import g4f import gradio as gr from gradio import ChatInterface from g4f.Provider import ( Ails, You, Bing, Yqcloud, Theb, Aichat, Bard, Vercel, Forefront, Lockchat, Liaobots, H2o, ChatgptLogin, DeepAi, GetGpt, AItianhu, EasyChat, Acytoo, DfeHub, AiService, BingHuan, Wewordle, ChatgptAi, opchatgpts ) import os import json import pandas as pd from langchain.tools.python.tool import PythonREPLTool from langchain.agents.agent_toolkits import create_python_agent from models_for_langchain.model import CustomLLM from langchain.memory import ConversationBufferWindowMemory, ConversationTokenBufferMemory from langchain import LLMChain, PromptTemplate from langchain.prompts import ( ChatPromptTemplate, PromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemplate, ) from langchain.agents.agent_types import AgentType from langchain.tools import WikipediaQueryRun from langchain.utilities import WikipediaAPIWrapper from langchain.tools import DuckDuckGoSearchRun from models_for_langchain.memory_func import validate_memory_len provider_dict = { 'Ails': Ails, 'You': You, 'Bing': Bing, 'Yqcloud': Yqcloud, 'Theb': Theb, 'Aichat': Aichat, 'Bard': Bard, 'Vercel': Vercel, 'Forefront': Forefront, 'Lockchat': Lockchat, 'Liaobots': Liaobots, 'H2o': H2o, 'ChatgptLogin': ChatgptLogin, 'DeepAi': DeepAi, 'GetGpt': GetGpt, 'AItianhu': AItianhu, 'EasyChat': EasyChat, 'Acytoo': Acytoo, 'DfeHub': DfeHub, 'AiService': AiService, 'BingHuan': BingHuan, 'Wewordle': Wewordle, 'ChatgptAi': ChatgptAi, 'opchatgpts': opchatgpts } with open("available_dict.txt", "r") as fp: # Load the dictionary from the file available_dict = json.load(fp) def change_prompt_set(prompt_set_name): return gr.Dropdown.update(choices=list(prompt_set_list[prompt_set_name].keys())) def change_model(model_name): new_choices = list(available_dict[model_name]) return gr.Dropdown.update(choices=new_choices, value=new_choices[0]) def change_prompt(prompt_set_name, prompt_name): return gr.update(value=prompt_set_list[prompt_set_name][prompt_name]) def user(user_message, history): return gr.update(value="", interactive=False), history + [[user_message, None]] def bot(history, model_name, provider_name, system_msg, agent): history[-1][1] = '' message = history[-1][0] if len(system_msg)>3000: system_msg = system_msg[:2000] + system_msg[-1000:] global template, memory llm.model_name = model_name llm.provider_name = provider_name if agent == '系统提示': new_template = template.format(system_instruction=system_msg) elif agent == '维基百科': wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper()) 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:') new_template = template.format(system_instruction=wikipedia.run(str(target))) elif agent == 'duckduckgo': search = DuckDuckGoSearchRun() 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:') new_template = template.format(system_instruction=search.run(str(target))) elif agent == 'python': py_agent = create_python_agent( llm, tool=PythonREPLTool(), # REPL,一种代码交互方式,类似jupyter,可以执行代码 verbose=True, # agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION handle_parsing_errors=True, # 输出无法解析,返回给llm要求改正。 ) response = py_agent.run(message) return str(response) else: new_template = template.format(system_instruction=system_msg) if len(history)>1 and history[-2][1]!=None: memory.chat_memory.add_ai_message(history[-2][1]) memory.chat_memory.add_user_message(history[-1][0]) validate_memory_len(memory=memory, max_token_limit=1800) if len(memory.chat_memory.messages)==0: for c in '文本长度超过限制,请清空后再试': history[-1][1] += c yield history else: prev_memory = memory.load_memory_variables({})['chat_history'] prompt = new_template.format( chat_history = prev_memory, ) print(f'prompt = \n --------\n{prompt}\n --------') for _ in range(3): try: bot_msg = llm._call(prompt=prompt) break except: bot_msg = '服务器无响应,请更换提供者或者清空对话后重试。' for c in bot_msg: history[-1][1] += c yield history def empty_fn(): global memory memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history") return [[None, None]] def undo_fn(history): return history[:-1] def retry_fn(history): history[-1][1] = None return history prompt_set_list = {} for prompt_file in os.listdir("prompt_set"): key = prompt_file if '.csv' in key: df = pd.read_csv("prompt_set/" + prompt_file) prompt_dict = dict(zip(df['act'], df['prompt'])) else: with open("prompt_set/" + prompt_file, encoding='utf-8') as f: ds = json.load(f) prompt_dict = {item["act"]: item["prompt"] for item in ds} prompt_set_list[key] = prompt_dict with gr.Blocks() as demo: llm = CustomLLM() template = """ Chat with human based on following instructions: ``` {system_instruction} ``` 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. {{chat_history}} AI:""" memory = ConversationBufferWindowMemory(k=6, memory_key="chat_history") with gr.Row(): model_name = gr.Dropdown(list(available_dict.keys()), value='gpt-3.5-turbo', label='模型') provider = gr.Dropdown(available_dict['gpt-3.5-turbo'], value='GetGpt', label='提供者', min_width=20) agent = gr.Dropdown(['系统提示', '维基百科'], value='系统提示', label='Agent') system_msg = gr.Textbox(value="你是一名助手,可以解答问题。", label='系统提示') chatbot = gr.Chatbot([[None, None]], label='AI') with gr.Group(): with gr.Row(): textbox = gr.Textbox( container=False, show_label=False, label="请输入:", scale=7, autofocus=True, ) submit = gr.Button('发送', scale=1, variant="primary", min_width=150,) with gr.Row(): retry = gr.Button('🔄 重试') undo = gr.Button('↩️ 撤销') clear = gr.Button("🗑️ 清空") with gr.Row(): default_prompt_set = "1 中文提示词.json" prompt_set_name = gr.Dropdown(prompt_set_list.keys(), value=default_prompt_set, label='提示词集合') prompt_name = gr.Dropdown(prompt_set_list[default_prompt_set].keys(), label='提示词', min_width=5, container=True) textbox.submit(user, [textbox, chatbot], [textbox, chatbot], queue=False).then( bot, [chatbot, model_name, provider, system_msg, agent], chatbot ).then(lambda: gr.update(interactive=True), None, [textbox], queue=False) response = submit.click(user, [textbox, chatbot], [textbox, chatbot], queue=False).then( bot, [chatbot, model_name, provider, system_msg, agent], chatbot ).then(lambda: gr.update(interactive=True), None, [textbox], queue=False) retry.click(retry_fn, [chatbot], [chatbot]).then( bot, [chatbot, model_name, provider, system_msg, agent], chatbot ) undo.click(undo_fn, [chatbot], [chatbot], queue=False) clear.click(empty_fn, None, [chatbot], queue=False) prompt_set_name.select(change_prompt_set, prompt_set_name, prompt_name) model_name.select(change_model, model_name, provider) prompt_name.select(change_prompt, [prompt_set_name, prompt_name], system_msg) demo.title = "AI Chat" demo.queue() demo.launch()