Compare commits
4 Commits
enable_cle
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CodeInterp
| Author | SHA1 | Date | |
|---|---|---|---|
| fb22f716ff | |||
| a8093b9dd8 | |||
| 4f55dfdc0e | |||
| 505b10965f |
44
.github/workflows/build-with-audio-assistant.yml
vendored
44
.github/workflows/build-with-audio-assistant.yml
vendored
@ -1,44 +0,0 @@
|
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# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
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name: build-with-audio-assistant
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on:
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push:
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branches:
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- 'master'
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env:
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REGISTRY: ghcr.io
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IMAGE_NAME: ${{ github.repository }}_audio_assistant
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jobs:
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build-and-push-image:
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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- name: Log in to the Container registry
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uses: docker/login-action@v2
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata (tags, labels) for Docker
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id: meta
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uses: docker/metadata-action@v4
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
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- name: Build and push Docker image
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uses: docker/build-push-action@v4
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with:
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context: .
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push: true
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file: docs/GithubAction+NoLocal+AudioAssistant
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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1
.gitignore
vendored
1
.gitignore
vendored
@ -151,3 +151,4 @@ multi-language
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request_llm/moss
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media
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flagged
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objdump.tmp
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13
README.md
13
README.md
@ -93,7 +93,7 @@ Latex论文一键校对 | [函数插件] 仿Grammarly对Latex文章进行语法
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1. 下载项目
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```sh
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git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
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git clone https://github.com/binary-husky/gpt_academic.git
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cd gpt_academic
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```
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@ -126,7 +126,7 @@ python -m pip install -r request_llm/requirements_chatglm.txt
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# 【可选步骤II】支持复旦MOSS
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python -m pip install -r request_llm/requirements_moss.txt
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git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llm/moss # 注意执行此行代码时，必须处于项目根路径
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git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # 注意执行此行代码时，必须处于项目根路径
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# 【可选步骤III】确保config.py配置文件的AVAIL_LLM_MODELS包含了期望的模型，目前支持的全部模型如下(jittorllms系列目前仅支持docker方案)：
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AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
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@ -147,10 +147,9 @@ python main.py
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1. 仅ChatGPT（推荐大多数人选择，等价于docker-compose方案1）
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[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-without-local-llms.yml)
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[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-latex.yml)
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[](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-audio-assistant.yml)
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``` sh
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git clone --depth=1 https://github.com/binary-husky/gpt_academic.git # 下载项目
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git clone https://github.com/binary-husky/gpt_academic.git # 下载项目
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cd gpt_academic # 进入路径
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nano config.py # 用任意文本编辑器编辑config.py, 配置 “Proxy”， “API_KEY” 以及 “WEB_PORT” (例如50923) 等
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docker build -t gpt-academic . # 安装
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@ -196,12 +195,10 @@ docker-compose up
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5. 远程云服务器部署（需要云服务器知识与经验）。
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请访问[部署wiki-1](https://github.com/binary-husky/gpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
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6. 使用Sealos[一键部署](https://github.com/binary-husky/gpt_academic/issues/993)。
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7. 使用WSL2（Windows Subsystem for Linux 子系统）。
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6. 使用WSL2（Windows Subsystem for Linux 子系统）。
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请访问[部署wiki-2](https://github.com/binary-husky/gpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
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8. 如何在二级网址（如`http://localhost/subpath`）下运行。
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7. 如何在二级网址（如`http://localhost/subpath`）下运行。
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请访问[FastAPI运行说明](docs/WithFastapi.md)
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@ -80,7 +80,6 @@ ChatGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b
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# 本地LLM模型如ChatGLM的执行方式 CPU/GPU
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LOCAL_MODEL_DEVICE = "cpu" # 可选 "cuda"
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LOCAL_MODEL_QUANT = "FP16" # 默认 "FP16" "INT4" 启用量化INT4版本 "INT8" 启用量化INT8版本
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# 设置gradio的并行线程数（不需要修改）
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@ -132,10 +131,9 @@ put your new bing cookies here
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# 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
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ENABLE_AUDIO = False
|
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ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
|
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ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
|
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ALIYUN_ACCESSKEY="" # （无需填写）
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ALIYUN_SECRET="" # （无需填写）
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ALIYUN_TOKEN="" # 例如 f37f30e0f9934c34a992f6f64f7eba4f
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ALIYUN_APPKEY="" # 例如 RoPlZrM88DnAFkZK
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||||
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# Claude API KEY
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ANTHROPIC_API_KEY = ""
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@ -1,7 +1,7 @@
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# 'primary' 颜色对应 theme.py 中的 primary_hue
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# 'secondary' 颜色对应 theme.py 中的 neutral_hue
|
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# 'stop' 颜色对应 theme.py 中的 color_er
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import importlib
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# 默认按钮颜色是 secondary
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from toolbox import clear_line_break
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@ -14,12 +14,7 @@ def get_core_functions():
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r"Furthermore, list all modification and explain the reasons to do so in markdown table." + "\n\n",
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# 后语
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"Suffix": r"",
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# 按钮颜色 (默认 secondary)
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"Color": r"secondary",
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||||
# 按钮是否可见 (默认 True，即可见)
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"Visible": True,
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# 是否在触发时清除历史 (默认 False，即不处理之前的对话历史)
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"AutoClearHistory": False
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"Color": r"secondary", # 按钮颜色
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},
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"中文学术润色": {
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"Prefix": r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，" +
|
||||
@ -81,14 +76,3 @@ def get_core_functions():
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||||
"Suffix": r"",
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||||
}
|
||||
}
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||||
|
||||
|
||||
def handle_core_functionality(additional_fn, inputs, history, chatbot):
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import core_functional
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importlib.reload(core_functional) # 热更新prompt
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core_functional = core_functional.get_core_functions()
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||||
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）
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inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
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if core_functional[additional_fn].get("AutoClearHistory", False):
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||||
history = []
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return inputs, history
|
||||
|
||||
@ -432,18 +432,18 @@ def get_crazy_functions():
|
||||
except:
|
||||
print('Load function plugin failed')
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||||
|
||||
# try:
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||||
# from crazy_functions.虚空终端 import 终端
|
||||
# function_plugins.update({
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||||
# "超级终端": {
|
||||
# "Color": "stop",
|
||||
# "AsButton": False,
|
||||
# # "AdvancedArgs": True,
|
||||
# # "ArgsReminder": "",
|
||||
# "Function": HotReload(终端)
|
||||
# }
|
||||
# })
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||||
# except:
|
||||
# print('Load function plugin failed')
|
||||
try:
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||||
from crazy_functions.虚空终端CodeInterpreter import 虚空终端CodeInterpreter
|
||||
function_plugins.update({
|
||||
"虚空终端CodeInterpreter": {
|
||||
"Color": "stop",
|
||||
"AsButton": True,
|
||||
# "AdvancedArgs": True,
|
||||
# "ArgsReminder": "",
|
||||
"Function": HotReload(虚空终端CodeInterpreter)
|
||||
}
|
||||
})
|
||||
except:
|
||||
print('Load function plugin failed')
|
||||
|
||||
return function_plugins
|
||||
|
||||
@ -228,6 +228,22 @@ def test_chatglm_finetune():
|
||||
for cookies, cb, hist, msg in (启动微调)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
cli_printer.print(cb)
|
||||
|
||||
def test_虚空终端CodeInterpreter():
|
||||
from crazy_functions.虚空终端CodeInterpreter import 虚空终端CodeInterpreter
|
||||
txt = 'Convert this dataset to excel.'
|
||||
plugin_kwargs = {"recently_uploaded_files":"build/assets/iris.csv"}
|
||||
|
||||
for cookies, cb, hist, msg in (虚空终端CodeInterpreter)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
cli_printer.print(cb)
|
||||
|
||||
|
||||
def test_解析项目源代码炫酷版():
|
||||
from crazy_functions.解析项目源代码炫酷版 import 解析一个Python项目炫酷版
|
||||
txt = './'
|
||||
|
||||
for cookies, cb, hist, msg in (解析一个Python项目炫酷版)(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
cli_printer.print(cb)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# test_解析一个Python项目()
|
||||
@ -243,7 +259,9 @@ if __name__ == "__main__":
|
||||
# test_数学动画生成manim()
|
||||
# test_Langchain知识库()
|
||||
# test_Langchain知识库读取()
|
||||
test_Latex()
|
||||
# test_Latex()
|
||||
# test_chatglm_finetune()
|
||||
# test_虚空终端CodeInterpreter()
|
||||
test_解析项目源代码炫酷版()
|
||||
input("程序完成，回车退出。")
|
||||
print("退出。")
|
||||
@ -141,7 +141,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
chatbot, history_array, sys_prompt_array,
|
||||
refresh_interval=0.2, max_workers=-1, scroller_max_len=30,
|
||||
handle_token_exceed=True, show_user_at_complete=False,
|
||||
retry_times_at_unknown_error=2,
|
||||
retry_times_at_unknown_error=2, callback_fn=None
|
||||
):
|
||||
"""
|
||||
Request GPT model using multiple threads with UI and high efficiency
|
||||
@ -166,6 +166,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
handle_token_exceed：是否自动处理token溢出的情况，如果选择自动处理，则会在溢出时暴力截断，默认开启
|
||||
show_user_at_complete (bool, optional): (在结束时，把完整输入-输出结果显示在聊天框)
|
||||
retry_times_at_unknown_error：子任务失败时的重试次数
|
||||
callback_fn: 当信息更新时，在主进程调用的回调函数
|
||||
|
||||
输出 Returns:
|
||||
list: List of GPT model responses （每个子任务的输出汇总，如果某个子任务出错，response中会携带traceback报错信息，方便调试和定位问题。）
|
||||
@ -283,6 +284,9 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
# 在前端打印些好玩的东西
|
||||
chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
|
||||
yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
|
||||
# 回调函数
|
||||
if callback_fn is not None: callback_fn([mutable[thread_index][0] for thread_index in range(len(futures))])
|
||||
# 结束了吗？
|
||||
if all(worker_done):
|
||||
executor.shutdown()
|
||||
break
|
||||
|
||||
@ -22,8 +22,7 @@ def split_subprocess(txt, project_folder, return_dict, opts):
|
||||
mask = np.zeros(len(txt), dtype=np.uint8) + TRANSFORM
|
||||
|
||||
# 吸收title与作者以上的部分
|
||||
text, mask = set_forbidden_text(text, mask, r"^(.*?)\\maketitle", re.DOTALL)
|
||||
text, mask = set_forbidden_text(text, mask, r"^(.*?)\\begin{document}", re.DOTALL)
|
||||
text, mask = set_forbidden_text(text, mask, r"(.*?)\\maketitle", re.DOTALL)
|
||||
# 吸收iffalse注释
|
||||
text, mask = set_forbidden_text(text, mask, r"\\iffalse(.*?)\\fi", re.DOTALL)
|
||||
# 吸收在42行以内的begin-end组合
|
||||
|
||||
@ -19,7 +19,7 @@ class AliyunASR():
|
||||
pass
|
||||
|
||||
def test_on_error(self, message, *args):
|
||||
print("on_error args=>{}".format(args))
|
||||
# print("on_error args=>{}".format(args))
|
||||
pass
|
||||
|
||||
def test_on_close(self, *args):
|
||||
@ -50,8 +50,6 @@ class AliyunASR():
|
||||
rad.clean_up()
|
||||
temp_folder = tempfile.gettempdir()
|
||||
TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
|
||||
if len(TOKEN) == 0:
|
||||
TOKEN = self.get_token()
|
||||
self.aliyun_service_ok = True
|
||||
URL="wss://nls-gateway.aliyuncs.com/ws/v1"
|
||||
sr = nls.NlsSpeechTranscriber(
|
||||
@ -93,38 +91,3 @@ class AliyunASR():
|
||||
self.stop = True
|
||||
self.stop_msg = 'Aliyun音频服务异常，请检查ALIYUN_TOKEN和ALIYUN_APPKEY是否过期。'
|
||||
r = sr.stop()
|
||||
|
||||
def get_token(self):
|
||||
from toolbox import get_conf
|
||||
import json
|
||||
from aliyunsdkcore.request import CommonRequest
|
||||
from aliyunsdkcore.client import AcsClient
|
||||
AccessKey_ID, AccessKey_secret = get_conf('ALIYUN_ACCESSKEY', 'ALIYUN_SECRET')
|
||||
|
||||
# 创建AcsClient实例
|
||||
client = AcsClient(
|
||||
AccessKey_ID,
|
||||
AccessKey_secret,
|
||||
"cn-shanghai"
|
||||
)
|
||||
|
||||
# 创建request，并设置参数。
|
||||
request = CommonRequest()
|
||||
request.set_method('POST')
|
||||
request.set_domain('nls-meta.cn-shanghai.aliyuncs.com')
|
||||
request.set_version('2019-02-28')
|
||||
request.set_action_name('CreateToken')
|
||||
|
||||
try:
|
||||
response = client.do_action_with_exception(request)
|
||||
print(response)
|
||||
jss = json.loads(response)
|
||||
if 'Token' in jss and 'Id' in jss['Token']:
|
||||
token = jss['Token']['Id']
|
||||
expireTime = jss['Token']['ExpireTime']
|
||||
print("token = " + token)
|
||||
print("expireTime = " + str(expireTime))
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
return token
|
||||
|
||||
@ -1,87 +1,70 @@
|
||||
from toolbox import CatchException, update_ui, gen_time_str
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
from .crazy_utils import input_clipping
|
||||
import copy, json
|
||||
|
||||
|
||||
prompt = """
|
||||
I have to achieve some functionalities by calling one of the functions below.
|
||||
Your job is to find the correct funtion to use to satisfy my requirement,
|
||||
and then write python code to call this function with correct parameters.
|
||||
|
||||
These are functions you are allowed to choose from:
|
||||
1.
|
||||
功能描述: 总结音视频内容
|
||||
调用函数: ConcludeAudioContent(txt, llm_kwargs)
|
||||
参数说明:
|
||||
txt: 音频文件的路径
|
||||
llm_kwargs: 模型参数, 永远给定None
|
||||
2.
|
||||
功能描述: 将每次对话记录写入Markdown格式的文件中
|
||||
调用函数: WriteMarkdown()
|
||||
3.
|
||||
功能描述: 将指定目录下的PDF文件从英文翻译成中文
|
||||
调用函数: BatchTranslatePDFDocuments_MultiThreaded(txt, llm_kwargs)
|
||||
参数说明:
|
||||
txt: PDF文件所在的路径
|
||||
llm_kwargs: 模型参数, 永远给定None
|
||||
4.
|
||||
功能描述: 根据文本使用GPT模型生成相应的图像
|
||||
调用函数: ImageGeneration(txt, llm_kwargs)
|
||||
参数说明:
|
||||
txt: 图像生成所用到的提示文本
|
||||
llm_kwargs: 模型参数, 永远给定None
|
||||
5.
|
||||
功能描述: 对输入的word文档进行摘要生成
|
||||
调用函数: SummarizingWordDocuments(input_path, output_path)
|
||||
参数说明:
|
||||
input_path: 待处理的word文档路径
|
||||
output_path: 摘要生成后的文档路径
|
||||
|
||||
|
||||
You should always anwser with following format:
|
||||
----------------
|
||||
Code:
|
||||
```
|
||||
class AutoAcademic(object):
|
||||
def __init__(self):
|
||||
self.selected_function = "FILL_CORRECT_FUNCTION_HERE" # e.g., "GenerateImage"
|
||||
self.txt = "FILL_MAIN_PARAMETER_HERE" # e.g., "荷叶上的蜻蜓"
|
||||
self.llm_kwargs = None
|
||||
```
|
||||
Explanation:
|
||||
只有GenerateImage和生成图像相关, 因此选择GenerateImage函数。
|
||||
----------------
|
||||
|
||||
Now, this is my requirement:
|
||||
|
||||
"""
|
||||
def get_fn_lib():
|
||||
return {
|
||||
"BatchTranslatePDFDocuments_MultiThreaded": ("crazy_functions.批量翻译PDF文档_多线程", "批量翻译PDF文档"),
|
||||
"SummarizingWordDocuments": ("crazy_functions.总结word文档", "总结word文档"),
|
||||
"ImageGeneration": ("crazy_functions.图片生成", "图片生成"),
|
||||
"TranslateMarkdownFromEnglishToChinese": ("crazy_functions.批量Markdown翻译", "Markdown中译英"),
|
||||
"SummaryAudioVideo": ("crazy_functions.总结音视频", "总结音视频"),
|
||||
"BatchTranslatePDFDocuments_MultiThreaded": {
|
||||
"module": "crazy_functions.批量翻译PDF文档_多线程",
|
||||
"function": "批量翻译PDF文档",
|
||||
"description": "Translate PDF Documents",
|
||||
"arg_1_description": "A path containing pdf files.",
|
||||
},
|
||||
"SummarizingWordDocuments": {
|
||||
"module": "crazy_functions.总结word文档",
|
||||
"function": "总结word文档",
|
||||
"description": "Summarize Word Documents",
|
||||
"arg_1_description": "A path containing Word files.",
|
||||
},
|
||||
"ImageGeneration": {
|
||||
"module": "crazy_functions.图片生成",
|
||||
"function": "图片生成",
|
||||
"description": "Generate a image that satisfies some description.",
|
||||
"arg_1_description": "Descriptions about the image to be generated.",
|
||||
},
|
||||
"TranslateMarkdownFromEnglishToChinese": {
|
||||
"module": "crazy_functions.批量Markdown翻译",
|
||||
"function": "Markdown中译英",
|
||||
"description": "Translate Markdown Documents from English to Chinese.",
|
||||
"arg_1_description": "A path containing Markdown files.",
|
||||
},
|
||||
"SummaryAudioVideo": {
|
||||
"module": "crazy_functions.总结音视频",
|
||||
"function": "总结音视频",
|
||||
"description": "Get text from a piece of audio and summarize this audio.",
|
||||
"arg_1_description": "A path containing audio files.",
|
||||
},
|
||||
}
|
||||
|
||||
functions = [
|
||||
{
|
||||
"name": k,
|
||||
"description": v['description'],
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"plugin_arg_1": {
|
||||
"type": "string",
|
||||
"description": v['arg_1_description'],
|
||||
},
|
||||
},
|
||||
"required": ["plugin_arg_1"],
|
||||
},
|
||||
} for k, v in get_fn_lib().items()
|
||||
]
|
||||
|
||||
def inspect_dependency(chatbot, history):
|
||||
return True
|
||||
|
||||
def eval_code(code, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
import subprocess, sys, os, shutil, importlib
|
||||
|
||||
with open('gpt_log/void_terminal_runtime.py', 'w', encoding='utf8') as f:
|
||||
f.write(code)
|
||||
|
||||
import importlib
|
||||
try:
|
||||
AutoAcademic = getattr(importlib.import_module('gpt_log.void_terminal_runtime', 'AutoAcademic'), 'AutoAcademic')
|
||||
# importlib.reload(AutoAcademic)
|
||||
auto_dict = AutoAcademic()
|
||||
selected_function = auto_dict.selected_function
|
||||
txt = auto_dict.txt
|
||||
fp, fn = get_fn_lib()[selected_function]
|
||||
tmp = get_fn_lib()[code['name']]
|
||||
fp, fn = tmp['module'], tmp['function']
|
||||
fn_plugin = getattr(importlib.import_module(fp, fn), fn)
|
||||
yield from fn_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
|
||||
arg = json.loads(code['arguments'])['plugin_arg_1']
|
||||
yield from fn_plugin(arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
chatbot.append(["执行错误", f"\n```\n{trimmed_format_exc()}\n```\n"])
|
||||
@ -110,22 +93,27 @@ def 终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_
|
||||
history = []
|
||||
|
||||
# 基本信息：功能、贡献者
|
||||
chatbot.append(["函数插件功能？", "根据自然语言执行插件命令, 作者: binary-husky, 插件初始化中 ..."])
|
||||
chatbot.append(["虚空终端插件的功能？", "根据自然语言的描述, 执行任意插件的命令."])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# # 尝试导入依赖, 如果缺少依赖, 则给出安装建议
|
||||
# dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面
|
||||
# if not dep_ok: return
|
||||
|
||||
# 输入
|
||||
i_say = prompt + txt
|
||||
i_say = txt
|
||||
# 开始
|
||||
llm_kwargs_function_call = copy.deepcopy(llm_kwargs)
|
||||
llm_kwargs_function_call['llm_model'] = 'gpt-call-fn' # 修改调用函数
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=i_say, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
|
||||
sys_prompt=""
|
||||
llm_kwargs=llm_kwargs_function_call, chatbot=chatbot, history=[],
|
||||
sys_prompt=functions
|
||||
)
|
||||
|
||||
# 将代码转为动画
|
||||
code = get_code_block(gpt_say)
|
||||
yield from eval_code(code, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
|
||||
res = json.loads(gpt_say)['choices'][0]
|
||||
if res['finish_reason'] == 'function_call':
|
||||
code = json.loads(gpt_say)['choices'][0]
|
||||
yield from eval_code(code['message']['function_call'], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
|
||||
else:
|
||||
chatbot.append(["无法调用相关功能", res])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
|
||||
213
crazy_functions/虚空终端CodeInterpreter.py
Normal file
213
crazy_functions/虚空终端CodeInterpreter.py
Normal file
@ -0,0 +1,213 @@
|
||||
from collections.abc import Callable, Iterable, Mapping
|
||||
from typing import Any
|
||||
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, promote_file_to_downloadzone, clear_file_downloadzone
|
||||
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
|
||||
|
||||
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'gpt_log/{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"gpt_log.{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)
|
||||
|
||||
@CatchException
|
||||
def 虚空终端CodeInterpreter(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，暂时没有用武之地
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
web_port 当前软件运行的端口号
|
||||
"""
|
||||
# 清空历史，以免输入溢出
|
||||
history = []; 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, 作者: binary-husky, 插件初始化中 ..."
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 尝试导入依赖, 如果缺少依赖, 则给出安装建议
|
||||
dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面
|
||||
if not dep_ok: return
|
||||
|
||||
# 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 'private_upload' in 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
|
||||
"""
|
||||
137
crazy_functions/解析项目源代码炫酷版.py
Normal file
137
crazy_functions/解析项目源代码炫酷版.py
Normal file
@ -0,0 +1,137 @@
|
||||
from toolbox import update_ui
|
||||
from toolbox import CatchException, report_execption, write_results_to_file
|
||||
from toolbox import objdump, objload
|
||||
from .crazy_utils import input_clipping
|
||||
|
||||
class ThreeJSPlot():
|
||||
def __init__(self) -> None:
|
||||
self.files = None
|
||||
|
||||
def read_files(self, files):
|
||||
self.files = files
|
||||
|
||||
def launch_render_interface(self):
|
||||
from vhmap.mcom import mcom
|
||||
self.visual_bridge = mcom()
|
||||
|
||||
|
||||
|
||||
def 解析源代码炫酷版(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||||
import os, copy
|
||||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
msg = '正常'
|
||||
summary_batch_isolation = True
|
||||
inputs_array = []
|
||||
inputs_show_user_array = []
|
||||
history_array = []
|
||||
sys_prompt_array = []
|
||||
report_part_1 = []
|
||||
|
||||
assert len(file_manifest) <= 512, "源文件太多（超过512个）, 请缩减输入文件的数量。或者，您也可以选择删除此行警告，并修改代码拆分file_manifest列表，从而实现分批次处理。"
|
||||
############################## <第一步，逐个文件分析，多线程> ##################################
|
||||
for index, fp in enumerate(file_manifest):
|
||||
# 读取文件
|
||||
with open(fp, 'r', encoding='utf-8', errors='replace') as f:
|
||||
file_content = f.read()
|
||||
prefix = "接下来请你逐文件分析下面的工程" if index==0 else ""
|
||||
i_say = prefix + f'请对下面的程序文件做一个概述文件名是{os.path.relpath(fp, project_folder)}，文件代码是 ```{file_content}```'
|
||||
i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的程序文件做一个概述: {os.path.abspath(fp)}'
|
||||
# 装载请求内容
|
||||
inputs_array.append(i_say)
|
||||
inputs_show_user_array.append(i_say_show_user)
|
||||
history_array.append([])
|
||||
sys_prompt_array.append("你是一个程序架构分析师，正在分析一个源代码项目。你的回答必须简单明了。")
|
||||
|
||||
def callback_when_intel_update(gpt_reply_array):
|
||||
objdump((gpt_reply_array, file_manifest))
|
||||
return
|
||||
|
||||
|
||||
# 文件读取完成，对每一个源代码文件，生成一个请求线程，发送到chatgpt进行分析
|
||||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||||
inputs_array = inputs_array,
|
||||
inputs_show_user_array = inputs_show_user_array,
|
||||
history_array = history_array,
|
||||
sys_prompt_array = sys_prompt_array,
|
||||
llm_kwargs = llm_kwargs,
|
||||
chatbot = chatbot,
|
||||
show_user_at_complete = True,
|
||||
callback_fn=callback_when_intel_update,
|
||||
)
|
||||
|
||||
# 全部文件解析完成，结果写入文件，准备对工程源代码进行汇总分析
|
||||
report_part_1 = copy.deepcopy(gpt_response_collection)
|
||||
history_to_return = report_part_1
|
||||
res = write_results_to_file(report_part_1)
|
||||
chatbot.append(("完成？", "逐个文件分析已完成。" + res + "\n\n正在开始汇总。"))
|
||||
yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
|
||||
############################## <第二步，综合，单线程，分组+迭代处理> ##################################
|
||||
|
||||
|
||||
############################## <END> ##################################
|
||||
history_to_return.extend([])
|
||||
res = write_results_to_file(history_to_return)
|
||||
chatbot.append(("完成了吗？", res))
|
||||
yield from update_ui(chatbot=chatbot, history=history_to_return) # 刷新界面
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析一个Python项目炫酷版(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
import glob, os
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.py', recursive=True)]
|
||||
if len(file_manifest) == 0:
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何python文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码炫酷版(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
|
||||
|
||||
|
||||
@CatchException
|
||||
def 解析任意code项目炫酷版(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
txt_pattern = plugin_kwargs.get("advanced_arg")
|
||||
txt_pattern = txt_pattern.replace("，", ",")
|
||||
# 将要匹配的模式(例如: *.c, *.cpp, *.py, config.toml)
|
||||
pattern_include = [_.lstrip(" ,").rstrip(" ,") for _ in txt_pattern.split(",") if _ != "" and not _.strip().startswith("^")]
|
||||
if not pattern_include: pattern_include = ["*"] # 不输入即全部匹配
|
||||
# 将要忽略匹配的文件后缀(例如: ^*.c, ^*.cpp, ^*.py)
|
||||
pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
|
||||
pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
|
||||
# 将要忽略匹配的文件名(例如: ^README.md)
|
||||
pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
|
||||
# 生成正则表达式
|
||||
pattern_except = '/[^/]+\.(' + "|".join(pattern_except_suffix) + ')$'
|
||||
pattern_except += '|/(' + "|".join(pattern_except_name) + ')$' if pattern_except_name != [] else ''
|
||||
|
||||
history.clear()
|
||||
import glob, os, re
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
if txt == "": txt = '空空如也的输入栏'
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
# 若上传压缩文件, 先寻找到解压的文件夹路径, 从而避免解析压缩文件
|
||||
maybe_dir = [f for f in glob.glob(f'{project_folder}/*') if os.path.isdir(f)]
|
||||
if len(maybe_dir)>0 and maybe_dir[0].endswith('.extract'):
|
||||
extract_folder_path = maybe_dir[0]
|
||||
else:
|
||||
extract_folder_path = project_folder
|
||||
# 按输入的匹配模式寻找上传的非压缩文件和已解压的文件
|
||||
file_manifest = [f for pattern in pattern_include for f in glob.glob(f'{extract_folder_path}/**/{pattern}', recursive=True) if "" != extract_folder_path and \
|
||||
os.path.isfile(f) and (not re.search(pattern_except, f) or pattern.endswith('.' + re.search(pattern_except, f).group().split('.')[-1]))]
|
||||
if len(file_manifest) == 0:
|
||||
report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何文件: {txt}")
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|
||||
@ -179,12 +179,12 @@ def 语音助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
|
||||
import nls
|
||||
from scipy import io
|
||||
except:
|
||||
chatbot.append(["导入依赖失败", "使用该模块需要额外依赖, 安装方法:```pip install --upgrade aliyun-python-sdk-core==2.13.3 pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
|
||||
chatbot.append(["导入依赖失败", "使用该模块需要额外依赖, 安装方法:```pip install --upgrade pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git```"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
APPKEY = get_conf('ALIYUN_APPKEY')
|
||||
if APPKEY == "":
|
||||
TOKEN, APPKEY = get_conf('ALIYUN_TOKEN', 'ALIYUN_APPKEY')
|
||||
if TOKEN == "" or APPKEY == "":
|
||||
chatbot.append(["导入依赖失败", "没有阿里云语音识别APPKEY和TOKEN, 详情见https://help.aliyun.com/document_detail/450255.html"])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
@ -115,36 +115,3 @@ services:
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
## ===================================================
|
||||
## 【方案五】 ChatGPT + 语音助手 （请先阅读 docs/use_audio.md）
|
||||
## ===================================================
|
||||
version: '3'
|
||||
services:
|
||||
gpt_academic_with_audio:
|
||||
image: ghcr.io/binary-husky/gpt_academic_audio_assistant:master
|
||||
environment:
|
||||
# 请查阅 `config.py` 以查看所有的配置信息
|
||||
API_KEY: ' fk195831-IdP0Pb3W6DCMUIbQwVX6MsSiyxwqybyS '
|
||||
USE_PROXY: ' False '
|
||||
proxies: ' None '
|
||||
LLM_MODEL: ' gpt-3.5-turbo '
|
||||
AVAIL_LLM_MODELS: ' ["gpt-3.5-turbo", "gpt-4"] '
|
||||
ENABLE_AUDIO: ' True '
|
||||
LOCAL_MODEL_DEVICE: ' cuda '
|
||||
DEFAULT_WORKER_NUM: ' 20 '
|
||||
WEB_PORT: ' 12343 '
|
||||
ADD_WAIFU: ' True '
|
||||
THEME: ' Chuanhu-Small-and-Beautiful '
|
||||
ALIYUN_APPKEY: ' RoP1ZrM84DnAFkZK '
|
||||
ALIYUN_TOKEN: ' f37f30e0f9934c34a992f6f64f7eba4f '
|
||||
# (无需填写) ALIYUN_ACCESSKEY: ' LTAI5q6BrFUzoRXVGUWnekh1 '
|
||||
# (无需填写) ALIYUN_SECRET: ' eHmI20AVWIaQZ0CiTD2bGQVsaP9i68 '
|
||||
|
||||
# 与宿主的网络融合
|
||||
network_mode: "host"
|
||||
|
||||
# 不使用代理网络拉取最新代码
|
||||
command: >
|
||||
bash -c "python3 -u main.py"
|
||||
|
||||
|
||||
@ -1,22 +0,0 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
|
||||
# 如何构建: 先修改 `config.py`， 然后 docker build -t gpt-academic-nolocal -f docs/Dockerfile+NoLocal .
|
||||
# 如何运行: docker run --rm -it --net=host gpt-academic-nolocal
|
||||
FROM python:3.11
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
# 安装语音插件的额外依赖
|
||||
RUN pip3 install pyOpenSSL scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
|
||||
|
||||
# 可选步骤，用于预热模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
@ -28,16 +28,6 @@ ALIYUN_APPKEY = "RoPlZrM88DnAFkZK" # 此appkey已经失效
|
||||
参考 https://help.aliyun.com/document_detail/450255.html
|
||||
先有阿里云开发者账号，登录之后，需要开通 智能语音交互 的功能，可以免费获得一个token，然后在 全部项目 中，创建一个项目，可以获得一个appkey.
|
||||
|
||||
- 进阶功能
|
||||
进一步填写ALIYUN_ACCESSKEY和ALIYUN_SECRET实现自动获取ALIYUN_TOKEN
|
||||
```
|
||||
ALIYUN_APPKEY = "RoP1ZrM84DnAFkZK"
|
||||
ALIYUN_TOKEN = ""
|
||||
ALIYUN_ACCESSKEY = "LTAI5q6BrFUzoRXVGUWnekh1"
|
||||
ALIYUN_SECRET = "eHmI20AVWIaQZ0CiTD2bGQVsaP9i68"
|
||||
```
|
||||
|
||||
|
||||
## 3.启动
|
||||
|
||||
启动gpt-academic `python main.py`
|
||||
@ -58,7 +48,7 @@ III `[把特殊软件（如腾讯会议）的外放声音用VoiceMeeter截留]`
|
||||
|
||||
VI 两种音频监听模式切换时，需要刷新页面才有效。
|
||||
|
||||
VII 非localhost运行+非https情况下无法打开录音功能的坑：https://blog.csdn.net/weixin_39461487/article/details/109594434
|
||||
|
||||
## 5.点击函数插件区“实时音频采集” 或者其他音频交互功能
|
||||
|
||||
|
||||
|
||||
|
||||
2
main.py
2
main.py
@ -160,7 +160,7 @@ def main():
|
||||
click_handle = functional[k]["Button"].click(fn=ArgsGeneralWrapper(predict), inputs=[*input_combo, gr.State(True), gr.State(k)], outputs=output_combo)
|
||||
cancel_handles.append(click_handle)
|
||||
# 文件上传区，接收文件后与chatbot的互动
|
||||
file_upload.upload(on_file_uploaded, [file_upload, chatbot, txt, txt2, checkboxes], [chatbot, txt, txt2])
|
||||
file_upload.upload(on_file_uploaded, [cookies, file_upload, chatbot, txt, txt2, checkboxes], [cookies, chatbot, txt, txt2])
|
||||
# 函数插件-固定按钮区
|
||||
for k in crazy_fns:
|
||||
if not crazy_fns[k].get("AsButton", True): continue
|
||||
|
||||
@ -37,23 +37,15 @@ class GetGLMHandle(Process):
|
||||
# 子进程执行
|
||||
# 第一次运行，加载参数
|
||||
retry = 0
|
||||
LOCAL_MODEL_QUANT, device = get_conf('LOCAL_MODEL_QUANT', 'LOCAL_MODEL_DEVICE')
|
||||
|
||||
if LOCAL_MODEL_QUANT == "INT4": # INT4
|
||||
_model_name_ = "THUDM/chatglm2-6b-int4"
|
||||
elif LOCAL_MODEL_QUANT == "INT8": # INT8
|
||||
_model_name_ = "THUDM/chatglm2-6b-int8"
|
||||
else:
|
||||
_model_name_ = "THUDM/chatglm2-6b" # FP16
|
||||
|
||||
while True:
|
||||
try:
|
||||
if self.chatglm_model is None:
|
||||
self.chatglm_tokenizer = AutoTokenizer.from_pretrained(_model_name_, trust_remote_code=True)
|
||||
self.chatglm_tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
|
||||
device, = get_conf('LOCAL_MODEL_DEVICE')
|
||||
if device=='cpu':
|
||||
self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).float()
|
||||
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).float()
|
||||
else:
|
||||
self.chatglm_model = AutoModel.from_pretrained(_model_name_, trust_remote_code=True).half().cuda()
|
||||
self.chatglm_model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).half().cuda()
|
||||
self.chatglm_model = self.chatglm_model.eval()
|
||||
break
|
||||
else:
|
||||
@ -144,8 +136,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
|
||||
@ -185,8 +185,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
|
||||
@ -129,8 +129,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
raw_input = inputs
|
||||
logging.info(f'[raw_input] {raw_input}')
|
||||
|
||||
@ -116,8 +116,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
raw_input = inputs
|
||||
logging.info(f'[raw_input] {raw_input}')
|
||||
|
||||
@ -290,8 +290,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
|
||||
@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
|
||||
@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
|
||||
@ -154,8 +154,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
|
||||
@ -224,8 +224,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
# 处理历史信息
|
||||
history_feedin = []
|
||||
|
||||
@ -224,8 +224,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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):
|
||||
|
||||
@ -248,8 +248,14 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
return
|
||||
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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):
|
||||
|
||||
@ -96,8 +96,11 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||||
"""
|
||||
if additional_fn is not None:
|
||||
from core_functional import handle_core_functionality
|
||||
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
||||
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"]
|
||||
|
||||
raw_input = "What I would like to say is the following: " + inputs
|
||||
history.extend([inputs, ""])
|
||||
|
||||
16
toolbox.py
16
toolbox.py
@ -60,6 +60,9 @@ def ArgsGeneralWrapper(f):
|
||||
plugin_kwargs = {
|
||||
"advanced_arg": plugin_advanced_arg,
|
||||
}
|
||||
if "recently_uploaded_files" in cookies:
|
||||
plugin_kwargs.update({"recently_uploaded_files": cookies["recently_uploaded_files"]})
|
||||
|
||||
chatbot_with_cookie = ChatBotWithCookies(cookies)
|
||||
chatbot_with_cookie.write_list(chatbot)
|
||||
if cookies.get('lock_plugin', None) is None:
|
||||
@ -462,6 +465,10 @@ def find_recent_files(directory):
|
||||
|
||||
return recent_files
|
||||
|
||||
def clear_file_downloadzone(chatbot):
|
||||
if chatbot:
|
||||
chatbot._cookies.update({'file_to_promote': []})
|
||||
|
||||
def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
|
||||
# 将文件复制一份到下载区
|
||||
import shutil
|
||||
@ -476,13 +483,14 @@ def promote_file_to_downloadzone(file, rename_file=None, chatbot=None):
|
||||
if 'file_to_promote' in chatbot._cookies: current = chatbot._cookies['file_to_promote']
|
||||
else: current = []
|
||||
chatbot._cookies.update({'file_to_promote': [new_path] + current})
|
||||
return new_path
|
||||
|
||||
def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
|
||||
def on_file_uploaded(cookies, files, chatbot, txt, txt2, checkboxes):
|
||||
"""
|
||||
当文件被上传时的回调函数
|
||||
"""
|
||||
if len(files) == 0:
|
||||
return chatbot, txt
|
||||
return cookies, chatbot, txt
|
||||
import shutil
|
||||
import os
|
||||
import time
|
||||
@ -512,7 +520,8 @@ def on_file_uploaded(files, chatbot, txt, txt2, checkboxes):
|
||||
f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
|
||||
f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
|
||||
f'\n\n现在您点击任意“红颜色”标识的函数插件时，以上文件将被作为输入参数'+err_msg])
|
||||
return chatbot, txt, txt2
|
||||
cookies.update({"recently_uploaded_files": moved_files})
|
||||
return cookies, chatbot, txt, txt2
|
||||
|
||||
|
||||
def on_report_generated(cookies, files, chatbot):
|
||||
@ -681,7 +690,6 @@ def read_single_conf_with_lru_cache(arg):
|
||||
else:
|
||||
print亮红( "[API_KEY] 您的 API_KEY 不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行。")
|
||||
if arg == 'proxies':
|
||||
if not read_single_conf_with_lru_cache('USE_PROXY'): r = None # 检查USE_PROXY，防止proxies单独起作用
|
||||
if r is None:
|
||||
print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
|
||||
else:
|
||||
|
||||
6
version
6
version
@ -1,5 +1,5 @@
|
||||
{
|
||||
"version": 3.47,
|
||||
"version": 3.46,
|
||||
"show_feature": true,
|
||||
"new_feature": "优化一键升级 <-> 提高arxiv翻译速度和成功率 <-> 支持自定义APIKEY格式 <-> 临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持"
|
||||
}
|
||||
"new_feature": "临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持 <-> 新增最强Arxiv论文翻译插件 <-> 修复gradio复制按钮BUG <-> 修复PDF翻译的BUG, 新增HTML中英双栏对照 <-> 添加了OpenAI图片生成插件"
|
||||
}
|
||||
Reference in New Issue
Block a user