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
71 次代码提交
version3.2
...
version3.3
| 作者 | SHA1 | 提交日期 | |
|---|---|---|---|
|
|
6538c58b8e | ||
|
|
e35eb9048e | ||
|
|
a0fa64de47 | ||
|
|
e04946c816 | ||
|
|
231c9c2e57 | ||
|
|
48555f570c | ||
|
|
7c9195ddd2 | ||
|
|
5500fbe682 | ||
|
|
5a83b3b096 | ||
|
|
4783fd6f37 | ||
|
|
9a4b56277c | ||
|
|
5eea959103 | ||
|
|
856df8fb62 | ||
|
|
8e59412c47 | ||
|
|
8f571ff68f | ||
|
|
b6d2766e59 | ||
|
|
73ce471a0e | ||
|
|
4e113139c8 | ||
|
|
e4c4b28ddf | ||
|
|
081acc6404 | ||
|
|
1a999497d7 | ||
|
|
6137963355 | ||
|
|
22bffdb737 | ||
|
|
75adcbffeb | ||
|
|
4451770061 | ||
|
|
09c413a272 | ||
|
|
ddb6c90a8f | ||
|
|
71590426f9 | ||
|
|
b3e5cdb3a5 | ||
|
|
6595ab813e | ||
|
|
d1efbd26da | ||
|
|
f04683732e | ||
|
|
cb0241db78 | ||
|
|
a097b6cd03 | ||
|
|
487ffe7888 | ||
|
|
51424a7d08 | ||
|
|
06e8e8f9a6 | ||
|
|
0512b311f8 | ||
|
|
81d53d0726 | ||
|
|
a141c5ccdc | ||
|
|
e361d741c3 | ||
|
|
f5bc58dbde | ||
|
|
e7b73f3041 | ||
|
|
ed8db8c8ae | ||
|
|
df97213d3b | ||
|
|
97443d1f83 | ||
|
|
59bed52faf | ||
|
|
3814c3a915 | ||
|
|
d98d0a291e | ||
|
|
ee94fa6dc4 | ||
|
|
d2e46f6684 | ||
|
|
5948dcacd5 | ||
|
|
3041858e7f | ||
|
|
9c2a6bc413 | ||
|
|
1cf8b6c6c8 | ||
|
|
781ef4487c | ||
|
|
4a494354b1 | ||
|
|
385c775aa5 | ||
|
|
518385dea2 | ||
|
|
4d1eea7bd5 | ||
|
|
9cb51ccc70 | ||
|
|
94dc398163 | ||
|
|
65317e33af | ||
|
|
06fbdf43af | ||
|
|
ab61418410 | ||
|
|
0785ff2aed | ||
|
|
676fe40d39 | ||
|
|
0b89673ee9 | ||
|
|
2f4e050612 | ||
|
|
2b96217f2b | ||
|
|
13342c2988 |
130
README.md
130
README.md
@@ -1,8 +1,13 @@
|
||||
|
||||
> **Note**
|
||||
>
|
||||
> 本项目依赖的Gradio组件的新版pip包(Gradio 3.26~3.27)有严重bug。所以,请在安装时严格选择requirements.txt中**指定的版本**。
|
||||
>
|
||||
> `pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
|
||||
>
|
||||
|
||||
# <img src="docs/logo.png" width="40" > ChatGPT 学术优化
|
||||
|
||||
**如果喜欢这个项目,请给它一个Star;如果你发明了更好用的快捷键或函数插件,欢迎发issue或者pull requests**
|
||||
**如果喜欢这个项目,请给它一个Star;如果你发明了更好用的快捷键或函数插件,欢迎发pull requests**
|
||||
|
||||
If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a README in [English|](docs/README_EN.md)[日本語|](docs/README_JP.md)[Русский|](docs/README_RS.md)[Français](docs/README_FR.md) translated by this project itself.
|
||||
|
||||
@@ -20,25 +25,25 @@ If you like this project, please give it a Star. If you've come up with more use
|
||||
--- | ---
|
||||
一键润色 | 支持一键润色、一键查找论文语法错误
|
||||
一键中英互译 | 一键中英互译
|
||||
一键代码解释 | 可以正确显示代码、解释代码
|
||||
一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释
|
||||
[自定义快捷键](https://www.bilibili.com/video/BV14s4y1E7jN) | 支持自定义快捷键
|
||||
[配置代理服务器](https://www.bilibili.com/video/BV1rc411W7Dr) | 支持配置代理服务器
|
||||
模块化设计 | 支持自定义高阶的函数插件与[函数插件],插件支持[热更新](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
|
||||
模块化设计 | 支持自定义强大的[函数插件](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions),插件支持[热更新](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
|
||||
[自我程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] [一键读懂](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)本项目的源代码
|
||||
[程序剖析](https://www.bilibili.com/video/BV1cj411A7VW) | [函数插件] 一键可以剖析其他Python/C/C++/Java/Lua/...项目树
|
||||
读论文 | [函数插件] 一键解读latex论文全文并生成摘要
|
||||
Latex全文翻译、润色 | [函数插件] 一键翻译或润色latex论文
|
||||
读论文、翻译论文 | [函数插件] 一键解读latex/pdf论文全文并生成摘要
|
||||
Latex全文[翻译](https://www.bilibili.com/video/BV1nk4y1Y7Js/)、[润色](https://www.bilibili.com/video/BV1FT411H7c5/) | [函数插件] 一键翻译或润色latex论文
|
||||
批量注释生成 | [函数插件] 一键批量生成函数注释
|
||||
Markdown[中英互译](https://www.bilibili.com/video/BV1yo4y157jV/) | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)了吗?
|
||||
chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
|
||||
Markdown中英互译 | [函数插件] 看到上面5种语言的[README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)了吗?
|
||||
[arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
|
||||
[PDF论文全文翻译功能](https://www.bilibili.com/video/BV1KT411x7Wn) | [函数插件] PDF论文提取题目&摘要+翻译全文(多线程)
|
||||
[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL,让gpt帮你选择有趣的文章
|
||||
公式/图片/表格显示 | 可以同时显示公式的tex形式和渲染形式,支持公式、代码高亮
|
||||
多线程函数插件支持 | 支持多线调用chatgpt,一键处理海量文本或程序
|
||||
[Arxiv小助手](https://www.bilibili.com/video/BV1LM4y1279X) | [函数插件] 输入arxiv文章url即可一键翻译摘要+下载PDF
|
||||
[谷歌学术统合小助手](https://www.bilibili.com/video/BV19L411U7ia) | [函数插件] 给定任意谷歌学术搜索页面URL,让gpt帮你[写relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
|
||||
互联网信息聚合+GPT | [函数插件] 一键让ChatGPT先Google搜索,再回答问题,信息流永不过时
|
||||
公式/图片/表格显示 | 可以同时显示公式的[tex形式和渲染形式](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png),支持公式、代码高亮
|
||||
多线程函数插件支持 | 支持多线调用chatgpt,一键处理[海量文本](https://www.bilibili.com/video/BV1FT411H7c5/)或程序
|
||||
启动暗色gradio[主题](https://github.com/binary-husky/chatgpt_academic/issues/173) | 在浏览器url后面添加```/?__dark-theme=true```可以切换dark主题
|
||||
[多LLM模型](https://www.bilibili.com/video/BV1wT411p7yf)支持,[API2D](https://api2d.com/)接口支持 | 同时被GPT3.5、GPT4和[清华ChatGLM](https://github.com/THUDM/ChatGLM-6B)伺候的感觉一定会很不错吧?
|
||||
huggingface免科学上网[在线体验](https://huggingface.co/spaces/qingxu98/gpt-academic) | 登陆huggingface后复制[此空间](https://huggingface.co/spaces/qingxu98/gpt-academic)
|
||||
更多LLM模型接入 | 新加入Newbing测试接口(新必应AI)
|
||||
…… | ……
|
||||
|
||||
</div>
|
||||
@@ -75,9 +80,6 @@ huggingface免科学上网[在线体验](https://huggingface.co/spaces/qingxu98/
|
||||
<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
|
||||
</div>
|
||||
|
||||
多种大语言模型混合调用[huggingface测试版](https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta)(huggingface版不支持chatglm)
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 安装-方法1:直接运行 (Windows, Linux or MacOS)
|
||||
@@ -88,14 +90,10 @@ git clone https://github.com/binary-husky/chatgpt_academic.git
|
||||
cd chatgpt_academic
|
||||
```
|
||||
|
||||
2. 配置API_KEY和代理设置
|
||||
2. 配置API_KEY
|
||||
|
||||
在`config.py`中,配置API KEY等[设置](https://github.com/binary-husky/gpt_academic/issues/1) 。
|
||||
|
||||
在`config.py`中,配置 海外Proxy 和 OpenAI API KEY,说明如下
|
||||
```
|
||||
1. 如果你在国内,需要设置海外代理才能够顺利使用OpenAI API,设置方法请仔细阅读config.py(1.修改其中的USE_PROXY为True; 2.按照说明修改其中的proxies)。
|
||||
2. 配置 OpenAI API KEY。支持任意数量的OpenAI的密钥和API2D的密钥共存/负载均衡,多个KEY用英文逗号分隔即可,例如输入 API_KEY="OpenAI密钥1,API2D密钥2,OpenAI密钥3,OpenAI密钥4"
|
||||
3. 与代理网络有关的issue(网络超时、代理不起作用)汇总到 https://github.com/binary-husky/chatgpt_academic/issues/1
|
||||
```
|
||||
(P.S. 程序运行时会优先检查是否存在名为`config_private.py`的私密配置文件,并用其中的配置覆盖`config.py`的同名配置。因此,如果您能理解我们的配置读取逻辑,我们强烈建议您在`config.py`旁边创建一个名为`config_private.py`的新配置文件,并把`config.py`中的配置转移(复制)到`config_private.py`中。`config_private.py`不受git管控,可以让您的隐私信息更加安全。)
|
||||
|
||||
|
||||
@@ -123,14 +121,8 @@ python main.py
|
||||
|
||||
5. 测试函数插件
|
||||
```
|
||||
- 测试Python项目分析
|
||||
(选择1)input区域 输入 `./crazy_functions/test_project/python/dqn` , 然后点击 "解析整个Python项目"
|
||||
(选择2)展开文件上传区,将python文件/包含python文件的压缩包拖拽进去,在出现反馈提示后, 然后点击 "解析整个Python项目"
|
||||
- 测试自我代码解读(本项目自译解)
|
||||
点击 "[多线程Demo] 解析此项目本身(源码自译解)"
|
||||
- 测试函数插件模板函数(要求gpt回答历史上的今天发生了什么),您可以根据此函数为模板,实现更复杂的功能
|
||||
点击 "[函数插件模板Demo] 历史上的今天"
|
||||
- 函数插件区下拉菜单中有更多功能可供选择
|
||||
```
|
||||
|
||||
## 安装-方法2:使用Docker
|
||||
@@ -141,7 +133,7 @@ python main.py
|
||||
# 下载项目
|
||||
git clone https://github.com/binary-husky/chatgpt_academic.git
|
||||
cd chatgpt_academic
|
||||
# 配置 “海外Proxy”, “API_KEY” 以及 “WEB_PORT” (例如50923) 等
|
||||
# 配置 “Proxy”, “API_KEY” 以及 “WEB_PORT” (例如50923) 等
|
||||
用任意文本编辑器编辑 config.py
|
||||
# 安装
|
||||
docker build -t gpt-academic .
|
||||
@@ -164,7 +156,6 @@ docker run --rm -it --net=host --gpus=all gpt-academic
|
||||
docker run --rm -it --net=host --gpus=all gpt-academic bash
|
||||
```
|
||||
|
||||
|
||||
## 安装-方法3:其他部署方式(需要云服务器知识与经验)
|
||||
|
||||
1. 远程云服务器部署
|
||||
@@ -176,14 +167,6 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
|
||||
3. 如何在二级网址(如`http://localhost/subpath`)下运行
|
||||
请访问[FastAPI运行说明](docs/WithFastapi.md)
|
||||
|
||||
## 安装-代理配置
|
||||
1. 常规方法
|
||||
[配置代理](https://github.com/binary-husky/chatgpt_academic/issues/1)
|
||||
|
||||
2. 纯新手教程
|
||||
[纯新手教程](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
|
||||
|
||||
|
||||
---
|
||||
|
||||
## 自定义新的便捷按钮 / 自定义函数插件
|
||||
@@ -211,73 +194,9 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
|
||||
详情请参考[函数插件指南](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)。
|
||||
|
||||
|
||||
---
|
||||
|
||||
|
||||
## 部分功能展示
|
||||
|
||||
1. 图片显示:
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/228737599-bf0a9d9c-1808-4f43-ae15-dfcc7af0f295.png" width="800" >
|
||||
</div>
|
||||
|
||||
2. 本项目的代码自译解(如果一个程序能够读懂并剖析自己):
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="800" >
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226936618-9b487e4b-ab5b-4b6e-84c6-16942102e917.png" width="800" >
|
||||
</div>
|
||||
|
||||
3. 其他任意Python/Cpp/Java/Go/Rect/...项目剖析:
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="800" >
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="800" >
|
||||
</div>
|
||||
|
||||
4. Latex论文一键阅读理解与摘要生成
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/227504406-86ab97cd-f208-41c3-8e4a-7000e51cf980.png" width="800" >
|
||||
</div>
|
||||
|
||||
5. 自动报告生成
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
|
||||
</div>
|
||||
|
||||
6. 模块化功能设计
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
|
||||
</div>
|
||||
|
||||
|
||||
7. 源代码转译英文
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/229720562-fe6c3508-6142-4635-a83d-21eb3669baee.png" height="400" >
|
||||
</div>
|
||||
|
||||
8. 互联网在线信息综合
|
||||
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/96192199/233575247-fb00819e-6d1b-4bb7-bd54-1d7528f03dd9.png" width="800" >
|
||||
<img src="https://user-images.githubusercontent.com/96192199/233779501-5ce826f0-6cca-4d59-9e5f-b4eacb8cc15f.png" width="800" >
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
## Todo 与 版本规划:
|
||||
- version 3.2+ (todo): 函数插件支持更多参数接口
|
||||
## 版本:
|
||||
- version 3.2: 函数插件支持更多参数接口 (保存对话功能, 解读任意语言代码+同时询问任意的LLM组合)
|
||||
- version 3.1: 支持同时问询多个gpt模型!支持api2d,支持多个apikey负载均衡
|
||||
- version 3.0: 对chatglm和其他小型llm的支持
|
||||
- version 2.6: 重构了插件结构,提高了交互性,加入更多插件
|
||||
@@ -289,7 +208,6 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
|
||||
- version 2.0: 引入模块化函数插件
|
||||
- version 1.0: 基础功能
|
||||
|
||||
chatgpt_academic开发者QQ群:734063350
|
||||
|
||||
## 参考与学习
|
||||
|
||||
|
||||
10
config.py
10
config.py
@@ -45,7 +45,7 @@ MAX_RETRY = 2
|
||||
|
||||
# OpenAI模型选择是(gpt4现在只对申请成功的人开放,体验gpt-4可以试试api2d)
|
||||
LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm"]
|
||||
AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing"]
|
||||
|
||||
# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
|
||||
LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
|
||||
@@ -58,8 +58,14 @@ CONCURRENT_COUNT = 100
|
||||
AUTHENTICATION = []
|
||||
|
||||
# 重新URL重新定向,实现更换API_URL的作用(常规情况下,不要修改!!)
|
||||
# 格式 {"https://api.openai.com/v1/chat/completions": "重定向的URL"}
|
||||
# 格式 {"https://api.openai.com/v1/chat/completions": "在这里填写重定向的api.openai.com的URL"}
|
||||
API_URL_REDIRECT = {}
|
||||
|
||||
# 如果需要在二级路径下运行(常规情况下,不要修改!!)(需要配合修改main.py才能生效!)
|
||||
CUSTOM_PATH = "/"
|
||||
|
||||
# 如果需要使用newbing,把newbing的长长的cookie放到这里
|
||||
NEWBING_STYLE = "creative" # ["creative", "balanced", "precise"]
|
||||
NEWBING_COOKIES = """
|
||||
your bing cookies here
|
||||
"""
|
||||
@@ -21,6 +21,7 @@ def get_crazy_functions():
|
||||
from crazy_functions.总结word文档 import 总结word文档
|
||||
from crazy_functions.解析JupyterNotebook import 解析ipynb文件
|
||||
from crazy_functions.对话历史存档 import 对话历史存档
|
||||
from crazy_functions.批量Markdown翻译 import Markdown英译中
|
||||
function_plugins = {
|
||||
|
||||
"解析整个Python项目": {
|
||||
@@ -35,6 +36,8 @@ def get_crazy_functions():
|
||||
"Color": "stop",
|
||||
"AsButton":False,
|
||||
"Function": HotReload(解析ipynb文件),
|
||||
"AdvancedArgs": True, # 调用时,唤起高级参数输入区(默认False)
|
||||
"ArgsReminder": "若输入0,则不解析notebook中的Markdown块", # 高级参数输入区的显示提示
|
||||
},
|
||||
"批量总结Word文档": {
|
||||
"Color": "stop",
|
||||
@@ -79,8 +82,14 @@ def get_crazy_functions():
|
||||
"Color": "stop", # 按钮颜色
|
||||
"Function": HotReload(读文章写摘要)
|
||||
},
|
||||
"Markdown/Readme英译中": {
|
||||
# HotReload 的意思是热更新,修改函数插件代码后,不需要重启程序,代码直接生效
|
||||
"Color": "stop",
|
||||
"Function": HotReload(Markdown英译中)
|
||||
},
|
||||
"批量生成函数注释": {
|
||||
"Color": "stop", # 按钮颜色
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Function": HotReload(批量生成函数注释)
|
||||
},
|
||||
"[多线程Demo] 解析此项目本身(源码自译解)": {
|
||||
@@ -108,7 +117,6 @@ def get_crazy_functions():
|
||||
from crazy_functions.Latex全文翻译 import Latex中译英
|
||||
from crazy_functions.Latex全文翻译 import Latex英译中
|
||||
from crazy_functions.批量Markdown翻译 import Markdown中译英
|
||||
from crazy_functions.批量Markdown翻译 import Markdown英译中
|
||||
|
||||
function_plugins.update({
|
||||
"批量翻译PDF文档(多线程)": {
|
||||
@@ -173,12 +181,7 @@ def get_crazy_functions():
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Function": HotReload(Markdown中译英)
|
||||
},
|
||||
"[测试功能] 批量Markdown英译中(输入路径或上传压缩包)": {
|
||||
# HotReload 的意思是热更新,修改函数插件代码后,不需要重启程序,代码直接生效
|
||||
"Color": "stop",
|
||||
"AsButton": False, # 加入下拉菜单中
|
||||
"Function": HotReload(Markdown英译中)
|
||||
},
|
||||
|
||||
|
||||
})
|
||||
|
||||
|
||||
@@ -1,5 +1,4 @@
|
||||
import traceback
|
||||
from toolbox import update_ui, get_conf
|
||||
from toolbox import update_ui, get_conf, trimmed_format_exc
|
||||
|
||||
def input_clipping(inputs, history, max_token_limit):
|
||||
import numpy as np
|
||||
@@ -94,12 +93,12 @@ def request_gpt_model_in_new_thread_with_ui_alive(
|
||||
continue # 返回重试
|
||||
else:
|
||||
# 【选择放弃】
|
||||
tb_str = '```\n' + traceback.format_exc() + '```'
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
mutable[0] += f"[Local Message] 警告,在执行过程中遭遇问题, Traceback:\n\n{tb_str}\n\n"
|
||||
return mutable[0] # 放弃
|
||||
except:
|
||||
# 【第三种情况】:其他错误:重试几次
|
||||
tb_str = '```\n' + traceback.format_exc() + '```'
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
print(tb_str)
|
||||
mutable[0] += f"[Local Message] 警告,在执行过程中遭遇问题, Traceback:\n\n{tb_str}\n\n"
|
||||
if retry_op > 0:
|
||||
@@ -173,7 +172,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
if max_workers == -1: # 读取配置文件
|
||||
try: max_workers, = get_conf('DEFAULT_WORKER_NUM')
|
||||
except: max_workers = 8
|
||||
if max_workers <= 0 or max_workers >= 20: max_workers = 8
|
||||
if max_workers <= 0: max_workers = 3
|
||||
# 屏蔽掉 chatglm的多线程,可能会导致严重卡顿
|
||||
if not (llm_kwargs['llm_model'].startswith('gpt-') or llm_kwargs['llm_model'].startswith('api2d-')):
|
||||
max_workers = 1
|
||||
@@ -220,14 +219,14 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
continue # 返回重试
|
||||
else:
|
||||
# 【选择放弃】
|
||||
tb_str = '```\n' + traceback.format_exc() + '```'
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
gpt_say += f"[Local Message] 警告,线程{index}在执行过程中遭遇问题, Traceback:\n\n{tb_str}\n\n"
|
||||
if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答:\n\n" + mutable[index][0]
|
||||
mutable[index][2] = "输入过长已放弃"
|
||||
return gpt_say # 放弃
|
||||
except:
|
||||
# 【第三种情况】:其他错误
|
||||
tb_str = '```\n' + traceback.format_exc() + '```'
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
print(tb_str)
|
||||
gpt_say += f"[Local Message] 警告,线程{index}在执行过程中遭遇问题, Traceback:\n\n{tb_str}\n\n"
|
||||
if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答:\n\n" + mutable[index][0]
|
||||
|
||||
@@ -84,7 +84,33 @@ 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):
|
||||
import glob, os
|
||||
|
||||
success = True
|
||||
if txt.startswith('http'):
|
||||
# 网络的远程文件
|
||||
txt = txt.replace("https://github.com/", "https://raw.githubusercontent.com/")
|
||||
txt = txt.replace("/blob/", "/")
|
||||
import requests
|
||||
from toolbox import get_conf
|
||||
proxies, = get_conf('proxies')
|
||||
r = requests.get(txt, proxies=proxies)
|
||||
with open('./gpt_log/temp.md', 'wb+') as f: f.write(r.content)
|
||||
project_folder = './gpt_log/'
|
||||
file_manifest = ['./gpt_log/temp.md']
|
||||
elif txt.endswith('.md'):
|
||||
# 直接给定文件
|
||||
file_manifest = [txt]
|
||||
project_folder = os.path.dirname(txt)
|
||||
elif os.path.exists(txt):
|
||||
# 本地路径,递归搜索
|
||||
project_folder = txt
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
|
||||
else:
|
||||
success = False
|
||||
|
||||
return success, file_manifest, project_folder
|
||||
|
||||
|
||||
@CatchException
|
||||
@@ -98,6 +124,7 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
# 尝试导入依赖,如果缺少依赖,则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
import glob, os
|
||||
except:
|
||||
report_execption(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
@@ -105,19 +132,21 @@ def Markdown英译中(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史,以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt)
|
||||
|
||||
if not success:
|
||||
# 什么都没有
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
|
||||
|
||||
if len(file_manifest) == 0:
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
yield from 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en->zh')
|
||||
|
||||
|
||||
@@ -135,6 +164,7 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
# 尝试导入依赖,如果缺少依赖,则给出安装建议
|
||||
try:
|
||||
import tiktoken
|
||||
import glob, os
|
||||
except:
|
||||
report_execption(chatbot, history,
|
||||
a=f"解析项目: {txt}",
|
||||
@@ -142,18 +172,13 @@ def Markdown中译英(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_p
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
history = [] # 清空历史,以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
success, file_manifest, project_folder = get_files_from_everything(txt)
|
||||
if not success:
|
||||
# 什么都没有
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
if txt.endswith('.md'):
|
||||
file_manifest = [txt]
|
||||
else:
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
@@ -67,11 +67,16 @@ def parseNotebook(filename, enable_markdown=1):
|
||||
def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
|
||||
enable_markdown = plugin_kwargs.get("advanced_arg", "1")
|
||||
try:
|
||||
enable_markdown = int(enable_markdown)
|
||||
except ValueError:
|
||||
enable_markdown = 1
|
||||
|
||||
pfg = PaperFileGroup()
|
||||
|
||||
print(file_manifest)
|
||||
for fp in file_manifest:
|
||||
file_content = parseNotebook(fp, enable_markdown=1)
|
||||
file_content = parseNotebook(fp, enable_markdown=enable_markdown)
|
||||
pfg.file_paths.append(fp)
|
||||
pfg.file_contents.append(file_content)
|
||||
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_execption, write_results_to_file
|
||||
from .crazy_utils import input_clipping
|
||||
|
||||
def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import os, copy
|
||||
@@ -61,13 +62,15 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
|
||||
previous_iteration_files.extend([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
|
||||
previous_iteration_files_string = ', '.join(previous_iteration_files)
|
||||
current_iteration_focus = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
|
||||
i_say = f'根据以上分析,对程序的整体功能和构架重新做出概括。然后用一张markdown表格整理每个文件的功能(包括{previous_iteration_files_string})。'
|
||||
i_say = f'用一张Markdown表格简要描述以下文件的功能:{previous_iteration_files_string}。根据以上分析,用一句话概括程序的整体功能。'
|
||||
inputs_show_user = f'根据以上分析,对程序的整体功能和构架重新做出概括,由于输入长度限制,可能需要分组处理,本组文件为 {current_iteration_focus} + 已经汇总的文件组。'
|
||||
this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
|
||||
this_iteration_history.append(last_iteration_result)
|
||||
# 裁剪input
|
||||
inputs, this_iteration_history_feed = input_clipping(inputs=i_say, history=this_iteration_history, max_token_limit=2560)
|
||||
result = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
|
||||
history=this_iteration_history, # 迭代之前的分析
|
||||
inputs=inputs, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
|
||||
history=this_iteration_history_feed, # 迭代之前的分析
|
||||
sys_prompt="你是一个程序架构分析师,正在分析一个项目的源代码。")
|
||||
report_part_2.extend([i_say, result])
|
||||
last_iteration_result = result
|
||||
|
||||
@@ -70,6 +70,7 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
# 尝试导入依赖,如果缺少依赖,则给出安装建议
|
||||
try:
|
||||
import arxiv
|
||||
import math
|
||||
from bs4 import BeautifulSoup
|
||||
except:
|
||||
report_execption(chatbot, history,
|
||||
@@ -80,25 +81,26 @@ def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
|
||||
|
||||
# 清空历史,以免输入溢出
|
||||
history = []
|
||||
|
||||
meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
|
||||
|
||||
if len(meta_paper_info_list[:10]) > 0:
|
||||
i_say = "下面是一些学术文献的数据,请从中提取出以下内容。" + \
|
||||
batchsize = 5
|
||||
for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
|
||||
if len(meta_paper_info_list[:batchsize]) > 0:
|
||||
i_say = "下面是一些学术文献的数据,提取出以下内容:" + \
|
||||
"1、英文题目;2、中文题目翻译;3、作者;4、arxiv公开(is_paper_in_arxiv);4、引用数量(cite);5、中文摘要翻译。" + \
|
||||
f"以下是信息源:{str(meta_paper_info_list[:10])}"
|
||||
f"以下是信息源:{str(meta_paper_info_list[:batchsize])}"
|
||||
|
||||
inputs_show_user = f"请分析此页面中出现的所有文章:{txt}"
|
||||
inputs_show_user = f"请分析此页面中出现的所有文章:{txt},这是第{batch+1}批"
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=inputs_show_user,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt="你是一个学术翻译,请从数据中提取信息。你必须使用Markdown格式。你必须逐个文献进行处理。"
|
||||
sys_prompt="你是一个学术翻译,请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
|
||||
)
|
||||
|
||||
history.extend([ "第一批", gpt_say ])
|
||||
meta_paper_info_list = meta_paper_info_list[10:]
|
||||
history.extend([ f"第{batch+1}批", gpt_say ])
|
||||
meta_paper_info_list = meta_paper_info_list[batchsize:]
|
||||
|
||||
chatbot.append(["状态?", "已经全部完成"])
|
||||
chatbot.append(["状态?",
|
||||
"已经全部完成,您可以试试让AI写一个Related Works,例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
|
||||
msg = '正常'
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
|
||||
res = write_results_to_file(history)
|
||||
|
||||
3
main.py
3
main.py
@@ -173,9 +173,6 @@ def main():
|
||||
yield from ArgsGeneralWrapper(crazy_fns[k]["Function"])(*args, **kwargs)
|
||||
click_handle = switchy_bt.click(route,[switchy_bt, *input_combo, gr.State(PORT)], output_combo)
|
||||
click_handle.then(on_report_generated, [file_upload, chatbot], [file_upload, chatbot])
|
||||
# def expand_file_area(file_upload, area_file_up):
|
||||
# if len(file_upload)>0: return {area_file_up: gr.update(open=True)}
|
||||
# click_handle.then(expand_file_area, [file_upload, area_file_up], [area_file_up])
|
||||
cancel_handles.append(click_handle)
|
||||
# 终止按钮的回调函数注册
|
||||
stopBtn.click(fn=None, inputs=None, outputs=None, cancels=cancel_handles)
|
||||
|
||||
@@ -11,7 +11,7 @@
|
||||
import tiktoken
|
||||
from functools import lru_cache
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from toolbox import get_conf
|
||||
from toolbox import get_conf, trimmed_format_exc
|
||||
|
||||
from .bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
|
||||
from .bridge_chatgpt import predict as chatgpt_ui
|
||||
@@ -19,6 +19,9 @@ from .bridge_chatgpt import predict as chatgpt_ui
|
||||
from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
|
||||
from .bridge_chatglm import predict as chatglm_ui
|
||||
|
||||
from .bridge_newbing import predict_no_ui_long_connection as newbing_noui
|
||||
from .bridge_newbing import predict as newbing_ui
|
||||
|
||||
# from .bridge_tgui import predict_no_ui_long_connection as tgui_noui
|
||||
# from .bridge_tgui import predict as tgui_ui
|
||||
|
||||
@@ -48,6 +51,7 @@ class LazyloadTiktoken(object):
|
||||
API_URL_REDIRECT, = get_conf("API_URL_REDIRECT")
|
||||
openai_endpoint = "https://api.openai.com/v1/chat/completions"
|
||||
api2d_endpoint = "https://openai.api2d.net/v1/chat/completions"
|
||||
newbing_endpoint = "wss://sydney.bing.com/sydney/ChatHub"
|
||||
# 兼容旧版的配置
|
||||
try:
|
||||
API_URL, = get_conf("API_URL")
|
||||
@@ -59,6 +63,7 @@ except:
|
||||
# 新版配置
|
||||
if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
|
||||
if api2d_endpoint in API_URL_REDIRECT: api2d_endpoint = API_URL_REDIRECT[api2d_endpoint]
|
||||
if newbing_endpoint in API_URL_REDIRECT: newbing_endpoint = API_URL_REDIRECT[newbing_endpoint]
|
||||
|
||||
|
||||
# 获取tokenizer
|
||||
@@ -116,7 +121,15 @@ model_info = {
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
|
||||
# newbing
|
||||
"newbing": {
|
||||
"fn_with_ui": newbing_ui,
|
||||
"fn_without_ui": newbing_noui,
|
||||
"endpoint": newbing_endpoint,
|
||||
"max_token": 4096,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@@ -128,10 +141,7 @@ def LLM_CATCH_EXCEPTION(f):
|
||||
try:
|
||||
return f(inputs, llm_kwargs, history, sys_prompt, observe_window, console_slience)
|
||||
except Exception as e:
|
||||
from toolbox import get_conf
|
||||
import traceback
|
||||
proxies, = get_conf('proxies')
|
||||
tb_str = '\n```\n' + traceback.format_exc() + '\n```\n'
|
||||
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
|
||||
observe_window[0] = tb_str
|
||||
return tb_str
|
||||
return decorated
|
||||
@@ -182,7 +192,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
|
||||
|
||||
def mutex_manager(window_mutex, observe_window):
|
||||
while True:
|
||||
time.sleep(0.5)
|
||||
time.sleep(0.25)
|
||||
if not window_mutex[-1]: break
|
||||
# 看门狗(watchdog)
|
||||
for i in range(n_model):
|
||||
|
||||
@@ -32,6 +32,7 @@ class GetGLMHandle(Process):
|
||||
return self.chatglm_model is not None
|
||||
|
||||
def run(self):
|
||||
# 子进程执行
|
||||
# 第一次运行,加载参数
|
||||
retry = 0
|
||||
while True:
|
||||
@@ -53,17 +54,24 @@ class GetGLMHandle(Process):
|
||||
self.child.send('[Local Message] Call ChatGLM fail 不能正常加载ChatGLM的参数。')
|
||||
raise RuntimeError("不能正常加载ChatGLM的参数!")
|
||||
|
||||
# 进入任务等待状态
|
||||
while True:
|
||||
# 进入任务等待状态
|
||||
kwargs = self.child.recv()
|
||||
# 收到消息,开始请求
|
||||
try:
|
||||
for response, history in self.chatglm_model.stream_chat(self.chatglm_tokenizer, **kwargs):
|
||||
self.child.send(response)
|
||||
# # 中途接收可能的终止指令(如果有的话)
|
||||
# if self.child.poll():
|
||||
# command = self.child.recv()
|
||||
# if command == '[Terminate]': break
|
||||
except:
|
||||
self.child.send('[Local Message] Call ChatGLM fail.')
|
||||
# 请求处理结束,开始下一个循环
|
||||
self.child.send('[Finish]')
|
||||
|
||||
def stream_chat(self, **kwargs):
|
||||
# 主进程执行
|
||||
self.parent.send(kwargs)
|
||||
while True:
|
||||
res = self.parent.recv()
|
||||
@@ -130,14 +138,17 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数(如果有的话)
|
||||
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
history_feedin.append(["What can I do?", system_prompt] )
|
||||
for i in range(len(history)//2):
|
||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
||||
|
||||
# 开始接收chatglm的回复
|
||||
for response in glm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
|
||||
chatbot[-1] = (inputs, response)
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
# 总结输出
|
||||
history.extend([inputs, response])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
@@ -21,7 +21,7 @@ import importlib
|
||||
|
||||
# config_private.py放自己的秘密如API和代理网址
|
||||
# 读取时首先看是否存在私密的config_private配置文件(不受git管控),如果有,则覆盖原config文件
|
||||
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys
|
||||
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc
|
||||
proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
|
||||
get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
|
||||
|
||||
@@ -198,21 +198,24 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
chunk_decoded = chunk.decode()
|
||||
error_msg = chunk_decoded
|
||||
if "reduce the length" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长,或历史数据过长. 历史缓存数据现已释放,您可以请再次尝试.")
|
||||
history = [] # 清除历史
|
||||
if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入:history[-2] 是本次输入, history[-1] 是本次输出
|
||||
history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
|
||||
max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
|
||||
# history = [] # 清除历史
|
||||
elif "does not exist" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在,或者您没有获得体验资格.")
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
|
||||
elif "Incorrect API key" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由,拒绝服务.")
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务.")
|
||||
elif "exceeded your current quota" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由,拒绝服务.")
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务.")
|
||||
elif "bad forward key" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
|
||||
elif "Not enough point" in error_msg:
|
||||
chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
|
||||
else:
|
||||
from toolbox import regular_txt_to_markdown
|
||||
tb_str = '```\n' + traceback.format_exc() + '```'
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded[4:])}")
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
|
||||
return
|
||||
|
||||
251
request_llm/bridge_newbing.py
普通文件
251
request_llm/bridge_newbing.py
普通文件
@@ -0,0 +1,251 @@
|
||||
"""
|
||||
========================================================================
|
||||
第一部分:来自EdgeGPT.py
|
||||
https://github.com/acheong08/EdgeGPT
|
||||
========================================================================
|
||||
"""
|
||||
from .edge_gpt import NewbingChatbot
|
||||
load_message = "等待NewBing响应。"
|
||||
|
||||
"""
|
||||
========================================================================
|
||||
第二部分:子进程Worker(调用主体)
|
||||
========================================================================
|
||||
"""
|
||||
import time
|
||||
import json
|
||||
import re
|
||||
import asyncio
|
||||
import importlib
|
||||
import threading
|
||||
from toolbox import update_ui, get_conf, trimmed_format_exc
|
||||
from multiprocessing import Process, Pipe
|
||||
|
||||
def preprocess_newbing_out(s):
|
||||
pattern = r'\^(\d+)\^' # 匹配^数字^
|
||||
sub = lambda m: '\['+m.group(1)+'\]' # 将匹配到的数字作为替换值
|
||||
result = re.sub(pattern, sub, s) # 替换操作
|
||||
if '[1]' in result:
|
||||
result += '\n\n```\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
|
||||
return result
|
||||
|
||||
def preprocess_newbing_out_simple(result):
|
||||
if '[1]' in result:
|
||||
result += '\n\n```\n' + "\n".join([r for r in result.split('\n') if r.startswith('[')]) + '\n```\n'
|
||||
return result
|
||||
|
||||
class NewBingHandle(Process):
|
||||
def __init__(self):
|
||||
super().__init__(daemon=True)
|
||||
self.parent, self.child = Pipe()
|
||||
self.newbing_model = None
|
||||
self.info = ""
|
||||
self.success = True
|
||||
self.local_history = []
|
||||
self.check_dependency()
|
||||
self.start()
|
||||
self.threadLock = threading.Lock()
|
||||
|
||||
def check_dependency(self):
|
||||
try:
|
||||
self.success = False
|
||||
import certifi, httpx, rich
|
||||
self.info = "依赖检测通过,等待NewBing响应。注意目前不能多人同时调用NewBing接口(有线程锁),否则将导致每个人的NewBing问询历史互相渗透。调用NewBing时,会自动使用已配置的代理。"
|
||||
self.success = True
|
||||
except:
|
||||
self.info = "缺少的依赖,如果要使用Newbing,除了基础的pip依赖以外,您还需要运行`pip install -r request_llm/requirements_newbing.txt`安装Newbing的依赖。"
|
||||
self.success = False
|
||||
|
||||
def ready(self):
|
||||
return self.newbing_model is not None
|
||||
|
||||
async def async_run(self):
|
||||
# 读取配置
|
||||
NEWBING_STYLE, = get_conf('NEWBING_STYLE')
|
||||
from request_llm.bridge_all import model_info
|
||||
endpoint = model_info['newbing']['endpoint']
|
||||
while True:
|
||||
# 等待
|
||||
kwargs = self.child.recv()
|
||||
question=kwargs['query']
|
||||
history=kwargs['history']
|
||||
system_prompt=kwargs['system_prompt']
|
||||
|
||||
# 是否重置
|
||||
if len(self.local_history) > 0 and len(history)==0:
|
||||
await self.newbing_model.reset()
|
||||
self.local_history = []
|
||||
|
||||
# 开始问问题
|
||||
prompt = ""
|
||||
if system_prompt not in self.local_history:
|
||||
self.local_history.append(system_prompt)
|
||||
prompt += system_prompt + '\n'
|
||||
|
||||
# 追加历史
|
||||
for ab in history:
|
||||
a, b = ab
|
||||
if a not in self.local_history:
|
||||
self.local_history.append(a)
|
||||
prompt += a + '\n'
|
||||
# if b not in self.local_history:
|
||||
# self.local_history.append(b)
|
||||
# prompt += b + '\n'
|
||||
|
||||
# 问题
|
||||
prompt += question
|
||||
self.local_history.append(question)
|
||||
print('question:', prompt)
|
||||
# 提交
|
||||
async for final, response in self.newbing_model.ask_stream(
|
||||
prompt=question,
|
||||
conversation_style=NEWBING_STYLE, # ["creative", "balanced", "precise"]
|
||||
wss_link=endpoint, # "wss://sydney.bing.com/sydney/ChatHub"
|
||||
):
|
||||
if not final:
|
||||
print(response)
|
||||
self.child.send(str(response))
|
||||
else:
|
||||
print('-------- receive final ---------')
|
||||
self.child.send('[Finish]')
|
||||
# self.local_history.append(response)
|
||||
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
这个函数运行在子进程
|
||||
"""
|
||||
# 第一次运行,加载参数
|
||||
self.success = False
|
||||
self.local_history = []
|
||||
if (self.newbing_model is None) or (not self.success):
|
||||
# 代理设置
|
||||
proxies, = get_conf('proxies')
|
||||
if proxies is None:
|
||||
self.proxies_https = None
|
||||
else:
|
||||
self.proxies_https = proxies['https']
|
||||
# cookie
|
||||
NEWBING_COOKIES, = get_conf('NEWBING_COOKIES')
|
||||
try:
|
||||
cookies = json.loads(NEWBING_COOKIES)
|
||||
except:
|
||||
self.success = False
|
||||
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
|
||||
self.child.send(f'[Local Message] 不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。')
|
||||
self.child.send('[Fail]')
|
||||
self.child.send('[Finish]')
|
||||
raise RuntimeError(f"不能加载Newbing组件。NEWBING_COOKIES未填写或有格式错误。")
|
||||
|
||||
try:
|
||||
self.newbing_model = NewbingChatbot(proxy=self.proxies_https, cookies=cookies)
|
||||
except:
|
||||
self.success = False
|
||||
tb_str = '\n```\n' + trimmed_format_exc() + '\n```\n'
|
||||
self.child.send(f'[Local Message] 不能加载Newbing组件。{tb_str}')
|
||||
self.child.send('[Fail]')
|
||||
self.child.send('[Finish]')
|
||||
raise RuntimeError(f"不能加载Newbing组件。")
|
||||
|
||||
self.success = True
|
||||
try:
|
||||
# 进入任务等待状态
|
||||
asyncio.run(self.async_run())
|
||||
except Exception:
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
self.child.send(f'[Local Message] Newbing失败 {tb_str}.')
|
||||
self.child.send('[Fail]')
|
||||
self.child.send('[Finish]')
|
||||
|
||||
def stream_chat(self, **kwargs):
|
||||
"""
|
||||
这个函数运行在主进程
|
||||
"""
|
||||
self.threadLock.acquire()
|
||||
self.parent.send(kwargs) # 发送请求到子进程
|
||||
while True:
|
||||
res = self.parent.recv() # 等待newbing回复的片段
|
||||
if res == '[Finish]':
|
||||
break # 结束
|
||||
elif res == '[Fail]':
|
||||
self.success = False
|
||||
break
|
||||
else:
|
||||
yield res # newbing回复的片段
|
||||
self.threadLock.release()
|
||||
|
||||
|
||||
"""
|
||||
========================================================================
|
||||
第三部分:主进程统一调用函数接口
|
||||
========================================================================
|
||||
"""
|
||||
global newbing_handle
|
||||
newbing_handle = None
|
||||
|
||||
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
|
||||
"""
|
||||
多线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
"""
|
||||
global newbing_handle
|
||||
if (newbing_handle is None) or (not newbing_handle.success):
|
||||
newbing_handle = NewBingHandle()
|
||||
observe_window[0] = load_message + "\n\n" + newbing_handle.info
|
||||
if not newbing_handle.success:
|
||||
error = newbing_handle.info
|
||||
newbing_handle = None
|
||||
raise RuntimeError(error)
|
||||
|
||||
# 没有 sys_prompt 接口,因此把prompt加入 history
|
||||
history_feedin = []
|
||||
for i in range(len(history)//2):
|
||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
||||
|
||||
watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
|
||||
response = ""
|
||||
observe_window[0] = "[Local Message]: 等待NewBing响应中 ..."
|
||||
for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
|
||||
observe_window[0] = preprocess_newbing_out_simple(response)
|
||||
if len(observe_window) >= 2:
|
||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||||
raise RuntimeError("程序终止。")
|
||||
return preprocess_newbing_out_simple(response)
|
||||
|
||||
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
单线程方法
|
||||
函数的说明请见 request_llm/bridge_all.py
|
||||
"""
|
||||
chatbot.append((inputs, "[Local Message]: 等待NewBing响应中 ..."))
|
||||
|
||||
global newbing_handle
|
||||
if (newbing_handle is None) or (not newbing_handle.success):
|
||||
newbing_handle = NewBingHandle()
|
||||
chatbot[-1] = (inputs, load_message + "\n\n" + newbing_handle.info)
|
||||
yield from update_ui(chatbot=chatbot, history=[])
|
||||
if not newbing_handle.success:
|
||||
newbing_handle = None
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
import core_functional
|
||||
importlib.reload(core_functional) # 热更新prompt
|
||||
core_functional = core_functional.get_core_functions()
|
||||
if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数(如果有的话)
|
||||
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||
|
||||
history_feedin = []
|
||||
for i in range(len(history)//2):
|
||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
||||
|
||||
chatbot[-1] = (inputs, "[Local Message]: 等待NewBing响应中 ...")
|
||||
response = "[Local Message]: 等待NewBing响应中 ..."
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢,尚未完成全部响应,请耐心完成后再提交新问题。")
|
||||
for response in newbing_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
|
||||
chatbot[-1] = (inputs, preprocess_newbing_out(response))
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="NewBing响应缓慢,尚未完成全部响应,请耐心完成后再提交新问题。")
|
||||
if response == "[Local Message]: 等待NewBing响应中 ...": response = "[Local Message]: NewBing响应异常,请刷新界面重试 ..."
|
||||
history.extend([inputs, response])
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="完成全部响应,请提交新问题。")
|
||||
|
||||
409
request_llm/edge_gpt.py
普通文件
409
request_llm/edge_gpt.py
普通文件
@@ -0,0 +1,409 @@
|
||||
"""
|
||||
========================================================================
|
||||
第一部分:来自EdgeGPT.py
|
||||
https://github.com/acheong08/EdgeGPT
|
||||
========================================================================
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
import re
|
||||
import ssl
|
||||
import sys
|
||||
import uuid
|
||||
from enum import Enum
|
||||
from typing import Generator
|
||||
from typing import Literal
|
||||
from typing import Optional
|
||||
from typing import Union
|
||||
import websockets.client as websockets
|
||||
|
||||
DELIMITER = "\x1e"
|
||||
|
||||
|
||||
# Generate random IP between range 13.104.0.0/14
|
||||
FORWARDED_IP = (
|
||||
f"13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}"
|
||||
)
|
||||
|
||||
HEADERS = {
|
||||
"accept": "application/json",
|
||||
"accept-language": "en-US,en;q=0.9",
|
||||
"content-type": "application/json",
|
||||
"sec-ch-ua": '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"',
|
||||
"sec-ch-ua-arch": '"x86"',
|
||||
"sec-ch-ua-bitness": '"64"',
|
||||
"sec-ch-ua-full-version": '"109.0.1518.78"',
|
||||
"sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
|
||||
"sec-ch-ua-mobile": "?0",
|
||||
"sec-ch-ua-model": "",
|
||||
"sec-ch-ua-platform": '"Windows"',
|
||||
"sec-ch-ua-platform-version": '"15.0.0"',
|
||||
"sec-fetch-dest": "empty",
|
||||
"sec-fetch-mode": "cors",
|
||||
"sec-fetch-site": "same-origin",
|
||||
"x-ms-client-request-id": str(uuid.uuid4()),
|
||||
"x-ms-useragent": "azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32",
|
||||
"Referer": "https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx",
|
||||
"Referrer-Policy": "origin-when-cross-origin",
|
||||
"x-forwarded-for": FORWARDED_IP,
|
||||
}
|
||||
|
||||
HEADERS_INIT_CONVER = {
|
||||
"authority": "edgeservices.bing.com",
|
||||
"accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
|
||||
"accept-language": "en-US,en;q=0.9",
|
||||
"cache-control": "max-age=0",
|
||||
"sec-ch-ua": '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"',
|
||||
"sec-ch-ua-arch": '"x86"',
|
||||
"sec-ch-ua-bitness": '"64"',
|
||||
"sec-ch-ua-full-version": '"110.0.1587.69"',
|
||||
"sec-ch-ua-full-version-list": '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"',
|
||||
"sec-ch-ua-mobile": "?0",
|
||||
"sec-ch-ua-model": '""',
|
||||
"sec-ch-ua-platform": '"Windows"',
|
||||
"sec-ch-ua-platform-version": '"15.0.0"',
|
||||
"sec-fetch-dest": "document",
|
||||
"sec-fetch-mode": "navigate",
|
||||
"sec-fetch-site": "none",
|
||||
"sec-fetch-user": "?1",
|
||||
"upgrade-insecure-requests": "1",
|
||||
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69",
|
||||
"x-edge-shopping-flag": "1",
|
||||
"x-forwarded-for": FORWARDED_IP,
|
||||
}
|
||||
|
||||
def get_ssl_context():
|
||||
import certifi
|
||||
ssl_context = ssl.create_default_context()
|
||||
ssl_context.load_verify_locations(certifi.where())
|
||||
return ssl_context
|
||||
|
||||
|
||||
|
||||
class NotAllowedToAccess(Exception):
|
||||
pass
|
||||
|
||||
|
||||
class ConversationStyle(Enum):
|
||||
creative = "h3imaginative,clgalileo,gencontentv3"
|
||||
balanced = "galileo"
|
||||
precise = "h3precise,clgalileo"
|
||||
|
||||
|
||||
CONVERSATION_STYLE_TYPE = Optional[
|
||||
Union[ConversationStyle, Literal["creative", "balanced", "precise"]]
|
||||
]
|
||||
|
||||
|
||||
def _append_identifier(msg: dict) -> str:
|
||||
"""
|
||||
Appends special character to end of message to identify end of message
|
||||
"""
|
||||
# Convert dict to json string
|
||||
return json.dumps(msg) + DELIMITER
|
||||
|
||||
|
||||
def _get_ran_hex(length: int = 32) -> str:
|
||||
"""
|
||||
Returns random hex string
|
||||
"""
|
||||
return "".join(random.choice("0123456789abcdef") for _ in range(length))
|
||||
|
||||
|
||||
class _ChatHubRequest:
|
||||
"""
|
||||
Request object for ChatHub
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
conversation_signature: str,
|
||||
client_id: str,
|
||||
conversation_id: str,
|
||||
invocation_id: int = 0,
|
||||
) -> None:
|
||||
self.struct: dict = {}
|
||||
|
||||
self.client_id: str = client_id
|
||||
self.conversation_id: str = conversation_id
|
||||
self.conversation_signature: str = conversation_signature
|
||||
self.invocation_id: int = invocation_id
|
||||
|
||||
def update(
|
||||
self,
|
||||
prompt,
|
||||
conversation_style,
|
||||
options,
|
||||
) -> None:
|
||||
"""
|
||||
Updates request object
|
||||
"""
|
||||
if options is None:
|
||||
options = [
|
||||
"deepleo",
|
||||
"enable_debug_commands",
|
||||
"disable_emoji_spoken_text",
|
||||
"enablemm",
|
||||
]
|
||||
if conversation_style:
|
||||
if not isinstance(conversation_style, ConversationStyle):
|
||||
conversation_style = getattr(ConversationStyle, conversation_style)
|
||||
options = [
|
||||
"nlu_direct_response_filter",
|
||||
"deepleo",
|
||||
"disable_emoji_spoken_text",
|
||||
"responsible_ai_policy_235",
|
||||
"enablemm",
|
||||
conversation_style.value,
|
||||
"dtappid",
|
||||
"cricinfo",
|
||||
"cricinfov2",
|
||||
"dv3sugg",
|
||||
]
|
||||
self.struct = {
|
||||
"arguments": [
|
||||
{
|
||||
"source": "cib",
|
||||
"optionsSets": options,
|
||||
"sliceIds": [
|
||||
"222dtappid",
|
||||
"225cricinfo",
|
||||
"224locals0",
|
||||
],
|
||||
"traceId": _get_ran_hex(32),
|
||||
"isStartOfSession": self.invocation_id == 0,
|
||||
"message": {
|
||||
"author": "user",
|
||||
"inputMethod": "Keyboard",
|
||||
"text": prompt,
|
||||
"messageType": "Chat",
|
||||
},
|
||||
"conversationSignature": self.conversation_signature,
|
||||
"participant": {
|
||||
"id": self.client_id,
|
||||
},
|
||||
"conversationId": self.conversation_id,
|
||||
},
|
||||
],
|
||||
"invocationId": str(self.invocation_id),
|
||||
"target": "chat",
|
||||
"type": 4,
|
||||
}
|
||||
self.invocation_id += 1
|
||||
|
||||
|
||||
class _Conversation:
|
||||
"""
|
||||
Conversation API
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cookies,
|
||||
proxy,
|
||||
) -> None:
|
||||
self.struct: dict = {
|
||||
"conversationId": None,
|
||||
"clientId": None,
|
||||
"conversationSignature": None,
|
||||
"result": {"value": "Success", "message": None},
|
||||
}
|
||||
import httpx
|
||||
self.proxy = proxy
|
||||
proxy = (
|
||||
proxy
|
||||
or os.environ.get("all_proxy")
|
||||
or os.environ.get("ALL_PROXY")
|
||||
or os.environ.get("https_proxy")
|
||||
or os.environ.get("HTTPS_PROXY")
|
||||
or None
|
||||
)
|
||||
if proxy is not None and proxy.startswith("socks5h://"):
|
||||
proxy = "socks5://" + proxy[len("socks5h://") :]
|
||||
self.session = httpx.Client(
|
||||
proxies=proxy,
|
||||
timeout=30,
|
||||
headers=HEADERS_INIT_CONVER,
|
||||
)
|
||||
for cookie in cookies:
|
||||
self.session.cookies.set(cookie["name"], cookie["value"])
|
||||
|
||||
# Send GET request
|
||||
response = self.session.get(
|
||||
url=os.environ.get("BING_PROXY_URL")
|
||||
or "https://edgeservices.bing.com/edgesvc/turing/conversation/create",
|
||||
)
|
||||
if response.status_code != 200:
|
||||
response = self.session.get(
|
||||
"https://edge.churchless.tech/edgesvc/turing/conversation/create",
|
||||
)
|
||||
if response.status_code != 200:
|
||||
print(f"Status code: {response.status_code}")
|
||||
print(response.text)
|
||||
print(response.url)
|
||||
raise Exception("Authentication failed")
|
||||
try:
|
||||
self.struct = response.json()
|
||||
except (json.decoder.JSONDecodeError, NotAllowedToAccess) as exc:
|
||||
raise Exception(
|
||||
"Authentication failed. You have not been accepted into the beta.",
|
||||
) from exc
|
||||
if self.struct["result"]["value"] == "UnauthorizedRequest":
|
||||
raise NotAllowedToAccess(self.struct["result"]["message"])
|
||||
|
||||
|
||||
class _ChatHub:
|
||||
"""
|
||||
Chat API
|
||||
"""
|
||||
|
||||
def __init__(self, conversation) -> None:
|
||||
self.wss = None
|
||||
self.request: _ChatHubRequest
|
||||
self.loop: bool
|
||||
self.task: asyncio.Task
|
||||
print(conversation.struct)
|
||||
self.request = _ChatHubRequest(
|
||||
conversation_signature=conversation.struct["conversationSignature"],
|
||||
client_id=conversation.struct["clientId"],
|
||||
conversation_id=conversation.struct["conversationId"],
|
||||
)
|
||||
|
||||
async def ask_stream(
|
||||
self,
|
||||
prompt: str,
|
||||
wss_link: str,
|
||||
conversation_style: CONVERSATION_STYLE_TYPE = None,
|
||||
raw: bool = False,
|
||||
options: dict = None,
|
||||
) -> Generator[str, None, None]:
|
||||
"""
|
||||
Ask a question to the bot
|
||||
"""
|
||||
if self.wss and not self.wss.closed:
|
||||
await self.wss.close()
|
||||
# Check if websocket is closed
|
||||
self.wss = await websockets.connect(
|
||||
wss_link,
|
||||
extra_headers=HEADERS,
|
||||
max_size=None,
|
||||
ssl=get_ssl_context()
|
||||
)
|
||||
await self._initial_handshake()
|
||||
# Construct a ChatHub request
|
||||
self.request.update(
|
||||
prompt=prompt,
|
||||
conversation_style=conversation_style,
|
||||
options=options,
|
||||
)
|
||||
# Send request
|
||||
await self.wss.send(_append_identifier(self.request.struct))
|
||||
final = False
|
||||
while not final:
|
||||
objects = str(await self.wss.recv()).split(DELIMITER)
|
||||
for obj in objects:
|
||||
if obj is None or not obj:
|
||||
continue
|
||||
response = json.loads(obj)
|
||||
if response.get("type") != 2 and raw:
|
||||
yield False, response
|
||||
elif response.get("type") == 1 and response["arguments"][0].get(
|
||||
"messages",
|
||||
):
|
||||
resp_txt = response["arguments"][0]["messages"][0]["adaptiveCards"][
|
||||
0
|
||||
]["body"][0].get("text")
|
||||
yield False, resp_txt
|
||||
elif response.get("type") == 2:
|
||||
final = True
|
||||
yield True, response
|
||||
|
||||
async def _initial_handshake(self) -> None:
|
||||
await self.wss.send(_append_identifier({"protocol": "json", "version": 1}))
|
||||
await self.wss.recv()
|
||||
|
||||
async def close(self) -> None:
|
||||
"""
|
||||
Close the connection
|
||||
"""
|
||||
if self.wss and not self.wss.closed:
|
||||
await self.wss.close()
|
||||
|
||||
|
||||
class NewbingChatbot:
|
||||
"""
|
||||
Combines everything to make it seamless
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cookies,
|
||||
proxy
|
||||
) -> None:
|
||||
if cookies is None:
|
||||
cookies = {}
|
||||
self.cookies = cookies
|
||||
self.proxy = proxy
|
||||
self.chat_hub: _ChatHub = _ChatHub(
|
||||
_Conversation(self.cookies, self.proxy),
|
||||
)
|
||||
|
||||
async def ask(
|
||||
self,
|
||||
prompt: str,
|
||||
wss_link: str,
|
||||
conversation_style: CONVERSATION_STYLE_TYPE = None,
|
||||
options: dict = None,
|
||||
) -> dict:
|
||||
"""
|
||||
Ask a question to the bot
|
||||
"""
|
||||
async for final, response in self.chat_hub.ask_stream(
|
||||
prompt=prompt,
|
||||
conversation_style=conversation_style,
|
||||
wss_link=wss_link,
|
||||
options=options,
|
||||
):
|
||||
if final:
|
||||
return response
|
||||
await self.chat_hub.wss.close()
|
||||
return None
|
||||
|
||||
async def ask_stream(
|
||||
self,
|
||||
prompt: str,
|
||||
wss_link: str,
|
||||
conversation_style: CONVERSATION_STYLE_TYPE = None,
|
||||
raw: bool = False,
|
||||
options: dict = None,
|
||||
) -> Generator[str, None, None]:
|
||||
"""
|
||||
Ask a question to the bot
|
||||
"""
|
||||
async for response in self.chat_hub.ask_stream(
|
||||
prompt=prompt,
|
||||
conversation_style=conversation_style,
|
||||
wss_link=wss_link,
|
||||
raw=raw,
|
||||
options=options,
|
||||
):
|
||||
yield response
|
||||
|
||||
async def close(self) -> None:
|
||||
"""
|
||||
Close the connection
|
||||
"""
|
||||
await self.chat_hub.close()
|
||||
|
||||
async def reset(self) -> None:
|
||||
"""
|
||||
Reset the conversation
|
||||
"""
|
||||
await self.close()
|
||||
self.chat_hub = _ChatHub(_Conversation(self.cookies, self.proxy))
|
||||
|
||||
|
||||
@@ -0,0 +1,8 @@
|
||||
BingImageCreator
|
||||
certifi
|
||||
httpx
|
||||
prompt_toolkit
|
||||
requests
|
||||
rich
|
||||
websockets
|
||||
httpx[socks]
|
||||
247
theme.py
247
theme.py
@@ -137,6 +137,16 @@ advanced_css = """
|
||||
|
||||
/* 行内代码的背景设为淡灰色,设定圆角和间距. */
|
||||
.markdown-body code {
|
||||
display: inline;
|
||||
white-space: break-spaces;
|
||||
border-radius: 6px;
|
||||
margin: 0 2px 0 2px;
|
||||
padding: .2em .4em .1em .4em;
|
||||
background-color: rgba(13, 17, 23, 0.95);
|
||||
color: #c9d1d9;
|
||||
}
|
||||
|
||||
.dark .markdown-body code {
|
||||
display: inline;
|
||||
white-space: break-spaces;
|
||||
border-radius: 6px;
|
||||
@@ -144,8 +154,19 @@ advanced_css = """
|
||||
padding: .2em .4em .1em .4em;
|
||||
background-color: rgba(175,184,193,0.2);
|
||||
}
|
||||
|
||||
/* 设定代码块的样式,包括背景颜色、内、外边距、圆角。 */
|
||||
.markdown-body pre code {
|
||||
display: block;
|
||||
overflow: auto;
|
||||
white-space: pre;
|
||||
background-color: rgba(13, 17, 23, 0.95);
|
||||
border-radius: 10px;
|
||||
padding: 1em;
|
||||
margin: 1em 2em 1em 0.5em;
|
||||
}
|
||||
|
||||
.dark .markdown-body pre code {
|
||||
display: block;
|
||||
overflow: auto;
|
||||
white-space: pre;
|
||||
@@ -160,72 +181,162 @@ advanced_css = """
|
||||
if CODE_HIGHLIGHT:
|
||||
advanced_css += """
|
||||
|
||||
.hll { background-color: #ffffcc }
|
||||
.c { color: #3D7B7B; font-style: italic } /* Comment */
|
||||
.err { border: 1px solid #FF0000 } /* Error */
|
||||
.k { color: hsl(197, 94%, 51%); font-weight: bold } /* Keyword */
|
||||
.o { color: #666666 } /* Operator */
|
||||
.ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
|
||||
.cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
|
||||
.cp { color: #9C6500 } /* Comment.Preproc */
|
||||
.cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
|
||||
.c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
|
||||
.cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
|
||||
.gd { color: #A00000 } /* Generic.Deleted */
|
||||
.ge { font-style: italic } /* Generic.Emph */
|
||||
.gr { color: #E40000 } /* Generic.Error */
|
||||
.gh { color: #000080; font-weight: bold } /* Generic.Heading */
|
||||
.gi { color: #008400 } /* Generic.Inserted */
|
||||
.go { color: #717171 } /* Generic.Output */
|
||||
.gp { color: #000080; font-weight: bold } /* Generic.Prompt */
|
||||
.gs { font-weight: bold } /* Generic.Strong */
|
||||
.gu { color: #800080; font-weight: bold } /* Generic.Subheading */
|
||||
.gt { color: #a9dd00 } /* Generic.Traceback */
|
||||
.kc { color: #008000; font-weight: bold } /* Keyword.Constant */
|
||||
.kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
|
||||
.kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
|
||||
.kp { color: #008000 } /* Keyword.Pseudo */
|
||||
.kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
|
||||
.kt { color: #B00040 } /* Keyword.Type */
|
||||
.m { color: #666666 } /* Literal.Number */
|
||||
.s { color: #BA2121 } /* Literal.String */
|
||||
.na { color: #687822 } /* Name.Attribute */
|
||||
.nb { color: #e5f8c3 } /* Name.Builtin */
|
||||
.nc { color: #ffad65; font-weight: bold } /* Name.Class */
|
||||
.no { color: #880000 } /* Name.Constant */
|
||||
.nd { color: #AA22FF } /* Name.Decorator */
|
||||
.ni { color: #717171; font-weight: bold } /* Name.Entity */
|
||||
.ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
|
||||
.nf { color: #f9f978 } /* Name.Function */
|
||||
.nl { color: #767600 } /* Name.Label */
|
||||
.nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
|
||||
.nt { color: #008000; font-weight: bold } /* Name.Tag */
|
||||
.nv { color: #19177C } /* Name.Variable */
|
||||
.ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
|
||||
.w { color: #bbbbbb } /* Text.Whitespace */
|
||||
.mb { color: #666666 } /* Literal.Number.Bin */
|
||||
.mf { color: #666666 } /* Literal.Number.Float */
|
||||
.mh { color: #666666 } /* Literal.Number.Hex */
|
||||
.mi { color: #666666 } /* Literal.Number.Integer */
|
||||
.mo { color: #666666 } /* Literal.Number.Oct */
|
||||
.sa { color: #BA2121 } /* Literal.String.Affix */
|
||||
.sb { color: #BA2121 } /* Literal.String.Backtick */
|
||||
.sc { color: #BA2121 } /* Literal.String.Char */
|
||||
.dl { color: #BA2121 } /* Literal.String.Delimiter */
|
||||
.sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
|
||||
.s2 { color: #2bf840 } /* Literal.String.Double */
|
||||
.se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
|
||||
.sh { color: #BA2121 } /* Literal.String.Heredoc */
|
||||
.si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
|
||||
.sx { color: #008000 } /* Literal.String.Other */
|
||||
.sr { color: #A45A77 } /* Literal.String.Regex */
|
||||
.s1 { color: #BA2121 } /* Literal.String.Single */
|
||||
.ss { color: #19177C } /* Literal.String.Symbol */
|
||||
.bp { color: #008000 } /* Name.Builtin.Pseudo */
|
||||
.fm { color: #0000FF } /* Name.Function.Magic */
|
||||
.vc { color: #19177C } /* Name.Variable.Class */
|
||||
.vg { color: #19177C } /* Name.Variable.Global */
|
||||
.vi { color: #19177C } /* Name.Variable.Instance */
|
||||
.vm { color: #19177C } /* Name.Variable.Magic */
|
||||
.il { color: #666666 } /* Literal.Number.Integer.Long */
|
||||
.codehilite .hll { background-color: #6e7681 }
|
||||
.codehilite .c { color: #8b949e; font-style: italic } /* Comment */
|
||||
.codehilite .err { color: #f85149 } /* Error */
|
||||
.codehilite .esc { color: #c9d1d9 } /* Escape */
|
||||
.codehilite .g { color: #c9d1d9 } /* Generic */
|
||||
.codehilite .k { color: #ff7b72 } /* Keyword */
|
||||
.codehilite .l { color: #a5d6ff } /* Literal */
|
||||
.codehilite .n { color: #c9d1d9 } /* Name */
|
||||
.codehilite .o { color: #ff7b72; font-weight: bold } /* Operator */
|
||||
.codehilite .x { color: #c9d1d9 } /* Other */
|
||||
.codehilite .p { color: #c9d1d9 } /* Punctuation */
|
||||
.codehilite .ch { color: #8b949e; font-style: italic } /* Comment.Hashbang */
|
||||
.codehilite .cm { color: #8b949e; font-style: italic } /* Comment.Multiline */
|
||||
.codehilite .cp { color: #8b949e; font-weight: bold; font-style: italic } /* Comment.Preproc */
|
||||
.codehilite .cpf { color: #8b949e; font-style: italic } /* Comment.PreprocFile */
|
||||
.codehilite .c1 { color: #8b949e; font-style: italic } /* Comment.Single */
|
||||
.codehilite .cs { color: #8b949e; font-weight: bold; font-style: italic } /* Comment.Special */
|
||||
.codehilite .gd { color: #ffa198; background-color: #490202 } /* Generic.Deleted */
|
||||
.codehilite .ge { color: #c9d1d9; font-style: italic } /* Generic.Emph */
|
||||
.codehilite .gr { color: #ffa198 } /* Generic.Error */
|
||||
.codehilite .gh { color: #79c0ff; font-weight: bold } /* Generic.Heading */
|
||||
.codehilite .gi { color: #56d364; background-color: #0f5323 } /* Generic.Inserted */
|
||||
.codehilite .go { color: #8b949e } /* Generic.Output */
|
||||
.codehilite .gp { color: #8b949e } /* Generic.Prompt */
|
||||
.codehilite .gs { color: #c9d1d9; font-weight: bold } /* Generic.Strong */
|
||||
.codehilite .gu { color: #79c0ff } /* Generic.Subheading */
|
||||
.codehilite .gt { color: #ff7b72 } /* Generic.Traceback */
|
||||
.codehilite .g-Underline { color: #c9d1d9; text-decoration: underline } /* Generic.Underline */
|
||||
.codehilite .kc { color: #79c0ff } /* Keyword.Constant */
|
||||
.codehilite .kd { color: #ff7b72 } /* Keyword.Declaration */
|
||||
.codehilite .kn { color: #ff7b72 } /* Keyword.Namespace */
|
||||
.codehilite .kp { color: #79c0ff } /* Keyword.Pseudo */
|
||||
.codehilite .kr { color: #ff7b72 } /* Keyword.Reserved */
|
||||
.codehilite .kt { color: #ff7b72 } /* Keyword.Type */
|
||||
.codehilite .ld { color: #79c0ff } /* Literal.Date */
|
||||
.codehilite .m { color: #a5d6ff } /* Literal.Number */
|
||||
.codehilite .s { color: #a5d6ff } /* Literal.String */
|
||||
.codehilite .na { color: #c9d1d9 } /* Name.Attribute */
|
||||
.codehilite .nb { color: #c9d1d9 } /* Name.Builtin */
|
||||
.codehilite .nc { color: #f0883e; font-weight: bold } /* Name.Class */
|
||||
.codehilite .no { color: #79c0ff; font-weight: bold } /* Name.Constant */
|
||||
.codehilite .nd { color: #d2a8ff; font-weight: bold } /* Name.Decorator */
|
||||
.codehilite .ni { color: #ffa657 } /* Name.Entity */
|
||||
.codehilite .ne { color: #f0883e; font-weight: bold } /* Name.Exception */
|
||||
.codehilite .nf { color: #d2a8ff; font-weight: bold } /* Name.Function */
|
||||
.codehilite .nl { color: #79c0ff; font-weight: bold } /* Name.Label */
|
||||
.codehilite .nn { color: #ff7b72 } /* Name.Namespace */
|
||||
.codehilite .nx { color: #c9d1d9 } /* Name.Other */
|
||||
.codehilite .py { color: #79c0ff } /* Name.Property */
|
||||
.codehilite .nt { color: #7ee787 } /* Name.Tag */
|
||||
.codehilite .nv { color: #79c0ff } /* Name.Variable */
|
||||
.codehilite .ow { color: #ff7b72; font-weight: bold } /* Operator.Word */
|
||||
.codehilite .pm { color: #c9d1d9 } /* Punctuation.Marker */
|
||||
.codehilite .w { color: #6e7681 } /* Text.Whitespace */
|
||||
.codehilite .mb { color: #a5d6ff } /* Literal.Number.Bin */
|
||||
.codehilite .mf { color: #a5d6ff } /* Literal.Number.Float */
|
||||
.codehilite .mh { color: #a5d6ff } /* Literal.Number.Hex */
|
||||
.codehilite .mi { color: #a5d6ff } /* Literal.Number.Integer */
|
||||
.codehilite .mo { color: #a5d6ff } /* Literal.Number.Oct */
|
||||
.codehilite .sa { color: #79c0ff } /* Literal.String.Affix */
|
||||
.codehilite .sb { color: #a5d6ff } /* Literal.String.Backtick */
|
||||
.codehilite .sc { color: #a5d6ff } /* Literal.String.Char */
|
||||
.codehilite .dl { color: #79c0ff } /* Literal.String.Delimiter */
|
||||
.codehilite .sd { color: #a5d6ff } /* Literal.String.Doc */
|
||||
.codehilite .s2 { color: #a5d6ff } /* Literal.String.Double */
|
||||
.codehilite .se { color: #79c0ff } /* Literal.String.Escape */
|
||||
.codehilite .sh { color: #79c0ff } /* Literal.String.Heredoc */
|
||||
.codehilite .si { color: #a5d6ff } /* Literal.String.Interpol */
|
||||
.codehilite .sx { color: #a5d6ff } /* Literal.String.Other */
|
||||
.codehilite .sr { color: #79c0ff } /* Literal.String.Regex */
|
||||
.codehilite .s1 { color: #a5d6ff } /* Literal.String.Single */
|
||||
.codehilite .ss { color: #a5d6ff } /* Literal.String.Symbol */
|
||||
.codehilite .bp { color: #c9d1d9 } /* Name.Builtin.Pseudo */
|
||||
.codehilite .fm { color: #d2a8ff; font-weight: bold } /* Name.Function.Magic */
|
||||
.codehilite .vc { color: #79c0ff } /* Name.Variable.Class */
|
||||
.codehilite .vg { color: #79c0ff } /* Name.Variable.Global */
|
||||
.codehilite .vi { color: #79c0ff } /* Name.Variable.Instance */
|
||||
.codehilite .vm { color: #79c0ff } /* Name.Variable.Magic */
|
||||
.codehilite .il { color: #a5d6ff } /* Literal.Number.Integer.Long */
|
||||
|
||||
.dark .codehilite .hll { background-color: #2C3B41 }
|
||||
.dark .codehilite .c { color: #79d618; font-style: italic } /* Comment */
|
||||
.dark .codehilite .err { color: #FF5370 } /* Error */
|
||||
.dark .codehilite .esc { color: #89DDFF } /* Escape */
|
||||
.dark .codehilite .g { color: #EEFFFF } /* Generic */
|
||||
.dark .codehilite .k { color: #BB80B3 } /* Keyword */
|
||||
.dark .codehilite .l { color: #C3E88D } /* Literal */
|
||||
.dark .codehilite .n { color: #EEFFFF } /* Name */
|
||||
.dark .codehilite .o { color: #89DDFF } /* Operator */
|
||||
.dark .codehilite .p { color: #89DDFF } /* Punctuation */
|
||||
.dark .codehilite .ch { color: #79d618; font-style: italic } /* Comment.Hashbang */
|
||||
.dark .codehilite .cm { color: #79d618; font-style: italic } /* Comment.Multiline */
|
||||
.dark .codehilite .cp { color: #79d618; font-style: italic } /* Comment.Preproc */
|
||||
.dark .codehilite .cpf { color: #79d618; font-style: italic } /* Comment.PreprocFile */
|
||||
.dark .codehilite .c1 { color: #79d618; font-style: italic } /* Comment.Single */
|
||||
.dark .codehilite .cs { color: #79d618; font-style: italic } /* Comment.Special */
|
||||
.dark .codehilite .gd { color: #FF5370 } /* Generic.Deleted */
|
||||
.dark .codehilite .ge { color: #89DDFF } /* Generic.Emph */
|
||||
.dark .codehilite .gr { color: #FF5370 } /* Generic.Error */
|
||||
.dark .codehilite .gh { color: #C3E88D } /* Generic.Heading */
|
||||
.dark .codehilite .gi { color: #C3E88D } /* Generic.Inserted */
|
||||
.dark .codehilite .go { color: #79d618 } /* Generic.Output */
|
||||
.dark .codehilite .gp { color: #FFCB6B } /* Generic.Prompt */
|
||||
.dark .codehilite .gs { color: #FF5370 } /* Generic.Strong */
|
||||
.dark .codehilite .gu { color: #89DDFF } /* Generic.Subheading */
|
||||
.dark .codehilite .gt { color: #FF5370 } /* Generic.Traceback */
|
||||
.dark .codehilite .kc { color: #89DDFF } /* Keyword.Constant */
|
||||
.dark .codehilite .kd { color: #BB80B3 } /* Keyword.Declaration */
|
||||
.dark .codehilite .kn { color: #89DDFF; font-style: italic } /* Keyword.Namespace */
|
||||
.dark .codehilite .kp { color: #89DDFF } /* Keyword.Pseudo */
|
||||
.dark .codehilite .kr { color: #BB80B3 } /* Keyword.Reserved */
|
||||
.dark .codehilite .kt { color: #BB80B3 } /* Keyword.Type */
|
||||
.dark .codehilite .ld { color: #C3E88D } /* Literal.Date */
|
||||
.dark .codehilite .m { color: #F78C6C } /* Literal.Number */
|
||||
.dark .codehilite .s { color: #C3E88D } /* Literal.String */
|
||||
.dark .codehilite .na { color: #BB80B3 } /* Name.Attribute */
|
||||
.dark .codehilite .nb { color: #82AAFF } /* Name.Builtin */
|
||||
.dark .codehilite .nc { color: #FFCB6B } /* Name.Class */
|
||||
.dark .codehilite .no { color: #EEFFFF } /* Name.Constant */
|
||||
.dark .codehilite .nd { color: #82AAFF } /* Name.Decorator */
|
||||
.dark .codehilite .ni { color: #89DDFF } /* Name.Entity */
|
||||
.dark .codehilite .ne { color: #FFCB6B } /* Name.Exception */
|
||||
.dark .codehilite .nf { color: #82AAFF } /* Name.Function */
|
||||
.dark .codehilite .nl { color: #82AAFF } /* Name.Label */
|
||||
.dark .codehilite .nn { color: #FFCB6B } /* Name.Namespace */
|
||||
.dark .codehilite .nx { color: #EEFFFF } /* Name.Other */
|
||||
.dark .codehilite .py { color: #FFCB6B } /* Name.Property */
|
||||
.dark .codehilite .nt { color: #FF5370 } /* Name.Tag */
|
||||
.dark .codehilite .nv { color: #89DDFF } /* Name.Variable */
|
||||
.dark .codehilite .ow { color: #89DDFF; font-style: italic } /* Operator.Word */
|
||||
.dark .codehilite .pm { color: #89DDFF } /* Punctuation.Marker */
|
||||
.dark .codehilite .w { color: #EEFFFF } /* Text.Whitespace */
|
||||
.dark .codehilite .mb { color: #F78C6C } /* Literal.Number.Bin */
|
||||
.dark .codehilite .mf { color: #F78C6C } /* Literal.Number.Float */
|
||||
.dark .codehilite .mh { color: #F78C6C } /* Literal.Number.Hex */
|
||||
.dark .codehilite .mi { color: #F78C6C } /* Literal.Number.Integer */
|
||||
.dark .codehilite .mo { color: #F78C6C } /* Literal.Number.Oct */
|
||||
.dark .codehilite .sa { color: #BB80B3 } /* Literal.String.Affix */
|
||||
.dark .codehilite .sb { color: #C3E88D } /* Literal.String.Backtick */
|
||||
.dark .codehilite .sc { color: #C3E88D } /* Literal.String.Char */
|
||||
.dark .codehilite .dl { color: #EEFFFF } /* Literal.String.Delimiter */
|
||||
.dark .codehilite .sd { color: #79d618; font-style: italic } /* Literal.String.Doc */
|
||||
.dark .codehilite .s2 { color: #C3E88D } /* Literal.String.Double */
|
||||
.dark .codehilite .se { color: #EEFFFF } /* Literal.String.Escape */
|
||||
.dark .codehilite .sh { color: #C3E88D } /* Literal.String.Heredoc */
|
||||
.dark .codehilite .si { color: #89DDFF } /* Literal.String.Interpol */
|
||||
.dark .codehilite .sx { color: #C3E88D } /* Literal.String.Other */
|
||||
.dark .codehilite .sr { color: #89DDFF } /* Literal.String.Regex */
|
||||
.dark .codehilite .s1 { color: #C3E88D } /* Literal.String.Single */
|
||||
.dark .codehilite .ss { color: #89DDFF } /* Literal.String.Symbol */
|
||||
.dark .codehilite .bp { color: #89DDFF } /* Name.Builtin.Pseudo */
|
||||
.dark .codehilite .fm { color: #82AAFF } /* Name.Function.Magic */
|
||||
.dark .codehilite .vc { color: #89DDFF } /* Name.Variable.Class */
|
||||
.dark .codehilite .vg { color: #89DDFF } /* Name.Variable.Global */
|
||||
.dark .codehilite .vi { color: #89DDFF } /* Name.Variable.Instance */
|
||||
.dark .codehilite .vm { color: #82AAFF } /* Name.Variable.Magic */
|
||||
.dark .codehilite .il { color: #F78C6C } /* Literal.Number.Integer.Long */
|
||||
|
||||
"""
|
||||
|
||||
106
toolbox.py
106
toolbox.py
@@ -5,7 +5,20 @@ import inspect
|
||||
import re
|
||||
from latex2mathml.converter import convert as tex2mathml
|
||||
from functools import wraps, lru_cache
|
||||
############################### 插件输入输出接驳区 #######################################
|
||||
|
||||
"""
|
||||
========================================================================
|
||||
第一部分
|
||||
函数插件输入输出接驳区
|
||||
- ChatBotWithCookies: 带Cookies的Chatbot类,为实现更多强大的功能做基础
|
||||
- ArgsGeneralWrapper: 装饰器函数,用于重组输入参数,改变输入参数的顺序与结构
|
||||
- update_ui: 刷新界面用 yield from update_ui(chatbot, history)
|
||||
- CatchException: 将插件中出的所有问题显示在界面上
|
||||
- HotReload: 实现插件的热更新
|
||||
- trimmed_format_exc: 打印traceback,为了安全而隐藏绝对地址
|
||||
========================================================================
|
||||
"""
|
||||
|
||||
class ChatBotWithCookies(list):
|
||||
def __init__(self, cookie):
|
||||
self._cookies = cookie
|
||||
@@ -20,6 +33,7 @@ class ChatBotWithCookies(list):
|
||||
def get_cookies(self):
|
||||
return self._cookies
|
||||
|
||||
|
||||
def ArgsGeneralWrapper(f):
|
||||
"""
|
||||
装饰器函数,用于重组输入参数,改变输入参数的顺序与结构。
|
||||
@@ -47,6 +61,7 @@ def ArgsGeneralWrapper(f):
|
||||
yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
|
||||
return decorated
|
||||
|
||||
|
||||
def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
|
||||
"""
|
||||
刷新用户界面
|
||||
@@ -54,10 +69,18 @@ def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面
|
||||
assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时,可用clear将其清空,然后用for+append循环重新赋值。"
|
||||
yield chatbot.get_cookies(), chatbot, history, msg
|
||||
|
||||
def trimmed_format_exc():
|
||||
import os, traceback
|
||||
str = traceback.format_exc()
|
||||
current_path = os.getcwd()
|
||||
replace_path = "."
|
||||
return str.replace(current_path, replace_path)
|
||||
|
||||
def CatchException(f):
|
||||
"""
|
||||
装饰器函数,捕捉函数f中的异常并封装到一个生成器中返回,并显示到聊天当中。
|
||||
"""
|
||||
|
||||
@wraps(f)
|
||||
def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
|
||||
try:
|
||||
@@ -66,7 +89,7 @@ def CatchException(f):
|
||||
from check_proxy import check_proxy
|
||||
from toolbox import get_conf
|
||||
proxies, = get_conf('proxies')
|
||||
tb_str = '```\n' + traceback.format_exc() + '```'
|
||||
tb_str = '```\n' + trimmed_format_exc() + '```'
|
||||
if chatbot is None or len(chatbot) == 0:
|
||||
chatbot = [["插件调度异常", "异常原因"]]
|
||||
chatbot[-1] = (chatbot[-1][0],
|
||||
@@ -93,7 +116,23 @@ def HotReload(f):
|
||||
return decorated
|
||||
|
||||
|
||||
####################################### 其他小工具 #####################################
|
||||
"""
|
||||
========================================================================
|
||||
第二部分
|
||||
其他小工具:
|
||||
- write_results_to_file: 将结果写入markdown文件中
|
||||
- regular_txt_to_markdown: 将普通文本转换为Markdown格式的文本。
|
||||
- report_execption: 向chatbot中添加简单的意外错误信息
|
||||
- text_divide_paragraph: 将文本按照段落分隔符分割开,生成带有段落标签的HTML代码。
|
||||
- markdown_convertion: 用多种方式组合,将markdown转化为好看的html
|
||||
- format_io: 接管gradio默认的markdown处理方式
|
||||
- on_file_uploaded: 处理文件的上传(自动解压)
|
||||
- on_report_generated: 将生成的报告自动投射到文件上传区
|
||||
- clip_history: 当历史上下文过长时,自动截断
|
||||
- get_conf: 获取设置
|
||||
- select_api_key: 根据当前的模型类别,抽取可用的api-key
|
||||
========================================================================
|
||||
"""
|
||||
|
||||
def get_reduce_token_percent(text):
|
||||
"""
|
||||
@@ -113,7 +152,6 @@ def get_reduce_token_percent(text):
|
||||
return 0.5, '不详'
|
||||
|
||||
|
||||
|
||||
def write_results_to_file(history, file_name=None):
|
||||
"""
|
||||
将对话记录history以Markdown格式写入文件中。如果没有指定文件名,则使用当前时间生成文件名。
|
||||
@@ -369,6 +407,9 @@ def find_recent_files(directory):
|
||||
|
||||
|
||||
def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
|
||||
"""
|
||||
当文件被上传时的回调函数
|
||||
"""
|
||||
if len(files) == 0:
|
||||
return chatbot, txt
|
||||
import shutil
|
||||
@@ -388,8 +429,7 @@ def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
|
||||
shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
|
||||
err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
|
||||
dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
|
||||
moved_files = [fp for fp in glob.glob(
|
||||
'private_upload/**/*', recursive=True)]
|
||||
moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)]
|
||||
if "底部输入区" in checkboxes:
|
||||
txt = ""
|
||||
txt2 = f'private_upload/{time_tag}'
|
||||
@@ -508,7 +548,7 @@ def clear_line_break(txt):
|
||||
class DummyWith():
|
||||
"""
|
||||
这段代码定义了一个名为DummyWith的空上下文管理器,
|
||||
它的作用是……额……没用,即在代码结构不变得情况下取代其他的上下文管理器。
|
||||
它的作用是……额……就是不起作用,即在代码结构不变得情况下取代其他的上下文管理器。
|
||||
上下文管理器是一种Python对象,用于与with语句一起使用,
|
||||
以确保一些资源在代码块执行期间得到正确的初始化和清理。
|
||||
上下文管理器必须实现两个方法,分别为 __enter__()和 __exit__()。
|
||||
@@ -522,6 +562,9 @@ class DummyWith():
|
||||
return
|
||||
|
||||
def run_gradio_in_subpath(demo, auth, port, custom_path):
|
||||
"""
|
||||
把gradio的运行地址更改到指定的二次路径上
|
||||
"""
|
||||
def is_path_legal(path: str)->bool:
|
||||
'''
|
||||
check path for sub url
|
||||
@@ -551,3 +594,52 @@ def run_gradio_in_subpath(demo, auth, port, custom_path):
|
||||
return {"message": f"Gradio is running at: {custom_path}"}
|
||||
app = gr.mount_gradio_app(app, demo, path=custom_path)
|
||||
uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth
|
||||
|
||||
|
||||
def clip_history(inputs, history, tokenizer, max_token_limit):
|
||||
"""
|
||||
reduce the length of history by clipping.
|
||||
this function search for the longest entries to clip, little by little,
|
||||
until the number of token of history is reduced under threshold.
|
||||
通过裁剪来缩短历史记录的长度。
|
||||
此函数逐渐地搜索最长的条目进行剪辑,
|
||||
直到历史记录的标记数量降低到阈值以下。
|
||||
"""
|
||||
import numpy as np
|
||||
from request_llm.bridge_all import model_info
|
||||
def get_token_num(txt):
|
||||
return len(tokenizer.encode(txt, disallowed_special=()))
|
||||
input_token_num = get_token_num(inputs)
|
||||
if input_token_num < max_token_limit * 3 / 4:
|
||||
# 当输入部分的token占比小于限制的3/4时,裁剪时
|
||||
# 1. 把input的余量留出来
|
||||
max_token_limit = max_token_limit - input_token_num
|
||||
# 2. 把输出用的余量留出来
|
||||
max_token_limit = max_token_limit - 128
|
||||
# 3. 如果余量太小了,直接清除历史
|
||||
if max_token_limit < 128:
|
||||
history = []
|
||||
return history
|
||||
else:
|
||||
# 当输入部分的token占比 > 限制的3/4时,直接清除历史
|
||||
history = []
|
||||
return history
|
||||
|
||||
everything = ['']
|
||||
everything.extend(history)
|
||||
n_token = get_token_num('\n'.join(everything))
|
||||
everything_token = [get_token_num(e) for e in everything]
|
||||
|
||||
# 截断时的颗粒度
|
||||
delta = max(everything_token) // 16
|
||||
|
||||
while n_token > max_token_limit:
|
||||
where = np.argmax(everything_token)
|
||||
encoded = tokenizer.encode(everything[where], disallowed_special=())
|
||||
clipped_encoded = encoded[:len(encoded)-delta]
|
||||
everything[where] = tokenizer.decode(clipped_encoded)[:-1] # -1 to remove the may-be illegal char
|
||||
everything_token[where] = get_token_num(everything[where])
|
||||
n_token = get_token_num('\n'.join(everything))
|
||||
|
||||
history = everything[1:]
|
||||
return history
|
||||
|
||||
4
version
4
version
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"version": 3.2,
|
||||
"version": 3.3,
|
||||
"show_feature": true,
|
||||
"new_feature": "保存对话功能 <-> 解读任意语言代码+同时询问任意的LLM组合 <-> 添加联网(Google)回答问题插件 <-> 修复ChatGLM上下文BUG <-> 添加支持清华ChatGLM和GPT-4 <-> 改进架构,支持与多个LLM模型同时对话 <-> 添加支持API2D(国内,可支持gpt4)"
|
||||
"new_feature": "支持NewBing <-> Markdown翻译功能支持直接输入Readme文件网址 <-> 保存对话功能 <-> 解读任意语言代码+同时询问任意的LLM组合 <-> 添加联网(Google)回答问题插件 <-> 修复ChatGLM上下文BUG <-> 添加支持清华ChatGLM和GPT-4 <-> 改进架构,支持与多个LLM模型同时对话 <-> 添加支持API2D(国内,可支持gpt4)"
|
||||
}
|
||||
|
||||
在新工单中引用
屏蔽一个用户