合并master修改
This commit is contained in:
w_xiaolizu
1
.gitignore
vendored
1
.gitignore
vendored
@ -145,3 +145,4 @@ cradle*
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debug*
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private*
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crazy_functions/test_project/pdf_and_word
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crazy_functions/test_samples
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@ -173,6 +173,8 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
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2. 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/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
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3. 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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## 安装-代理配置
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1. 常规方法
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@ -268,6 +270,8 @@ docker run --rm -it --net=host --gpus=all gpt-academic bash
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<div align="center">
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<img src="https://user-images.githubusercontent.com/96192199/233575247-fb00819e-6d1b-4bb7-bd54-1d7528f03dd9.png" width="800" >
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<img src="https://user-images.githubusercontent.com/96192199/233779501-5ce826f0-6cca-4d59-9e5f-b4eacb8cc15f.png" width="800" >
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</div>
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@ -56,3 +56,10 @@ CONCURRENT_COUNT = 100
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# 设置用户名和密码（不需要修改）（相关功能不稳定，与gradio版本和网络都相关，如果本地使用不建议加这个）
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# [("username", "password"), ("username2", "password2"), ...]
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AUTHENTICATION = []
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# 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!!）
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# 格式 {"https://api.openai.com/v1/chat/completions": "重定向的URL"}
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API_URL_REDIRECT = {}
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# 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!）
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CUSTOM_PATH = "/"
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@ -20,6 +20,7 @@ def get_crazy_functions():
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from crazy_functions.解析项目源代码 import 解析一个CSharp项目
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from crazy_functions.总结word文档 import 总结word文档
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from crazy_functions.辅助回答 import 猜你想问
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from crazy_functions.解析JupyterNotebook import 解析ipynb文件
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function_plugins = {
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"猜你想问": {
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"Function": HotReload(猜你想问)
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@ -28,6 +29,11 @@ def get_crazy_functions():
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"Color": "stop", # 按钮颜色
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"Function": HotReload(解析一个Python项目)
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},
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"[测试功能] 解析Jupyter Notebook文件": {
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"Color": "stop",
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"AsButton":False,
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"Function": HotReload(解析ipynb文件),
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},
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"批量总结Word文档": {
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"Color": "stop",
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"Function": HotReload(总结word文档)
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@ -172,7 +178,7 @@ def get_crazy_functions():
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"AsButton": False, # 加入下拉菜单中
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"Function": HotReload(Markdown英译中)
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},
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})
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###################### 第三组插件 ###########################
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@ -185,7 +191,7 @@ def get_crazy_functions():
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"Function": HotReload(下载arxiv论文并翻译摘要)
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}
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})
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from crazy_functions.联网的ChatGPT import 连接网络回答问题
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function_plugins.update({
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"连接网络回答问题（先输入问题，再点击按钮，需要访问谷歌）": {
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@ -195,5 +201,25 @@ def get_crazy_functions():
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}
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})
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from crazy_functions.解析项目源代码 import 解析任意code项目
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function_plugins.update({
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"解析项目源代码（手动指定和筛选源代码文件类型）": {
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"Color": "stop",
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"AsButton": False,
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"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
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"ArgsReminder": "输入时用逗号隔开, *代表通配符, 加了^代表不匹配; 不输入代表全部匹配。例如: \"*.c, ^*.cpp, config.toml, ^*.toml\"", # 高级参数输入区的显示提示
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"Function": HotReload(解析任意code项目)
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},
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})
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from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
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function_plugins.update({
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"询问多个GPT模型（手动指定询问哪些模型）": {
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"Color": "stop",
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"AsButton": False,
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"AdvancedArgs": True, # 调用时，唤起高级参数输入区（默认False）
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"ArgsReminder": "支持任意数量的llm接口，用&符号分隔。例如chatglm&gpt-3.5-turbo&api2d-gpt-4", # 高级参数输入区的显示提示
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"Function": HotReload(同时问询_指定模型)
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},
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})
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###################### 第n组插件 ###########################
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return function_plugins
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@ -108,6 +108,13 @@ def test_联网回答问题():
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print("当前问答：", cb[-1][-1].replace("\n"," "))
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for i, it in enumerate(cb): print亮蓝(it[0]); print亮黄(it[1])
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def test_解析ipynb文件():
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from crazy_functions.解析JupyterNotebook import 解析ipynb文件
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txt = "crazy_functions/test_samples"
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for cookies, cb, hist, msg in 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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print(cb)
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# test_解析一个Python项目()
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# test_Latex英文润色()
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# test_Markdown中译英()
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@ -116,9 +123,8 @@ def test_联网回答问题():
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# test_总结word文档()
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# test_下载arxiv论文并翻译摘要()
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# test_解析一个Cpp项目()
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test_联网回答问题()
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# test_联网回答问题()
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test_解析ipynb文件()
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input("程序完成，回车退出。")
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print("退出。")
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140
crazy_functions/解析JupyterNotebook.py
Normal file
140
crazy_functions/解析JupyterNotebook.py
Normal file
@ -0,0 +1,140 @@
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from toolbox import update_ui
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from toolbox import CatchException, report_execption, write_results_to_file
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fast_debug = True
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class PaperFileGroup():
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def __init__(self):
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self.file_paths = []
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self.file_contents = []
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self.sp_file_contents = []
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self.sp_file_index = []
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self.sp_file_tag = []
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# count_token
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from request_llm.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(
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enc.encode(txt, disallowed_special=()))
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self.get_token_num = get_token_num
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def run_file_split(self, max_token_limit=1900):
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"""
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将长文本分离开来
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"""
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for index, file_content in enumerate(self.file_contents):
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if self.get_token_num(file_content) < max_token_limit:
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self.sp_file_contents.append(file_content)
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self.sp_file_index.append(index)
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self.sp_file_tag.append(self.file_paths[index])
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else:
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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segments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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file_content, self.get_token_num, max_token_limit)
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for j, segment in enumerate(segments):
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self.sp_file_contents.append(segment)
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self.sp_file_index.append(index)
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self.sp_file_tag.append(
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self.file_paths[index] + f".part-{j}.txt")
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def parseNotebook(filename, enable_markdown=1):
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import json
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CodeBlocks = []
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with open(filename, 'r', encoding='utf-8', errors='replace') as f:
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notebook = json.load(f)
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for cell in notebook['cells']:
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if cell['cell_type'] == 'code' and cell['source']:
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# remove blank lines
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cell['source'] = [line for line in cell['source'] if line.strip()
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!= '']
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CodeBlocks.append("".join(cell['source']))
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elif enable_markdown and cell['cell_type'] == 'markdown' and cell['source']:
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cell['source'] = [line for line in cell['source'] if line.strip()
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!= '']
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CodeBlocks.append("Markdown:"+"".join(cell['source']))
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Code = ""
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for idx, code in enumerate(CodeBlocks):
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Code += f"This is {idx+1}th code block: \n"
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Code += code+"\n"
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return Code
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def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
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from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
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pfg = PaperFileGroup()
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print(file_manifest)
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for fp in file_manifest:
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file_content = parseNotebook(fp, enable_markdown=1)
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pfg.file_paths.append(fp)
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pfg.file_contents.append(file_content)
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# <-------- 拆分过长的IPynb文件 ---------->
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pfg.run_file_split(max_token_limit=1024)
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n_split = len(pfg.sp_file_contents)
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inputs_array = [r"This is a Jupyter Notebook file, tell me about Each Block in Chinese. Focus Just On Code." +
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r"If a block starts with `Markdown` which means it's a markdown block in ipynbipynb. " +
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r"Start a new line for a block and block num use Chinese." +
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f"\n\n{frag}" for frag in pfg.sp_file_contents]
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inputs_show_user_array = [f"{f}的分析如下" for f in pfg.sp_file_tag]
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sys_prompt_array = ["You are a professional programmer."] * n_split
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gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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inputs_array=inputs_array,
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inputs_show_user_array=inputs_show_user_array,
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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history_array=[[""] for _ in range(n_split)],
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sys_prompt_array=sys_prompt_array,
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# max_workers=5, # OpenAI所允许的最大并行过载
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scroller_max_len=80
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)
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# <-------- 整理结果，退出 ---------->
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block_result = " \n".join(gpt_response_collection)
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chatbot.append(("解析的结果如下", block_result))
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history.extend(["解析的结果如下", block_result])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# <-------- 写入文件，退出 ---------->
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res = write_results_to_file(history)
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chatbot.append(("完成了吗？", res))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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@CatchException
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def 解析ipynb文件(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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chatbot.append([
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"函数插件功能？",
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"对IPynb文件进行解析。Contributor: codycjy."])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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history = [] # 清空历史
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import glob
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import os
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if os.path.exists(txt):
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project_folder = txt
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else:
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if txt == "":
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txt = '空空如也的输入栏'
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report_execption(chatbot, history,
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a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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if txt.endswith('.ipynb'):
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file_manifest = [txt]
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else:
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file_manifest = [f for f in glob.glob(
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f'{project_folder}/**/*.ipynb', recursive=True)]
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if len(file_manifest) == 0:
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report_execption(chatbot, history,
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a=f"解析项目: {txt}", b=f"找不到任何.ipynb文件: {txt}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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yield from ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, )
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@ -11,7 +11,7 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
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history_array = []
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sys_prompt_array = []
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report_part_1 = []
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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。"
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############################## <第一步，逐个文件分析，多线程> ##################################
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for index, fp in enumerate(file_manifest):
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@ -63,10 +63,10 @@ def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
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current_iteration_focus = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(this_iteration_file_manifest)])
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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}）。'
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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。'
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this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
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this_iteration_history = copy.deepcopy(this_iteration_gpt_response_collection)
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this_iteration_history.append(last_iteration_result)
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result = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
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inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot,
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history=this_iteration_history, # 迭代之前的分析
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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。")
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report_part_2.extend([i_say, result])
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@ -222,8 +222,8 @@ def 解析一个Golang项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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@CatchException
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def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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history = [] # 清空历史，以免输入溢出
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@ -243,9 +243,9 @@ def 解析一个Lua项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
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report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何lua文件: {txt}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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@CatchException
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def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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history = [] # 清空历史，以免输入溢出
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@ -263,4 +263,45 @@ def 解析一个CSharp项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
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report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何CSharp文件: {txt}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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yield from 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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@CatchException
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def 解析任意code项目(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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txt_pattern = plugin_kwargs.get("advanced_arg")
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txt_pattern = txt_pattern.replace("，", ",")
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# 将要匹配的模式(例如: *.c, *.cpp, *.py, config.toml)
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pattern_include = [_.lstrip(" ,").rstrip(" ,") for _ in txt_pattern.split(",") if _ != "" and not _.strip().startswith("^")]
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if not pattern_include: pattern_include = ["*"] # 不输入即全部匹配
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# 将要忽略匹配的文件后缀(例如: ^*.c, ^*.cpp, ^*.py)
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pattern_except_suffix = [_.lstrip(" ^*.,").rstrip(" ,") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^*.")]
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pattern_except_suffix += ['zip', 'rar', '7z', 'tar', 'gz'] # 避免解析压缩文件
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# 将要忽略匹配的文件名(例如: ^README.md)
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pattern_except_name = [_.lstrip(" ^*,").rstrip(" ,").replace(".", "\.") for _ in txt_pattern.split(" ") if _ != "" and _.strip().startswith("^") and not _.strip().startswith("^*.")]
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# 生成正则表达式
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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)
|
||||
@ -25,6 +25,35 @@ def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt
|
||||
retry_times_at_unknown_error=0
|
||||
)
|
||||
|
||||
history.append(txt)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
|
||||
|
||||
@CatchException
|
||||
def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||||
"""
|
||||
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
|
||||
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
|
||||
plugin_kwargs 插件模型的参数，如温度和top_p等，一般原样传递下去就行
|
||||
chatbot 聊天显示框的句柄，用于显示给用户
|
||||
history 聊天历史，前情提要
|
||||
system_prompt 给gpt的静默提醒
|
||||
web_port 当前软件运行的端口号
|
||||
"""
|
||||
history = [] # 清空历史，以免输入溢出
|
||||
chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
|
||||
|
||||
# llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
|
||||
llm_kwargs['llm_model'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
|
||||
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=txt, inputs_show_user=txt,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
sys_prompt=system_prompt,
|
||||
retry_times_at_unknown_error=0
|
||||
)
|
||||
|
||||
history.append(txt)
|
||||
history.append(gpt_say)
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
|
||||
43
docs/WithFastapi.md
Normal file
43
docs/WithFastapi.md
Normal file
@ -0,0 +1,43 @@
|
||||
# Running with fastapi
|
||||
|
||||
We currently support fastapi in order to solve sub-path deploy issue.
|
||||
|
||||
1. change CUSTOM_PATH setting in `config.py`
|
||||
|
||||
``` sh
|
||||
nano config.py
|
||||
```
|
||||
|
||||
2. Edit main.py
|
||||
|
||||
```diff
|
||||
auto_opentab_delay()
|
||||
- demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
+ demo.queue(concurrency_count=CONCURRENT_COUNT)
|
||||
|
||||
- # 如果需要在二级路径下运行
|
||||
- # CUSTOM_PATH, = get_conf('CUSTOM_PATH')
|
||||
- # if CUSTOM_PATH != "/":
|
||||
- # from toolbox import run_gradio_in_subpath
|
||||
- # run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
|
||||
- # else:
|
||||
- # demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
|
||||
+ 如果需要在二级路径下运行
|
||||
+ CUSTOM_PATH, = get_conf('CUSTOM_PATH')
|
||||
+ if CUSTOM_PATH != "/":
|
||||
+ from toolbox import run_gradio_in_subpath
|
||||
+ run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
|
||||
+ else:
|
||||
+ demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
```
|
||||
|
||||
|
||||
3. Go!
|
||||
|
||||
``` sh
|
||||
python main.py
|
||||
```
|
||||
37
main.py
37
main.py
@ -45,7 +45,7 @@ def main():
|
||||
|
||||
gr_L1 = lambda: gr.Row().style()
|
||||
gr_L2 = lambda scale: gr.Column(scale=scale)
|
||||
if LAYOUT == "TOP-DOWN":
|
||||
if LAYOUT == "TOP-DOWN":
|
||||
gr_L1 = lambda: DummyWith()
|
||||
gr_L2 = lambda scale: gr.Row()
|
||||
CHATBOT_HEIGHT /= 2
|
||||
@ -88,9 +88,12 @@ def main():
|
||||
with gr.Row():
|
||||
with gr.Accordion("更多函数插件", open=True):
|
||||
dropdown_fn_list = [k for k in crazy_fns.keys() if not crazy_fns[k].get("AsButton", True)]
|
||||
with gr.Column(scale=1):
|
||||
with gr.Row():
|
||||
dropdown = gr.Dropdown(dropdown_fn_list, value=r"打开插件列表", label="").style(container=False)
|
||||
with gr.Column(scale=1):
|
||||
with gr.Row():
|
||||
plugin_advanced_arg = gr.Textbox(show_label=True, label="高级参数输入区", visible=False,
|
||||
placeholder="这里是特殊函数插件的高级参数输入区").style(container=False)
|
||||
with gr.Row():
|
||||
switchy_bt = gr.Button(r"请先从插件列表中选择", variant="secondary")
|
||||
with gr.Row():
|
||||
with gr.Accordion("点击展开“文件上传区”。上传本地文件可供红色函数插件调用。", open=False) as area_file_up:
|
||||
@ -100,7 +103,7 @@ def main():
|
||||
top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.01,interactive=True, label="Top-p (nucleus sampling)",)
|
||||
temperature = gr.Slider(minimum=-0, maximum=2.0, value=1.0, step=0.01, interactive=True, label="Temperature",)
|
||||
max_length_sl = gr.Slider(minimum=256, maximum=4096, value=512, step=1, interactive=True, label="Local LLM MaxLength",)
|
||||
checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
|
||||
checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "底部输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区")
|
||||
md_dropdown = gr.Dropdown(AVAIL_LLM_MODELS, value=LLM_MODEL, label="更换LLM模型/请求源").style(container=False)
|
||||
|
||||
gr.Markdown(description)
|
||||
@ -122,11 +125,12 @@ def main():
|
||||
ret.update({area_input_secondary: gr.update(visible=("底部输入区" in a))})
|
||||
ret.update({clearBtn: gr.update(visible=("输入清除键" in a))})
|
||||
ret.update({clearBtn2: gr.update(visible=("输入清除键" in a))})
|
||||
ret.update({plugin_advanced_arg: gr.update(visible=("插件参数区" in a))})
|
||||
if "底部输入区" in a: ret.update({txt: gr.update(value="")})
|
||||
return ret
|
||||
checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2] )
|
||||
checkboxes.select(fn_area_visibility, [checkboxes], [area_basic_fn, area_crazy_fn, area_input_primary, area_input_secondary, txt, txt2, clearBtn, clearBtn2, plugin_advanced_arg] )
|
||||
# 整理反复出现的控件句柄组合
|
||||
input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt]
|
||||
input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg]
|
||||
output_combo = [cookies, chatbot, history, status]
|
||||
predict_args = dict(fn=ArgsGeneralWrapper(predict), inputs=input_combo, outputs=output_combo)
|
||||
# 提交按钮、重置按钮
|
||||
@ -153,14 +157,19 @@ def main():
|
||||
# 函数插件-下拉菜单与随变按钮的互动
|
||||
def on_dropdown_changed(k):
|
||||
variant = crazy_fns[k]["Color"] if "Color" in crazy_fns[k] else "secondary"
|
||||
return {switchy_bt: gr.update(value=k, variant=variant)}
|
||||
dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt] )
|
||||
ret = {switchy_bt: gr.update(value=k, variant=variant)}
|
||||
if crazy_fns[k].get("AdvancedArgs", False): # 是否唤起高级插件参数区
|
||||
ret.update({plugin_advanced_arg: gr.update(visible=True, label=f"插件[{k}]的高级参数说明：" + crazy_fns[k].get("ArgsReminder", [f"没有提供高级参数功能说明"]))})
|
||||
else:
|
||||
ret.update({plugin_advanced_arg: gr.update(visible=False, label=f"插件[{k}]不需要高级参数。")})
|
||||
return ret
|
||||
dropdown.select(on_dropdown_changed, [dropdown], [switchy_bt, plugin_advanced_arg] )
|
||||
def on_md_dropdown_changed(k):
|
||||
return {chatbot: gr.update(label="当前模型："+k)}
|
||||
md_dropdown.select(on_md_dropdown_changed, [md_dropdown], [chatbot] )
|
||||
# 随变按钮的回调函数注册
|
||||
def route(k, *args, **kwargs):
|
||||
if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
|
||||
if k in [r"打开插件列表", r"请先从插件列表中选择"]: return
|
||||
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])
|
||||
@ -178,7 +187,7 @@ def main():
|
||||
print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
|
||||
print(f"\t（亮色主题）: http://localhost:{PORT}")
|
||||
print(f"\t（暗色主题）: http://localhost:{PORT}/?__dark-theme=true")
|
||||
def open():
|
||||
def open():
|
||||
time.sleep(2) # 打开浏览器
|
||||
webbrowser.open_new_tab(f"http://localhost:{PORT}/?__dark-theme=true")
|
||||
threading.Thread(target=open, name="open-browser", daemon=True).start()
|
||||
@ -188,5 +197,13 @@ def main():
|
||||
auto_opentab_delay()
|
||||
demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
|
||||
# 如果需要在二级路径下运行
|
||||
# CUSTOM_PATH, = get_conf('CUSTOM_PATH')
|
||||
# if CUSTOM_PATH != "/":
|
||||
# from toolbox import run_gradio_in_subpath
|
||||
# run_gradio_in_subpath(demo, auth=AUTHENTICATION, port=PORT, custom_path=CUSTOM_PATH)
|
||||
# else:
|
||||
# demo.launch(server_name="0.0.0.0", server_port=PORT, auth=AUTHENTICATION, favicon_path="docs/logo.png")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# 如何使用其他大语言模型（v3.0分支测试中）
|
||||
# 如何使用其他大语言模型
|
||||
|
||||
## ChatGLM
|
||||
|
||||
@ -15,7 +15,7 @@ LLM_MODEL = "chatglm"
|
||||
|
||||
|
||||
---
|
||||
## Text-Generation-UI (TGUI)
|
||||
## Text-Generation-UI (TGUI，调试中，暂不可用)
|
||||
|
||||
### 1. 部署TGUI
|
||||
``` sh
|
||||
|
||||
@ -1,16 +1,17 @@
|
||||
|
||||
"""
|
||||
该文件中主要包含2个函数
|
||||
该文件中主要包含2个函数，是所有LLM的通用接口，它们会继续向下调用更底层的LLM模型，处理多模型并行等细节
|
||||
|
||||
不具备多线程能力的函数：
|
||||
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
|
||||
不具备多线程能力的函数：正常对话时使用，具备完备的交互功能，不可多线程
|
||||
1. predict(...)
|
||||
|
||||
具备多线程调用能力的函数
|
||||
2. predict_no_ui_long_connection：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
|
||||
具备多线程调用能力的函数：在函数插件中被调用，灵活而简洁
|
||||
2. predict_no_ui_long_connection(...)
|
||||
"""
|
||||
import tiktoken
|
||||
from functools import wraps, lru_cache
|
||||
from functools import lru_cache
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from toolbox import get_conf
|
||||
|
||||
from request_llm.bridge_chatgpt import predict_no_ui_long_connection as chatgpt_noui
|
||||
from request_llm.bridge_chatgpt import predict as chatgpt_ui
|
||||
@ -42,18 +43,37 @@ class LazyloadTiktoken(object):
|
||||
def decode(self, *args, **kwargs):
|
||||
encoder = self.get_encoder(self.model)
|
||||
return encoder.decode(*args, **kwargs)
|
||||
|
||||
|
||||
# Endpoint 重定向
|
||||
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"
|
||||
# 兼容旧版的配置
|
||||
try:
|
||||
API_URL, = get_conf("API_URL")
|
||||
if API_URL != "https://api.openai.com/v1/chat/completions":
|
||||
openai_endpoint = API_URL
|
||||
print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
|
||||
except:
|
||||
pass
|
||||
# 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]
|
||||
|
||||
|
||||
# 获取tokenizer
|
||||
tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
|
||||
tokenizer_gpt4 = LazyloadTiktoken("gpt-4")
|
||||
get_token_num_gpt35 = lambda txt: len(tokenizer_gpt35.encode(txt, disallowed_special=()))
|
||||
get_token_num_gpt4 = lambda txt: len(tokenizer_gpt4.encode(txt, disallowed_special=()))
|
||||
|
||||
|
||||
model_info = {
|
||||
# openai
|
||||
"gpt-3.5-turbo": {
|
||||
"fn_with_ui": chatgpt_ui,
|
||||
"fn_without_ui": chatgpt_noui,
|
||||
"endpoint": "https://api.openai.com/v1/chat/completions",
|
||||
"endpoint": openai_endpoint,
|
||||
"max_token": 4096,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
@ -62,7 +82,7 @@ model_info = {
|
||||
"gpt-4": {
|
||||
"fn_with_ui": chatgpt_ui,
|
||||
"fn_without_ui": chatgpt_noui,
|
||||
"endpoint": "https://api.openai.com/v1/chat/completions",
|
||||
"endpoint": openai_endpoint,
|
||||
"max_token": 8192,
|
||||
"tokenizer": tokenizer_gpt4,
|
||||
"token_cnt": get_token_num_gpt4,
|
||||
@ -72,7 +92,7 @@ model_info = {
|
||||
"api2d-gpt-3.5-turbo": {
|
||||
"fn_with_ui": chatgpt_ui,
|
||||
"fn_without_ui": chatgpt_noui,
|
||||
"endpoint": "https://openai.api2d.net/v1/chat/completions",
|
||||
"endpoint": api2d_endpoint,
|
||||
"max_token": 4096,
|
||||
"tokenizer": tokenizer_gpt35,
|
||||
"token_cnt": get_token_num_gpt35,
|
||||
@ -81,7 +101,7 @@ model_info = {
|
||||
"api2d-gpt-4": {
|
||||
"fn_with_ui": chatgpt_ui,
|
||||
"fn_without_ui": chatgpt_noui,
|
||||
"endpoint": "https://openai.api2d.net/v1/chat/completions",
|
||||
"endpoint": api2d_endpoint,
|
||||
"max_token": 8192,
|
||||
"tokenizer": tokenizer_gpt4,
|
||||
"token_cnt": get_token_num_gpt4,
|
||||
@ -190,7 +210,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history, sys_prompt, obser
|
||||
return_string_collect.append( f"【{str(models[i])} 说】: <font color=\"{colors[i]}\"> {future.result()} </font>" )
|
||||
|
||||
window_mutex[-1] = False # stop mutex thread
|
||||
res = '<br/>\n\n---\n\n'.join(return_string_collect)
|
||||
res = '<br/><br/>\n\n---\n\n'.join(return_string_collect)
|
||||
return res
|
||||
|
||||
|
||||
|
||||
@ -92,8 +92,8 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
|
||||
|
||||
# chatglm 没有 sys_prompt 接口，因此把prompt加入 history
|
||||
history_feedin = []
|
||||
history_feedin.append(["What can I do?", sys_prompt])
|
||||
for i in range(len(history)//2):
|
||||
history_feedin.append(["What can I do?", sys_prompt] )
|
||||
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
|
||||
@ -131,10 +131,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
|
||||
|
||||
history_feedin = []
|
||||
history_feedin.append(["What can I do?", system_prompt] )
|
||||
for i in range(len(history)//2):
|
||||
history_feedin.append(["What can I do?", system_prompt] )
|
||||
history_feedin.append([history[2*i], history[2*i+1]] )
|
||||
|
||||
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)
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
|
||||
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
|
||||
from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys
|
||||
proxies, API_KEY, TIMEOUT_SECONDS, MAX_RETRY = \
|
||||
get_conf('proxies', 'API_KEY', 'TIMEOUT_SECONDS', 'MAX_RETRY')
|
||||
|
||||
@ -118,7 +118,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
"""
|
||||
if is_any_api_key(inputs):
|
||||
chatbot._cookies['api_key'] = inputs
|
||||
chatbot.append(("输入已识别为openai的api_key", "api_key已导入"))
|
||||
chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
|
||||
return
|
||||
elif not is_any_api_key(chatbot._cookies['api_key']):
|
||||
@ -141,7 +141,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
|
||||
try:
|
||||
headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
|
||||
except RuntimeError as e:
|
||||
chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。")
|
||||
chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
|
||||
yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
|
||||
return
|
||||
|
||||
|
||||
65
toolbox.py
65
toolbox.py
@ -32,23 +32,23 @@ 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。
|
||||
"""
|
||||
def decorated(cookies, max_length, llm_model, txt, top_p, temperature,
|
||||
chatbot, history, system_prompt, models, ipaddr: gr.Request, *args):
|
||||
chatbot, history, system_prompt, plugin_advanced_arg, models, ipaddr: gr.Request, *args):
|
||||
""""""
|
||||
# 引入一个有cookie的chatbot
|
||||
cookies.update({
|
||||
'top_p':top_p,
|
||||
'top_p':top_p,
|
||||
'temperature':temperature,
|
||||
})
|
||||
llm_kwargs = {
|
||||
'api_key': cookies['api_key'],
|
||||
'llm_model': llm_model,
|
||||
'top_p':top_p,
|
||||
'top_p':top_p,
|
||||
'max_length': max_length,
|
||||
'temperature': temperature,
|
||||
'ipaddr': ipaddr.client.host
|
||||
}
|
||||
plugin_kwargs = {
|
||||
# 目前还没有
|
||||
"advanced_arg": plugin_advanced_arg,
|
||||
}
|
||||
chatbot_with_cookie = ChatBotWithCookies(cookies)
|
||||
chatbot_with_cookie.write_list(chatbot)
|
||||
@ -237,7 +237,7 @@ def markdown_convertion(txt):
|
||||
return content
|
||||
else:
|
||||
return tex2mathml_catch_exception(content)
|
||||
|
||||
|
||||
def markdown_bug_hunt(content):
|
||||
"""
|
||||
解决一个mdx_math的bug（单$包裹begin命令时多余<script>）
|
||||
@ -245,7 +245,7 @@ def markdown_convertion(txt):
|
||||
content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
|
||||
content = content.replace('</script>\n</script>', '</script>')
|
||||
return content
|
||||
|
||||
|
||||
|
||||
if ('$' in txt) and ('```' not in txt): # 有$标识的公式符号，且没有代码段```的标识
|
||||
# convert everything to html format
|
||||
@ -266,7 +266,7 @@ def markdown_convertion(txt):
|
||||
def close_up_code_segment_during_stream(gpt_reply):
|
||||
"""
|
||||
在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
|
||||
|
||||
|
||||
Args:
|
||||
gpt_reply (str): GPT模型返回的回复字符串。
|
||||
|
||||
@ -473,6 +473,19 @@ def is_any_api_key(key):
|
||||
else:
|
||||
return is_openai_api_key(key) or is_api2d_key(key)
|
||||
|
||||
def what_keys(keys):
|
||||
avail_key_list = {'OpenAI Key':0, "API2D Key":0}
|
||||
key_list = keys.split(',')
|
||||
|
||||
for k in key_list:
|
||||
if is_openai_api_key(k):
|
||||
avail_key_list['OpenAI Key'] += 1
|
||||
|
||||
for k in key_list:
|
||||
if is_api2d_key(k):
|
||||
avail_key_list['API2D Key'] += 1
|
||||
|
||||
return f"检测到： OpenAI Key {avail_key_list['OpenAI Key']} 个，API2D Key {avail_key_list['API2D Key']} 个"
|
||||
|
||||
def select_api_key(keys, llm_model):
|
||||
import random
|
||||
@ -488,7 +501,7 @@ def select_api_key(keys, llm_model):
|
||||
if is_api2d_key(k): avail_key_list.append(k)
|
||||
|
||||
if len(avail_key_list) == 0:
|
||||
raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。")
|
||||
raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。")
|
||||
|
||||
api_key = random.choice(avail_key_list) # 随机负载均衡
|
||||
return api_key
|
||||
@ -539,7 +552,7 @@ class DummyWith():
|
||||
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。
|
||||
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__()。
|
||||
上下文管理器必须实现两个方法，分别为 __enter__()和 __exit__()。
|
||||
在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，
|
||||
而在上下文执行结束时，__exit__()方法则会被调用。
|
||||
"""
|
||||
@ -550,7 +563,33 @@ class DummyWith():
|
||||
return
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
private_upload = './private_upload'
|
||||
for r, d, f in os.walk(private_upload):
|
||||
print(r, f)
|
||||
def run_gradio_in_subpath(demo, auth, port, custom_path):
|
||||
def is_path_legal(path: str)->bool:
|
||||
'''
|
||||
check path for sub url
|
||||
path: path to check
|
||||
return value: do sub url wrap
|
||||
'''
|
||||
if path == "/": return True
|
||||
if len(path) == 0:
|
||||
print("ilegal custom path: {}\npath must not be empty\ndeploy on root url".format(path))
|
||||
return False
|
||||
if path[0] == '/':
|
||||
if path[1] != '/':
|
||||
print("deploy on sub-path {}".format(path))
|
||||
return True
|
||||
return False
|
||||
print("ilegal custom path: {}\npath should begin with \'/\'\ndeploy on root url".format(path))
|
||||
return False
|
||||
|
||||
if not is_path_legal(custom_path): raise RuntimeError('Ilegal custom path')
|
||||
import uvicorn
|
||||
import gradio as gr
|
||||
from fastapi import FastAPI
|
||||
app = FastAPI()
|
||||
if custom_path != "/":
|
||||
@app.get("/")
|
||||
def read_main():
|
||||
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
|
||||
4
version
4
version
@ -1,5 +1,5 @@
|
||||
{
|
||||
"version": 3.1,
|
||||
"version": 3.2,
|
||||
"show_feature": true,
|
||||
"new_feature": "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）<-> 支持多API-KEY负载均衡（并列填写，逗号分割） <-> 添加输入区文本清除按键"
|
||||
"new_feature": "现函数插件可读取高级参数（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）"
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user