文件, 并将其存储在日志文件夹中。
|
||||
|
||||
输入参数:
|
||||
folder (str): 需要压缩的文件夹的路径。
|
||||
返回值:
|
||||
str: 日志文件夹中创建的ZIP文件的路径。
|
||||
"""
|
||||
t = gen_time_str()
|
||||
zip_folder(folder, get_log_folder(), f"result.zip") # ⭐ 执行文件夹的压缩
|
||||
return os.path.join(get_log_folder(), f"result.zip")
|
||||
```
|
||||
</instruction_2_revised_code>
|
||||
------------------ End of Example ------------------
|
||||
|
||||
|
||||
------------------ the real INPUT you need to process NOW ({FILE_BASENAME}) ------------------
|
||||
```
|
||||
{THE_CODE}
|
||||
```
|
||||
{INDENT_REMINDER}
|
||||
{BRIEF_REMINDER}
|
||||
{HINT_REMINDER}
|
||||
'''
|
||||
|
||||
|
||||
class PythonCodeComment():
|
||||
|
||||
def __init__(self, llm_kwargs, plugin_kwargs, language, observe_window_update) -> None:
|
||||
self.original_content = ""
|
||||
self.full_context = []
|
||||
self.full_context_with_line_no = []
|
||||
self.current_page_start = 0
|
||||
self.page_limit = 100 # 100 lines of code each page
|
||||
self.ignore_limit = 20
|
||||
self.llm_kwargs = llm_kwargs
|
||||
self.plugin_kwargs = plugin_kwargs
|
||||
self.language = language
|
||||
self.observe_window_update = observe_window_update
|
||||
if self.language == "chinese":
|
||||
self.core_prompt = revise_function_prompt_chinese
|
||||
else:
|
||||
self.core_prompt = revise_function_prompt
|
||||
self.path = None
|
||||
self.file_basename = None
|
||||
self.file_brief = ""
|
||||
|
||||
def generate_tagged_code_from_full_context(self):
|
||||
for i, code in enumerate(self.full_context):
|
||||
number = i
|
||||
padded_number = f"{number:04}"
|
||||
result = f"L{padded_number}"
|
||||
self.full_context_with_line_no.append(f"{result} | {code}")
|
||||
return self.full_context_with_line_no
|
||||
|
||||
def read_file(self, path, brief):
|
||||
with open(path, 'r', encoding='utf8') as f:
|
||||
self.full_context = f.readlines()
|
||||
self.original_content = ''.join(self.full_context)
|
||||
self.file_basename = os.path.basename(path)
|
||||
self.file_brief = brief
|
||||
self.full_context_with_line_no = self.generate_tagged_code_from_full_context()
|
||||
self.path = path
|
||||
|
||||
def find_next_function_begin(self, tagged_code:list, begin_and_end):
|
||||
begin, end = begin_and_end
|
||||
THE_TAGGED_CODE = ''.join(tagged_code)
|
||||
self.llm_kwargs['temperature'] = 0
|
||||
result = predict_no_ui_long_connection(
|
||||
inputs=find_function_end_prompt.format(THE_TAGGED_CODE=THE_TAGGED_CODE),
|
||||
llm_kwargs=self.llm_kwargs,
|
||||
history=[],
|
||||
sys_prompt="",
|
||||
observe_window=[],
|
||||
console_silence=True
|
||||
)
|
||||
|
||||
def extract_number(text):
|
||||
# 使用正则表达式匹配模式
|
||||
match = re.search(r'<next_function_begin_from>L(\d+)</next_function_begin_from>', text)
|
||||
if match:
|
||||
# 提取匹配的数字部分并转换为整数
|
||||
return int(match.group(1))
|
||||
return None
|
||||
|
||||
line_no = extract_number(result)
|
||||
if line_no is not None:
|
||||
return line_no
|
||||
else:
|
||||
return end
|
||||
|
||||
def _get_next_window(self):
|
||||
#
|
||||
current_page_start = self.current_page_start
|
||||
|
||||
if self.current_page_start == len(self.full_context) + 1:
|
||||
raise StopIteration
|
||||
|
||||
# 如果剩余的行数非常少，一鼓作气处理掉
|
||||
if len(self.full_context) - self.current_page_start < self.ignore_limit:
|
||||
future_page_start = len(self.full_context) + 1
|
||||
self.current_page_start = future_page_start
|
||||
return current_page_start, future_page_start
|
||||
|
||||
|
||||
tagged_code = self.full_context_with_line_no[ self.current_page_start: self.current_page_start + self.page_limit]
|
||||
line_no = self.find_next_function_begin(tagged_code, [self.current_page_start, self.current_page_start + self.page_limit])
|
||||
|
||||
if line_no > len(self.full_context) - 5:
|
||||
line_no = len(self.full_context) + 1
|
||||
|
||||
future_page_start = line_no
|
||||
self.current_page_start = future_page_start
|
||||
|
||||
# ! consider eof
|
||||
return current_page_start, future_page_start
|
||||
|
||||
def dedent(self, text):
|
||||
"""Remove any common leading whitespace from every line in `text`.
|
||||
"""
|
||||
# Look for the longest leading string of spaces and tabs common to
|
||||
# all lines.
|
||||
margin = None
|
||||
_whitespace_only_re = re.compile('^[ \t]+$', re.MULTILINE)
|
||||
_leading_whitespace_re = re.compile('(^[ \t]*)(?:[^ \t\n])', re.MULTILINE)
|
||||
text = _whitespace_only_re.sub('', text)
|
||||
indents = _leading_whitespace_re.findall(text)
|
||||
for indent in indents:
|
||||
if margin is None:
|
||||
margin = indent
|
||||
|
||||
# Current line more deeply indented than previous winner:
|
||||
# no change (previous winner is still on top).
|
||||
elif indent.startswith(margin):
|
||||
pass
|
||||
|
||||
# Current line consistent with and no deeper than previous winner:
|
||||
# it's the new winner.
|
||||
elif margin.startswith(indent):
|
||||
margin = indent
|
||||
|
||||
# Find the largest common whitespace between current line and previous
|
||||
# winner.
|
||||
else:
|
||||
for i, (x, y) in enumerate(zip(margin, indent)):
|
||||
if x != y:
|
||||
margin = margin[:i]
|
||||
break
|
||||
|
||||
# sanity check (testing/debugging only)
|
||||
if 0 and margin:
|
||||
for line in text.split("\n"):
|
||||
assert not line or line.startswith(margin), \
|
||||
"line = %r, margin = %r" % (line, margin)
|
||||
|
||||
if margin:
|
||||
text = re.sub(r'(?m)^' + margin, '', text)
|
||||
return text, len(margin)
|
||||
else:
|
||||
return text, 0
|
||||
|
||||
def get_next_batch(self):
|
||||
current_page_start, future_page_start = self._get_next_window()
|
||||
return ''.join(self.full_context[current_page_start: future_page_start]), current_page_start, future_page_start
|
||||
|
||||
def tag_code(self, fn, hint):
|
||||
code = fn
|
||||
_, n_indent = self.dedent(code)
|
||||
indent_reminder = "" if n_indent == 0 else "(Reminder: as you can see, this piece of code has indent made up with {n_indent} whitespace, please preserve them in the OUTPUT.)"
|
||||
brief_reminder = "" if self.file_brief == "" else f"({self.file_basename} abstract: {self.file_brief})"
|
||||
hint_reminder = "" if hint is None else f"(Reminder: do not ignore or modify code such as `{hint}`, provide complete code in the OUTPUT.)"
|
||||
self.llm_kwargs['temperature'] = 0
|
||||
result = predict_no_ui_long_connection(
|
||||
inputs=self.core_prompt.format(
|
||||
LANG=self.language,
|
||||
FILE_BASENAME=self.file_basename,
|
||||
THE_CODE=code,
|
||||
INDENT_REMINDER=indent_reminder,
|
||||
BRIEF_REMINDER=brief_reminder,
|
||||
HINT_REMINDER=hint_reminder
|
||||
),
|
||||
llm_kwargs=self.llm_kwargs,
|
||||
history=[],
|
||||
sys_prompt="",
|
||||
observe_window=[],
|
||||
console_silence=True
|
||||
)
|
||||
|
||||
def get_code_block(reply):
|
||||
import re
|
||||
pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks
|
||||
matches = re.findall(pattern, reply) # find all code blocks in text
|
||||
if len(matches) == 1:
|
||||
return matches[0].strip('python') # code block
|
||||
return None
|
||||
|
||||
code_block = get_code_block(result)
|
||||
if code_block is not None:
|
||||
code_block = self.sync_and_patch(original=code, revised=code_block)
|
||||
return code_block
|
||||
else:
|
||||
return code
|
||||
|
||||
def get_markdown_block_in_html(self, html):
|
||||
from bs4 import BeautifulSoup
|
||||
soup = BeautifulSoup(html, 'lxml')
|
||||
found_list = soup.find_all("div", class_="markdown-body")
|
||||
if found_list:
|
||||
res = found_list[0]
|
||||
return res.prettify()
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def sync_and_patch(self, original, revised):
|
||||
"""Ensure the number of pre-string empty lines in revised matches those in original."""
|
||||
|
||||
def count_leading_empty_lines(s, reverse=False):
|
||||
"""Count the number of leading empty lines in a string."""
|
||||
lines = s.split('\n')
|
||||
if reverse: lines = list(reversed(lines))
|
||||
count = 0
|
||||
for line in lines:
|
||||
if line.strip() == '':
|
||||
count += 1
|
||||
else:
|
||||
break
|
||||
return count
|
||||
|
||||
original_empty_lines = count_leading_empty_lines(original)
|
||||
revised_empty_lines = count_leading_empty_lines(revised)
|
||||
|
||||
if original_empty_lines > revised_empty_lines:
|
||||
additional_lines = '\n' * (original_empty_lines - revised_empty_lines)
|
||||
revised = additional_lines + revised
|
||||
elif original_empty_lines < revised_empty_lines:
|
||||
lines = revised.split('\n')
|
||||
revised = '\n'.join(lines[revised_empty_lines - original_empty_lines:])
|
||||
|
||||
original_empty_lines = count_leading_empty_lines(original, reverse=True)
|
||||
revised_empty_lines = count_leading_empty_lines(revised, reverse=True)
|
||||
|
||||
if original_empty_lines > revised_empty_lines:
|
||||
additional_lines = '\n' * (original_empty_lines - revised_empty_lines)
|
||||
revised = revised + additional_lines
|
||||
elif original_empty_lines < revised_empty_lines:
|
||||
lines = revised.split('\n')
|
||||
revised = '\n'.join(lines[:-(revised_empty_lines - original_empty_lines)])
|
||||
|
||||
return revised
|
||||
|
||||
def begin_comment_source_code(self, chatbot=None, history=None):
|
||||
# from toolbox import update_ui_latest_msg
|
||||
assert self.path is not None
|
||||
assert '.py' in self.path # must be python source code
|
||||
# write_target = self.path + '.revised.py'
|
||||
|
||||
write_content = ""
|
||||
# with open(self.path + '.revised.py', 'w+', encoding='utf8') as f:
|
||||
while True:
|
||||
try:
|
||||
# yield from update_ui_latest_msg(f"({self.file_basename}) 正在读取下一段代码片段:\n", chatbot=chatbot, history=history, delay=0)
|
||||
next_batch, line_no_start, line_no_end = self.get_next_batch()
|
||||
self.observe_window_update(f"正在处理{self.file_basename} - {line_no_start}/{len(self.full_context)}\n")
|
||||
# yield from update_ui_latest_msg(f"({self.file_basename}) 处理代码片段:\n\n{next_batch}", chatbot=chatbot, history=history, delay=0)
|
||||
|
||||
hint = None
|
||||
MAX_ATTEMPT = 2
|
||||
for attempt in range(MAX_ATTEMPT):
|
||||
result = self.tag_code(next_batch, hint)
|
||||
try:
|
||||
successful, hint = self.verify_successful(next_batch, result)
|
||||
except Exception as e:
|
||||
logger.error('ignored exception:\n' + str(e))
|
||||
break
|
||||
if successful:
|
||||
break
|
||||
if attempt == MAX_ATTEMPT - 1:
|
||||
# cannot deal with this, give up
|
||||
result = next_batch
|
||||
break
|
||||
|
||||
# f.write(result)
|
||||
write_content += result
|
||||
except StopIteration:
|
||||
next_batch, line_no_start, line_no_end = [], -1, -1
|
||||
return None, write_content
|
||||
|
||||
def verify_successful(self, original, revised):
|
||||
""" Determine whether the revised code contains every line that already exists
|
||||
"""
|
||||
from crazy_functions.ast_fns.comment_remove import remove_python_comments
|
||||
original = remove_python_comments(original)
|
||||
original_lines = original.split('\n')
|
||||
revised_lines = revised.split('\n')
|
||||
|
||||
for l in original_lines:
|
||||
l = l.strip()
|
||||
if '\'' in l or '\"' in l: continue # ast sometimes toggle " to '
|
||||
found = False
|
||||
for lt in revised_lines:
|
||||
if l in lt:
|
||||
found = True
|
||||
break
|
||||
if not found:
|
||||
return False, l
|
||||
return True, None
|
||||
@@ -1,45 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="zh-CN">
|
||||
<head>
|
||||
<style>ADVANCED_CSS</style>
|
||||
<meta charset="UTF-8">
|
||||
<title>源文件对比</title>
|
||||
<style>
|
||||
body {
|
||||
font-family: Arial, sans-serif;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
height: 100vh;
|
||||
margin: 0;
|
||||
}
|
||||
.container {
|
||||
display: flex;
|
||||
width: 95%;
|
||||
height: -webkit-fill-available;
|
||||
}
|
||||
.code-container {
|
||||
flex: 1;
|
||||
margin: 0px;
|
||||
padding: 0px;
|
||||
border: 1px solid #ccc;
|
||||
background-color: #f9f9f9;
|
||||
overflow: auto;
|
||||
}
|
||||
pre {
|
||||
white-space: pre-wrap;
|
||||
word-wrap: break-word;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<div class="code-container">
|
||||
REPLACE_CODE_FILE_LEFT
|
||||
</div>
|
||||
<div class="code-container">
|
||||
REPLACE_CODE_FILE_RIGHT
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,5 +1,4 @@
|
||||
import threading, time
|
||||
from loguru import logger
|
||||
|
||||
class WatchDog():
|
||||
def __init__(self, timeout, bark_fn, interval=3, msg="") -> None:
|
||||
@@ -14,7 +13,7 @@ class WatchDog():
|
||||
while True:
|
||||
if self.kill_dog: break
|
||||
if time.time() - self.last_feed > self.timeout:
|
||||
if len(self.msg) > 0: logger.info(self.msg)
|
||||
if len(self.msg) > 0: print(self.msg)
|
||||
self.bark_fn()
|
||||
break
|
||||
time.sleep(self.interval)
|
||||
|
||||
@@ -1,54 +0,0 @@
|
||||
import token
|
||||
import tokenize
|
||||
import copy
|
||||
import io
|
||||
|
||||
|
||||
def remove_python_comments(input_source: str) -> str:
|
||||
source_flag = copy.copy(input_source)
|
||||
source = io.StringIO(input_source)
|
||||
ls = input_source.split('\n')
|
||||
prev_toktype = token.INDENT
|
||||
readline = source.readline
|
||||
|
||||
def get_char_index(lineno, col):
|
||||
# find the index of the char in the source code
|
||||
if lineno == 1:
|
||||
return len('\n'.join(ls[:(lineno-1)])) + col
|
||||
else:
|
||||
return len('\n'.join(ls[:(lineno-1)])) + col + 1
|
||||
|
||||
def replace_char_between(start_lineno, start_col, end_lineno, end_col, source, replace_char, ls):
|
||||
# replace char between start_lineno, start_col and end_lineno, end_col with replace_char, but keep '\n' and ' '
|
||||
b = get_char_index(start_lineno, start_col)
|
||||
e = get_char_index(end_lineno, end_col)
|
||||
for i in range(b, e):
|
||||
if source[i] == '\n':
|
||||
source = source[:i] + '\n' + source[i+1:]
|
||||
elif source[i] == ' ':
|
||||
source = source[:i] + ' ' + source[i+1:]
|
||||
else:
|
||||
source = source[:i] + replace_char + source[i+1:]
|
||||
return source
|
||||
|
||||
tokgen = tokenize.generate_tokens(readline)
|
||||
for toktype, ttext, (slineno, scol), (elineno, ecol), ltext in tokgen:
|
||||
if toktype == token.STRING and (prev_toktype == token.INDENT):
|
||||
source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
|
||||
elif toktype == token.STRING and (prev_toktype == token.NEWLINE):
|
||||
source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
|
||||
elif toktype == tokenize.COMMENT:
|
||||
source_flag = replace_char_between(slineno, scol, elineno, ecol, source_flag, ' ', ls)
|
||||
prev_toktype = toktype
|
||||
return source_flag
|
||||
|
||||
|
||||
# 示例使用
|
||||
if __name__ == "__main__":
|
||||
with open("source.py", "r", encoding="utf-8") as f:
|
||||
source_code = f.read()
|
||||
|
||||
cleaned_code = remove_python_comments(source_code)
|
||||
|
||||
with open("cleaned_source.py", "w", encoding="utf-8") as f:
|
||||
f.write(cleaned_code)
|
||||
141
crazy_functions/chatglm微调工具.py
普通文件
141
crazy_functions/chatglm微调工具.py
普通文件
@@ -0,0 +1,141 @@
|
||||
from toolbox import CatchException, update_ui, promote_file_to_downloadzone
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
import datetime, json
|
||||
|
||||
def fetch_items(list_of_items, batch_size):
|
||||
for i in range(0, len(list_of_items), batch_size):
|
||||
yield list_of_items[i:i + batch_size]
|
||||
|
||||
def string_to_options(arguments):
|
||||
import argparse
|
||||
import shlex
|
||||
|
||||
# Create an argparse.ArgumentParser instance
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
# Add command-line arguments
|
||||
parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
|
||||
parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
|
||||
parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
|
||||
parser.add_argument("--batch", type=int, help="System prompt", default=50)
|
||||
parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50)
|
||||
parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2)
|
||||
parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1)
|
||||
parser.add_argument("--json_dataset", type=str, help="json_dataset", default="")
|
||||
parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="")
|
||||
|
||||
|
||||
|
||||
# Parse the arguments
|
||||
args = parser.parse_args(shlex.split(arguments))
|
||||
|
||||
return args
|
||||
|
||||
@CatchException
|
||||
def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
args = plugin_kwargs.get("advanced_arg", None)
|
||||
if args is None:
|
||||
chatbot.append(("没给定指令", "退出"))
|
||||
yield from update_ui(chatbot=chatbot, history=history); return
|
||||
else:
|
||||
arguments = string_to_options(arguments=args)
|
||||
|
||||
dat = []
|
||||
with open(txt, 'r', encoding='utf8') as f:
|
||||
for line in f.readlines():
|
||||
json_dat = json.loads(line)
|
||||
dat.append(json_dat["content"])
|
||||
|
||||
llm_kwargs['llm_model'] = arguments.llm_to_learn
|
||||
for batch in fetch_items(dat, arguments.batch):
|
||||
res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
|
||||
inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history_array=[[] for _ in (batch)],
|
||||
sys_prompt_array=[arguments.system_prompt for _ in (batch)],
|
||||
max_workers=10 # OpenAI所允许的最大并行过载
|
||||
)
|
||||
|
||||
with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
|
||||
for b, r in zip(batch, res[1::2]):
|
||||
f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
|
||||
|
||||
promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
|
||||
return
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
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
|
||||
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
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
import subprocess
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
args = plugin_kwargs.get("advanced_arg", None)
|
||||
if args is None:
|
||||
chatbot.append(("没给定指令", "退出"))
|
||||
yield from update_ui(chatbot=chatbot, history=history); return
|
||||
else:
|
||||
arguments = string_to_options(arguments=args)
|
||||
|
||||
|
||||
|
||||
pre_seq_len = arguments.pre_seq_len # 128
|
||||
learning_rate = arguments.learning_rate # 2e-2
|
||||
num_gpus = arguments.num_gpus # 1
|
||||
json_dataset = arguments.json_dataset # 't_code.json'
|
||||
ptuning_directory = arguments.ptuning_directory # '/home/hmp/ChatGLM2-6B/ptuning'
|
||||
|
||||
command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \
|
||||
--do_train \
|
||||
--train_file AdvertiseGen/{json_dataset} \
|
||||
--validation_file AdvertiseGen/{json_dataset} \
|
||||
--preprocessing_num_workers 20 \
|
||||
--prompt_column content \
|
||||
--response_column summary \
|
||||
--overwrite_cache \
|
||||
--model_name_or_path THUDM/chatglm2-6b \
|
||||
--output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \
|
||||
--overwrite_output_dir \
|
||||
--max_source_length 256 \
|
||||
--max_target_length 256 \
|
||||
--per_device_train_batch_size 1 \
|
||||
--per_device_eval_batch_size 1 \
|
||||
--gradient_accumulation_steps 16 \
|
||||
--predict_with_generate \
|
||||
--max_steps 100 \
|
||||
--logging_steps 10 \
|
||||
--save_steps 20 \
|
||||
--learning_rate {learning_rate} \
|
||||
--pre_seq_len {pre_seq_len} \
|
||||
--quantization_bit 4"
|
||||
|
||||
process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)
|
||||
try:
|
||||
process.communicate(timeout=3600*24)
|
||||
except subprocess.TimeoutExpired:
|
||||
process.kill()
|
||||
return
|
||||
@@ -1,40 +1,25 @@
|
||||
import os
|
||||
import threading
|
||||
from loguru import logger
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
from shared_utils.char_visual_effect import scrolling_visual_effect
|
||||
from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
|
||||
import threading
|
||||
import os
|
||||
import logging
|
||||
|
||||
def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
|
||||
"""
|
||||
当输入文本 + 历史文本超出最大限制时，采取措施丢弃一部分文本。
|
||||
输入：
|
||||
- inputs 本次请求
|
||||
- history 历史上下文
|
||||
- max_token_limit 最大token限制
|
||||
输出:
|
||||
- inputs 本次请求（经过clip）
|
||||
- history 历史上下文（经过clip）
|
||||
"""
|
||||
def input_clipping(inputs, history, max_token_limit):
|
||||
import numpy as np
|
||||
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=()))
|
||||
|
||||
|
||||
mode = 'input-and-history'
|
||||
# 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
|
||||
input_token_num = get_token_num(inputs)
|
||||
original_input_len = len(inputs)
|
||||
if input_token_num < max_token_limit//2:
|
||||
mode = 'only-history'
|
||||
max_token_limit = max_token_limit - input_token_num
|
||||
|
||||
everything = [inputs] if mode == 'input-and-history' else ['']
|
||||
everything.extend(history)
|
||||
full_token_num = n_token = get_token_num('\n'.join(everything))
|
||||
n_token = get_token_num('\n'.join(everything))
|
||||
everything_token = [get_token_num(e) for e in everything]
|
||||
everything_token_num = sum(everything_token)
|
||||
delta = max(everything_token) // 16 # 截断时的颗粒度
|
||||
|
||||
while n_token > max_token_limit:
|
||||
@@ -47,24 +32,10 @@ def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
|
||||
|
||||
if mode == 'input-and-history':
|
||||
inputs = everything[0]
|
||||
full_token_num = everything_token_num
|
||||
else:
|
||||
full_token_num = everything_token_num + input_token_num
|
||||
|
||||
pass
|
||||
history = everything[1:]
|
||||
|
||||
flags = {
|
||||
"mode": mode,
|
||||
"original_input_token_num": input_token_num,
|
||||
"original_full_token_num": full_token_num,
|
||||
"original_input_len": original_input_len,
|
||||
"clipped_input_len": len(inputs),
|
||||
}
|
||||
|
||||
if not return_clip_flags:
|
||||
return inputs, history
|
||||
else:
|
||||
return inputs, history, flags
|
||||
|
||||
def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs, inputs_show_user, llm_kwargs,
|
||||
@@ -134,7 +105,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
except:
|
||||
# 【第三种情况】：其他错误：重试几次
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
logger.error(tb_str)
|
||||
print(tb_str)
|
||||
mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
|
||||
if retry_op > 0:
|
||||
retry_op -= 1
|
||||
@@ -164,31 +135,18 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
yield from update_ui(chatbot=chatbot, history=[]) # 如果最后成功了，则删除报错信息
|
||||
return final_result
|
||||
|
||||
def can_multi_process(llm) -> bool:
|
||||
from request_llms.bridge_all import model_info
|
||||
|
||||
def default_condition(llm) -> bool:
|
||||
# legacy condition
|
||||
def can_multi_process(llm):
|
||||
if llm.startswith('gpt-'): return True
|
||||
if llm.startswith('chatgpt-'): return True
|
||||
if llm.startswith('api2d-'): return True
|
||||
if llm.startswith('azure-'): return True
|
||||
if llm.startswith('spark'): return True
|
||||
if llm.startswith('zhipuai') or llm.startswith('glm-'): return True
|
||||
if llm.startswith('zhipuai'): return True
|
||||
return False
|
||||
|
||||
if llm in model_info:
|
||||
if 'can_multi_thread' in model_info[llm]:
|
||||
return model_info[llm]['can_multi_thread']
|
||||
else:
|
||||
return default_condition(llm)
|
||||
else:
|
||||
return default_condition(llm)
|
||||
|
||||
def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array, inputs_show_user_array, llm_kwargs,
|
||||
chatbot, history_array, sys_prompt_array,
|
||||
refresh_interval=0.2, max_workers=-1, scroller_max_len=75,
|
||||
refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
|
||||
handle_token_exceed=True, show_user_at_complete=False,
|
||||
retry_times_at_unknown_error=2,
|
||||
):
|
||||
@@ -257,7 +215,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
# 【第一种情况】：顺利完成
|
||||
gpt_say = predict_no_ui_long_connection(
|
||||
inputs=inputs, llm_kwargs=llm_kwargs, history=history,
|
||||
sys_prompt=sys_prompt, observe_window=mutable[index], console_silence=True
|
||||
sys_prompt=sys_prompt, observe_window=mutable[index], console_slience=True
|
||||
)
|
||||
mutable[index][2] = "已成功"
|
||||
return gpt_say
|
||||
@@ -285,7 +243,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
# 【第三种情况】：其他错误
|
||||
if detect_timeout(): raise RuntimeError("检测到程序终止。")
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
logger.error(tb_str)
|
||||
print(tb_str)
|
||||
gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
|
||||
if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
|
||||
if retry_op > 0:
|
||||
@@ -313,8 +271,6 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
|
||||
range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
|
||||
cnt = 0
|
||||
|
||||
|
||||
while True:
|
||||
# yield一次以刷新前端页面
|
||||
time.sleep(refresh_interval)
|
||||
@@ -327,7 +283,8 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
mutable[thread_index][1] = time.time()
|
||||
# 在前端打印些好玩的东西
|
||||
for thread_index, _ in enumerate(worker_done):
|
||||
print_something_really_funny = f"[ ...`{scrolling_visual_effect(mutable[thread_index][0], scroller_max_len)}`... ]"
|
||||
print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
|
||||
replace('\n', '').replace('`', '.').replace(' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
|
||||
observe_win.append(print_something_really_funny)
|
||||
# 在前端打印些好玩的东西
|
||||
stat_str = ''.join([f'`{mutable[thread_index][2]}`: {obs}\n\n'
|
||||
@@ -380,7 +337,7 @@ def read_and_clean_pdf_text(fp):
|
||||
import fitz, copy
|
||||
import re
|
||||
import numpy as np
|
||||
# from shared_utils.colorful import print亮黄, print亮绿
|
||||
from colorful import print亮黄, print亮绿
|
||||
fc = 0 # Index 0 文本
|
||||
fs = 1 # Index 1 字体
|
||||
fb = 2 # Index 2 框框
|
||||
@@ -390,11 +347,11 @@ def read_and_clean_pdf_text(fp):
|
||||
"""
|
||||
提取文本块主字体
|
||||
"""
|
||||
fsize_statistics = {}
|
||||
fsize_statiscs = {}
|
||||
for wtf in l['spans']:
|
||||
if wtf['size'] not in fsize_statistics: fsize_statistics[wtf['size']] = 0
|
||||
fsize_statistics[wtf['size']] += len(wtf['text'])
|
||||
return max(fsize_statistics, key=fsize_statistics.get)
|
||||
if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
|
||||
fsize_statiscs[wtf['size']] += len(wtf['text'])
|
||||
return max(fsize_statiscs, key=fsize_statiscs.get)
|
||||
|
||||
def ffsize_same(a,b):
|
||||
"""
|
||||
@@ -434,11 +391,11 @@ def read_and_clean_pdf_text(fp):
|
||||
|
||||
############################## <第 2 步，获取正文主字体> ##################################
|
||||
try:
|
||||
fsize_statistics = {}
|
||||
fsize_statiscs = {}
|
||||
for span in meta_span:
|
||||
if span[1] not in fsize_statistics: fsize_statistics[span[1]] = 0
|
||||
fsize_statistics[span[1]] += span[2]
|
||||
main_fsize = max(fsize_statistics, key=fsize_statistics.get)
|
||||
if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
|
||||
fsize_statiscs[span[1]] += span[2]
|
||||
main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
|
||||
if REMOVE_FOOT_NOTE:
|
||||
give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
|
||||
except:
|
||||
@@ -540,7 +497,7 @@ def read_and_clean_pdf_text(fp):
|
||||
return meta_txt, page_one_meta
|
||||
|
||||
|
||||
def get_files_from_everything(txt, type, chatbot=None): # type='.md'
|
||||
def get_files_from_everything(txt, type): # type='.md'
|
||||
"""
|
||||
这个函数是用来获取指定目录下所有指定类型（如.md）的文件，并且对于网络上的文件，也可以获取它。
|
||||
下面是对每个参数和返回值的说明：
|
||||
@@ -552,7 +509,6 @@ def get_files_from_everything(txt, type, chatbot=None): # type='.md'
|
||||
- file_manifest: 文件路径列表，里面包含以指定类型为后缀名的所有文件的绝对路径。
|
||||
- project_folder: 字符串，表示文件所在的文件夹路径。如果是网络上的文件，就是临时文件夹的路径。
|
||||
该函数详细注释已添加，请确认是否满足您的需要。
|
||||
- chatbot 带Cookies的Chatbot类，为实现更多强大的功能做基础
|
||||
"""
|
||||
import glob, os
|
||||
|
||||
@@ -575,13 +531,9 @@ def get_files_from_everything(txt, type, chatbot=None): # type='.md'
|
||||
# 直接给定文件
|
||||
file_manifest = [txt]
|
||||
project_folder = os.path.dirname(txt)
|
||||
if chatbot is not None:
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
elif os.path.exists(txt):
|
||||
# 本地路径，递归搜索
|
||||
project_folder = txt
|
||||
if chatbot is not None:
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*'+type, recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
success = False
|
||||
@@ -602,23 +554,23 @@ class nougat_interface():
|
||||
def nougat_with_timeout(self, command, cwd, timeout=3600):
|
||||
import subprocess
|
||||
from toolbox import ProxyNetworkActivate
|
||||
logger.info(f'正在执行命令 {command}')
|
||||
logging.info(f'正在执行命令 {command}')
|
||||
with ProxyNetworkActivate("Nougat_Download"):
|
||||
process = subprocess.Popen(command, shell=False, cwd=cwd, env=os.environ)
|
||||
process = subprocess.Popen(command, shell=True, cwd=cwd, env=os.environ)
|
||||
try:
|
||||
stdout, stderr = process.communicate(timeout=timeout)
|
||||
except subprocess.TimeoutExpired:
|
||||
process.kill()
|
||||
stdout, stderr = process.communicate()
|
||||
logger.error("Process timed out!")
|
||||
print("Process timed out!")
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def NOUGAT_parse_pdf(self, fp, chatbot, history):
|
||||
from toolbox import update_ui_latest_msg
|
||||
from toolbox import update_ui_lastest_msg
|
||||
|
||||
yield from update_ui_latest_msg("正在解析论文, 请稍候。进度：正在排队, 等待线程锁...",
|
||||
yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在排队, 等待线程锁...",
|
||||
chatbot=chatbot, history=history, delay=0)
|
||||
self.threadLock.acquire()
|
||||
import glob, threading, os
|
||||
@@ -626,10 +578,9 @@ class nougat_interface():
|
||||
dst = os.path.join(get_log_folder(plugin_name='nougat'), gen_time_str())
|
||||
os.makedirs(dst)
|
||||
|
||||
yield from update_ui_latest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
|
||||
yield from update_ui_lastest_msg("正在解析论文, 请稍候。进度：正在加载NOUGAT... （提示：首次运行需要花费较长时间下载NOUGAT参数）",
|
||||
chatbot=chatbot, history=history, delay=0)
|
||||
command = ['nougat', '--out', os.path.abspath(dst), os.path.abspath(fp)]
|
||||
self.nougat_with_timeout(command, cwd=os.getcwd(), timeout=3600)
|
||||
self.nougat_with_timeout(f'nougat --out "{os.path.abspath(dst)}" "{os.path.abspath(fp)}"', os.getcwd(), timeout=3600)
|
||||
res = glob.glob(os.path.join(dst,'*.mmd'))
|
||||
if len(res) == 0:
|
||||
self.threadLock.release()
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
import os
|
||||
from textwrap import indent
|
||||
from loguru import logger
|
||||
|
||||
class FileNode:
|
||||
def __init__(self, name, build_manifest=False):
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
self.children = []
|
||||
self.is_leaf = False
|
||||
@@ -11,8 +10,6 @@ class FileNode:
|
||||
self.parenting_ship = []
|
||||
self.comment = ""
|
||||
self.comment_maxlen_show = 50
|
||||
self.build_manifest = build_manifest
|
||||
self.manifest = {}
|
||||
|
||||
@staticmethod
|
||||
def add_linebreaks_at_spaces(string, interval=10):
|
||||
@@ -32,7 +29,6 @@ class FileNode:
|
||||
level = 1
|
||||
if directory_names == "":
|
||||
new_node = FileNode(file_name)
|
||||
self.manifest[file_path] = new_node
|
||||
current_node.children.append(new_node)
|
||||
new_node.is_leaf = True
|
||||
new_node.comment = self.sanitize_comment(file_comment)
|
||||
@@ -54,14 +50,13 @@ class FileNode:
|
||||
new_node.level = level - 1
|
||||
current_node = new_node
|
||||
term = FileNode(file_name)
|
||||
self.manifest[file_path] = term
|
||||
term.level = level
|
||||
term.comment = self.sanitize_comment(file_comment)
|
||||
term.is_leaf = True
|
||||
current_node.children.append(term)
|
||||
|
||||
def print_files_recursively(self, level=0, code="R0"):
|
||||
logger.info(' '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
|
||||
print(' '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
|
||||
for j, child in enumerate(self.children):
|
||||
child.print_files_recursively(level=level+1, code=code+str(j))
|
||||
self.parenting_ship.extend(child.parenting_ship)
|
||||
@@ -124,4 +119,4 @@ if __name__ == "__main__":
|
||||
"用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器",
|
||||
"包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类",
|
||||
]
|
||||
logger.info(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))
|
||||
print(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))
|
||||
@@ -1,4 +1,4 @@
|
||||
from toolbox import CatchException, update_ui, update_ui_latest_msg
|
||||
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
|
||||
@@ -13,7 +13,7 @@ class MiniGame_ASCII_Art(GptAcademicGameBaseState):
|
||||
else:
|
||||
if prompt.strip() == 'exit':
|
||||
self.delete_game = True
|
||||
yield from update_ui_latest_msg(lastmsg=f"谜底是{self.obj}，游戏结束。", chatbot=chatbot, history=history, delay=0.)
|
||||
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)
|
||||
@@ -31,12 +31,12 @@ class MiniGame_ASCII_Art(GptAcademicGameBaseState):
|
||||
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_latest_msg(lastmsg=res, chatbot=chatbot, history=history, delay=0.)
|
||||
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_latest_msg(lastmsg="你猜对了！", chatbot=chatbot, history=history, delay=0.)
|
||||
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_latest_msg(lastmsg="猜错了，再试试，输入“exit”获取答案。", chatbot=chatbot, history=history, delay=0.)
|
||||
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.)
|
||||
@@ -63,7 +63,7 @@ prompts_terminate = """小说的前文回顾：
|
||||
"""
|
||||
|
||||
|
||||
from toolbox import CatchException, update_ui, update_ui_latest_msg
|
||||
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
|
||||
@@ -92,7 +92,7 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
|
||||
def generate_story_image(self, story_paragraph):
|
||||
try:
|
||||
from crazy_functions.Image_Generate import gen_image
|
||||
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>'
|
||||
@@ -112,7 +112,7 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
if prompt.strip() == 'exit' or prompt.strip() == '结束剧情':
|
||||
# should we terminate game here?
|
||||
self.delete_game = True
|
||||
yield from update_ui_latest_msg(lastmsg=f"游戏结束。", chatbot=chatbot, history=history, delay=0.)
|
||||
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'
|
||||
@@ -137,8 +137,8 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
)
|
||||
self.story.append(story_paragraph)
|
||||
# # 配图
|
||||
yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
|
||||
yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
|
||||
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 = ""
|
||||
@@ -171,8 +171,8 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
)
|
||||
self.story.append(story_paragraph)
|
||||
# # 配图
|
||||
yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
|
||||
yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
|
||||
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 = ""
|
||||
@@ -204,8 +204,8 @@ class MiniGame_ResumeStory(GptAcademicGameBaseState):
|
||||
chatbot, history_, self.sys_prompt_
|
||||
)
|
||||
# # 配图
|
||||
yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>正在生成插图中 ...', chatbot=chatbot, history=history, delay=0.)
|
||||
yield from update_ui_latest_msg(lastmsg=story_paragraph + '<br/>'+ self.generate_story_image(story_paragraph), chatbot=chatbot, history=history, delay=0.)
|
||||
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
|
||||
|
||||
@@ -2,7 +2,7 @@ import time
|
||||
import importlib
|
||||
from toolbox import trimmed_format_exc, gen_time_str, get_log_folder
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
|
||||
from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_latest_msg
|
||||
from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
|
||||
import multiprocessing
|
||||
|
||||
def get_class_name(class_string):
|
||||
|
||||
@@ -24,8 +24,8 @@ class Actor(BaseModel):
|
||||
film_names: List[str] = Field(description="list of names of films they starred in")
|
||||
"""
|
||||
|
||||
import json, re
|
||||
from loguru import logger as logging
|
||||
import json, re, logging
|
||||
|
||||
|
||||
PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
|
||||
|
||||
@@ -62,8 +62,8 @@ class GptJsonIO():
|
||||
if "type" in reduced_schema:
|
||||
del reduced_schema["type"]
|
||||
# Ensure json in context is well-formed with double quotes.
|
||||
schema_str = json.dumps(reduced_schema)
|
||||
if self.example_instruction:
|
||||
schema_str = json.dumps(reduced_schema)
|
||||
return PYDANTIC_FORMAT_INSTRUCTIONS.format(schema=schema_str)
|
||||
else:
|
||||
return PYDANTIC_FORMAT_INSTRUCTIONS_SIMPLE.format(schema=schema_str)
|
||||
@@ -102,10 +102,10 @@ class GptJsonIO():
|
||||
logging.info(f'Repairing json：{response}')
|
||||
repair_prompt = self.generate_repair_prompt(broken_json = response, error=repr(e))
|
||||
result = self.generate_output(gpt_gen_fn(repair_prompt, self.format_instructions))
|
||||
logging.info('Repair json success.')
|
||||
logging.info('Repaire json success.')
|
||||
except Exception as e:
|
||||
# 没辙了，放弃治疗
|
||||
logging.info('Repair json fail.')
|
||||
logging.info('Repaire json fail.')
|
||||
raise JsonStringError('Cannot repair json.', str(e))
|
||||
return result
|
||||
|
||||
|
||||
@@ -1,26 +0,0 @@
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
|
||||
def structure_output(txt, prompt, err_msg, run_gpt_fn, pydantic_cls):
|
||||
gpt_json_io = GptJsonIO(pydantic_cls)
|
||||
analyze_res = run_gpt_fn(
|
||||
txt,
|
||||
sys_prompt=prompt + gpt_json_io.format_instructions
|
||||
)
|
||||
try:
|
||||
friend = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
|
||||
except JsonStringError as e:
|
||||
return None, err_msg
|
||||
|
||||
err_msg = ""
|
||||
return friend, err_msg
|
||||
|
||||
|
||||
def select_tool(prompt, run_gpt_fn, pydantic_cls):
|
||||
pydantic_cls_instance, err_msg = structure_output(
|
||||
txt=prompt,
|
||||
prompt="根据提示, 分析应该调用哪个工具函数\n\n",
|
||||
err_msg=f"不能理解该联系人",
|
||||
run_gpt_fn=run_gpt_fn,
|
||||
pydantic_cls=pydantic_cls
|
||||
)
|
||||
return pydantic_cls_instance, err_msg
|
||||
@@ -1,17 +1,14 @@
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
from toolbox import update_ui, update_ui_latest_msg, get_log_folder, gen_time_str
|
||||
from toolbox import get_conf, promote_file_to_downloadzone
|
||||
from crazy_functions.latex_fns.latex_toolbox import PRESERVE, TRANSFORM
|
||||
from crazy_functions.latex_fns.latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
||||
from crazy_functions.latex_fns.latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
|
||||
from crazy_functions.latex_fns.latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
|
||||
from crazy_functions.latex_fns.latex_toolbox import find_title_and_abs
|
||||
from crazy_functions.latex_fns.latex_pickle_io import objdump, objload
|
||||
from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
|
||||
from toolbox import get_conf, objdump, objload, promote_file_to_downloadzone
|
||||
from .latex_toolbox import PRESERVE, TRANSFORM
|
||||
from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
||||
from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
|
||||
from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
|
||||
from .latex_toolbox import find_title_and_abs
|
||||
|
||||
import os, shutil
|
||||
import re
|
||||
import numpy as np
|
||||
|
||||
pj = os.path.join
|
||||
|
||||
@@ -20,7 +17,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
|
||||
"""
|
||||
break down latex file to a linked list,
|
||||
each node use a preserve flag to indicate whether it should
|
||||
be processed by GPT.
|
||||
be proccessed by GPT.
|
||||
"""
|
||||
text = txt
|
||||
mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
|
||||
@@ -85,14 +82,14 @@ class LatexPaperSplit():
|
||||
"""
|
||||
break down latex file to a linked list,
|
||||
each node use a preserve flag to indicate whether it should
|
||||
be processed by GPT.
|
||||
be proccessed by GPT.
|
||||
"""
|
||||
def __init__(self) -> None:
|
||||
self.nodes = None
|
||||
self.msg = "*{\\scriptsize\\textbf{警告：该PDF由GPT-Academic开源项目调用大语言模型+Latex翻译插件一键生成，" + \
|
||||
"版权归原文作者所有。翻译内容可靠性无保障，请仔细鉴别并以原文为准。" + \
|
||||
"项目Github地址 \\url{https://github.com/binary-husky/gpt_academic/}。"
|
||||
# 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加README中的QQ联系开发者）
|
||||
# 请您不要删除或修改这行警告，除非您是论文的原作者（如果您是论文原作者，欢迎加REAME中的QQ联系开发者）
|
||||
self.msg_declare = "为了防止大语言模型的意外谬误产生扩散影响，禁止移除或修改此警告。}}\\\\"
|
||||
self.title = "unknown"
|
||||
self.abstract = "unknown"
|
||||
@@ -151,7 +148,7 @@ class LatexPaperSplit():
|
||||
"""
|
||||
break down latex file to a linked list,
|
||||
each node use a preserve flag to indicate whether it should
|
||||
be processed by GPT.
|
||||
be proccessed by GPT.
|
||||
P.S. use multiprocessing to avoid timeout error
|
||||
"""
|
||||
import multiprocessing
|
||||
@@ -300,8 +297,7 @@ def Latex精细分解与转化(file_manifest, project_folder, llm_kwargs, plugin
|
||||
write_html(pfg.sp_file_contents, pfg.sp_file_result, chatbot=chatbot, project_folder=project_folder)
|
||||
|
||||
# <-------- 写出文件 ---------->
|
||||
model_name = llm_kwargs['llm_model'].replace('_', '\\_') # 替换LLM模型名称中的下划线为转义字符
|
||||
msg = f"当前大语言模型: {model_name}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
|
||||
msg = f"当前大语言模型: {llm_kwargs['llm_model']}，当前语言模型温度设定: {llm_kwargs['temperature']}。"
|
||||
final_tex = lps.merge_result(pfg.file_result, mode, msg)
|
||||
objdump((lps, pfg.file_result, mode, msg), file=pj(project_folder,'merge_result.pkl'))
|
||||
|
||||
@@ -326,7 +322,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
|
||||
buggy_lines = [int(l) for l in buggy_lines]
|
||||
buggy_lines = sorted(buggy_lines)
|
||||
buggy_line = buggy_lines[0]-1
|
||||
logger.warning("reversing tex line that has errors", buggy_line)
|
||||
print("reversing tex line that has errors", buggy_line)
|
||||
|
||||
# 重组，逆转出错的段落
|
||||
if buggy_line not in fixed_line:
|
||||
@@ -340,7 +336,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
|
||||
|
||||
return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
|
||||
except:
|
||||
logger.error("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
|
||||
print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
|
||||
return False, -1, [-1]
|
||||
|
||||
|
||||
@@ -351,42 +347,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
max_try = 32
|
||||
chatbot.append([f"正在编译PDF文档", f'编译已经开始。当前工作路径为{work_folder}，如果程序停顿5分钟以上，请直接去该路径下取回翻译结果，或者重启之后再度尝试 ...']); yield from update_ui(chatbot=chatbot, history=history)
|
||||
chatbot.append([f"正在编译PDF文档", '...']); yield from update_ui(chatbot=chatbot, history=history); time.sleep(1); chatbot[-1] = list(chatbot[-1]) # 刷新界面
|
||||
yield from update_ui_latest_msg('编译已经开始...', chatbot, history) # 刷新Gradio前端界面
|
||||
# 检查是否需要使用xelatex
|
||||
def check_if_need_xelatex(tex_path):
|
||||
try:
|
||||
with open(tex_path, 'r', encoding='utf-8', errors='replace') as f:
|
||||
content = f.read(5000)
|
||||
# 检查是否有使用xelatex的宏包
|
||||
need_xelatex = any(
|
||||
pkg in content
|
||||
for pkg in ['fontspec', 'xeCJK', 'xetex', 'unicode-math', 'xltxtra', 'xunicode']
|
||||
)
|
||||
if need_xelatex:
|
||||
logger.info(f"检测到宏包需要xelatex编译, 切换至xelatex编译")
|
||||
else:
|
||||
logger.info(f"未检测到宏包需要xelatex编译, 使用pdflatex编译")
|
||||
return need_xelatex
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
# 根据编译器类型返回编译命令
|
||||
def get_compile_command(compiler, filename):
|
||||
compile_command = f'{compiler} -interaction=batchmode -file-line-error {filename}.tex'
|
||||
logger.info('Latex 编译指令: ' + compile_command)
|
||||
return compile_command
|
||||
|
||||
# 确定使用的编译器
|
||||
compiler = 'pdflatex'
|
||||
if check_if_need_xelatex(pj(work_folder_modified, f'{main_file_modified}.tex')):
|
||||
logger.info("检测到宏包需要xelatex编译，切换至xelatex编译")
|
||||
# Check if xelatex is installed
|
||||
try:
|
||||
import subprocess
|
||||
subprocess.run(['xelatex', '--version'], capture_output=True, check=True)
|
||||
compiler = 'xelatex'
|
||||
except (subprocess.CalledProcessError, FileNotFoundError):
|
||||
raise RuntimeError("检测到需要使用xelatex编译，但系统中未安装xelatex。请先安装texlive或其他提供xelatex的LaTeX发行版。")
|
||||
yield from update_ui_lastest_msg('编译已经开始...', chatbot, history) # 刷新Gradio前端界面
|
||||
|
||||
while True:
|
||||
import os
|
||||
@@ -396,36 +357,36 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
shutil.copyfile(may_exist_bbl, target_bbl)
|
||||
|
||||
# https://stackoverflow.com/questions/738755/dont-make-me-manually-abort-a-latex-compile-when-theres-an-error
|
||||
yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_original), work_folder_original)
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译原始PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
|
||||
|
||||
yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_modified), work_folder_modified)
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译转化后的PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
|
||||
|
||||
if ok and os.path.exists(pj(work_folder_modified, f'{main_file_modified}.pdf')):
|
||||
# 只有第二步成功，才能继续下面的步骤
|
||||
yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history) # 刷新Gradio前端界面
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译BibTex ...', chatbot, history) # 刷新Gradio前端界面
|
||||
if not os.path.exists(pj(work_folder_original, f'{main_file_original}.bbl')):
|
||||
ok = compile_latex_with_timeout(f'bibtex {main_file_original}.aux', work_folder_original)
|
||||
if not os.path.exists(pj(work_folder_modified, f'{main_file_modified}.bbl')):
|
||||
ok = compile_latex_with_timeout(f'bibtex {main_file_modified}.aux', work_folder_modified)
|
||||
|
||||
yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_original), work_folder_original)
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_modified), work_folder_modified)
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_original), work_folder_original)
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, main_file_modified), work_folder_modified)
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 编译文献交叉引用 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_original}.tex', work_folder_original)
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error {main_file_modified}.tex', work_folder_modified)
|
||||
|
||||
if mode!='translate_zh':
|
||||
yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
logger.info( f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
print( f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
|
||||
ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex', os.getcwd())
|
||||
|
||||
yield from update_ui_latest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, 'merge_diff'), work_folder)
|
||||
yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history) # 刷新Gradio前端界面
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
|
||||
ok = compile_latex_with_timeout(f'bibtex merge_diff.aux', work_folder)
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, 'merge_diff'), work_folder)
|
||||
ok = compile_latex_with_timeout(get_compile_command(compiler, 'merge_diff'), work_folder)
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
|
||||
ok = compile_latex_with_timeout(f'pdflatex -interaction=batchmode -file-line-error merge_diff.tex', work_folder)
|
||||
|
||||
# <---------- 检查结果 ----------->
|
||||
results_ = ""
|
||||
@@ -435,13 +396,13 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
results_ += f"原始PDF编译是否成功: {original_pdf_success};"
|
||||
results_ += f"转化PDF编译是否成功: {modified_pdf_success};"
|
||||
results_ += f"对比PDF编译是否成功: {diff_pdf_success};"
|
||||
yield from update_ui_latest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面
|
||||
yield from update_ui_lastest_msg(f'第{n_fix}编译结束:<br/>{results_}...', chatbot, history) # 刷新Gradio前端界面
|
||||
|
||||
if diff_pdf_success:
|
||||
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_latest_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
|
||||
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')):
|
||||
@@ -457,7 +418,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
shutil.copyfile(concat_pdf, pj(work_folder, '..', 'translation', 'comparison.pdf'))
|
||||
promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot) # promote file to web UI
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
print(e)
|
||||
pass
|
||||
return True # 成功啦
|
||||
else:
|
||||
@@ -472,7 +433,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
work_folder_modified=work_folder_modified,
|
||||
fixed_line=fixed_line
|
||||
)
|
||||
yield from update_ui_latest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
yield from update_ui_lastest_msg(f'由于最为关键的转化PDF编译失败, 将根据报错信息修正tex源文件并重试, 当前报错的latex代码处于第{buggy_lines}行 ...', chatbot, history) # 刷新Gradio前端界面
|
||||
if not can_retry: break
|
||||
|
||||
return False # 失败啦
|
||||
@@ -503,71 +464,4 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
|
||||
promote_file_to_downloadzone(file=res, chatbot=chatbot)
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
logger.error('writing html result failed:', trimmed_format_exc())
|
||||
|
||||
|
||||
def upload_to_gptac_cloud_if_user_allow(chatbot, arxiv_id):
|
||||
try:
|
||||
# 如果用户允许，我们将arxiv论文PDF上传到GPTAC学术云
|
||||
from toolbox import map_file_to_sha256
|
||||
# 检查是否顺利，如果没有生成预期的文件，则跳过
|
||||
is_result_good = False
|
||||
for file_path in chatbot._cookies.get("files_to_promote", []):
|
||||
if file_path.endswith('translate_zh.pdf'):
|
||||
is_result_good = True
|
||||
if not is_result_good:
|
||||
return
|
||||
# 上传文件
|
||||
for file_path in chatbot._cookies.get("files_to_promote", []):
|
||||
align_name = None
|
||||
# normalized name
|
||||
for name in ['translate_zh.pdf', 'comparison.pdf']:
|
||||
if file_path.endswith(name): align_name = name
|
||||
# if match any align name
|
||||
if align_name:
|
||||
logger.info(f'Uploading to GPTAC cloud as the user has set `allow_cloud_io`: {file_path}')
|
||||
with open(file_path, 'rb') as f:
|
||||
import requests
|
||||
url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_upload'
|
||||
files = {'file': (align_name, f, 'application/octet-stream')}
|
||||
data = {
|
||||
'arxiv_id': arxiv_id,
|
||||
'file_hash': map_file_to_sha256(file_path),
|
||||
'language': 'zh',
|
||||
'trans_prompt': 'to_be_implemented',
|
||||
'llm_model': 'to_be_implemented',
|
||||
'llm_model_param': 'to_be_implemented',
|
||||
}
|
||||
resp = requests.post(url=url, files=files, data=data, timeout=30)
|
||||
logger.info(f'Uploading terminate ({resp.status_code})`: {file_path}')
|
||||
except:
|
||||
# 如果上传失败，不会中断程序，因为这是次要功能
|
||||
pass
|
||||
|
||||
def check_gptac_cloud(arxiv_id, chatbot):
|
||||
import requests
|
||||
success = False
|
||||
downloaded = []
|
||||
try:
|
||||
for pdf_target in ['translate_zh.pdf', 'comparison.pdf']:
|
||||
url = 'https://cloud-2.agent-matrix.com/arxiv_tf_paper_normal_exist'
|
||||
data = {
|
||||
'arxiv_id': arxiv_id,
|
||||
'name': pdf_target,
|
||||
}
|
||||
resp = requests.post(url=url, data=data)
|
||||
cache_hit_result = resp.text.strip('"')
|
||||
if cache_hit_result.startswith("http"):
|
||||
url = cache_hit_result
|
||||
logger.info(f'Downloading from GPTAC cloud: {url}')
|
||||
resp = requests.get(url=url, timeout=30)
|
||||
target = os.path.join(get_log_folder(plugin_name='gptac_cloud'), gen_time_str(), pdf_target)
|
||||
os.makedirs(os.path.dirname(target), exist_ok=True)
|
||||
with open(target, 'wb') as f:
|
||||
f.write(resp.content)
|
||||
new_path = promote_file_to_downloadzone(target, chatbot=chatbot)
|
||||
success = True
|
||||
downloaded.append(new_path)
|
||||
except:
|
||||
pass
|
||||
return success, downloaded
|
||||
print('writing html result failed:', trimmed_format_exc())
|
||||
|
||||
@@ -1,48 +0,0 @@
|
||||
import pickle
|
||||
|
||||
|
||||
class SafeUnpickler(pickle.Unpickler):
|
||||
|
||||
def get_safe_classes(self):
|
||||
from crazy_functions.latex_fns.latex_actions import LatexPaperFileGroup, LatexPaperSplit
|
||||
from crazy_functions.latex_fns.latex_toolbox import LinkedListNode
|
||||
from numpy.core.multiarray import scalar
|
||||
from numpy import dtype
|
||||
# 定义允许的安全类
|
||||
safe_classes = {
|
||||
# 在这里添加其他安全的类
|
||||
'LatexPaperFileGroup': LatexPaperFileGroup,
|
||||
'LatexPaperSplit': LatexPaperSplit,
|
||||
'LinkedListNode': LinkedListNode,
|
||||
'scalar': scalar,
|
||||
'dtype': dtype,
|
||||
}
|
||||
return safe_classes
|
||||
|
||||
def find_class(self, module, name):
|
||||
# 只允许特定的类进行反序列化
|
||||
self.safe_classes = self.get_safe_classes()
|
||||
match_class_name = None
|
||||
for class_name in self.safe_classes.keys():
|
||||
if (class_name in f'{module}.{name}'):
|
||||
match_class_name = class_name
|
||||
if match_class_name is not None:
|
||||
return self.safe_classes[match_class_name]
|
||||
# 如果尝试加载未授权的类，则抛出异常
|
||||
raise pickle.UnpicklingError(f"Attempted to deserialize unauthorized class '{name}' from module '{module}'")
|
||||
|
||||
def objdump(obj, file="objdump.tmp"):
|
||||
|
||||
with open(file, "wb+") as f:
|
||||
pickle.dump(obj, f)
|
||||
return
|
||||
|
||||
|
||||
def objload(file="objdump.tmp"):
|
||||
import os
|
||||
|
||||
if not os.path.exists(file):
|
||||
return
|
||||
with open(file, "rb") as f:
|
||||
unpickler = SafeUnpickler(f)
|
||||
return unpickler.load()
|
||||
@@ -1,8 +1,6 @@
|
||||
import os
|
||||
import os, shutil
|
||||
import re
|
||||
import shutil
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
|
||||
PRESERVE = 0
|
||||
TRANSFORM = 1
|
||||
@@ -57,7 +55,7 @@ def post_process(root):
|
||||
str_stack.append("{")
|
||||
elif c == "}":
|
||||
if len(str_stack) == 1:
|
||||
logger.warning("fixing brace error")
|
||||
print("stack fix")
|
||||
return i
|
||||
str_stack.pop(-1)
|
||||
else:
|
||||
@@ -168,7 +166,7 @@ def set_forbidden_text(text, mask, pattern, flags=0):
|
||||
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 complete text area.
|
||||
count the number of the braces so as to catch compelete text area.
|
||||
e.g.
|
||||
\begin{abstract} blablablablablabla. \end{abstract}
|
||||
"""
|
||||
@@ -188,7 +186,7 @@ def reverse_forbidden_text(text, mask, pattern, flags=0, forbid_wrapper=True):
|
||||
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 complete text area.
|
||||
count the number of the braces so as to catch compelete text area.
|
||||
e.g.
|
||||
\caption{blablablablabla\texbf{blablabla}blablabla.}
|
||||
"""
|
||||
@@ -214,7 +212,7 @@ def reverse_forbidden_text_careful_brace(
|
||||
):
|
||||
"""
|
||||
Move area out of preserve area (make text editable for GPT)
|
||||
count the number of the braces so as to catch complete text area.
|
||||
count the number of the braces so as to catch compelete text area.
|
||||
e.g.
|
||||
\caption{blablablablabla\texbf{blablabla}blablabla.}
|
||||
"""
|
||||
@@ -287,23 +285,23 @@ 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。
|
||||
P.S. 但愿没人把latex模板放在里面传进来 (6.25 加入判定latex模板的代码)
|
||||
"""
|
||||
candidates = []
|
||||
canidates = []
|
||||
for texf in file_manifest:
|
||||
if os.path.basename(texf).startswith("merge"):
|
||||
continue
|
||||
with open(texf, "r", encoding="utf8", errors="ignore") as f:
|
||||
file_content = f.read()
|
||||
if r"\documentclass" in file_content:
|
||||
candidates.append(texf)
|
||||
canidates.append(texf)
|
||||
else:
|
||||
continue
|
||||
|
||||
if len(candidates) == 0:
|
||||
if len(canidates) == 0:
|
||||
raise RuntimeError("无法找到一个主Tex文件（包含documentclass关键字）")
|
||||
elif len(candidates) == 1:
|
||||
return candidates[0]
|
||||
else: # if len(candidates) >= 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
|
||||
candidates_score = []
|
||||
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
|
||||
canidates_score = []
|
||||
# 给出一些判定模板文档的词作为扣分项
|
||||
unexpected_words = [
|
||||
"\\LaTeX",
|
||||
@@ -316,19 +314,19 @@ def find_main_tex_file(file_manifest, mode):
|
||||
"reviewers",
|
||||
]
|
||||
expected_words = ["\\input", "\\ref", "\\cite"]
|
||||
for texf in candidates:
|
||||
candidates_score.append(0)
|
||||
for texf in canidates:
|
||||
canidates_score.append(0)
|
||||
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:
|
||||
if uw in file_content:
|
||||
candidates_score[-1] -= 1
|
||||
canidates_score[-1] -= 1
|
||||
for uw in expected_words:
|
||||
if uw in file_content:
|
||||
candidates_score[-1] += 1
|
||||
select = np.argmax(candidates_score) # 取评分最高者返回
|
||||
return candidates[select]
|
||||
canidates_score[-1] += 1
|
||||
select = np.argmax(canidates_score) # 取评分最高者返回
|
||||
return canidates[select]
|
||||
|
||||
|
||||
def rm_comments(main_file):
|
||||
@@ -374,7 +372,7 @@ def find_tex_file_ignore_case(fp):
|
||||
|
||||
def merge_tex_files_(project_foler, main_file, mode):
|
||||
"""
|
||||
Merge Tex project recursively
|
||||
Merge Tex project recrusively
|
||||
"""
|
||||
main_file = rm_comments(main_file)
|
||||
for s in reversed([q for q in re.finditer(r"\\input\{(.*?)\}", main_file, re.M)]):
|
||||
@@ -429,7 +427,7 @@ def find_title_and_abs(main_file):
|
||||
|
||||
def merge_tex_files(project_foler, main_file, mode):
|
||||
"""
|
||||
Merge Tex project recursively
|
||||
Merge Tex project recrusively
|
||||
P.S. 顺便把CTEX塞进去以支持中文
|
||||
P.S. 顺便把Latex的注释去除
|
||||
"""
|
||||
@@ -603,7 +601,7 @@ def compile_latex_with_timeout(command, cwd, timeout=60):
|
||||
except subprocess.TimeoutExpired:
|
||||
process.kill()
|
||||
stdout, stderr = process.communicate()
|
||||
logger.error("Process timed out (compile_latex_with_timeout)!")
|
||||
print("Process timed out!")
|
||||
return False
|
||||
return True
|
||||
|
||||
@@ -644,216 +642,6 @@ def run_in_subprocess(func):
|
||||
|
||||
|
||||
def _merge_pdfs(pdf1_path, pdf2_path, output_path):
|
||||
try:
|
||||
logger.info("Merging PDFs using _merge_pdfs_ng")
|
||||
_merge_pdfs_ng(pdf1_path, pdf2_path, output_path)
|
||||
except:
|
||||
logger.info("Merging PDFs using _merge_pdfs_legacy")
|
||||
_merge_pdfs_legacy(pdf1_path, pdf2_path, output_path)
|
||||
|
||||
|
||||
def _merge_pdfs_ng(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
|
||||
from PyPDF2.generic import NameObject, TextStringObject, ArrayObject, FloatObject, NumberObject
|
||||
|
||||
Percent = 1
|
||||
# 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:
|
||||
pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
|
||||
# Open the second PDF 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()
|
||||
# Determine the number of pages in each PDF file
|
||||
num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
|
||||
# Merge the pages from the two PDF files
|
||||
for page_num in range(num_pages):
|
||||
# Add the page from the first PDF file
|
||||
if page_num < pdf1_reader.numPages:
|
||||
page1 = pdf1_reader.getPage(page_num)
|
||||
else:
|
||||
page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
|
||||
# Add the page from the second PDF file
|
||||
if page_num < pdf2_reader.numPages:
|
||||
page2 = pdf2_reader.getPage(page_num)
|
||||
else:
|
||||
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()),
|
||||
)
|
||||
new_page.mergeTranslatedPage(page1, 0, 0)
|
||||
new_page.mergeTranslatedPage(
|
||||
page2,
|
||||
int(
|
||||
int(page1.mediaBox.getWidth())
|
||||
- int(page2.mediaBox.getWidth()) * (1 - Percent)
|
||||
),
|
||||
0,
|
||||
)
|
||||
if "/Annots" in new_page:
|
||||
annotations = new_page["/Annots"]
|
||||
for i, annot in enumerate(annotations):
|
||||
annot_obj = annot.get_object()
|
||||
|
||||
# 检查注释类型是否是链接（/Link）
|
||||
if annot_obj.get("/Subtype") == "/Link":
|
||||
# 检查是否为内部链接跳转（/GoTo）或外部URI链接（/URI）
|
||||
action = annot_obj.get("/A")
|
||||
if action:
|
||||
|
||||
if "/S" in action and action["/S"] == "/GoTo":
|
||||
# 内部链接：跳转到文档中的某个页面
|
||||
dest = action.get("/D") # 目标页或目标位置
|
||||
# if dest and annot.idnum in page2_annot_id:
|
||||
# if dest in pdf2_reader.named_destinations:
|
||||
if dest and page2.annotations:
|
||||
if annot in page2.annotations:
|
||||
# 获取原始文件中跳转信息，包括跳转页面
|
||||
destination = pdf2_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
page_number = (
|
||||
pdf2_reader.get_destination_page_number(
|
||||
destination
|
||||
)
|
||||
)
|
||||
# 更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
if destination.dest_array[1] == "/XYZ":
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[
|
||||
2
|
||||
]
|
||||
+ int(
|
||||
page1.mediaBox.getWidth()
|
||||
)
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
else:
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
rect = annot_obj.get("/Rect")
|
||||
# 更新点击坐标
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(
|
||||
rect[0]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[1],
|
||||
FloatObject(
|
||||
rect[2]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[3],
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
# if dest and annot.idnum in page1_annot_id:
|
||||
# if dest in pdf1_reader.named_destinations:
|
||||
if dest and page1.annotations:
|
||||
if annot in page1.annotations:
|
||||
# 获取原始文件中跳转信息，包括跳转页面
|
||||
destination = pdf1_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
page_number = (
|
||||
pdf1_reader.get_destination_page_number(
|
||||
destination
|
||||
)
|
||||
)
|
||||
# 更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
if destination.dest_array[1] == "/XYZ":
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[
|
||||
2
|
||||
]
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
else:
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
rect = annot_obj.get("/Rect")
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(rect[0]),
|
||||
rect[1],
|
||||
FloatObject(rect[2]),
|
||||
rect[3],
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
elif "/S" in action and action["/S"] == "/URI":
|
||||
# 外部链接：跳转到某个URI
|
||||
uri = action.get("/URI")
|
||||
output_writer.addPage(new_page)
|
||||
# Save the merged PDF file
|
||||
with open(output_path, "wb") as output_file:
|
||||
output_writer.write(output_file)
|
||||
|
||||
|
||||
def _merge_pdfs_legacy(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
|
||||
|
||||
Percent = 0.95
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import time, json, sys, struct
|
||||
import time, logging, json, sys, struct
|
||||
import numpy as np
|
||||
from loguru import logger as logging
|
||||
from scipy.io.wavfile import WAVE_FORMAT
|
||||
|
||||
def write_numpy_to_wave(filename, rate, data, add_header=False):
|
||||
@@ -107,14 +106,18 @@ def is_speaker_speaking(vad, data, sample_rate):
|
||||
class AliyunASR():
|
||||
|
||||
def test_on_sentence_begin(self, message, *args):
|
||||
# print("test_on_sentence_begin:{}".format(message))
|
||||
pass
|
||||
|
||||
def test_on_sentence_end(self, message, *args):
|
||||
# print("test_on_sentence_end:{}".format(message))
|
||||
message = json.loads(message)
|
||||
self.parsed_sentence = message['payload']['result']
|
||||
self.event_on_entence_end.set()
|
||||
# print(self.parsed_sentence)
|
||||
|
||||
def test_on_start(self, message, *args):
|
||||
# print("test_on_start:{}".format(message))
|
||||
pass
|
||||
|
||||
def test_on_error(self, message, *args):
|
||||
@@ -126,11 +129,13 @@ class AliyunASR():
|
||||
pass
|
||||
|
||||
def test_on_result_chg(self, message, *args):
|
||||
# print("test_on_chg:{}".format(message))
|
||||
message = json.loads(message)
|
||||
self.parsed_text = message['payload']['result']
|
||||
self.event_on_result_chg.set()
|
||||
|
||||
def test_on_completed(self, message, *args):
|
||||
# print("on_completed:args=>{} message=>{}".format(args, message))
|
||||
pass
|
||||
|
||||
def audio_convertion_thread(self, uuid):
|
||||
@@ -243,14 +248,14 @@ class AliyunASR():
|
||||
|
||||
try:
|
||||
response = client.do_action_with_exception(request)
|
||||
logging.info(response)
|
||||
print(response)
|
||||
jss = json.loads(response)
|
||||
if 'Token' in jss and 'Id' in jss['Token']:
|
||||
token = jss['Token']['Id']
|
||||
expireTime = jss['Token']['ExpireTime']
|
||||
logging.info("token = " + token)
|
||||
logging.info("expireTime = " + str(expireTime))
|
||||
print("token = " + token)
|
||||
print("expireTime = " + str(expireTime))
|
||||
except Exception as e:
|
||||
logging.error(e)
|
||||
print(e)
|
||||
|
||||
return token
|
||||
|
||||
@@ -1,43 +0,0 @@
|
||||
from toolbox import update_ui, get_conf, promote_file_to_downloadzone, update_ui_latest_msg, generate_file_link
|
||||
from shared_utils.docker_as_service_api import stream_daas
|
||||
from shared_utils.docker_as_service_api import DockerServiceApiComModel
|
||||
import random
|
||||
|
||||
def download_video(video_id, only_audio, user_name, chatbot, history):
|
||||
from toolbox import get_log_folder
|
||||
chatbot.append([None, "Processing..."])
|
||||
yield from update_ui(chatbot, history)
|
||||
client_command = f'{video_id} --audio-only' if only_audio else video_id
|
||||
server_urls = get_conf('DAAS_SERVER_URLS')
|
||||
server_url = random.choice(server_urls)
|
||||
docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
|
||||
save_file_dir = get_log_folder(user_name, plugin_name='media_downloader')
|
||||
for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
|
||||
status_buf = ""
|
||||
status_buf += "DaaS message: \n\n"
|
||||
status_buf += output_manifest['server_message'].replace('\n', '<br/>')
|
||||
status_buf += "\n\n"
|
||||
status_buf += "DaaS standard error: \n\n"
|
||||
status_buf += output_manifest['server_std_err'].replace('\n', '<br/>')
|
||||
status_buf += "\n\n"
|
||||
status_buf += "DaaS standard output: \n\n"
|
||||
status_buf += output_manifest['server_std_out'].replace('\n', '<br/>')
|
||||
status_buf += "\n\n"
|
||||
status_buf += "DaaS file attach: \n\n"
|
||||
status_buf += str(output_manifest['server_file_attach'])
|
||||
yield from update_ui_latest_msg(status_buf, chatbot, history)
|
||||
|
||||
return output_manifest['server_file_attach']
|
||||
|
||||
|
||||
def search_videos(keywords):
|
||||
from toolbox import get_log_folder
|
||||
client_command = keywords
|
||||
server_urls = get_conf('DAAS_SERVER_URLS')
|
||||
server_url = random.choice(server_urls)
|
||||
server_url = server_url.replace('stream', 'search')
|
||||
docker_service_api_com_model = DockerServiceApiComModel(client_command=client_command)
|
||||
save_file_dir = get_log_folder("default_user", plugin_name='media_downloader')
|
||||
for output_manifest in stream_daas(docker_service_api_com_model, server_url, save_file_dir):
|
||||
return output_manifest['server_message']
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import List
|
||||
from toolbox import update_ui_latest_msg, disable_auto_promotion
|
||||
from toolbox import update_ui_lastest_msg, disable_auto_promotion
|
||||
from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
|
||||
from loguru import logger
|
||||
|
||||
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
|
||||
@@ -77,7 +76,7 @@ def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=F
|
||||
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
|
||||
logger.info(f'正在文本切分 {int(process*100)}%')
|
||||
print(f'正在文本切分 {int(process*100)}%')
|
||||
if len(remain_txt_to_cut.strip()) == 0:
|
||||
break
|
||||
return res
|
||||
@@ -120,7 +119,7 @@ if __name__ == '__main__':
|
||||
for i in range(5):
|
||||
file_content += file_content
|
||||
|
||||
logger.info(len(file_content))
|
||||
print(len(file_content))
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@ from toolbox import promote_file_to_downloadzone
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from toolbox import get_conf
|
||||
from toolbox import ProxyNetworkActivate
|
||||
from shared_utils.colorful import *
|
||||
from colorful import *
|
||||
import requests
|
||||
import random
|
||||
import copy
|
||||
@@ -72,7 +72,7 @@ def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chat
|
||||
generated_conclusion_files.append(res_path)
|
||||
return res_path
|
||||
|
||||
def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs={}):
|
||||
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.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
|
||||
@@ -113,7 +113,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
|
||||
return [txt]
|
||||
else:
|
||||
# raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
|
||||
# find a smooth token limit to achieve even separation
|
||||
# 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_text_to_satisfy_token_limit(txt, limit=token_limit_smooth, llm_model=llm_kwargs['llm_model'])
|
||||
@@ -138,7 +138,7 @@ def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_fi
|
||||
chatbot=chatbot,
|
||||
history_array=[meta for _ in inputs_array],
|
||||
sys_prompt_array=[
|
||||
"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "") for _ in inputs_array],
|
||||
"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
|
||||
)
|
||||
# -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-=
|
||||
produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files)
|
||||
|
||||
@@ -1,26 +0,0 @@
|
||||
import os
|
||||
from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_latest_msg, disable_auto_promotion
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone, get_conf, extract_archive
|
||||
from crazy_functions.pdf_fns.parse_pdf import parse_pdf, translate_pdf
|
||||
|
||||
def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
|
||||
import copy, json
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 1024
|
||||
generated_conclusion_files = []
|
||||
generated_html_files = []
|
||||
DST_LANG = "中文"
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
for index, fp in enumerate(file_manifest):
|
||||
chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
article_dict = parse_pdf(fp, grobid_url)
|
||||
grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
|
||||
with open(grobid_json_res, 'w+', encoding='utf8') as f:
|
||||
f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
|
||||
promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
|
||||
if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
|
||||
yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG, plugin_kwargs=plugin_kwargs)
|
||||
chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
@@ -1,333 +0,0 @@
|
||||
from toolbox import get_log_folder, gen_time_str, get_conf
|
||||
from toolbox import update_ui, promote_file_to_downloadzone
|
||||
from toolbox import promote_file_to_downloadzone, extract_archive
|
||||
from toolbox import generate_file_link, zip_folder
|
||||
from crazy_functions.crazy_utils import get_files_from_everything
|
||||
from shared_utils.colorful import *
|
||||
from loguru import logger
|
||||
import os
|
||||
import requests
|
||||
import time
|
||||
|
||||
|
||||
def retry_request(max_retries=3, delay=3):
|
||||
"""
|
||||
Decorator for retrying HTTP requests
|
||||
Args:
|
||||
max_retries: Maximum number of retry attempts
|
||||
delay: Delay between retries in seconds
|
||||
"""
|
||||
|
||||
def decorator(func):
|
||||
def wrapper(*args, **kwargs):
|
||||
for attempt in range(max_retries):
|
||||
try:
|
||||
return func(*args, **kwargs)
|
||||
except Exception as e:
|
||||
if attempt < max_retries - 1:
|
||||
logger.error(
|
||||
f"Request failed, retrying... ({attempt + 1}/{max_retries}) Error: {e}"
|
||||
)
|
||||
time.sleep(delay)
|
||||
continue
|
||||
raise e
|
||||
return None
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
@retry_request()
|
||||
def make_request(method, url, **kwargs):
|
||||
"""
|
||||
Make HTTP request with retry mechanism
|
||||
"""
|
||||
return requests.request(method, url, **kwargs)
|
||||
|
||||
|
||||
def doc2x_api_response_status(response, uid=""):
|
||||
"""
|
||||
Check the status of Doc2x API response
|
||||
Args:
|
||||
response_data: Response object from Doc2x API
|
||||
"""
|
||||
response_json = response.json()
|
||||
response_data = response_json.get("data", {})
|
||||
code = response_json.get("code", "Unknown")
|
||||
meg = response_data.get("message", response_json)
|
||||
trace_id = response.headers.get("trace-id", "Failed to get trace-id")
|
||||
if response.status_code != 200:
|
||||
raise RuntimeError(
|
||||
f"Doc2x return an error:\nTrace ID: {trace_id} {uid}\n{response.status_code} - {response_json}"
|
||||
)
|
||||
if code in ["parse_page_limit_exceeded", "parse_concurrency_limit"]:
|
||||
raise RuntimeError(
|
||||
f"Reached the limit of Doc2x:\nTrace ID: {trace_id} {uid}\n{code} - {meg}"
|
||||
)
|
||||
if code not in ["ok", "success"]:
|
||||
raise RuntimeError(
|
||||
f"Doc2x return an error:\nTrace ID: {trace_id} {uid}\n{code} - {meg}"
|
||||
)
|
||||
return response_data
|
||||
|
||||
|
||||
def 解析PDF_DOC2X_转Latex(pdf_file_path):
|
||||
zip_file_path, unzipped_folder = 解析PDF_DOC2X(pdf_file_path, format="tex")
|
||||
return unzipped_folder
|
||||
|
||||
|
||||
def 解析PDF_DOC2X(pdf_file_path, format="tex"):
|
||||
"""
|
||||
format: 'tex', 'md', 'docx'
|
||||
"""
|
||||
|
||||
DOC2X_API_KEY = get_conf("DOC2X_API_KEY")
|
||||
latex_dir = get_log_folder(plugin_name="pdf_ocr_latex")
|
||||
markdown_dir = get_log_folder(plugin_name="pdf_ocr")
|
||||
doc2x_api_key = DOC2X_API_KEY
|
||||
|
||||
# < ------ 第1步：预上传获取URL，然后上传文件 ------ >
|
||||
logger.info("Doc2x 上传文件：预上传获取URL")
|
||||
res = make_request(
|
||||
"POST",
|
||||
"https://v2.doc2x.noedgeai.com/api/v2/parse/preupload",
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
timeout=15,
|
||||
)
|
||||
res_data = doc2x_api_response_status(res)
|
||||
upload_url = res_data["url"]
|
||||
uuid = res_data["uid"]
|
||||
|
||||
logger.info("Doc2x 上传文件：上传文件")
|
||||
with open(pdf_file_path, "rb") as file:
|
||||
res = make_request("PUT", upload_url, data=file, timeout=60)
|
||||
res.raise_for_status()
|
||||
|
||||
# < ------ 第2步：轮询等待 ------ >
|
||||
logger.info("Doc2x 处理文件中：轮询等待")
|
||||
params = {"uid": uuid}
|
||||
max_attempts = 60
|
||||
attempt = 0
|
||||
while attempt < max_attempts:
|
||||
res = make_request(
|
||||
"GET",
|
||||
"https://v2.doc2x.noedgeai.com/api/v2/parse/status",
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
params=params,
|
||||
timeout=15,
|
||||
)
|
||||
res_data = doc2x_api_response_status(res)
|
||||
if res_data["status"] == "success":
|
||||
break
|
||||
elif res_data["status"] == "processing":
|
||||
time.sleep(5)
|
||||
logger.info(f"Doc2x is processing at {res_data['progress']}%")
|
||||
attempt += 1
|
||||
else:
|
||||
raise RuntimeError(f"Doc2x return an error: {res_data}")
|
||||
if attempt >= max_attempts:
|
||||
raise RuntimeError("Doc2x processing timeout after maximum attempts")
|
||||
|
||||
# < ------ 第3步：提交转化 ------ >
|
||||
logger.info("Doc2x 第3步：提交转化")
|
||||
data = {
|
||||
"uid": uuid,
|
||||
"to": format,
|
||||
"formula_mode": "dollar",
|
||||
"filename": "output"
|
||||
}
|
||||
res = make_request(
|
||||
"POST",
|
||||
"https://v2.doc2x.noedgeai.com/api/v2/convert/parse",
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
json=data,
|
||||
timeout=15,
|
||||
)
|
||||
doc2x_api_response_status(res, uid=f"uid: {uuid}")
|
||||
|
||||
# < ------ 第4步：等待结果 ------ >
|
||||
logger.info("Doc2x 第4步：等待结果")
|
||||
params = {"uid": uuid}
|
||||
max_attempts = 36
|
||||
attempt = 0
|
||||
while attempt < max_attempts:
|
||||
res = make_request(
|
||||
"GET",
|
||||
"https://v2.doc2x.noedgeai.com/api/v2/convert/parse/result",
|
||||
headers={"Authorization": "Bearer " + doc2x_api_key},
|
||||
params=params,
|
||||
timeout=15,
|
||||
)
|
||||
res_data = doc2x_api_response_status(res, uid=f"uid: {uuid}")
|
||||
if res_data["status"] == "success":
|
||||
break
|
||||
elif res_data["status"] == "processing":
|
||||
time.sleep(3)
|
||||
logger.info("Doc2x still processing to convert file")
|
||||
attempt += 1
|
||||
if attempt >= max_attempts:
|
||||
raise RuntimeError("Doc2x conversion timeout after maximum attempts")
|
||||
|
||||
# < ------ 第5步：最后的处理 ------ >
|
||||
logger.info("Doc2x 第5步：下载转换后的文件")
|
||||
|
||||
if format == "tex":
|
||||
target_path = latex_dir
|
||||
if format == "md":
|
||||
target_path = markdown_dir
|
||||
os.makedirs(target_path, exist_ok=True)
|
||||
|
||||
max_attempt = 3
|
||||
# < ------ 下载 ------ >
|
||||
for attempt in range(max_attempt):
|
||||
try:
|
||||
result_url = res_data["url"]
|
||||
res = make_request("GET", result_url, timeout=60)
|
||||
zip_path = os.path.join(target_path, gen_time_str() + ".zip")
|
||||
unzip_path = os.path.join(target_path, gen_time_str())
|
||||
if res.status_code == 200:
|
||||
with open(zip_path, "wb") as f:
|
||||
f.write(res.content)
|
||||
else:
|
||||
raise RuntimeError(f"Doc2x return an error: {res.json()}")
|
||||
except Exception as e:
|
||||
if attempt < max_attempt - 1:
|
||||
logger.error(f"Failed to download uid = {uuid} file, retrying... {e}")
|
||||
time.sleep(3)
|
||||
continue
|
||||
else:
|
||||
raise e
|
||||
|
||||
# < ------ 解压 ------ >
|
||||
import zipfile
|
||||
with zipfile.ZipFile(zip_path, "r") as zip_ref:
|
||||
zip_ref.extractall(unzip_path)
|
||||
return zip_path, unzip_path
|
||||
|
||||
|
||||
def 解析PDF_DOC2X_单文件(
|
||||
fp,
|
||||
project_folder,
|
||||
llm_kwargs,
|
||||
plugin_kwargs,
|
||||
chatbot,
|
||||
history,
|
||||
system_prompt,
|
||||
DOC2X_API_KEY,
|
||||
user_request,
|
||||
):
|
||||
def pdf2markdown(filepath):
|
||||
chatbot.append((None, f"Doc2x 解析中"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
md_zip_path, unzipped_folder = 解析PDF_DOC2X(filepath, format="md")
|
||||
|
||||
promote_file_to_downloadzone(md_zip_path, chatbot=chatbot)
|
||||
chatbot.append((None, f"完成解析 {md_zip_path} ..."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return md_zip_path
|
||||
|
||||
def deliver_to_markdown_plugin(md_zip_path, user_request):
|
||||
from crazy_functions.Markdown_Translate import Markdown英译中
|
||||
import shutil, re
|
||||
|
||||
time_tag = gen_time_str()
|
||||
target_path_base = get_log_folder(chatbot.get_user())
|
||||
file_origin_name = os.path.basename(md_zip_path)
|
||||
this_file_path = os.path.join(target_path_base, file_origin_name)
|
||||
os.makedirs(target_path_base, exist_ok=True)
|
||||
shutil.copyfile(md_zip_path, this_file_path)
|
||||
ex_folder = this_file_path + ".extract"
|
||||
extract_archive(file_path=this_file_path, dest_dir=ex_folder)
|
||||
|
||||
# edit markdown files
|
||||
success, file_manifest, project_folder = get_files_from_everything(ex_folder, type='.md', chatbot=chatbot)
|
||||
for generated_fp in file_manifest:
|
||||
# 修正一些公式问题
|
||||
with open(generated_fp, "r", encoding="utf8") as f:
|
||||
content = f.read()
|
||||
# 将公式中的\[ \]替换成$$
|
||||
content = content.replace(r"\[", r"$$").replace(r"\]", r"$$")
|
||||
# 将公式中的\( \)替换成$
|
||||
content = content.replace(r"\(", r"$").replace(r"\)", r"$")
|
||||
content = content.replace("```markdown", "\n").replace("```", "\n")
|
||||
with open(generated_fp, "w", encoding="utf8") as f:
|
||||
f.write(content)
|
||||
promote_file_to_downloadzone(generated_fp, chatbot=chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 生成在线预览html
|
||||
file_name = "在线预览翻译（原文）" + gen_time_str() + ".html"
|
||||
preview_fp = os.path.join(ex_folder, file_name)
|
||||
from shared_utils.advanced_markdown_format import (
|
||||
markdown_convertion_for_file,
|
||||
)
|
||||
|
||||
with open(generated_fp, "r", encoding="utf-8") as f:
|
||||
md = f.read()
|
||||
# # Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
|
||||
# md = re.sub(r'^<table>', r'.<table>', md, flags=re.MULTILINE)
|
||||
html = markdown_convertion_for_file(md)
|
||||
with open(preview_fp, "w", encoding="utf-8") as f:
|
||||
f.write(html)
|
||||
chatbot.append([None, f"生成在线预览：{generate_file_link([preview_fp])}"])
|
||||
promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
|
||||
|
||||
chatbot.append((None, f"调用Markdown插件 {ex_folder} ..."))
|
||||
plugin_kwargs["markdown_expected_output_dir"] = ex_folder
|
||||
|
||||
translated_f_name = "translated_markdown.md"
|
||||
generated_fp = plugin_kwargs["markdown_expected_output_path"] = os.path.join(
|
||||
ex_folder, translated_f_name
|
||||
)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
yield from Markdown英译中(
|
||||
ex_folder,
|
||||
llm_kwargs,
|
||||
plugin_kwargs,
|
||||
chatbot,
|
||||
history,
|
||||
system_prompt,
|
||||
user_request,
|
||||
)
|
||||
if os.path.exists(generated_fp):
|
||||
# 修正一些公式问题
|
||||
with open(generated_fp, "r", encoding="utf8") as f:
|
||||
content = f.read()
|
||||
content = content.replace("```markdown", "\n").replace("```", "\n")
|
||||
# Markdown中使用不标准的表格，需要在表格前加上一个emoji，以便公式渲染
|
||||
# content = re.sub(r'^<table>', r'.<table>', content, flags=re.MULTILINE)
|
||||
with open(generated_fp, "w", encoding="utf8") as f:
|
||||
f.write(content)
|
||||
# 生成在线预览html
|
||||
file_name = "在线预览翻译" + gen_time_str() + ".html"
|
||||
preview_fp = os.path.join(ex_folder, file_name)
|
||||
from shared_utils.advanced_markdown_format import (
|
||||
markdown_convertion_for_file,
|
||||
)
|
||||
|
||||
with open(generated_fp, "r", encoding="utf-8") as f:
|
||||
md = f.read()
|
||||
html = markdown_convertion_for_file(md)
|
||||
with open(preview_fp, "w", encoding="utf-8") as f:
|
||||
f.write(html)
|
||||
promote_file_to_downloadzone(preview_fp, chatbot=chatbot)
|
||||
# 生成包含图片的压缩包
|
||||
dest_folder = get_log_folder(chatbot.get_user())
|
||||
zip_name = "翻译后的带图文档.zip"
|
||||
zip_folder(
|
||||
source_folder=ex_folder, dest_folder=dest_folder, zip_name=zip_name
|
||||
)
|
||||
zip_fp = os.path.join(dest_folder, zip_name)
|
||||
promote_file_to_downloadzone(zip_fp, chatbot=chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
md_zip_path = yield from pdf2markdown(fp)
|
||||
yield from deliver_to_markdown_plugin(md_zip_path, user_request)
|
||||
|
||||
|
||||
def 解析PDF_基于DOC2X(file_manifest, *args):
|
||||
for index, fp in enumerate(file_manifest):
|
||||
yield from 解析PDF_DOC2X_单文件(fp, *args)
|
||||
return
|
||||
@@ -1,85 +0,0 @@
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text, get_files_from_everything
|
||||
import os
|
||||
import re
|
||||
def extract_text_from_files(txt, chatbot, history):
|
||||
"""
|
||||
查找pdf/md/word并获取文本内容并返回状态以及文本
|
||||
|
||||
输入参数 Args:
|
||||
chatbot: chatbot inputs and outputs （用户界面对话窗口句柄，用于数据流可视化）
|
||||
history (list): List of chat history （历史，对话历史列表）
|
||||
|
||||
输出 Returns:
|
||||
文件是否存在(bool)
|
||||
final_result(list):文本内容
|
||||
page_one(list):第一页内容/摘要
|
||||
file_manifest(list):文件路径
|
||||
exception(string):需要用户手动处理的信息,如没出错则保持为空
|
||||
"""
|
||||
|
||||
final_result = []
|
||||
page_one = []
|
||||
file_manifest = []
|
||||
exception = ""
|
||||
|
||||
if txt == "":
|
||||
final_result.append(txt)
|
||||
return False, final_result, page_one, file_manifest, exception #如输入区内容不是文件则直接返回输入区内容
|
||||
|
||||
#查找输入区内容中的文件
|
||||
file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf', chatbot=chatbot)
|
||||
file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md', chatbot=chatbot)
|
||||
file_word,word_manifest,folder_word = get_files_from_everything(txt, '.docx', chatbot=chatbot)
|
||||
file_doc,doc_manifest,folder_doc = get_files_from_everything(txt, '.doc', chatbot=chatbot)
|
||||
|
||||
if file_doc:
|
||||
exception = "word"
|
||||
return False, final_result, page_one, file_manifest, exception
|
||||
|
||||
file_num = len(pdf_manifest) + len(md_manifest) + len(word_manifest)
|
||||
if file_num == 0:
|
||||
final_result.append(txt)
|
||||
return False, final_result, page_one, file_manifest, exception #如输入区内容不是文件则直接返回输入区内容
|
||||
|
||||
if file_pdf:
|
||||
try: # 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
import fitz
|
||||
except:
|
||||
exception = "pdf"
|
||||
return False, final_result, page_one, file_manifest, exception
|
||||
for index, fp in enumerate(pdf_manifest):
|
||||
file_content, pdf_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
pdf_one = str(pdf_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
final_result.append(file_content)
|
||||
page_one.append(pdf_one)
|
||||
file_manifest.append(os.path.relpath(fp, folder_pdf))
|
||||
|
||||
if file_md:
|
||||
for index, fp in enumerate(md_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode()
|
||||
headers = re.findall(r'^#\s(.*)$', file_content, re.MULTILINE) #接下来提取md中的一级/二级标题作为摘要
|
||||
if len(headers) > 0:
|
||||
page_one.append("\n".join(headers)) #合并所有的标题,以换行符分割
|
||||
else:
|
||||
page_one.append("")
|
||||
final_result.append(file_content)
|
||||
file_manifest.append(os.path.relpath(fp, folder_md))
|
||||
|
||||
if file_word:
|
||||
try: # 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
from docx import Document
|
||||
except:
|
||||
exception = "word_pip"
|
||||
return False, final_result, page_one, file_manifest, exception
|
||||
for index, fp in enumerate(word_manifest):
|
||||
doc = Document(fp)
|
||||
file_content = '\n'.join([p.text for p in doc.paragraphs])
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode()
|
||||
page_one.append(file_content[:200])
|
||||
final_result.append(file_content)
|
||||
file_manifest.append(os.path.relpath(fp, folder_word))
|
||||
|
||||
return True, final_result, page_one, file_manifest, exception
|
||||
@@ -1,73 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html xmlns="http://www.w3.org/1999/xhtml">
|
||||
|
||||
<head>
|
||||
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
|
||||
<title>GPT-Academic 翻译报告书</title>
|
||||
<style>
|
||||
.centered-a {
|
||||
color: red;
|
||||
text-align: center;
|
||||
margin-bottom: 2%;
|
||||
font-size: 1.5em;
|
||||
}
|
||||
.centered-b {
|
||||
color: red;
|
||||
text-align: center;
|
||||
margin-top: 10%;
|
||||
margin-bottom: 20%;
|
||||
font-size: 1.5em;
|
||||
}
|
||||
.centered-c {
|
||||
color: rgba(255, 0, 0, 0);
|
||||
text-align: center;
|
||||
margin-top: 2%;
|
||||
margin-bottom: 20%;
|
||||
font-size: 7em;
|
||||
}
|
||||
</style>
|
||||
<script>
|
||||
// Configure MathJax settings
|
||||
MathJax = {
|
||||
tex: {
|
||||
inlineMath: [
|
||||
['$', '$'],
|
||||
['\(', '\)']
|
||||
]
|
||||
}
|
||||
}
|
||||
addEventListener('zero-md-rendered', () => {MathJax.typeset(); console.log('MathJax typeset!');})
|
||||
</script>
|
||||
<!-- Load MathJax library -->
|
||||
<script src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
|
||||
<script
|
||||
type="module"
|
||||
src="https://cdn.jsdelivr.net/gh/zerodevx/zero-md@2/dist/zero-md.min.js"
|
||||
></script>
|
||||
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="test_temp1" style="width:10%; height: 500px; float:left;">
|
||||
|
||||
</div>
|
||||
<div class="test_temp2" style="width:80%; height: 500px; float:left;">
|
||||
<!-- Simply set the `src` attribute to your MD file and win -->
|
||||
<div class="centered-a">
|
||||
请按Ctrl+S保存此页面，否则该页面可能在几分钟后失效。
|
||||
</div>
|
||||
<zero-md src="translated_markdown.md" no-shadow>
|
||||
</zero-md>
|
||||
<div class="centered-b">
|
||||
本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
|
||||
</div>
|
||||
<div class="centered-c">
|
||||
本报告由GPT-Academic开源项目生成，地址：https://github.com/binary-husky/gpt_academic。
|
||||
</div>
|
||||
</div>
|
||||
<div class="test_temp3" style="width:10%; height: 500px; float:left;">
|
||||
</div>
|
||||
|
||||
</body>
|
||||
|
||||
</html>
|
||||
@@ -1,52 +0,0 @@
|
||||
import os, json, base64
|
||||
from pydantic import BaseModel, Field
|
||||
from textwrap import dedent
|
||||
from typing import List
|
||||
|
||||
class ArgProperty(BaseModel): # PLUGIN_ARG_MENU
|
||||
title: str = Field(description="The title", default="")
|
||||
description: str = Field(description="The description", default="")
|
||||
default_value: str = Field(description="The default value", default="")
|
||||
type: str = Field(description="The type", default="") # currently we support ['string', 'dropdown']
|
||||
options: List[str] = Field(default=[], description="List of options available for the argument") # only used when type is 'dropdown'
|
||||
|
||||
class GptAcademicPluginTemplate():
|
||||
def __init__(self):
|
||||
# please note that `execute` method may run in different threads,
|
||||
# thus you should not store any state in the plugin instance,
|
||||
# which may be accessed by multiple threads
|
||||
pass
|
||||
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
An example as below:
|
||||
```
|
||||
def define_arg_selection_menu(self):
|
||||
gui_definition = {
|
||||
"main_input":
|
||||
ArgProperty(title="main input", description="description", default_value="default_value", type="string").model_dump_json(),
|
||||
"advanced_arg":
|
||||
ArgProperty(title="advanced arguments", description="description", default_value="default_value", type="string").model_dump_json(),
|
||||
"additional_arg_01":
|
||||
ArgProperty(title="additional", description="description", default_value="default_value", type="string").model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
```
|
||||
"""
|
||||
raise NotImplementedError("You need to implement this method in your plugin class")
|
||||
|
||||
|
||||
def get_js_code_for_generating_menu(self, btnName):
|
||||
define_arg_selection = self.define_arg_selection_menu()
|
||||
|
||||
if len(define_arg_selection.keys()) > 8:
|
||||
raise ValueError("You can only have up to 8 arguments in the define_arg_selection")
|
||||
# if "main_input" not in define_arg_selection:
|
||||
# raise ValueError("You must have a 'main_input' in the define_arg_selection")
|
||||
|
||||
DEFINE_ARG_INPUT_INTERFACE = json.dumps(define_arg_selection)
|
||||
return base64.b64encode(DEFINE_ARG_INPUT_INTERFACE.encode('utf-8')).decode('utf-8')
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
raise NotImplementedError("You need to implement this method in your plugin class")
|
||||
@@ -1,87 +0,0 @@
|
||||
SearchOptimizerPrompt="""作为一个网页搜索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高网页检索的精度。生成的问题要求指向对象清晰明确，并与“原问题语言相同”。例如：
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于 Nginx 的介绍和在Ubuntu上的使用等。
|
||||
"
|
||||
原问题: 怎么下载
|
||||
检索词: ["Nginx 下载","Ubuntu Nginx","Ubuntu安装Nginx"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于 Nginx 的介绍和使用等。
|
||||
Q: 报错 "no connection"
|
||||
A: 报错"no connection"可能是因为……
|
||||
"
|
||||
原问题: 怎么解决
|
||||
检索词: ["Nginx报错"no connection" 解决","Nginx'no connection'报错 原因","Nginx提示'no connection'"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
|
||||
"
|
||||
原问题: 你知道 Python 么？
|
||||
检索词: ["Python","Python 使用教程。","Python 特点和优势"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 列出Java的三种特点？
|
||||
A: 1. Java 是一种编译型语言。
|
||||
2. Java 是一种面向对象的编程语言。
|
||||
3. Java 是一种跨平台的编程语言。
|
||||
"
|
||||
原问题: 介绍下第2点。
|
||||
检索词: ["Java 面向对象特点","Java 面向对象编程优势。","Java 面向对象编程"]
|
||||
----------------
|
||||
现在有历史记录:
|
||||
"
|
||||
{history}
|
||||
"
|
||||
有其原问题: {query}
|
||||
直接给出最多{num}个检索词，必须以json形式给出，不得有多余字符:
|
||||
"""
|
||||
|
||||
SearchAcademicOptimizerPrompt="""作为一个学术论文搜索助手，你的任务是结合历史记录，从不同角度，为“原问题”生成个不同版本的“检索词”，从而提高学术论文检索的精度。生成的问题要求指向对象清晰明确，并与“原问题语言相同”。例如：
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于深度学习的介绍和在图像识别中的应用等。
|
||||
"
|
||||
原问题: 怎么下载相关论文
|
||||
检索词: ["深度学习 图像识别 论文下载","图像识别 深度学习 研究论文","深度学习 图像识别 论文资源","Deep Learning Image Recognition Paper Download","Image Recognition Deep Learning Research Paper"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 对话背景。
|
||||
A: 当前对话是关于深度学习的介绍和应用等。
|
||||
Q: 报错 "模型不收敛"
|
||||
A: 报错"模型不收敛"可能是因为……
|
||||
"
|
||||
原问题: 怎么解决
|
||||
检索词: ["深度学习 模型不收敛 解决方案 论文","深度学习 模型不收敛 原因 研究","深度学习 模型不收敛 论文","Deep Learning Model Convergence Issue Solution Paper","Deep Learning Model Convergence Problem Research"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
|
||||
"
|
||||
原问题: 你知道 GAN 么？
|
||||
检索词: ["生成对抗网络 论文","GAN 使用教程 论文","GAN 特点和优势 研究","Generative Adversarial Network Paper","GAN Usage Tutorial Paper"]
|
||||
----------------
|
||||
历史记录:
|
||||
"
|
||||
Q: 列出机器学习的三种应用？
|
||||
A: 1. 机器学习在图像识别中的应用。
|
||||
2. 机器学习在自然语言处理中的应用。
|
||||
3. 机器学习在推荐系统中的应用。
|
||||
"
|
||||
原问题: 介绍下第2点。
|
||||
检索词: ["机器学习 自然语言处理 应用 论文","机器学习 自然语言处理 研究","机器学习 NLP 应用 论文","Machine Learning Natural Language Processing Application Paper","Machine Learning NLP Research"]
|
||||
----------------
|
||||
现在有历史记录:
|
||||
"
|
||||
{history}
|
||||
"
|
||||
有其原问题: {query}
|
||||
直接给出最多{num}个检索词，必须以json形式给出，不得有多余字符:
|
||||
"""
|
||||
@@ -1,138 +0,0 @@
|
||||
import atexit
|
||||
from loguru import logger
|
||||
from typing import List
|
||||
|
||||
from llama_index.core import Document
|
||||
from llama_index.core.ingestion import run_transformations
|
||||
from llama_index.core.schema import TextNode
|
||||
|
||||
from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
|
||||
from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
|
||||
|
||||
DEFAULT_QUERY_GENERATION_PROMPT = """\
|
||||
Now, you have context information as below:
|
||||
---------------------
|
||||
{context_str}
|
||||
---------------------
|
||||
Answer the user request below (use the context information if necessary, otherwise you can ignore them):
|
||||
---------------------
|
||||
{query_str}
|
||||
"""
|
||||
|
||||
QUESTION_ANSWER_RECORD = """\
|
||||
{{
|
||||
"type": "This is a previous conversation with the user",
|
||||
"question": "{question}",
|
||||
"answer": "{answer}",
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
class SaveLoad():
|
||||
|
||||
def does_checkpoint_exist(self, checkpoint_dir=None):
|
||||
import os, glob
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if not os.path.exists(checkpoint_dir): return False
|
||||
if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
|
||||
return True
|
||||
|
||||
def save_to_checkpoint(self, checkpoint_dir=None):
|
||||
logger.info(f'saving vector store to: {checkpoint_dir}')
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
|
||||
|
||||
def load_from_checkpoint(self, checkpoint_dir=None):
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
|
||||
logger.info('loading checkpoint from disk')
|
||||
from llama_index.core import StorageContext, load_index_from_storage
|
||||
storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
|
||||
self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
|
||||
return self.vs_index
|
||||
else:
|
||||
return self.create_new_vs()
|
||||
|
||||
def create_new_vs(self):
|
||||
return GptacVectorStoreIndex.default_vector_store(embed_model=self.embed_model)
|
||||
|
||||
def purge(self):
|
||||
import shutil
|
||||
shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
|
||||
self.vs_index = self.create_new_vs(self.checkpoint_dir)
|
||||
|
||||
|
||||
class LlamaIndexRagWorker(SaveLoad):
|
||||
def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
|
||||
self.debug_mode = True
|
||||
self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
|
||||
self.user_name = user_name
|
||||
self.checkpoint_dir = checkpoint_dir
|
||||
if auto_load_checkpoint:
|
||||
self.vs_index = self.load_from_checkpoint(checkpoint_dir)
|
||||
else:
|
||||
self.vs_index = self.create_new_vs()
|
||||
atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
|
||||
|
||||
def assign_embedding_model(self):
|
||||
pass
|
||||
|
||||
def inspect_vector_store(self):
|
||||
# This function is for debugging
|
||||
self.vs_index.storage_context.index_store.to_dict()
|
||||
docstore = self.vs_index.storage_context.docstore.docs
|
||||
vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
|
||||
logger.info('\n++ --------inspect_vector_store begin--------')
|
||||
logger.info(vector_store_preview)
|
||||
logger.info('oo --------inspect_vector_store end--------')
|
||||
return vector_store_preview
|
||||
|
||||
def add_documents_to_vector_store(self, document_list: List[Document]):
|
||||
"""
|
||||
Adds a list of Document objects to the vector store after processing.
|
||||
"""
|
||||
documents = document_list
|
||||
documents_nodes = run_transformations(
|
||||
documents, # type: ignore
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
self.vs_index.insert_nodes(documents_nodes)
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
|
||||
def add_text_to_vector_store(self, text: str):
|
||||
node = TextNode(text=text)
|
||||
documents_nodes = run_transformations(
|
||||
[node],
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
self.vs_index.insert_nodes(documents_nodes)
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
|
||||
def remember_qa(self, question, answer):
|
||||
formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
|
||||
self.add_text_to_vector_store(formatted_str)
|
||||
|
||||
def retrieve_from_store_with_query(self, query):
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
retriever = self.vs_index.as_retriever()
|
||||
return retriever.retrieve(query)
|
||||
|
||||
def build_prompt(self, query, nodes):
|
||||
context_str = self.generate_node_array_preview(nodes)
|
||||
return DEFAULT_QUERY_GENERATION_PROMPT.format(context_str=context_str, query_str=query)
|
||||
|
||||
def generate_node_array_preview(self, nodes):
|
||||
buf = "\n".join(([f"(No.{i+1} | score {n.score:.3f}): {n.text}" for i, n in enumerate(nodes)]))
|
||||
if self.debug_mode: logger.info(buf)
|
||||
return buf
|
||||
|
||||
def purge_vector_store(self):
|
||||
"""
|
||||
Purges the current vector store and creates a new one.
|
||||
"""
|
||||
self.purge()
|
||||
@@ -1,108 +0,0 @@
|
||||
import llama_index
|
||||
import os
|
||||
import atexit
|
||||
from typing import List
|
||||
from loguru import logger
|
||||
from llama_index.core import Document
|
||||
from llama_index.core.schema import TextNode
|
||||
from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
|
||||
from shared_utils.connect_void_terminal import get_chat_default_kwargs
|
||||
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
|
||||
from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
|
||||
from llama_index.core.ingestion import run_transformations
|
||||
from llama_index.core import PromptTemplate
|
||||
from llama_index.core.response_synthesizers import TreeSummarize
|
||||
from llama_index.core import StorageContext
|
||||
from llama_index.vector_stores.milvus import MilvusVectorStore
|
||||
from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
|
||||
|
||||
DEFAULT_QUERY_GENERATION_PROMPT = """\
|
||||
Now, you have context information as below:
|
||||
---------------------
|
||||
{context_str}
|
||||
---------------------
|
||||
Answer the user request below (use the context information if necessary, otherwise you can ignore them):
|
||||
---------------------
|
||||
{query_str}
|
||||
"""
|
||||
|
||||
QUESTION_ANSWER_RECORD = """\
|
||||
{{
|
||||
"type": "This is a previous conversation with the user",
|
||||
"question": "{question}",
|
||||
"answer": "{answer}",
|
||||
}}
|
||||
"""
|
||||
|
||||
|
||||
class MilvusSaveLoad():
|
||||
|
||||
def does_checkpoint_exist(self, checkpoint_dir=None):
|
||||
import os, glob
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if not os.path.exists(checkpoint_dir): return False
|
||||
if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
|
||||
return True
|
||||
|
||||
def save_to_checkpoint(self, checkpoint_dir=None):
|
||||
logger.info(f'saving vector store to: {checkpoint_dir}')
|
||||
# if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
# self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
|
||||
|
||||
def load_from_checkpoint(self, checkpoint_dir=None):
|
||||
if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
|
||||
if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
|
||||
logger.info('loading checkpoint from disk')
|
||||
from llama_index.core import StorageContext, load_index_from_storage
|
||||
storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
|
||||
try:
|
||||
self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
|
||||
return self.vs_index
|
||||
except:
|
||||
return self.create_new_vs(checkpoint_dir)
|
||||
else:
|
||||
return self.create_new_vs(checkpoint_dir)
|
||||
|
||||
def create_new_vs(self, checkpoint_dir, overwrite=False):
|
||||
vector_store = MilvusVectorStore(
|
||||
uri=os.path.join(checkpoint_dir, "milvus_demo.db"),
|
||||
dim=self.embed_model.embedding_dimension(),
|
||||
overwrite=overwrite
|
||||
)
|
||||
storage_context = StorageContext.from_defaults(vector_store=vector_store)
|
||||
index = GptacVectorStoreIndex.default_vector_store(storage_context=storage_context, embed_model=self.embed_model)
|
||||
return index
|
||||
|
||||
def purge(self):
|
||||
self.vs_index = self.create_new_vs(self.checkpoint_dir, overwrite=True)
|
||||
|
||||
class MilvusRagWorker(MilvusSaveLoad, LlamaIndexRagWorker):
|
||||
|
||||
def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
|
||||
self.debug_mode = True
|
||||
self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
|
||||
self.user_name = user_name
|
||||
self.checkpoint_dir = checkpoint_dir
|
||||
if auto_load_checkpoint:
|
||||
self.vs_index = self.load_from_checkpoint(checkpoint_dir)
|
||||
else:
|
||||
self.vs_index = self.create_new_vs(checkpoint_dir)
|
||||
atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
|
||||
|
||||
def inspect_vector_store(self):
|
||||
# This function is for debugging
|
||||
try:
|
||||
self.vs_index.storage_context.index_store.to_dict()
|
||||
docstore = self.vs_index.storage_context.docstore.docs
|
||||
if not docstore.items():
|
||||
raise ValueError("cannot inspect")
|
||||
vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
|
||||
except:
|
||||
dummy_retrieve_res: List["NodeWithScore"] = self.vs_index.as_retriever().retrieve(' ')
|
||||
vector_store_preview = "\n".join(
|
||||
[f"{node.id_} | {node.text}" for node in dummy_retrieve_res]
|
||||
)
|
||||
logger.info('\n++ --------inspect_vector_store begin--------')
|
||||
logger.info(vector_store_preview)
|
||||
logger.info('oo --------inspect_vector_store end--------')
|
||||
return vector_store_preview
|
||||
@@ -1,22 +0,0 @@
|
||||
import os
|
||||
from llama_index.core import SimpleDirectoryReader
|
||||
|
||||
supports_format = ['.csv', '.docx', '.epub', '.ipynb', '.mbox', '.md', '.pdf', '.txt', '.ppt',
|
||||
'.pptm', '.pptx']
|
||||
|
||||
|
||||
# 修改后的 extract_text 函数，结合 SimpleDirectoryReader 和自定义解析逻辑
|
||||
def extract_text(file_path):
|
||||
_, ext = os.path.splitext(file_path.lower())
|
||||
|
||||
# 使用 SimpleDirectoryReader 处理它支持的文件格式
|
||||
if ext in supports_format:
|
||||
try:
|
||||
reader = SimpleDirectoryReader(input_files=[file_path])
|
||||
documents = reader.load_data()
|
||||
if len(documents) > 0:
|
||||
return documents[0].text
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return None
|
||||
@@ -1,58 +0,0 @@
|
||||
from llama_index.core import VectorStoreIndex
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from llama_index.core.callbacks.base import CallbackManager
|
||||
from llama_index.core.schema import TransformComponent
|
||||
from llama_index.core.service_context import ServiceContext
|
||||
from llama_index.core.settings import (
|
||||
Settings,
|
||||
callback_manager_from_settings_or_context,
|
||||
transformations_from_settings_or_context,
|
||||
)
|
||||
from llama_index.core.storage.storage_context import StorageContext
|
||||
|
||||
|
||||
class GptacVectorStoreIndex(VectorStoreIndex):
|
||||
|
||||
@classmethod
|
||||
def default_vector_store(
|
||||
cls,
|
||||
storage_context: Optional[StorageContext] = None,
|
||||
show_progress: bool = False,
|
||||
callback_manager: Optional[CallbackManager] = None,
|
||||
transformations: Optional[List[TransformComponent]] = None,
|
||||
# deprecated
|
||||
service_context: Optional[ServiceContext] = None,
|
||||
embed_model = None,
|
||||
**kwargs: Any,
|
||||
):
|
||||
"""Create index from documents.
|
||||
|
||||
Args:
|
||||
documents (Optional[Sequence[BaseDocument]]): List of documents to
|
||||
build the index from.
|
||||
|
||||
"""
|
||||
storage_context = storage_context or StorageContext.from_defaults()
|
||||
docstore = storage_context.docstore
|
||||
callback_manager = (
|
||||
callback_manager
|
||||
or callback_manager_from_settings_or_context(Settings, service_context)
|
||||
)
|
||||
transformations = transformations or transformations_from_settings_or_context(
|
||||
Settings, service_context
|
||||
)
|
||||
|
||||
with callback_manager.as_trace("index_construction"):
|
||||
|
||||
return cls(
|
||||
nodes=[],
|
||||
storage_context=storage_context,
|
||||
callback_manager=callback_manager,
|
||||
show_progress=show_progress,
|
||||
transformations=transformations,
|
||||
service_context=service_context,
|
||||
embed_model=embed_model,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@@ -1,17 +1,16 @@
|
||||
# From project chatglm-langchain
|
||||
|
||||
import threading
|
||||
from toolbox import Singleton
|
||||
import os
|
||||
import shutil
|
||||
import os
|
||||
import uuid
|
||||
import tqdm
|
||||
import shutil
|
||||
import threading
|
||||
import numpy as np
|
||||
from toolbox import Singleton
|
||||
from loguru import logger
|
||||
from langchain.vectorstores import FAISS
|
||||
from langchain.docstore.document import Document
|
||||
from typing import List, Tuple
|
||||
import numpy as np
|
||||
from crazy_functions.vector_fns.general_file_loader import load_file
|
||||
|
||||
embedding_model_dict = {
|
||||
@@ -60,7 +59,7 @@ def similarity_search_with_score_by_vector(
|
||||
self, embedding: List[float], k: int = 4
|
||||
) -> List[Tuple[Document, float]]:
|
||||
|
||||
def separate_list(ls: List[int]) -> List[List[int]]:
|
||||
def seperate_list(ls: List[int]) -> List[List[int]]:
|
||||
lists = []
|
||||
ls1 = [ls[0]]
|
||||
for i in range(1, len(ls)):
|
||||
@@ -82,7 +81,7 @@ def similarity_search_with_score_by_vector(
|
||||
continue
|
||||
_id = self.index_to_docstore_id[i]
|
||||
doc = self.docstore.search(_id)
|
||||
if not self.chunk_content:
|
||||
if not self.chunk_conent:
|
||||
if not isinstance(doc, Document):
|
||||
raise ValueError(f"Could not find document for id {_id}, got {doc}")
|
||||
doc.metadata["score"] = int(scores[0][j])
|
||||
@@ -104,12 +103,12 @@ def similarity_search_with_score_by_vector(
|
||||
id_set.add(l)
|
||||
if break_flag:
|
||||
break
|
||||
if not self.chunk_content:
|
||||
if not self.chunk_conent:
|
||||
return docs
|
||||
if len(id_set) == 0 and self.score_threshold > 0:
|
||||
return []
|
||||
id_list = sorted(list(id_set))
|
||||
id_lists = separate_list(id_list)
|
||||
id_lists = seperate_list(id_list)
|
||||
for id_seq in id_lists:
|
||||
for id in id_seq:
|
||||
if id == id_seq[0]:
|
||||
@@ -132,7 +131,7 @@ class LocalDocQA:
|
||||
embeddings: object = None
|
||||
top_k: int = VECTOR_SEARCH_TOP_K
|
||||
chunk_size: int = CHUNK_SIZE
|
||||
chunk_content: bool = True
|
||||
chunk_conent: bool = True
|
||||
score_threshold: int = VECTOR_SEARCH_SCORE_THRESHOLD
|
||||
|
||||
def init_cfg(self,
|
||||
@@ -151,17 +150,17 @@ class LocalDocQA:
|
||||
failed_files = []
|
||||
if isinstance(filepath, str):
|
||||
if not os.path.exists(filepath):
|
||||
logger.error("路径不存在")
|
||||
print("路径不存在")
|
||||
return None
|
||||
elif os.path.isfile(filepath):
|
||||
file = os.path.split(filepath)[-1]
|
||||
try:
|
||||
docs = load_file(filepath, SENTENCE_SIZE)
|
||||
logger.info(f"{file} 已成功加载")
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(filepath)
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
logger.error(f"{file} 未能成功加载")
|
||||
print(e)
|
||||
print(f"{file} 未能成功加载")
|
||||
return None
|
||||
elif os.path.isdir(filepath):
|
||||
docs = []
|
||||
@@ -171,23 +170,23 @@ class LocalDocQA:
|
||||
docs += load_file(fullfilepath, SENTENCE_SIZE)
|
||||
loaded_files.append(fullfilepath)
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
print(e)
|
||||
failed_files.append(file)
|
||||
|
||||
if len(failed_files) > 0:
|
||||
logger.error("以下文件未能成功加载：")
|
||||
print("以下文件未能成功加载：")
|
||||
for file in failed_files:
|
||||
logger.error(f"{file}\n")
|
||||
print(f"{file}\n")
|
||||
|
||||
else:
|
||||
docs = []
|
||||
for file in filepath:
|
||||
docs += load_file(file, SENTENCE_SIZE)
|
||||
logger.info(f"{file} 已成功加载")
|
||||
print(f"{file} 已成功加载")
|
||||
loaded_files.append(file)
|
||||
|
||||
if len(docs) > 0:
|
||||
logger.info("文件加载完毕，正在生成向量库")
|
||||
print("文件加载完毕，正在生成向量库")
|
||||
if vs_path and os.path.isdir(vs_path):
|
||||
try:
|
||||
self.vector_store = FAISS.load_local(vs_path, text2vec)
|
||||
@@ -209,16 +208,16 @@ class LocalDocQA:
|
||||
|
||||
# query 查询内容
|
||||
# vs_path 知识库路径
|
||||
# chunk_content 是否启用上下文关联
|
||||
# chunk_conent 是否启用上下文关联
|
||||
# score_threshold 搜索匹配score阈值
|
||||
# vector_search_top_k 搜索知识库内容条数，默认搜索5条结果
|
||||
# chunk_sizes 匹配单段内容的连接上下文长度
|
||||
def get_knowledge_based_content_test(self, query, vs_path, chunk_content,
|
||||
def get_knowledge_based_conent_test(self, query, vs_path, chunk_conent,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_size=CHUNK_SIZE,
|
||||
text2vec=None):
|
||||
self.vector_store = FAISS.load_local(vs_path, text2vec)
|
||||
self.vector_store.chunk_content = chunk_content
|
||||
self.vector_store.chunk_conent = chunk_conent
|
||||
self.vector_store.score_threshold = score_threshold
|
||||
self.vector_store.chunk_size = chunk_size
|
||||
|
||||
@@ -234,14 +233,14 @@ class LocalDocQA:
|
||||
prompt += "\n\n".join([f"({k}): " + doc.page_content for k, doc in enumerate(related_docs_with_score)])
|
||||
prompt += "\n\n---\n\n"
|
||||
prompt = prompt.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
# logger.info(prompt)
|
||||
# print(prompt)
|
||||
response = {"query": query, "source_documents": related_docs_with_score}
|
||||
return response, prompt
|
||||
|
||||
|
||||
|
||||
|
||||
def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_content, one_content_segmentation, text2vec):
|
||||
def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_conent, one_content_segmentation, text2vec):
|
||||
for file in files:
|
||||
assert os.path.exists(file), "输入文件不存在：" + file
|
||||
import nltk
|
||||
@@ -263,7 +262,7 @@ def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_co
|
||||
else:
|
||||
pass
|
||||
# file_status = "文件未成功加载，请重新上传文件"
|
||||
# logger.info(file_status)
|
||||
# print(file_status)
|
||||
return local_doc_qa, vs_path
|
||||
|
||||
@Singleton
|
||||
@@ -279,7 +278,7 @@ class knowledge_archive_interface():
|
||||
if self.text2vec_large_chinese is None:
|
||||
# < -------------------预热文本向量化模组--------------- >
|
||||
from toolbox import ProxyNetworkActivate
|
||||
logger.info('Checking Text2vec ...')
|
||||
print('Checking Text2vec ...')
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
|
||||
@@ -297,7 +296,7 @@ class knowledge_archive_interface():
|
||||
files=file_manifest,
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_content="",
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
@@ -319,19 +318,19 @@ class knowledge_archive_interface():
|
||||
files=[],
|
||||
sentence_size=100,
|
||||
history=[],
|
||||
one_content="",
|
||||
one_conent="",
|
||||
one_content_segmentation="",
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
VECTOR_SEARCH_SCORE_THRESHOLD = 0
|
||||
VECTOR_SEARCH_TOP_K = 4
|
||||
CHUNK_SIZE = 512
|
||||
resp, prompt = self.qa_handle.get_knowledge_based_content_test(
|
||||
resp, prompt = self.qa_handle.get_knowledge_based_conent_test(
|
||||
query = txt,
|
||||
vs_path = self.kai_path,
|
||||
score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
|
||||
vector_search_top_k=VECTOR_SEARCH_TOP_K,
|
||||
chunk_content=True,
|
||||
chunk_conent=True,
|
||||
chunk_size=CHUNK_SIZE,
|
||||
text2vec = self.get_chinese_text2vec(),
|
||||
)
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import List
|
||||
from toolbox import update_ui_latest_msg, disable_auto_promotion
|
||||
from toolbox import update_ui_lastest_msg, disable_auto_promotion
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
|
||||
import copy, json, pickle, os, sys, time
|
||||
@@ -9,14 +9,14 @@ import copy, json, pickle, os, sys, time
|
||||
def read_avail_plugin_enum():
|
||||
from crazy_functional import get_crazy_functions
|
||||
plugin_arr = get_crazy_functions()
|
||||
# remove plugins with out explanation
|
||||
plugin_arr = {k:v for k, v in plugin_arr.items() if ('Info' in v) and ('Function' in v)}
|
||||
# remove plugins with out explaination
|
||||
plugin_arr = {k:v for k, v in plugin_arr.items() if 'Info' in v}
|
||||
plugin_arr_info = {"F_{:04d}".format(i):v["Info"] for i, v in enumerate(plugin_arr.values(), start=1)}
|
||||
plugin_arr_dict = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
|
||||
plugin_arr_dict_parse = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
|
||||
plugin_arr_dict_parse.update({f"F_{i}":v for i, v in enumerate(plugin_arr.values(), start=1)})
|
||||
prompt = json.dumps(plugin_arr_info, ensure_ascii=False, indent=2)
|
||||
prompt = "\n\nThe definition of PluginEnum:\nPluginEnum=" + prompt
|
||||
prompt = "\n\nThe defination of PluginEnum:\nPluginEnum=" + prompt
|
||||
return prompt, plugin_arr_dict, plugin_arr_dict_parse
|
||||
|
||||
def wrap_code(txt):
|
||||
@@ -55,7 +55,7 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
|
||||
plugin_selection: str = Field(description="The most related plugin from one of the PluginEnum.", default="F_0000")
|
||||
reason_of_selection: str = Field(description="The reason why you should select this plugin.", default="This plugin satisfy user requirement most")
|
||||
# ⭐ ⭐ ⭐ 选择插件
|
||||
yield from update_ui_latest_msg(lastmsg=f"正在执行任务: {txt}\n\n查找可用插件中...", chatbot=chatbot, history=history, delay=0)
|
||||
yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n查找可用插件中...", chatbot=chatbot, history=history, delay=0)
|
||||
gpt_json_io = GptJsonIO(Plugin)
|
||||
gpt_json_io.format_instructions = "The format of your output should be a json that can be parsed by json.loads.\n"
|
||||
gpt_json_io.format_instructions += """Output example: {"plugin_selection":"F_1234", "reason_of_selection":"F_1234 plugin satisfy user requirement most"}\n"""
|
||||
@@ -74,13 +74,13 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
|
||||
msg += "请求的Prompt为：\n" + wrap_code(get_inputs_show_user(inputs, plugin_arr_enum_prompt))
|
||||
msg += "语言模型回复为：\n" + wrap_code(gpt_reply)
|
||||
msg += "\n但您可以尝试再试一次\n"
|
||||
yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
|
||||
yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
|
||||
return
|
||||
if plugin_sel.plugin_selection not in plugin_arr_dict_parse:
|
||||
msg = f"抱歉, 找不到合适插件执行该任务, 或者{llm_kwargs['llm_model']}无法理解您的需求。"
|
||||
msg += f"语言模型{llm_kwargs['llm_model']}选择了不存在的插件：\n" + wrap_code(gpt_reply)
|
||||
msg += "\n但您可以尝试再试一次\n"
|
||||
yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
|
||||
yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
|
||||
return
|
||||
|
||||
# ⭐ ⭐ ⭐ 确认插件参数
|
||||
@@ -90,7 +90,7 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
|
||||
appendix_info = get_recent_file_prompt_support(chatbot)
|
||||
|
||||
plugin = plugin_arr_dict_parse[plugin_sel.plugin_selection]
|
||||
yield from update_ui_latest_msg(lastmsg=f"正在执行任务: {txt}\n\n提取插件参数...", chatbot=chatbot, history=history, delay=0)
|
||||
yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n提取插件参数...", chatbot=chatbot, history=history, delay=0)
|
||||
class PluginExplicit(BaseModel):
|
||||
plugin_selection: str = plugin_sel.plugin_selection
|
||||
plugin_arg: str = Field(description="The argument of the plugin.", default="")
|
||||
@@ -109,6 +109,6 @@ def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prom
|
||||
fn = plugin['Function']
|
||||
fn_name = fn.__name__
|
||||
msg = f'{llm_kwargs["llm_model"]}为您选择了插件: `{fn_name}`\n\n插件说明：{plugin["Info"]}\n\n插件参数：{plugin_sel.plugin_arg}\n\n假如偏离了您的要求，按停止键终止。'
|
||||
yield from update_ui_latest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
|
||||
yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
|
||||
yield from fn(plugin_sel.plugin_arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, -1)
|
||||
return
|
||||
@@ -1,6 +1,6 @@
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import List
|
||||
from toolbox import update_ui_latest_msg, get_conf
|
||||
from toolbox import update_ui_lastest_msg, get_conf
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.json_fns.pydantic_io import GptJsonIO
|
||||
import copy, json, pickle, os, sys
|
||||
@@ -9,7 +9,7 @@ import copy, json, pickle, os, sys
|
||||
def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
|
||||
ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
|
||||
if not ALLOW_RESET_CONFIG:
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
|
||||
chatbot=chatbot, history=history, delay=2
|
||||
)
|
||||
@@ -30,7 +30,7 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
new_option_value: str = Field(description="the new value of the option", default=None)
|
||||
|
||||
# ⭐ ⭐ ⭐ 分析用户意图
|
||||
yield from update_ui_latest_msg(lastmsg=f"正在执行任务: {txt}\n\n读取新配置中", chatbot=chatbot, history=history, delay=0)
|
||||
yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n读取新配置中", chatbot=chatbot, history=history, delay=0)
|
||||
gpt_json_io = GptJsonIO(ModifyConfigurationIntention)
|
||||
inputs = "Analyze how to change configuration according to following user input, answer me with json: \n\n" + \
|
||||
">> " + txt.rstrip('\n').replace('\n','\n>> ') + '\n\n' + \
|
||||
@@ -44,11 +44,11 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
|
||||
ok = (explicit_conf in txt)
|
||||
if ok:
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}",
|
||||
chatbot=chatbot, history=history, delay=1
|
||||
)
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"正在执行任务: {txt}\n\n新配置{explicit_conf}={user_intention.new_option_value}\n\n正在修改配置中",
|
||||
chatbot=chatbot, history=history, delay=2
|
||||
)
|
||||
@@ -57,25 +57,25 @@ def modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
from toolbox import set_conf
|
||||
set_conf(explicit_conf, user_intention.new_option_value)
|
||||
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，重新页面即可生效。", chatbot=chatbot, history=history, delay=1
|
||||
)
|
||||
else:
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"失败，如果需要配置{explicit_conf}，您需要明确说明并在指令中提到它。", chatbot=chatbot, history=history, delay=5
|
||||
)
|
||||
|
||||
def modify_configuration_reboot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
|
||||
ALLOW_RESET_CONFIG = get_conf('ALLOW_RESET_CONFIG')
|
||||
if not ALLOW_RESET_CONFIG:
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"当前配置不允许被修改！如需激活本功能，请在config.py中设置ALLOW_RESET_CONFIG=True后重启软件。",
|
||||
chatbot=chatbot, history=history, delay=2
|
||||
)
|
||||
return
|
||||
|
||||
yield from modify_configuration_hot(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention)
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"正在执行任务: {txt}\n\n配置修改完成，五秒后即将重启！若出现报错请无视即可。", chatbot=chatbot, history=history, delay=5
|
||||
)
|
||||
os.execl(sys.executable, sys.executable, *sys.argv)
|
||||
|
||||
@@ -5,7 +5,7 @@ class VoidTerminalState():
|
||||
self.reset_state()
|
||||
|
||||
def reset_state(self):
|
||||
self.has_provided_explanation = False
|
||||
self.has_provided_explaination = False
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.虚空终端->虚空终端'
|
||||
|
||||
文件差异内容过多而无法显示
加载差异
@@ -1,19 +1,17 @@
|
||||
import re, requests, unicodedata, os
|
||||
from toolbox import update_ui, get_log_folder
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, get_conf
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from loguru import logger
|
||||
|
||||
import re, requests, unicodedata, os
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
def download_arxiv_(url_pdf):
|
||||
if 'arxiv.org' not in url_pdf:
|
||||
if ('.' in url_pdf) and ('/' not in url_pdf):
|
||||
new_url = 'https://arxiv.org/abs/'+url_pdf
|
||||
logger.info('下载编号：', url_pdf, '自动定位：', new_url)
|
||||
print('下载编号：', url_pdf, '自动定位：', new_url)
|
||||
# download_arxiv_(new_url)
|
||||
return download_arxiv_(new_url)
|
||||
else:
|
||||
logger.info('不能识别的URL！')
|
||||
print('不能识别的URL！')
|
||||
return None
|
||||
if 'abs' in url_pdf:
|
||||
url_pdf = url_pdf.replace('abs', 'pdf')
|
||||
@@ -44,12 +42,15 @@ def download_arxiv_(url_pdf):
|
||||
requests_pdf_url = url_pdf
|
||||
file_path = download_dir+title_str
|
||||
|
||||
logger.info('下载中')
|
||||
print('下载中')
|
||||
proxies = get_conf('proxies')
|
||||
r = requests.get(requests_pdf_url, proxies=proxies)
|
||||
with open(file_path, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
logger.info('下载完成')
|
||||
print('下载完成')
|
||||
|
||||
# print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
|
||||
# subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
|
||||
|
||||
x = "%s %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
|
||||
x = x.replace('?', '？')\
|
||||
@@ -62,9 +63,19 @@ def download_arxiv_(url_pdf):
|
||||
|
||||
|
||||
def get_name(_url_):
|
||||
import os
|
||||
from bs4 import BeautifulSoup
|
||||
logger.info('正在获取文献名！')
|
||||
logger.info(_url_)
|
||||
print('正在获取文献名！')
|
||||
print(_url_)
|
||||
|
||||
# arxiv_recall = {}
|
||||
# if os.path.exists('./arxiv_recall.pkl'):
|
||||
# with open('./arxiv_recall.pkl', 'rb') as f:
|
||||
# arxiv_recall = pickle.load(f)
|
||||
|
||||
# if _url_ in arxiv_recall:
|
||||
# print('在缓存中')
|
||||
# return arxiv_recall[_url_]
|
||||
|
||||
proxies = get_conf('proxies')
|
||||
res = requests.get(_url_, proxies=proxies)
|
||||
@@ -81,7 +92,7 @@ def get_name(_url_):
|
||||
other_details['abstract'] = abstract
|
||||
except:
|
||||
other_details['year'] = ''
|
||||
logger.info('年份获取失败')
|
||||
print('年份获取失败')
|
||||
|
||||
# get author
|
||||
try:
|
||||
@@ -90,7 +101,7 @@ def get_name(_url_):
|
||||
other_details['authors'] = authors
|
||||
except:
|
||||
other_details['authors'] = ''
|
||||
logger.info('authors获取失败')
|
||||
print('authors获取失败')
|
||||
|
||||
# get comment
|
||||
try:
|
||||
@@ -105,11 +116,11 @@ def get_name(_url_):
|
||||
other_details['comment'] = ''
|
||||
except:
|
||||
other_details['comment'] = ''
|
||||
logger.info('年份获取失败')
|
||||
print('年份获取失败')
|
||||
|
||||
title_str = BeautifulSoup(
|
||||
res.text, 'html.parser').find('title').contents[0]
|
||||
logger.info('获取成功：', title_str)
|
||||
print('获取成功：', title_str)
|
||||
# arxiv_recall[_url_] = (title_str+'.pdf', other_details)
|
||||
# with open('./arxiv_recall.pkl', 'wb') as f:
|
||||
# pickle.dump(arxiv_recall, f)
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from toolbox import CatchException, update_ui, update_ui_latest_msg
|
||||
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
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
|
||||
@CatchException
|
||||
def 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
@@ -15,9 +15,9 @@ Testing:
|
||||
|
||||
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
|
||||
from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_latest_msg
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
|
||||
from crazy_functions.crazy_utils import input_clipping, try_install_deps
|
||||
from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
|
||||
from .crazy_utils import input_clipping, try_install_deps
|
||||
from crazy_functions.gen_fns.gen_fns_shared import is_function_successfully_generated
|
||||
from crazy_functions.gen_fns.gen_fns_shared import get_class_name
|
||||
from crazy_functions.gen_fns.gen_fns_shared import subprocess_worker
|
||||
@@ -27,7 +27,7 @@ import time
|
||||
import glob
|
||||
import multiprocessing
|
||||
|
||||
template = """
|
||||
templete = """
|
||||
```python
|
||||
import ... # Put dependencies here, e.g. import numpy as np.
|
||||
|
||||
@@ -77,10 +77,10 @@ def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history):
|
||||
|
||||
# 第二步
|
||||
prompt_compose = [
|
||||
"If previous stage is successful, rewrite the function you have just written to satisfy following template: \n",
|
||||
template
|
||||
"If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n",
|
||||
templete
|
||||
]
|
||||
i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable template. "
|
||||
i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. "
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
@@ -164,18 +164,18 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
|
||||
if get_plugin_arg(plugin_kwargs, key="file_path_arg", default=False):
|
||||
file_path = get_plugin_arg(plugin_kwargs, key="file_path_arg", default=None)
|
||||
file_list.append(file_path)
|
||||
yield from update_ui_latest_msg(f"当前文件: {file_path}", chatbot, history, 1)
|
||||
yield from update_ui_lastest_msg(f"当前文件: {file_path}", chatbot, history, 1)
|
||||
elif have_any_recent_upload_files(chatbot):
|
||||
file_dir = get_recent_file_prompt_support(chatbot)
|
||||
file_list = glob.glob(os.path.join(file_dir, '**/*'), recursive=True)
|
||||
yield from update_ui_latest_msg(f"当前文件处理列表: {file_list}", chatbot, history, 1)
|
||||
yield from update_ui_lastest_msg(f"当前文件处理列表: {file_list}", chatbot, history, 1)
|
||||
else:
|
||||
chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
|
||||
yield from update_ui_latest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
|
||||
yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
|
||||
return # 2. 如果没有文件
|
||||
if len(file_list) == 0:
|
||||
chatbot.append(["文件检索", "没有发现任何近期上传的文件。"])
|
||||
yield from update_ui_latest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
|
||||
yield from update_ui_lastest_msg("没有发现任何近期上传的文件。", chatbot, history, 1)
|
||||
return # 2. 如果没有文件
|
||||
|
||||
# 读取文件
|
||||
@@ -183,7 +183,7 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
|
||||
|
||||
# 粗心检查
|
||||
if is_the_upload_folder(txt):
|
||||
yield from update_ui_latest_msg(f"请在输入框内填写需求, 然后再次点击该插件! 至于您的文件，不用担心, 文件路径 {txt} 已经被记忆. ", chatbot, history, 1)
|
||||
yield from update_ui_lastest_msg(f"请在输入框内填写需求, 然后再次点击该插件! 至于您的文件，不用担心, 文件路径 {txt} 已经被记忆. ", chatbot, history, 1)
|
||||
return
|
||||
|
||||
# 开始干正事
|
||||
@@ -195,7 +195,7 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
|
||||
code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \
|
||||
yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history)
|
||||
chatbot.append(["代码生成阶段结束", ""])
|
||||
yield from update_ui_latest_msg(f"正在验证上述代码的有效性 ...", chatbot, history, 1)
|
||||
yield from update_ui_lastest_msg(f"正在验证上述代码的有效性 ...", chatbot, history, 1)
|
||||
# ⭐ 分离代码块
|
||||
code = get_code_block(code)
|
||||
# ⭐ 检查模块
|
||||
@@ -206,11 +206,11 @@ def 函数动态生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
|
||||
if not traceback: traceback = trimmed_format_exc()
|
||||
# 处理异常
|
||||
if not traceback: traceback = trimmed_format_exc()
|
||||
yield from update_ui_latest_msg(f"第 {j+1}/{MAX_TRY} 次代码生成尝试, 失败了~ 别担心, 我们5秒后再试一次... \n\n此次我们的错误追踪是\n```\n{traceback}\n```\n", chatbot, history, 5)
|
||||
yield from update_ui_lastest_msg(f"第 {j+1}/{MAX_TRY} 次代码生成尝试, 失败了~ 别担心, 我们5秒后再试一次... \n\n此次我们的错误追踪是\n```\n{traceback}\n```\n", chatbot, history, 5)
|
||||
|
||||
# 代码生成结束, 开始执行
|
||||
TIME_LIMIT = 15
|
||||
yield from update_ui_latest_msg(f"开始创建新进程并执行代码! 时间限制 {TIME_LIMIT} 秒. 请等待任务完成... ", chatbot, history, 1)
|
||||
yield from update_ui_lastest_msg(f"开始创建新进程并执行代码! 时间限制 {TIME_LIMIT} 秒. 请等待任务完成... ", chatbot, history, 1)
|
||||
manager = multiprocessing.Manager()
|
||||
return_dict = manager.dict()
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from toolbox import CatchException, update_ui, gen_time_str
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import input_clipping
|
||||
import copy, json
|
||||
|
||||
@CatchException
|
||||
|
||||
@@ -30,7 +30,7 @@ def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", qual
|
||||
if style is not None:
|
||||
data['style'] = style
|
||||
response = requests.post(url, headers=headers, json=data, proxies=proxies)
|
||||
# logger.info(response.content)
|
||||
print(response.content)
|
||||
try:
|
||||
image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
|
||||
except:
|
||||
@@ -76,7 +76,7 @@ def edit_image(llm_kwargs, prompt, image_path, resolution="1024x1024", model="da
|
||||
}
|
||||
|
||||
response = requests.post(url, headers=headers, files=files, proxies=proxies)
|
||||
# logger.info(response.content)
|
||||
print(response.content)
|
||||
try:
|
||||
image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
|
||||
except:
|
||||
@@ -108,7 +108,7 @@ def 图片生成_DALLE2(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
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, 使用前请切换模型到GPT系列。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
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')
|
||||
@@ -129,7 +129,7 @@ def 图片生成_DALLE3(prompt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
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, 使用前请切换模型到GPT系列。如果中文Prompt效果不理想, 请尝试英文Prompt。正在处理中 ....."))
|
||||
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()
|
||||
@@ -166,7 +166,7 @@ class ImageEditState(GptAcademicState):
|
||||
return confirm, file
|
||||
|
||||
def lock_plugin(self, chatbot):
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.Image_Generate->图片修改_DALLE2'
|
||||
chatbot._cookies['lock_plugin'] = 'crazy_functions.图片生成->图片修改_DALLE2'
|
||||
self.dump_state(chatbot)
|
||||
|
||||
def unlock_plugin(self, chatbot):
|
||||
@@ -6,14 +6,13 @@
|
||||
"""
|
||||
|
||||
|
||||
import time
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
|
||||
from toolbox import get_conf, select_api_key, update_ui_latest_msg, Singleton
|
||||
from toolbox import get_conf, select_api_key, update_ui_lastest_msg, Singleton
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
|
||||
from crazy_functions.crazy_utils import input_clipping, try_install_deps
|
||||
from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersistentClasses
|
||||
from crazy_functions.agent_fns.auto_agent import AutoGenMath
|
||||
from loguru import logger
|
||||
import time
|
||||
|
||||
def remove_model_prefix(llm):
|
||||
if llm.startswith('api2d-'): llm = llm.replace('api2d-', '')
|
||||
@@ -81,12 +80,12 @@ def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
|
||||
persistent_key = f"{user_uuid}->多智能体终端"
|
||||
if persistent_class_multi_user_manager.already_alive(persistent_key):
|
||||
# 当已经存在一个正在运行的多智能体终端时，直接将用户输入传递给它，而不是再次启动一个新的多智能体终端
|
||||
logger.info('[debug] feed new user input')
|
||||
print('[debug] feed new user input')
|
||||
executor = persistent_class_multi_user_manager.get(persistent_key)
|
||||
exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="resume")
|
||||
else:
|
||||
# 运行多智能体终端 (首次)
|
||||
logger.info('[debug] create new executor instance')
|
||||
print('[debug] create new executor instance')
|
||||
history = []
|
||||
chatbot.append(["正在启动: 多智能体终端", "插件动态生成, 执行开始, 作者 Microsoft & Binary-Husky."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
from toolbox import CatchException, update_ui, promote_file_to_downloadzone, get_log_folder, get_user
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
import re
|
||||
|
||||
f_prefix = 'GPT-Academic对话存档'
|
||||
@@ -10,61 +9,27 @@ def write_chat_to_file(chatbot, history=None, file_name=None):
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
from themes.theme import advanced_css
|
||||
|
||||
if file_name is None:
|
||||
file_name = f_prefix + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.html'
|
||||
fp = os.path.join(get_log_folder(get_user(chatbot), plugin_name='chat_history'), file_name)
|
||||
|
||||
with open(fp, 'w', encoding='utf8') as f:
|
||||
from textwrap import dedent
|
||||
form = dedent("""
|
||||
<!DOCTYPE html><head><meta charset="utf-8"><title>对话存档</title><style>{CSS}</style></head>
|
||||
<body>
|
||||
<div class="test_temp1" style="width:10%; height: 500px; float:left;"></div>
|
||||
<div class="test_temp2" style="width:80%;padding: 40px;float:left;padding-left: 20px;padding-right: 20px;box-shadow: rgba(0, 0, 0, 0.2) 0px 0px 8px 8px;border-radius: 10px;">
|
||||
<div class="chat-body" style="display: flex;justify-content: center;flex-direction: column;align-items: center;flex-wrap: nowrap;">
|
||||
{CHAT_PREVIEW}
|
||||
<div></div>
|
||||
<div></div>
|
||||
<div style="text-align: center;width:80%;padding: 0px;float:left;padding-left:20px;padding-right:20px;box-shadow: rgba(0, 0, 0, 0.05) 0px 0px 1px 2px;border-radius: 1px;">对话（原始数据）</div>
|
||||
{HISTORY_PREVIEW}
|
||||
</div>
|
||||
</div>
|
||||
<div class="test_temp3" style="width:10%; height: 500px; float:left;"></div>
|
||||
</body>
|
||||
""")
|
||||
|
||||
qa_from = dedent("""
|
||||
<div class="QaBox" style="width:80%;padding: 20px;margin-bottom: 20px;box-shadow: rgb(0 255 159 / 50%) 0px 0px 1px 2px;border-radius: 4px;">
|
||||
<div class="Question" style="border-radius: 2px;">{QUESTION}</div>
|
||||
<hr color="blue" style="border-top: dotted 2px #ccc;">
|
||||
<div class="Answer" style="border-radius: 2px;">{ANSWER}</div>
|
||||
</div>
|
||||
""")
|
||||
|
||||
history_from = dedent("""
|
||||
<div class="historyBox" style="width:80%;padding: 0px;float:left;padding-left:20px;padding-right:20px;box-shadow: rgba(0, 0, 0, 0.05) 0px 0px 1px 2px;border-radius: 1px;">
|
||||
<div class="entry" style="border-radius: 2px;">{ENTRY}</div>
|
||||
</div>
|
||||
""")
|
||||
CHAT_PREVIEW_BUF = ""
|
||||
from themes.theme import advanced_css
|
||||
f.write(f'<!DOCTYPE html><head><meta charset="utf-8"><title>对话历史</title><style>{advanced_css}</style></head>')
|
||||
for i, contents in enumerate(chatbot):
|
||||
question, answer = contents[0], contents[1]
|
||||
if question is None: question = ""
|
||||
try: question = str(question)
|
||||
except: question = ""
|
||||
if answer is None: answer = ""
|
||||
try: answer = str(answer)
|
||||
except: answer = ""
|
||||
CHAT_PREVIEW_BUF += qa_from.format(QUESTION=question, ANSWER=answer)
|
||||
|
||||
HISTORY_PREVIEW_BUF = ""
|
||||
for j, content in enumerate(contents):
|
||||
try: # 这个bug没找到触发条件，暂时先这样顶一下
|
||||
if type(content) != str: content = str(content)
|
||||
except:
|
||||
continue
|
||||
f.write(content)
|
||||
if j == 0:
|
||||
f.write('<hr style="border-top: dotted 3px #ccc;">')
|
||||
f.write('<hr color="red"> \n\n')
|
||||
f.write('<hr color="blue"> \n\n raw chat context:\n')
|
||||
f.write('<code>')
|
||||
for h in history:
|
||||
HISTORY_PREVIEW_BUF += history_from.format(ENTRY=h)
|
||||
html_content = form.format(CHAT_PREVIEW=CHAT_PREVIEW_BUF, HISTORY_PREVIEW=HISTORY_PREVIEW_BUF, CSS=advanced_css)
|
||||
f.write(html_content)
|
||||
|
||||
f.write("\n>>>" + h)
|
||||
f.write('</code>')
|
||||
promote_file_to_downloadzone(fp, rename_file=file_name, chatbot=chatbot)
|
||||
return '对话历史写入：' + fp
|
||||
|
||||
@@ -75,7 +40,7 @@ def gen_file_preview(file_name):
|
||||
# pattern to match the text between <head> and </head>
|
||||
pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
|
||||
file_content = re.sub(pattern, '', file_content)
|
||||
html, history = file_content.split('<hr color="blue"> \n\n 对话数据 (无渲染):\n')
|
||||
html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
|
||||
history = history.strip('<code>')
|
||||
history = history.strip('</code>')
|
||||
history = history.split("\n>>>")
|
||||
@@ -86,25 +51,21 @@ def gen_file_preview(file_name):
|
||||
def read_file_to_chat(chatbot, history, file_name):
|
||||
with open(file_name, 'r', encoding='utf8') as f:
|
||||
file_content = f.read()
|
||||
from bs4 import BeautifulSoup
|
||||
soup = BeautifulSoup(file_content, 'lxml')
|
||||
# 提取QaBox信息
|
||||
# pattern to match the text between <head> and </head>
|
||||
pattern = re.compile(r'<head>.*?</head>', flags=re.DOTALL)
|
||||
file_content = re.sub(pattern, '', file_content)
|
||||
html, history = file_content.split('<hr color="blue"> \n\n raw chat context:\n')
|
||||
history = history.strip('<code>')
|
||||
history = history.strip('</code>')
|
||||
history = history.split("\n>>>")
|
||||
history = list(filter(lambda x:x!="", history))
|
||||
html = html.split('<hr color="red"> \n\n')
|
||||
html = list(filter(lambda x:x!="", html))
|
||||
chatbot.clear()
|
||||
qa_box_list = []
|
||||
qa_boxes = soup.find_all("div", class_="QaBox")
|
||||
for box in qa_boxes:
|
||||
question = box.find("div", class_="Question").get_text(strip=False)
|
||||
answer = box.find("div", class_="Answer").get_text(strip=False)
|
||||
qa_box_list.append({"Question": question, "Answer": answer})
|
||||
chatbot.append([question, answer])
|
||||
# 提取historyBox信息
|
||||
history_box_list = []
|
||||
history_boxes = soup.find_all("div", class_="historyBox")
|
||||
for box in history_boxes:
|
||||
entry = box.find("div", class_="entry").get_text(strip=False)
|
||||
history_box_list.append(entry)
|
||||
history = history_box_list
|
||||
chatbot.append([None, f"[Local Message] 载入对话{len(qa_box_list)}条，上下文{len(history)}条。"])
|
||||
for i, h in enumerate(html):
|
||||
i_say, gpt_say = h.split('<hr style="border-top: dotted 3px #ccc;">')
|
||||
chatbot.append([i_say, gpt_say])
|
||||
chatbot.append([f"存档文件详情？", f"[Local Message] 载入对话{len(html)}条，上下文{len(history)}条。"])
|
||||
return chatbot, history
|
||||
|
||||
@CatchException
|
||||
@@ -118,42 +79,11 @@ def 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
file_name = plugin_kwargs.get("file_name", None)
|
||||
if (file_name is not None) and (file_name != "") and (not file_name.endswith('.html')): file_name += '.html'
|
||||
else: file_name = None
|
||||
|
||||
chatbot.append((None, f"[Local Message] {write_chat_to_file(chatbot, history, file_name)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
|
||||
chatbot.append(("保存当前对话",
|
||||
f"[Local Message] {write_chat_to_file(chatbot, history)}，您可以调用下拉菜单中的“载入对话历史存档”还原当下的对话。"))
|
||||
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
|
||||
|
||||
|
||||
class Conversation_To_File_Wrap(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
|
||||
第一个参数，名称`file_name`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
|
||||
"""
|
||||
gui_definition = {
|
||||
"file_name": ArgProperty(title="保存文件名", description="输入对话存档文件名，留空则使用时间作为文件名", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
yield from 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def hide_cwd(str):
|
||||
import os
|
||||
current_path = os.getcwd()
|
||||
@@ -171,8 +101,8 @@ def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
system_prompt 给gpt的静默提醒
|
||||
user_request 当前用户的请求信息（IP地址等）
|
||||
"""
|
||||
from crazy_functions.crazy_utils import get_files_from_everything
|
||||
success, file_manifest, _ = get_files_from_everything(txt, type='.html',chatbot=chatbot)
|
||||
from .crazy_utils import get_files_from_everything
|
||||
success, file_manifest, _ = get_files_from_everything(txt, type='.html')
|
||||
|
||||
if not success:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
@@ -218,3 +148,5 @@ def 删除所有本地对话历史记录(txt, llm_kwargs, plugin_kwargs, chatbot
|
||||
chatbot.append([f"删除所有历史对话文件", f"已删除<br/>{local_history}"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
fast_debug = False
|
||||
|
||||
|
||||
@@ -104,8 +104,6 @@ def 总结word文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_pr
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from toolbox import CatchException, report_exception, select_api_key, update_ui, get_conf
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone, get_log_folder
|
||||
|
||||
def split_audio_file(filename, split_duration=1000):
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
import glob, shutil, os, re
|
||||
from loguru import logger
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
from toolbox import update_ui, trimmed_format_exc, gen_time_str
|
||||
import glob, time, os, re, logging
|
||||
from toolbox import update_ui, trimmed_format_exc, gen_time_str, disable_auto_promotion
|
||||
from toolbox import CatchException, report_exception, get_log_folder
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
fast_debug = False
|
||||
@@ -20,7 +18,7 @@ class PaperFileGroup():
|
||||
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=2048):
|
||||
def run_file_split(self, max_token_limit=1900):
|
||||
"""
|
||||
将长文本分离开来
|
||||
"""
|
||||
@@ -36,7 +34,7 @@ class PaperFileGroup():
|
||||
self.sp_file_contents.append(segment)
|
||||
self.sp_file_index.append(index)
|
||||
self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.md")
|
||||
logger.info('Segmentation: done')
|
||||
logging.info('Segmentation: done')
|
||||
|
||||
def merge_result(self):
|
||||
self.file_result = ["" for _ in range(len(self.file_paths))]
|
||||
@@ -53,7 +51,7 @@ class PaperFileGroup():
|
||||
return manifest
|
||||
|
||||
def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
# <-------- 读取Markdown文件，删除其中的所有注释 ---------->
|
||||
pfg = PaperFileGroup()
|
||||
@@ -66,25 +64,25 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
|
||||
pfg.file_contents.append(file_content)
|
||||
|
||||
# <-------- 拆分过长的Markdown文件 ---------->
|
||||
pfg.run_file_split(max_token_limit=1024)
|
||||
pfg.run_file_split(max_token_limit=1500)
|
||||
n_split = len(pfg.sp_file_contents)
|
||||
|
||||
# <-------- 多线程翻译开始 ---------->
|
||||
if language == 'en->zh':
|
||||
inputs_array = ["This is a Markdown file, translate it into Chinese, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
|
||||
inputs_array = ["This is a Markdown file, translate it into Chinese, do not modify any existing Markdown commands:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
elif language == 'zh->en':
|
||||
inputs_array = [f"This is a Markdown file, translate it into English, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
|
||||
inputs_array = [f"This is a Markdown file, translate it into English, do not modify any existing Markdown commands:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
else:
|
||||
inputs_array = [f"This is a Markdown file, translate it into {language}, do NOT modify any existing Markdown commands, do NOT use code wrapper (```), ONLY answer me with translated results:" +
|
||||
inputs_array = [f"This is a Markdown file, translate it into {language}, do not modify any existing Markdown commands, only answer me with translated results:" +
|
||||
f"\n\n{frag}" for frag in pfg.sp_file_contents]
|
||||
inputs_show_user_array = [f"翻译 {f}" for f in pfg.sp_file_tag]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." + plugin_kwargs.get("additional_prompt", "") for _ in range(n_split)]
|
||||
sys_prompt_array = ["You are a professional academic paper translator." for _ in range(n_split)]
|
||||
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array=inputs_array,
|
||||
@@ -101,14 +99,9 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
|
||||
for i_say, gpt_say in zip(gpt_response_collection[0::2], gpt_response_collection[1::2]):
|
||||
pfg.sp_file_result.append(gpt_say)
|
||||
pfg.merge_result()
|
||||
output_file_arr = pfg.write_result(language)
|
||||
for output_file in output_file_arr:
|
||||
promote_file_to_downloadzone(output_file, chatbot=chatbot)
|
||||
if 'markdown_expected_output_path' in plugin_kwargs:
|
||||
expected_f_name = plugin_kwargs['markdown_expected_output_path']
|
||||
shutil.copyfile(output_file, expected_f_name)
|
||||
pfg.write_result(language)
|
||||
except:
|
||||
logger.error(trimmed_format_exc())
|
||||
logging.error(trimmed_format_exc())
|
||||
|
||||
# <-------- 整理结果，退出 ---------->
|
||||
create_report_file_name = gen_time_str() + f"-chatgpt.md"
|
||||
@@ -119,7 +112,7 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
def get_files_from_everything(txt, preference='', chatbox=None):
|
||||
def get_files_from_everything(txt, preference=''):
|
||||
if txt == "": return False, None, None
|
||||
success = True
|
||||
if txt.startswith('http'):
|
||||
@@ -128,7 +121,7 @@ def get_files_from_everything(txt, preference='', chatbox=None):
|
||||
proxies = get_conf('proxies')
|
||||
# 网络的远程文件
|
||||
if preference == 'Github':
|
||||
logger.info('正在从github下载资源 ...')
|
||||
logging.info('正在从github下载资源 ...')
|
||||
if not txt.endswith('.md'):
|
||||
# Make a request to the GitHub API to retrieve the repository information
|
||||
url = txt.replace("https://github.com/", "https://api.github.com/repos/") + '/readme'
|
||||
@@ -147,11 +140,9 @@ def get_files_from_everything(txt, preference='', chatbox=None):
|
||||
# 直接给定文件
|
||||
file_manifest = [txt]
|
||||
project_folder = os.path.dirname(txt)
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
elif os.path.exists(txt):
|
||||
# 本地路径，递归搜索
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
|
||||
else:
|
||||
project_folder = None
|
||||
@@ -168,6 +159,7 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
"函数插件功能？",
|
||||
"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
disable_auto_promotion(chatbot)
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
@@ -180,7 +172,7 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
return
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt, preference="Github", chatbox=chatbot)
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt, preference="Github")
|
||||
|
||||
if not success:
|
||||
# 什么都没有
|
||||
@@ -207,6 +199,7 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
"函数插件功能？",
|
||||
"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
disable_auto_promotion(chatbot)
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
@@ -239,6 +232,7 @@ def Markdown翻译指定语言(txt, llm_kwargs, plugin_kwargs, chatbot, history,
|
||||
"函数插件功能？",
|
||||
"对整个Markdown项目进行翻译。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
disable_auto_promotion(chatbot)
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
@@ -1,18 +1,16 @@
|
||||
from loguru import logger
|
||||
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
|
||||
from toolbox import CatchException, report_exception
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
from .crazy_utils import input_clipping
|
||||
|
||||
|
||||
|
||||
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
file_write_buffer = []
|
||||
for file_name in file_manifest:
|
||||
logger.info('begin analysis on:', file_name)
|
||||
print('begin analysis on:', file_name)
|
||||
############################## <第 0 步，切割PDF> ##################################
|
||||
# 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
|
||||
# 的长度必须小于 2500 个 Token
|
||||
@@ -40,7 +38,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
|
||||
last_iteration_result = paper_meta # 初始值是摘要
|
||||
MAX_WORD_TOTAL = 4096 * 0.7
|
||||
n_fragment = len(paper_fragments)
|
||||
if n_fragment >= 20: logger.warning('文章极长，不能达到预期效果')
|
||||
if n_fragment >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_fragment):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}"
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from loguru import logger
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
|
||||
fast_debug = False
|
||||
@@ -58,6 +57,7 @@ def readPdf(pdfPath):
|
||||
layout = device.get_result()
|
||||
for obj in layout._objs:
|
||||
if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
|
||||
# print(obj.get_text())
|
||||
outTextList.append(obj.get_text())
|
||||
|
||||
return outTextList
|
||||
@@ -66,7 +66,7 @@ def readPdf(pdfPath):
|
||||
def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, glob, os
|
||||
from bs4 import BeautifulSoup
|
||||
logger.info('begin analysis on:', file_manifest)
|
||||
print('begin analysis on:', file_manifest)
|
||||
for index, fp in enumerate(file_manifest):
|
||||
if ".tex" in fp:
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
@@ -77,7 +77,7 @@ def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbo
|
||||
|
||||
prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index+1}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
from toolbox import CatchException, report_exception, get_log_folder, gen_time_str
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_latest_msg, disable_auto_promotion
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
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
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
|
||||
from shared_utils.colorful import *
|
||||
from colorful import *
|
||||
import copy
|
||||
import os
|
||||
import math
|
||||
@@ -60,8 +60,8 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
from crazy_functions.crazy_utils import get_files_from_everything
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf', chatbot=chatbot)
|
||||
from .crazy_utils import get_files_from_everything
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
|
||||
if len(file_manifest) > 0:
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
@@ -73,7 +73,7 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade nougat-ocr tiktoken```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
success_mmd, file_manifest_mmd, _ = get_files_from_everything(txt, type='.mmd', chatbot=chatbot)
|
||||
success_mmd, file_manifest_mmd, _ = get_files_from_everything(txt, type='.mmd')
|
||||
success = success or success_mmd
|
||||
file_manifest += file_manifest_mmd
|
||||
chatbot.append(["文件列表：", ", ".join([e.split('/')[-1] for e in file_manifest])]);
|
||||
|
||||
@@ -1,16 +1,83 @@
|
||||
from toolbox import get_log_folder
|
||||
from toolbox import update_ui, promote_file_to_downloadzone
|
||||
from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
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
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text
|
||||
from shared_utils.colorful import *
|
||||
from loguru import logger
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
|
||||
from colorful import *
|
||||
import os
|
||||
|
||||
def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
|
||||
@CatchException
|
||||
def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
disable_auto_promotion(chatbot)
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"批量翻译PDF文档。函数插件贡献者: Binary-Husky"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
check_packages(["fitz", "tiktoken", "scipdf"])
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf tiktoken scipdf_parser```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 清空历史，以免输入溢出
|
||||
history = []
|
||||
|
||||
from .crazy_utils import get_files_from_everything
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if not success:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
|
||||
# 如果没找到任何文件
|
||||
if len(file_manifest) == 0:
|
||||
report_exception(chatbot, history,
|
||||
a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# 开始正式执行任务
|
||||
grobid_url = get_avail_grobid_url()
|
||||
if grobid_url is not None:
|
||||
yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
|
||||
else:
|
||||
yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
|
||||
yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
|
||||
import copy, json
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 1024
|
||||
generated_conclusion_files = []
|
||||
generated_html_files = []
|
||||
DST_LANG = "中文"
|
||||
from crazy_functions.pdf_fns.report_gen_html import construct_html
|
||||
for index, fp in enumerate(file_manifest):
|
||||
chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
article_dict = parse_pdf(fp, grobid_url)
|
||||
grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json")
|
||||
with open(grobid_json_res, 'w+', encoding='utf8') as f:
|
||||
f.write(json.dumps(article_dict, indent=4, ensure_ascii=False))
|
||||
promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot)
|
||||
|
||||
if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。")
|
||||
yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG)
|
||||
chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
"""
|
||||
注意：此函数已经弃用！！新函数位于：crazy_functions/pdf_fns/parse_pdf.py
|
||||
此函数已经弃用
|
||||
"""
|
||||
import copy
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 1024
|
||||
@@ -49,8 +116,7 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
|
||||
chatbot=chatbot,
|
||||
history_array=[[paper_meta] for _ in paper_fragments],
|
||||
sys_prompt_array=[
|
||||
"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" + plugin_kwargs.get("additional_prompt", "")
|
||||
for _ in paper_fragments],
|
||||
"请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments],
|
||||
# max_workers=5 # OpenAI所允许的最大并行过载
|
||||
)
|
||||
gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
|
||||
@@ -94,7 +160,7 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
|
||||
generated_html_files.append(ch.save_file(create_report_file_name))
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
logger.error('writing html result failed:', trimmed_format_exc())
|
||||
print('writing html result failed:', trimmed_format_exc())
|
||||
|
||||
# 准备文件的下载
|
||||
for pdf_path in generated_conclusion_files:
|
||||
@@ -1,5 +1,4 @@
|
||||
import os
|
||||
from loguru import logger
|
||||
from toolbox import CatchException, update_ui, gen_time_str, promote_file_to_downloadzone
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
@@ -35,10 +34,10 @@ def eval_manim(code):
|
||||
return f'gpt_log/{time_str}.mp4'
|
||||
except subprocess.CalledProcessError as e:
|
||||
output = e.output.decode()
|
||||
logger.error(f"Command returned non-zero exit status {e.returncode}: {output}.")
|
||||
print(f"Command returned non-zero exit status {e.returncode}: {output}.")
|
||||
return f"Evaluating python script failed: {e.output}."
|
||||
except:
|
||||
logger.error('generating mp4 failed')
|
||||
print('generating mp4 failed')
|
||||
return "Generating mp4 failed."
|
||||
|
||||
|
||||
@@ -166,7 +165,7 @@ class PointWithTrace(Scene):
|
||||
|
||||
```
|
||||
|
||||
# do not use get_graph, this function is deprecated
|
||||
# do not use get_graph, this funciton is deprecated
|
||||
|
||||
class ExampleFunctionGraph(Scene):
|
||||
def construct(self):
|
||||
|
||||
@@ -1,12 +1,13 @@
|
||||
from loguru import logger
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from crazy_functions.crazy_utils import read_and_clean_pdf_text
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
fast_debug = False
|
||||
|
||||
|
||||
def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
logger.info('begin analysis on:', file_name)
|
||||
import tiktoken
|
||||
print('begin analysis on:', file_name)
|
||||
|
||||
############################## <第 0 步，切割PDF> ##################################
|
||||
# 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
|
||||
@@ -35,7 +36,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
last_iteration_result = paper_meta # 初始值是摘要
|
||||
MAX_WORD_TOTAL = 4096
|
||||
n_fragment = len(paper_fragments)
|
||||
if n_fragment >= 20: logger.warning('文章极长，不能达到预期效果')
|
||||
if n_fragment >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_fragment):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
|
||||
@@ -56,7 +57,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
|
||||
chatbot.append([i_say_show_user, gpt_say])
|
||||
|
||||
############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from .crazy_utils import input_clipping
|
||||
_, final_results = input_clipping("", final_results, max_token_limit=3200)
|
||||
yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
|
||||
|
||||
@@ -87,8 +88,6 @@ def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chat
|
||||
# 检测输入参数，如没有给定输入参数，直接退出
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "":
|
||||
txt = '空空如也的输入栏'
|
||||
|
||||
@@ -1,21 +1,22 @@
|
||||
from loguru import logger
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
fast_debug = False
|
||||
|
||||
def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, os
|
||||
logger.info('begin analysis on:', file_manifest)
|
||||
print('begin analysis on:', file_manifest)
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
|
||||
i_say = f'请对下面的程序文件做一个概述，并对文件中的所有函数生成注释，使用markdown表格输出结果，文件名是{os.path.relpath(fp, project_folder)}，文件内容是 ```{file_content}```'
|
||||
i_say_show_user = f'[{index+1}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
|
||||
i_say_show_user = f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述，并对文件中的所有函数生成注释: {os.path.abspath(fp)}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if not fast_debug:
|
||||
msg = '正常'
|
||||
# ** gpt request **
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
@@ -24,8 +25,9 @@ def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
chatbot[-1] = (i_say_show_user, gpt_say)
|
||||
history.append(i_say_show_user); history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
time.sleep(2)
|
||||
if not fast_debug: time.sleep(2)
|
||||
|
||||
if not fast_debug:
|
||||
res = write_history_to_file(history)
|
||||
promote_file_to_downloadzone(res, chatbot=chatbot)
|
||||
chatbot.append(("完成了吗？", res))
|
||||
@@ -39,8 +41,6 @@ def 批量生成函数注释(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
|
||||
@@ -1,11 +1,9 @@
|
||||
from toolbox import CatchException, update_ui, report_exception
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.plugin_template.plugin_class_template import (
|
||||
GptAcademicPluginTemplate,
|
||||
)
|
||||
from crazy_functions.plugin_template.plugin_class_template import ArgProperty
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import read_and_clean_pdf_text
|
||||
import datetime
|
||||
|
||||
# 以下是每类图表的PROMPT
|
||||
#以下是每类图表的PROMPT
|
||||
SELECT_PROMPT = """
|
||||
“{subject}”
|
||||
=============
|
||||
@@ -20,24 +18,22 @@ SELECT_PROMPT = """
|
||||
8 象限提示图
|
||||
不需要解释原因，仅需要输出单个不带任何标点符号的数字。
|
||||
"""
|
||||
# 没有思维导图!!!测试发现模型始终会优先选择思维导图
|
||||
# 流程图
|
||||
#没有思维导图!!!测试发现模型始终会优先选择思维导图
|
||||
#流程图
|
||||
PROMPT_1 = """
|
||||
请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，注意需要使用双引号将内容括起来。
|
||||
mermaid语法举例：
|
||||
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")
|
||||
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)
|
||||
```
|
||||
"""
|
||||
# 序列图
|
||||
#序列图
|
||||
PROMPT_2 = """
|
||||
请你给出围绕“{subject}”的序列图，使用mermaid语法。
|
||||
mermaid语法举例：
|
||||
请你给出围绕“{subject}”的序列图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
sequenceDiagram
|
||||
participant A as 用户
|
||||
@@ -48,10 +44,9 @@ sequenceDiagram
|
||||
B->>A: 返回数据
|
||||
```
|
||||
"""
|
||||
# 类图
|
||||
#类图
|
||||
PROMPT_3 = """
|
||||
请你给出围绕“{subject}”的类图，使用mermaid语法。
|
||||
mermaid语法举例：
|
||||
请你给出围绕“{subject}”的类图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
classDiagram
|
||||
Class01 <|-- AveryLongClass : Cool
|
||||
@@ -69,10 +64,9 @@ classDiagram
|
||||
Class08 <--> C2: Cool label
|
||||
```
|
||||
"""
|
||||
# 饼图
|
||||
#饼图
|
||||
PROMPT_4 = """
|
||||
请你给出围绕“{subject}”的饼图，使用mermaid语法，注意需要使用双引号将内容括起来。
|
||||
mermaid语法举例：
|
||||
请你给出围绕“{subject}”的饼图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
pie title Pets adopted by volunteers
|
||||
"狗" : 386
|
||||
@@ -80,41 +74,38 @@ pie title Pets adopted by volunteers
|
||||
"兔子" : 15
|
||||
```
|
||||
"""
|
||||
# 甘特图
|
||||
#甘特图
|
||||
PROMPT_5 = """
|
||||
请你给出围绕“{subject}”的甘特图，使用mermaid语法，注意需要使用双引号将内容括起来。
|
||||
mermaid语法举例：
|
||||
请你给出围绕“{subject}”的甘特图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
gantt
|
||||
title "项目开发流程"
|
||||
title 项目开发流程
|
||||
dateFormat YYYY-MM-DD
|
||||
section "设计"
|
||||
"需求分析" :done, des1, 2024-01-06,2024-01-08
|
||||
"原型设计" :active, des2, 2024-01-09, 3d
|
||||
"UI设计" : des3, after des2, 5d
|
||||
section "开发"
|
||||
"前端开发" :2024-01-20, 10d
|
||||
"后端开发" :2024-01-20, 10d
|
||||
section 设计
|
||||
需求分析 :done, des1, 2024-01-06,2024-01-08
|
||||
原型设计 :active, des2, 2024-01-09, 3d
|
||||
UI设计 : des3, after des2, 5d
|
||||
section 开发
|
||||
前端开发 :2024-01-20, 10d
|
||||
后端开发 :2024-01-20, 10d
|
||||
```
|
||||
"""
|
||||
# 状态图
|
||||
#状态图
|
||||
PROMPT_6 = """
|
||||
请你给出围绕“{subject}”的状态图，使用mermaid语法，注意需要使用双引号将内容括起来。
|
||||
mermaid语法举例：
|
||||
请你给出围绕“{subject}”的状态图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
stateDiagram-v2
|
||||
[*] --> "Still"
|
||||
"Still" --> [*]
|
||||
"Still" --> "Moving"
|
||||
"Moving" --> "Still"
|
||||
"Moving" --> "Crash"
|
||||
"Crash" --> [*]
|
||||
[*] --> Still
|
||||
Still --> [*]
|
||||
Still --> Moving
|
||||
Moving --> Still
|
||||
Moving --> Crash
|
||||
Crash --> [*]
|
||||
```
|
||||
"""
|
||||
# 实体关系图
|
||||
#实体关系图
|
||||
PROMPT_7 = """
|
||||
请你给出围绕“{subject}”的实体关系图，使用mermaid语法。
|
||||
mermaid语法举例：
|
||||
请你给出围绕“{subject}”的实体关系图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
erDiagram
|
||||
CUSTOMER ||--o{ ORDER : places
|
||||
@@ -134,172 +125,144 @@ erDiagram
|
||||
}
|
||||
```
|
||||
"""
|
||||
# 象限提示图
|
||||
#象限提示图
|
||||
PROMPT_8 = """
|
||||
请你给出围绕“{subject}”的象限图，使用mermaid语法，注意需要使用双引号将内容括起来。
|
||||
mermaid语法举例：
|
||||
请你给出围绕“{subject}”的象限图，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
graph LR
|
||||
A["Hard skill"] --> B("Programming")
|
||||
A["Hard skill"] --> C("Design")
|
||||
D["Soft skill"] --> E("Coordination")
|
||||
D["Soft skill"] --> F("Communication")
|
||||
A[Hard skill] --> B(Programming)
|
||||
A[Hard skill] --> C(Design)
|
||||
D[Soft skill] --> E(Coordination)
|
||||
D[Soft skill] --> F(Communication)
|
||||
```
|
||||
"""
|
||||
# 思维导图
|
||||
#思维导图
|
||||
PROMPT_9 = """
|
||||
{subject}
|
||||
==========
|
||||
请给出上方内容的思维导图，充分考虑其之间的逻辑，使用mermaid语法，注意需要使用双引号将内容括起来。
|
||||
mermaid语法举例：
|
||||
请给出上方内容的思维导图，充分考虑其之间的逻辑，使用mermaid语法，mermaid语法举例：
|
||||
```mermaid
|
||||
mindmap
|
||||
root((mindmap))
|
||||
("Origins")
|
||||
("Long history")
|
||||
Origins
|
||||
Long history
|
||||
::icon(fa fa-book)
|
||||
("Popularisation")
|
||||
("British popular psychology author Tony Buzan")
|
||||
::icon(fa fa-user)
|
||||
("Research")
|
||||
("On effectiveness<br/>and features")
|
||||
::icon(fa fa-search)
|
||||
("On Automatic creation")
|
||||
::icon(fa fa-robot)
|
||||
("Uses")
|
||||
("Creative techniques")
|
||||
::icon(fa fa-lightbulb-o)
|
||||
("Strategic planning")
|
||||
::icon(fa fa-flag)
|
||||
("Argument mapping")
|
||||
::icon(fa fa-comments)
|
||||
("Tools")
|
||||
("Pen and paper")
|
||||
::icon(fa fa-pencil)
|
||||
("Mermaid")
|
||||
::icon(fa fa-code)
|
||||
Popularisation
|
||||
British popular psychology author Tony Buzan
|
||||
Research
|
||||
On effectiveness<br/>and features
|
||||
On Automatic creation
|
||||
Uses
|
||||
Creative techniques
|
||||
Strategic planning
|
||||
Argument mapping
|
||||
Tools
|
||||
Pen and paper
|
||||
Mermaid
|
||||
```
|
||||
"""
|
||||
|
||||
|
||||
def 解析历史输入(history, llm_kwargs, file_manifest, chatbot, plugin_kwargs):
|
||||
def 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs):
|
||||
############################## <第 0 步，切割输入> ##################################
|
||||
# 借用PDF切割中的函数对文本进行切割
|
||||
TOKEN_LIMIT_PER_FRAGMENT = 2500
|
||||
txt = (
|
||||
str(history).encode("utf-8", "ignore").decode()
|
||||
) # avoid reading non-utf8 chars
|
||||
from crazy_functions.pdf_fns.breakdown_txt import (
|
||||
breakdown_text_to_satisfy_token_limit,
|
||||
)
|
||||
|
||||
txt = breakdown_text_to_satisfy_token_limit(
|
||||
txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs["llm_model"]
|
||||
)
|
||||
txt = str(history).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
|
||||
txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
|
||||
############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ##################################
|
||||
i_say_show_user = f'首先你从历史记录或文件中提取摘要。'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=history) # 更新UI
|
||||
results = []
|
||||
MAX_WORD_TOTAL = 4096
|
||||
n_txt = len(txt)
|
||||
last_iteration_result = "从以下文本中提取摘要。"
|
||||
|
||||
if n_txt >= 20: print('文章极长，不能达到预期效果')
|
||||
for i in range(n_txt):
|
||||
NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}"
|
||||
i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i]}"
|
||||
i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[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 content of the previous section is?",
|
||||
last_iteration_result,
|
||||
], # 迭代上一次的结果
|
||||
sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese.", # 提示
|
||||
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 content of the previous section is?", last_iteration_result], # 迭代上一次的结果
|
||||
sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese." # 提示
|
||||
)
|
||||
results.append(gpt_say)
|
||||
last_iteration_result = gpt_say
|
||||
############################## <第 2 步，根据整理的摘要选择图表类型> ##################################
|
||||
gpt_say = str(plugin_kwargs) # 将图表类型参数赋值为插件参数
|
||||
results_txt = "\n".join(results) # 合并摘要
|
||||
if gpt_say not in [
|
||||
"1",
|
||||
"2",
|
||||
"3",
|
||||
"4",
|
||||
"5",
|
||||
"6",
|
||||
"7",
|
||||
"8",
|
||||
"9",
|
||||
]: # 如插件参数不正确则使用对话模型判断
|
||||
i_say_show_user = (
|
||||
f"接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制"
|
||||
)
|
||||
gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
gpt_say = plugin_kwargs.get("advanced_arg", "") #将图表类型参数赋值为插件参数
|
||||
results_txt = '\n'.join(results) #合并摘要
|
||||
if gpt_say not in ['1','2','3','4','5','6','7','8','9']: #如插件参数不正确则使用对话模型判断
|
||||
i_say_show_user = f'接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制'; gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
|
||||
i_say = SELECT_PROMPT.format(subject=results_txt)
|
||||
i_say_show_user = f'请判断适合使用的流程图类型,其中数字对应关系为:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图。由于不管提供文本是什么,模型大概率认为"思维导图"最合适,因此思维导图仅能通过参数调用。'
|
||||
for i in range(3):
|
||||
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="",
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt=""
|
||||
)
|
||||
if gpt_say in [
|
||||
"1",
|
||||
"2",
|
||||
"3",
|
||||
"4",
|
||||
"5",
|
||||
"6",
|
||||
"7",
|
||||
"8",
|
||||
"9",
|
||||
]: # 判断返回是否正确
|
||||
if gpt_say in ['1','2','3','4','5','6','7','8','9']: #判断返回是否正确
|
||||
break
|
||||
if gpt_say not in ["1", "2", "3", "4", "5", "6", "7", "8", "9"]:
|
||||
gpt_say = "1"
|
||||
if gpt_say not in ['1','2','3','4','5','6','7','8','9']:
|
||||
gpt_say = '1'
|
||||
############################## <第 3 步，根据选择的图表类型绘制图表> ##################################
|
||||
if gpt_say == "1":
|
||||
if gpt_say == '1':
|
||||
i_say = PROMPT_1.format(subject=results_txt)
|
||||
elif gpt_say == "2":
|
||||
elif gpt_say == '2':
|
||||
i_say = PROMPT_2.format(subject=results_txt)
|
||||
elif gpt_say == "3":
|
||||
elif gpt_say == '3':
|
||||
i_say = PROMPT_3.format(subject=results_txt)
|
||||
elif gpt_say == "4":
|
||||
elif gpt_say == '4':
|
||||
i_say = PROMPT_4.format(subject=results_txt)
|
||||
elif gpt_say == "5":
|
||||
elif gpt_say == '5':
|
||||
i_say = PROMPT_5.format(subject=results_txt)
|
||||
elif gpt_say == "6":
|
||||
elif gpt_say == '6':
|
||||
i_say = PROMPT_6.format(subject=results_txt)
|
||||
elif gpt_say == "7":
|
||||
i_say = PROMPT_7.replace("{subject}", results_txt) # 由于实体关系图用到了{}符号
|
||||
elif gpt_say == "8":
|
||||
elif gpt_say == '7':
|
||||
i_say = PROMPT_7.replace("{subject}", results_txt) #由于实体关系图用到了{}符号
|
||||
elif gpt_say == '8':
|
||||
i_say = PROMPT_8.format(subject=results_txt)
|
||||
elif gpt_say == "9":
|
||||
elif gpt_say == '9':
|
||||
i_say = PROMPT_9.format(subject=results_txt)
|
||||
i_say_show_user = f"请根据判断结果绘制相应的图表。如需绘制思维导图请使用参数调用,同时过大的图表可能需要复制到在线编辑器中进行渲染。"
|
||||
i_say_show_user = f'请根据判断结果绘制相应的图表。如需绘制思维导图请使用参数调用,同时过大的图表可能需要复制到在线编辑器中进行渲染。'
|
||||
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="",
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="你精通使用mermaid语法来绘制图表,首先确保语法正确,其次避免在mermaid语法中使用不允许的字符,此外也应当分考虑图表的可读性。"
|
||||
)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
def 输入区文件处理(txt):
|
||||
if txt == "": return False, txt
|
||||
success = True
|
||||
import glob
|
||||
from .crazy_utils import get_files_from_everything
|
||||
file_pdf,pdf_manifest,folder_pdf = get_files_from_everything(txt, '.pdf')
|
||||
file_md,md_manifest,folder_md = get_files_from_everything(txt, '.md')
|
||||
if len(pdf_manifest) == 0 and len(md_manifest) == 0:
|
||||
return False, txt #如输入区内容不是文件则直接返回输入区内容
|
||||
|
||||
final_result = ""
|
||||
if file_pdf:
|
||||
for index, fp in enumerate(pdf_manifest):
|
||||
file_content, page_one = read_and_clean_pdf_text(fp) # （尝试）按照章节切割PDF
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
|
||||
final_result += "\n" + file_content
|
||||
if file_md:
|
||||
for index, fp in enumerate(md_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
file_content = file_content.encode('utf-8', 'ignore').decode()
|
||||
final_result += "\n" + file_content
|
||||
return True, final_result
|
||||
|
||||
@CatchException
|
||||
def 生成多种Mermaid图表(
|
||||
txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port
|
||||
):
|
||||
def 生成多种Mermaid图表(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
|
||||
@@ -312,126 +275,28 @@ def 生成多种Mermaid图表(
|
||||
import os
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append(
|
||||
[
|
||||
chatbot.append([
|
||||
"函数插件功能？",
|
||||
"根据当前聊天历史或指定的路径文件(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\
|
||||
\n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918",
|
||||
]
|
||||
)
|
||||
"根据当前聊天历史或文件中(文件内容优先)绘制多种mermaid图表，将会由对话模型首先判断适合的图表类型，随后绘制图表。\
|
||||
\n您也可以使用插件参数指定绘制的图表类型,函数插件贡献者: Menghuan1918"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
if os.path.exists(txt): # 如输入区无内容则直接解析历史记录
|
||||
from crazy_functions.pdf_fns.parse_word import extract_text_from_files
|
||||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||||
try:
|
||||
import fitz
|
||||
except:
|
||||
report_exception(chatbot, history,
|
||||
a = f"解析项目: {txt}",
|
||||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
file_exist, final_result, page_one, file_manifest, exception = (
|
||||
extract_text_from_files(txt, chatbot, history)
|
||||
)
|
||||
if os.path.exists(txt): #如输入区无内容则直接解析历史记录
|
||||
file_exist, txt = 输入区文件处理(txt)
|
||||
else:
|
||||
file_exist = False
|
||||
exception = ""
|
||||
file_manifest = []
|
||||
|
||||
if exception != "":
|
||||
if exception == "word":
|
||||
report_exception(
|
||||
chatbot,
|
||||
history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"找到了.doc文件，但是该文件格式不被支持，请先转化为.docx格式。",
|
||||
)
|
||||
if file_exist : history = [] #如输入区内容为文件则清空历史记录
|
||||
history.append(txt) #将解析后的txt传递加入到历史中
|
||||
|
||||
elif exception == "pdf":
|
||||
report_exception(
|
||||
chatbot,
|
||||
history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。",
|
||||
)
|
||||
|
||||
elif exception == "word_pip":
|
||||
report_exception(
|
||||
chatbot,
|
||||
history,
|
||||
a=f"解析项目: {txt}",
|
||||
b=f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade python-docx pywin32```。",
|
||||
)
|
||||
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
else:
|
||||
if not file_exist:
|
||||
history.append(txt) # 如输入区不是文件则将输入区内容加入历史记录
|
||||
i_say_show_user = f"首先你从历史记录中提取摘要。"
|
||||
gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 更新UI
|
||||
yield from 解析历史输入(
|
||||
history, llm_kwargs, file_manifest, chatbot, plugin_kwargs
|
||||
)
|
||||
else:
|
||||
file_num = len(file_manifest)
|
||||
for i in range(file_num): # 依次处理文件
|
||||
i_say_show_user = f"[{i+1}/{file_num}]处理文件{file_manifest[i]}"
|
||||
gpt_say = "[Local Message] 收到。" # 用户提示
|
||||
chatbot.append([i_say_show_user, gpt_say])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 更新UI
|
||||
history = [] # 如输入区内容为文件则清空历史记录
|
||||
history.append(final_result[i])
|
||||
yield from 解析历史输入(
|
||||
history, llm_kwargs, file_manifest, chatbot, plugin_kwargs
|
||||
)
|
||||
|
||||
|
||||
class Mermaid_Gen(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
gui_definition = {
|
||||
"Type_of_Mermaid": ArgProperty(
|
||||
title="绘制的Mermaid图表类型",
|
||||
options=[
|
||||
"由LLM决定",
|
||||
"流程图",
|
||||
"序列图",
|
||||
"类图",
|
||||
"饼图",
|
||||
"甘特图",
|
||||
"状态图",
|
||||
"实体关系图",
|
||||
"象限提示图",
|
||||
"思维导图",
|
||||
],
|
||||
default_value="由LLM决定",
|
||||
description="选择'由LLM决定'时将由对话模型判断适合的图表类型(不包括思维导图)，选择其他类型时将直接绘制指定的图表类型。",
|
||||
type="dropdown",
|
||||
).model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(
|
||||
txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request
|
||||
):
|
||||
options = [
|
||||
"由LLM决定",
|
||||
"流程图",
|
||||
"序列图",
|
||||
"类图",
|
||||
"饼图",
|
||||
"甘特图",
|
||||
"状态图",
|
||||
"实体关系图",
|
||||
"象限提示图",
|
||||
"思维导图",
|
||||
]
|
||||
plugin_kwargs = options.index(plugin_kwargs['Type_of_Mermaid'])
|
||||
yield from 生成多种Mermaid图表(
|
||||
txt,
|
||||
llm_kwargs,
|
||||
plugin_kwargs,
|
||||
chatbot,
|
||||
history,
|
||||
system_prompt,
|
||||
user_request,
|
||||
)
|
||||
yield from 解析历史输入(history,llm_kwargs,chatbot,plugin_kwargs)
|
||||
@@ -1,6 +1,6 @@
|
||||
from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_latest_msg, get_log_folder, get_user
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
|
||||
from loguru import logger
|
||||
from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg, get_log_folder, get_user
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
|
||||
|
||||
install_msg ="""
|
||||
|
||||
1. python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
|
||||
@@ -40,16 +40,16 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
# from crazy_functions.crazy_utils import try_install_deps
|
||||
# from .crazy_utils import try_install_deps
|
||||
# try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
# yield from update_ui_latest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
# yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
return
|
||||
|
||||
# < --------------------读取文件--------------- >
|
||||
file_manifest = []
|
||||
spl = ["txt", "doc", "docx", "email", "epub", "html", "json", "md", "msg", "pdf", "ppt", "pptx", "rtf"]
|
||||
for sp in spl:
|
||||
_, file_manifest_tmp, _ = get_files_from_everything(txt, type=f'.{sp}', chatbot=chatbot)
|
||||
_, file_manifest_tmp, _ = get_files_from_everything(txt, type=f'.{sp}')
|
||||
file_manifest += file_manifest_tmp
|
||||
|
||||
if len(file_manifest) == 0:
|
||||
@@ -60,7 +60,7 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
# < -------------------预热文本向量化模组--------------- >
|
||||
chatbot.append(['<br/>'.join(file_manifest), "正在预热文本向量化模组, 如果是第一次运行, 将消耗较长时间下载中文向量化模型..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
logger.info('Checking Text2vec ...')
|
||||
print('Checking Text2vec ...')
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
|
||||
@@ -68,7 +68,7 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
# < -------------------构建知识库--------------- >
|
||||
chatbot.append(['<br/>'.join(file_manifest), "正在构建知识库..."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
logger.info('Establishing knowledge archive ...')
|
||||
print('Establishing knowledge archive ...')
|
||||
with ProxyNetworkActivate('Download_LLM'): # 临时地激活代理网络
|
||||
kai = knowledge_archive_interface()
|
||||
vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
|
||||
@@ -93,9 +93,9 @@ def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
except Exception as e:
|
||||
chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
# from crazy_functions.crazy_utils import try_install_deps
|
||||
# from .crazy_utils import try_install_deps
|
||||
# try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
|
||||
# yield from update_ui_latest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
# yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
|
||||
return
|
||||
|
||||
# < ------------------- --------------- >
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from request_llms.bridge_all import model_info
|
||||
@@ -23,8 +23,8 @@ def google(query, proxies):
|
||||
item = {'title': title, 'link': link}
|
||||
results.append(item)
|
||||
|
||||
# for r in results:
|
||||
# print(r['link'])
|
||||
for r in results:
|
||||
print(r['link'])
|
||||
return results
|
||||
|
||||
def scrape_text(url, proxies) -> str:
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
from toolbox import CatchException, update_ui
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from request_llms.bridge_all import model_info
|
||||
@@ -22,8 +22,8 @@ def bing_search(query, proxies=None):
|
||||
item = {'title': title, 'link': link}
|
||||
results.append(item)
|
||||
|
||||
# for r in results:
|
||||
# print(r['link'])
|
||||
for r in results:
|
||||
print(r['link'])
|
||||
return results
|
||||
|
||||
|
||||
|
||||
@@ -47,7 +47,7 @@ explain_msg = """
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import List
|
||||
from toolbox import CatchException, update_ui, is_the_upload_folder
|
||||
from toolbox import update_ui_latest_msg, disable_auto_promotion
|
||||
from toolbox import update_ui_lastest_msg, disable_auto_promotion
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
@@ -113,19 +113,19 @@ def 虚空终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
|
||||
# 用简单的关键词检测用户意图
|
||||
is_certain, _ = analyze_intention_with_simple_rules(txt)
|
||||
if is_the_upload_folder(txt):
|
||||
state.set_state(chatbot=chatbot, key='has_provided_explanation', value=False)
|
||||
state.set_state(chatbot=chatbot, key='has_provided_explaination', value=False)
|
||||
appendix_msg = "\n\n**很好，您已经上传了文件**，现在请您描述您的需求。"
|
||||
|
||||
if is_certain or (state.has_provided_explanation):
|
||||
if is_certain or (state.has_provided_explaination):
|
||||
# 如果意图明确，跳过提示环节
|
||||
state.set_state(chatbot=chatbot, key='has_provided_explanation', value=True)
|
||||
state.set_state(chatbot=chatbot, key='has_provided_explaination', value=True)
|
||||
state.unlock_plugin(chatbot=chatbot)
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
yield from 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
return
|
||||
else:
|
||||
# 如果意图模糊，提示
|
||||
state.set_state(chatbot=chatbot, key='has_provided_explanation', value=True)
|
||||
state.set_state(chatbot=chatbot, key='has_provided_explaination', value=True)
|
||||
state.lock_plugin(chatbot=chatbot)
|
||||
chatbot.append(("虚空终端状态:", explain_msg+appendix_msg))
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
@@ -141,7 +141,7 @@ def 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
# ⭐ ⭐ ⭐ 分析用户意图
|
||||
is_certain, user_intention = analyze_intention_with_simple_rules(txt)
|
||||
if not is_certain:
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"正在执行任务: {txt}\n\n分析用户意图中", chatbot=chatbot, history=history, delay=0)
|
||||
gpt_json_io = GptJsonIO(UserIntention)
|
||||
rf_req = "\nchoose from ['ModifyConfiguration', 'ExecutePlugin', 'Chat']"
|
||||
@@ -154,13 +154,13 @@ def 虚空终端主路由(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
user_intention = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
|
||||
lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 意图={explain_intention_to_user[user_intention.intention_type]}",
|
||||
except JsonStringError as e:
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 失败 当前语言模型（{llm_kwargs['llm_model']}）不能理解您的意图", chatbot=chatbot, history=history, delay=0)
|
||||
return
|
||||
else:
|
||||
pass
|
||||
|
||||
yield from update_ui_latest_msg(
|
||||
yield from update_ui_lastest_msg(
|
||||
lastmsg=f"正在执行任务: {txt}\n\n用户意图理解: 意图={explain_intention_to_user[user_intention.intention_type]}",
|
||||
chatbot=chatbot, history=history, delay=0)
|
||||
|
||||
|
||||
@@ -12,12 +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
|
||||
@@ -64,7 +58,7 @@ def parseNotebook(filename, enable_markdown=1):
|
||||
|
||||
|
||||
def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
enable_markdown = plugin_kwargs.get("advanced_arg", "1")
|
||||
@@ -126,8 +120,6 @@ def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
import os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "":
|
||||
txt = '空空如也的输入栏'
|
||||
|
||||
@@ -1,12 +1,12 @@
|
||||
from toolbox import update_ui, promote_file_to_downloadzone
|
||||
from toolbox import update_ui, promote_file_to_downloadzone, disable_auto_promotion
|
||||
from toolbox import CatchException, report_exception, write_history_to_file
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from .crazy_utils import input_clipping
|
||||
|
||||
def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import os, copy
|
||||
from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
disable_auto_promotion(chatbot=chatbot)
|
||||
|
||||
summary_batch_isolation = True
|
||||
inputs_array = []
|
||||
@@ -23,7 +23,7 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
file_content = f.read()
|
||||
prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index+1}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {fp}'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {fp}'
|
||||
# 装载请求内容
|
||||
inputs_array.append(i_say)
|
||||
inputs_show_user_array.append(i_say_show_user)
|
||||
@@ -128,7 +128,6 @@ def 解析一个Python项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -147,7 +146,6 @@ def 解析一个Matlab项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析Matlab项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -166,7 +164,6 @@ def 解析一个C项目的头文件(txt, llm_kwargs, plugin_kwargs, chatbot, his
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -187,7 +184,6 @@ def 解析一个C项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -210,7 +206,6 @@ def 解析一个Java项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -233,7 +228,6 @@ def 解析一个前端项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -263,7 +257,6 @@ def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -285,7 +278,6 @@ def 解析一个Rust项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -306,7 +298,6 @@ def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -329,7 +320,6 @@ def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -355,19 +345,15 @@ def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys
|
||||
pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
|
||||
pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
|
||||
# 将要忽略匹配的文件名(例如: ^README.md)
|
||||
pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", r"\.") # 移除左边通配符，移除右侧逗号，转义点号
|
||||
for _ in txt_pattern.split(" ") # 以空格分割
|
||||
if (_ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")) # ^开始，但不是^*.开始
|
||||
]
|
||||
pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
|
||||
# 生成正则表达式
|
||||
pattern_except = r'/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
|
||||
pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
|
||||
pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
|
||||
|
||||
history.clear()
|
||||
import glob, os, re
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
@@ -1,5 +1,5 @@
|
||||
from toolbox import CatchException, update_ui, get_conf
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
import datetime
|
||||
@CatchException
|
||||
def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
@@ -1,13 +1,11 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, get_conf, markdown_convertion
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from crazy_functions.agent_fns.watchdog import WatchDog
|
||||
from crazy_functions.live_audio.aliyunASR import AliyunASR
|
||||
from loguru import logger
|
||||
|
||||
from request_llms.bridge_all import predict_no_ui_long_connection
|
||||
import threading, time
|
||||
import numpy as np
|
||||
from .live_audio.aliyunASR import AliyunASR
|
||||
import json
|
||||
import re
|
||||
|
||||
@@ -42,11 +40,11 @@ class AsyncGptTask():
|
||||
MAX_TOKEN_ALLO = 2560
|
||||
i_say, history = input_clipping(i_say, history, max_token_limit=MAX_TOKEN_ALLO)
|
||||
gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt,
|
||||
observe_window=observe_window[index], console_silence=True)
|
||||
observe_window=observe_window[index], console_slience=True)
|
||||
except ConnectionAbortedError as token_exceed_err:
|
||||
logger.error('至少一个线程任务Token溢出而失败', e)
|
||||
print('至少一个线程任务Token溢出而失败', e)
|
||||
except Exception as e:
|
||||
logger.error('至少一个线程任务意外失败', e)
|
||||
print('至少一个线程任务意外失败', e)
|
||||
|
||||
def add_async_gpt_task(self, i_say, chatbot_index, llm_kwargs, history, system_prompt):
|
||||
self.observe_future.append([""])
|
||||
|
||||
@@ -1,18 +1,19 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_exception
|
||||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
|
||||
def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import time, glob, os
|
||||
print('begin analysis on:', file_manifest)
|
||||
for index, fp in enumerate(file_manifest):
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
|
||||
prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index+1}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
|
||||
chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
@@ -48,8 +49,6 @@ def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from toolbox import CatchException, report_exception, promote_file_to_downloadzone
|
||||
from toolbox import update_ui, update_ui_latest_msg, disable_auto_promotion, write_history_to_file
|
||||
from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file
|
||||
import logging
|
||||
import requests
|
||||
import time
|
||||
@@ -156,7 +156,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
history = []
|
||||
meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
|
||||
if len(meta_paper_info_list) == 0:
|
||||
yield from update_ui_latest_msg(lastmsg='获取文献失败，可能触发了google反爬虫机制。',chatbot=chatbot, history=history, delay=0)
|
||||
yield from update_ui_lastest_msg(lastmsg='获取文献失败，可能触发了google反爬虫机制。',chatbot=chatbot, history=history, delay=0)
|
||||
return
|
||||
batchsize = 5
|
||||
for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
|
||||
|
||||
@@ -2,10 +2,6 @@ from toolbox import CatchException, update_ui
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
import datetime
|
||||
|
||||
####################################################################################################################
|
||||
# Demo 1: 一个非常简单的插件 #########################################################################################
|
||||
####################################################################################################################
|
||||
|
||||
高阶功能模板函数示意图 = f"""
|
||||
```mermaid
|
||||
flowchart TD
|
||||
@@ -30,7 +26,7 @@ flowchart TD
|
||||
"""
|
||||
|
||||
@CatchException
|
||||
def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, num_day=5):
|
||||
def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
# 高阶功能模板函数示意图：https://mermaid.live/edit#pako:eNptk1tvEkEYhv8KmattQpvlvOyFCcdeeaVXuoYssBwie8gyhCIlqVoLhrbbtAWNUpEGUkyMEDW2Fmn_DDOL_8LZHdOwxrnamX3f7_3mmZk6yKhZCfAgV1KrmYKoQ9fDuKC4yChX0nld1Aou1JzjznQ5fWmejh8LYHW6vG2a47YAnlCLNSIRolnenKBXI_zRIBrcuqRT890u7jZx7zMDt-AaMbnW1--5olGiz2sQjwfoQxsZL0hxplSSU0-rop4vrzmKR6O2JxYjHmwcL2Y_HDatVMkXlf86YzHbGY9bO5j8XE7O8Nsbc3iNB3ukL2SMcH-XIQBgWoVOZzxuOxOJOyc63EPGV6ZQLENVrznViYStTiaJ2vw2M2d9bByRnOXkgCnXylCSU5quyto_IcmkbdvctELmJ-j1ASW3uB3g5xOmKqVTmqr_Na3AtuS_dtBFm8H90XJyHkDDT7S9xXWb4HGmRChx64AOL5HRpUm411rM5uh4H78Z4V7fCZzytjZz2seto9XaNPFue07clLaVZF8UNLygJ-VES8lah_n-O-5Ozc7-77NzJ0-K0yr0ZYrmHdqAk50t2RbA4qq9uNohBASw7YpSgaRkLWCCAtxAlnRZLGbJba9bPwUAC5IsCYAnn1kpJ1ZKUACC0iBSsQLVBzUlA3ioVyQ3qGhZEUrxokiehAz4nFgqk1VNVABfB1uAD_g2_AGPl-W8nMcbCvsDblADfNCz4feyobDPy3rYEMtxwYYbPFNVUoHdCPmDHBv2cP4AMfrCbiBli-Q-3afv0X6WdsIjW2-10fgDy1SAig
|
||||
|
||||
@@ -47,7 +43,7 @@ def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
"您正在调用插件：历史上的今天",
|
||||
"[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板（该函数只有20多行代码）。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！" + 高阶功能模板函数示意图))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
for i in range(int(num_day)):
|
||||
for i in range(5):
|
||||
currentMonth = (datetime.date.today() + datetime.timedelta(days=i)).month
|
||||
currentDay = (datetime.date.today() + datetime.timedelta(days=i)).day
|
||||
i_say = f'历史中哪些事件发生在{currentMonth}月{currentDay}日？列举两条并发送相关图片。发送图片时，请使用Markdown，将Unsplash API中的PUT_YOUR_QUERY_HERE替换成描述该事件的一个最重要的单词。'
|
||||
@@ -63,56 +59,6 @@ def 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
####################################################################################################################
|
||||
# Demo 2: 一个带二级菜单的插件 #######################################################################################
|
||||
####################################################################################################################
|
||||
|
||||
from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
|
||||
class Demo_Wrap(GptAcademicPluginTemplate):
|
||||
def __init__(self):
|
||||
"""
|
||||
请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
|
||||
"""
|
||||
pass
|
||||
|
||||
def define_arg_selection_menu(self):
|
||||
"""
|
||||
定义插件的二级选项菜单
|
||||
"""
|
||||
gui_definition = {
|
||||
"num_day":
|
||||
ArgProperty(title="日期选择", options=["仅今天", "未来3天", "未来5天"], default_value="未来3天", description="无", type="dropdown").model_dump_json(),
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
"""
|
||||
执行插件
|
||||
"""
|
||||
num_day = plugin_kwargs["num_day"]
|
||||
if num_day == "仅今天": num_day = 1
|
||||
if num_day == "未来3天": num_day = 3
|
||||
if num_day == "未来5天": num_day = 5
|
||||
yield from 高阶功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, num_day=num_day)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
####################################################################################################################
|
||||
# Demo 3: 绘制脑图的Demo ############################################################################################
|
||||
####################################################################################################################
|
||||
|
||||
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
|
||||
|
||||
@@ -4,9 +4,9 @@
|
||||
# 1. 请在以下方案中选择任意一种，然后删除其他的方案
|
||||
# 2. 修改你选择的方案中的environment环境变量，详情请见github wiki或者config.py
|
||||
# 3. 选择一种暴露服务端口的方法，并对相应的配置做出修改：
|
||||
# 「方法1: 适用于Linux，很方便，可惜windows不支持」与宿主的网络融合为一体，这个是默认配置
|
||||
# 【方法1: 适用于Linux，很方便，可惜windows不支持】与宿主的网络融合为一体，这个是默认配置
|
||||
# network_mode: "host"
|
||||
# 「方法2: 适用于所有系统包括Windows和MacOS」端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容）
|
||||
# 【方法2: 适用于所有系统包括Windows和MacOS】端口映射，把容器的端口映射到宿主的端口（注意您需要先删除network_mode: "host"，再追加以下内容）
|
||||
# ports:
|
||||
# - "12345:12345" # 注意！12345必须与WEB_PORT环境变量相互对应
|
||||
# 4. 最后`docker-compose up`运行
|
||||
@@ -25,7 +25,7 @@
|
||||
## ===================================================
|
||||
|
||||
## ===================================================
|
||||
## 「方案零」 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
|
||||
## 【方案零】 部署项目的全部能力（这个是包含cuda和latex的大型镜像。如果您网速慢、硬盘小或没有显卡，则不推荐使用这个）
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
@@ -63,10 +63,10 @@ services:
|
||||
# count: 1
|
||||
# capabilities: [gpu]
|
||||
|
||||
# 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
|
||||
# 【WEB_PORT暴露方法1: 适用于Linux】与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 「WEB_PORT暴露方法2: 适用于所有系统」端口映射
|
||||
# 【WEB_PORT暴露方法2: 适用于所有系统】端口映射
|
||||
# ports:
|
||||
# - "12345:12345" # 12345必须与WEB_PORT相互对应
|
||||
|
||||
@@ -75,8 +75,10 @@ services:
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
|
||||
|
||||
## ===================================================
|
||||
## 「方案一」 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
|
||||
## 【方案一】 如果不需要运行本地模型（仅 chatgpt, azure, 星火, 千帆, claude 等在线大模型服务）
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
@@ -95,16 +97,16 @@ services:
|
||||
# DEFAULT_WORKER_NUM: ' 10 '
|
||||
# AUTHENTICATION: ' [("username", "passwd"), ("username2", "passwd2")] '
|
||||
|
||||
# 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 启动命令
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
### ===================================================
|
||||
### 「方案二」 如果需要运行ChatGLM + Qwen + MOSS等本地模型
|
||||
### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
|
||||
### ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
@@ -128,10 +130,8 @@ services:
|
||||
devices:
|
||||
- /dev/nvidia0:/dev/nvidia0
|
||||
|
||||
# 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 启动命令
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
@@ -139,9 +139,8 @@ services:
|
||||
# command: >
|
||||
# bash -c "pip install -r request_llms/requirements_qwen.txt && python3 -u main.py"
|
||||
|
||||
|
||||
### ===================================================
|
||||
### 「方案三」 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
|
||||
### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
|
||||
### ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
@@ -165,22 +164,21 @@ services:
|
||||
devices:
|
||||
- /dev/nvidia0:/dev/nvidia0
|
||||
|
||||
# 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 启动命令
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
python3 -u main.py
|
||||
|
||||
|
||||
## ===================================================
|
||||
## 「方案四」 ChatGPT + Latex
|
||||
## 【方案四】 ChatGPT + Latex
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_latex:
|
||||
image: ghcr.io/binary-husky/gpt_academic_with_latex:master # (Auto Built by Dockerfile: docs/GithubAction+NoLocal+Latex)
|
||||
# 对于ARM64设备，请将以上镜像名称替换为 ghcr.io/binary-husky/gpt_academic_with_latex_arm:master
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx '
|
||||
@@ -192,16 +190,16 @@ services:
|
||||
DEFAULT_WORKER_NUM: ' 10 '
|
||||
WEB_PORT: ' 12303 '
|
||||
|
||||
# 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 启动命令
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
## ===================================================
|
||||
## 「方案五」 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
|
||||
## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
@@ -225,9 +223,9 @@ services:
|
||||
# (无需填写) ALIYUN_ACCESSKEY: ' LTAI5q6BrFUzoRXVGUWnekh1 '
|
||||
# (无需填写) ALIYUN_SECRET: ' eHmI20AVWIaQZ0CiTD2bGQVsaP9i68 '
|
||||
|
||||
# 「WEB_PORT暴露方法1: 适用于Linux」与宿主的网络融合
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 启动命令
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
1
docs/Dockerfile+JittorLLM
普通文件
1
docs/Dockerfile+JittorLLM
普通文件
@@ -0,0 +1 @@
|
||||
# 此Dockerfile不再维护，请前往docs/GithubAction+JittorLLMs
|
||||
@@ -3,13 +3,6 @@
|
||||
# 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
|
||||
|
||||
# edge-tts需要的依赖，某些pip包所需的依赖
|
||||
RUN apt update && apt install ffmpeg build-essential -y
|
||||
RUN apt-get install -y fontconfig
|
||||
RUN ln -s /usr/local/texlive/2023/texmf-dist/fonts/truetype /usr/share/fonts/truetype/texlive
|
||||
RUN fc-cache -fv
|
||||
RUN apt-get clean
|
||||
|
||||
# use python3 as the system default python
|
||||
WORKDIR /gpt
|
||||
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
|
||||
@@ -34,7 +27,7 @@ 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
|
||||
RUN python3 -m pip cache purge
|
||||
|
||||
|
||||
# 预热Tiktoken模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
某些文件未显示，因为此 diff 中更改的文件太多 显示更多
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