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import g4f |
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import gradio as gr |
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from gradio import ChatInterface |
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from g4f.Provider import ( |
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Ails, |
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You, |
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Bing, |
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Yqcloud, |
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Theb, |
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Aichat, |
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Bard, |
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Vercel, |
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Forefront, |
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Lockchat, |
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Liaobots, |
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H2o, |
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ChatgptLogin, |
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DeepAi, |
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GetGpt, |
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AItianhu, |
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EasyChat, |
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Acytoo, |
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DfeHub, |
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AiService, |
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Wewordle, |
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ChatgptAi, |
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) |
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import os |
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import json |
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import pandas as pd |
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from langchain.tools.python.tool import PythonREPLTool |
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from langchain.agents.agent_toolkits import create_python_agent |
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from models_for_langchain.model import CustomLLM |
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from langchain.memory import ConversationBufferWindowMemory, ConversationTokenBufferMemory |
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from langchain import LLMChain, PromptTemplate |
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from langchain.prompts import ( |
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ChatPromptTemplate, |
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PromptTemplate, |
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SystemMessagePromptTemplate, |
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AIMessagePromptTemplate, |
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HumanMessagePromptTemplate, |
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) |
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from langchain.agents.agent_types import AgentType |
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from langchain.tools import WikipediaQueryRun |
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from langchain.utilities import WikipediaAPIWrapper |
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from langchain.tools import DuckDuckGoSearchRun |
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from models_for_langchain.memory_func import validate_memory_len |
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provider_dict = { |
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'Ails': Ails, |
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'You': You, |
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'Bing': Bing, |
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'Yqcloud': Yqcloud, |
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'Theb': Theb, |
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'Aichat': Aichat, |
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'Bard': Bard, |
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'Vercel': Vercel, |
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'Forefront': Forefront, |
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'Lockchat': Lockchat, |
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'Liaobots': Liaobots, |
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'H2o': H2o, |
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'ChatgptLogin': ChatgptLogin, |
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'DeepAi': DeepAi, |
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'GetGpt': GetGpt, |
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'AItianhu': AItianhu, |
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'EasyChat': EasyChat, |
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'Acytoo': Acytoo, |
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'DfeHub': DfeHub, |
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'AiService': AiService, |
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'Wewordle': Wewordle, |
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'ChatgptAi': ChatgptAi, |
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} |
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with open("available_dict.txt", "r") as fp: |
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available_dict = json.load(fp) |
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def change_prompt_set(prompt_set_name): |
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return gr.Dropdown.update(choices=list(prompt_set_list[prompt_set_name].keys())) |
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def change_model(model_name): |
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new_choices = list(available_dict[model_name]) |
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return gr.Dropdown.update(choices=new_choices, value=new_choices[0]) |
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def change_prompt(prompt_set_name, prompt_name): |
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return gr.update(value=prompt_set_list[prompt_set_name][prompt_name]) |
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def user(user_message, history): |
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return gr.update(value="", interactive=False), history + [[user_message, None]] |
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def bot(history, model_name, provider_name, system_msg, agent): |
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history[-1][1] = '' |
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message = history[-1][0] |
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if len(system_msg)>3000: |
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system_msg = system_msg[:2000] + system_msg[-1000:] |
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global template, memory |
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llm.model_name = model_name |
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llm.provider_name = provider_name |
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if agent == '系统提示': |
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new_template = template.format(system_instruction=system_msg) |
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elif agent == '维基百科': |
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wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper()) |
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target = llm(f'用户的问题：```{message}```。为了回答用户的问题，你需要在维基百科上进行搜索，只有一次搜索的机会，请返回需要搜索的词汇，只需要返回一个英文词汇，不要加任何解释：') |
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new_template = template.format(system_instruction=wikipedia.run(str(target))) |
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elif agent == 'duckduckgo': |
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search = DuckDuckGoSearchRun() |
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target = llm(f'用户的问题：```{message}```。为了回答用户的问题，你需要在duckduckgo搜索引擎上进行搜索，只有一次搜索的机会，请返回需要搜索的内容，只需要返回纯英文的搜索语句，不要加任何解释：') |
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new_template = template.format(system_instruction=search.run(str(target))) |
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elif agent == 'python': |
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py_agent = create_python_agent( |
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llm, |
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tool=PythonREPLTool(), |
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verbose=True, |
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handle_parsing_errors=True, |
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) |
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response = py_agent.run(message) |
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return str(response) |
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else: |
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new_template = template.format(system_instruction=system_msg) |
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if len(history)>1 and history[-2][1]!=None: |
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memory.chat_memory.add_ai_message(history[-2][1]) |
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memory.chat_memory.add_user_message(history[-1][0]) |
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validate_memory_len(memory=memory, max_token_limit=1800) |
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if len(memory.chat_memory.messages)==0: |
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for c in '文本长度超过限制，请清空后再试': |
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history[-1][1] += c |
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yield history |
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else: |
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prev_memory = memory.load_memory_variables({})['chat_history'] |
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prompt = new_template.format( |
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chat_history = prev_memory, |
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) |
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print(f'prompt = \n --------\n{prompt}\n --------') |
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for _ in range(3): |
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try: |
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bot_msg = llm._call(prompt=prompt) |
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break |
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except: |
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bot_msg = '服务器无响应，请更换提供者或者清空对话后重试。' |
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for c in bot_msg: |
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history[-1][1] += c |
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yield history |
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def empty_fn(): |
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global memory |
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memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history") |
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return [[None, None]] |
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def undo_fn(history): |
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return history[:-1] |
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def retry_fn(history): |
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history[-1][1] = None |
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return history |
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prompt_set_list = {} |
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for prompt_file in os.listdir("prompt_set"): |
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key = prompt_file |
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if '.csv' in key: |
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df = pd.read_csv("prompt_set/" + prompt_file) |
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prompt_dict = dict(zip(df['act'], df['prompt'])) |
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else: |
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with open("prompt_set/" + prompt_file, encoding='utf-8') as f: |
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ds = json.load(f) |
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prompt_dict = {item["act"]: item["prompt"] for item in ds} |
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prompt_set_list[key] = prompt_dict |
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with gr.Blocks() as demo: |
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llm = CustomLLM() |
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template = """ |
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Chat with human based on following instructions: |
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``` |
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{system_instruction} |
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``` |
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The following is a conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. |
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{{chat_history}} |
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AI:""" |
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memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history") |
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with gr.Row(): |
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model_name = gr.Dropdown(list(available_dict.keys()), value='gpt-3.5-turbo', label='模型') |
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provider = gr.Dropdown(available_dict['gpt-3.5-turbo'], value=available_dict['gpt-3.5-turbo'][0], label='提供者', min_width=20) |
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agent = gr.Dropdown(['系统提示', '维基百科'], value='系统提示', label='Agent') |
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system_msg = gr.Textbox(value="你是一名助手，可以解答问题。", label='系统提示') |
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chatbot = gr.Chatbot([[None, None]], label='AI') |
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with gr.Group(): |
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with gr.Row(): |
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textbox = gr.Textbox( |
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container=False, |
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show_label=False, |
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label="请输入：", |
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scale=7, |
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autofocus=True, |
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) |
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submit = gr.Button('发送', scale=1, variant="primary", min_width=150,) |
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with gr.Row(): |
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retry = gr.Button('🔄 重试') |
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undo = gr.Button('↩️ 撤销') |
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clear = gr.Button("🗑️ 清空") |
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with gr.Row(): |
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default_prompt_set = "1 中文提示词.json" |
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prompt_set_name = gr.Dropdown(prompt_set_list.keys(), value=default_prompt_set, label='提示词集合') |
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prompt_name = gr.Dropdown(prompt_set_list[default_prompt_set].keys(), label='提示词', min_width=5, container=True) |
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textbox.submit(user, [textbox, chatbot], [textbox, chatbot], queue=False).then( |
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bot, [chatbot, model_name, provider, system_msg, agent], chatbot |
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).then(lambda: gr.update(interactive=True), None, [textbox], queue=False) |
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response = submit.click(user, [textbox, chatbot], [textbox, chatbot], queue=False).then( |
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bot, [chatbot, model_name, provider, system_msg, agent], chatbot |
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).then(lambda: gr.update(interactive=True), None, [textbox], queue=False) |
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retry.click(retry_fn, [chatbot], [chatbot]).then( |
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bot, [chatbot, model_name, provider, system_msg, agent], chatbot |
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) |
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undo.click(undo_fn, [chatbot], [chatbot], queue=False) |
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clear.click(empty_fn, None, [chatbot], queue=False) |
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prompt_set_name.select(change_prompt_set, prompt_set_name, prompt_name) |
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model_name.select(change_model, model_name, provider) |
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prompt_name.select(change_prompt, [prompt_set_name, prompt_name], system_msg) |
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demo.title = "AI Chat" |
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demo.queue() |
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demo.launch() |
