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import g4f |
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import gradio as gr |
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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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) |
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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 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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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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} |
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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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Human: {{human_input}} |
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Chatbot:""" |
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memory = ConversationBufferWindowMemory(k=10, memory_key="chat_history") |
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chatbot = gr.Chatbot([], label='AI') |
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msg = gr.Textbox(value="", label='请输入：') |
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with gr.Row(): |
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clear = gr.Button("清空对话", scale=2) |
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chat_mode = gr.Checkbox(value=True, label='聊天模式', interactive=True, scale=1) |
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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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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=20) |
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with gr.Row(): |
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model_name = gr.Dropdown(['gpt-3.5-turbo', 'gpt-4'], value='gpt-3.5-turbo', label='模型') |
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provider_name = gr.Dropdown(provider_dict.keys(), value='GetGpt', label='提供者', min_width=20) |
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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_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, chat_mode): |
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history[-1][1] = '' |
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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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if not chat_mode: |
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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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prompt = PromptTemplate( |
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input_variables=["chat_history", "human_input"], template=template.format(system_instruction=system_msg) |
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) |
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llm_chain = LLMChain( |
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llm=llm, |
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prompt=prompt, |
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verbose=False, |
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memory=memory, |
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) |
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bot_msg = llm_chain.run(history[-1][0]) |
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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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else: |
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prompt = """ |
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请你仔细阅读以下提示，然后针对用户的话进行回答。 |
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提示： |
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``` |
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{} |
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``` |
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用户最新的话： |
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``` |
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{} |
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``` |
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请回答： |
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""" |
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messages = [] |
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for user_message, assistant_message in history[:-1]: |
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messages.append({"role": "user", "content": user_message}) |
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messages.append({"role": "assistant", "content": assistant_message}) |
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messages.append({"role": "user", "content": history[-1][0]}) |
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bot_msg = g4f.ChatCompletion.create( |
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model=model_name, |
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provider=provider_dict[provider_name], |
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messages=messages, |
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stream=True) |
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for c in bot_msg: |
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history[-1][1] += c |
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print(c, flush=True, end='') |
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yield history |
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def empty_chat(): |
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global memory |
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memory = ConversationBufferWindowMemory(k=10, memory_key="chat_history") |
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return None |
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response = msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( |
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bot, [chatbot, model_name, provider_name, system_msg, chat_mode], chatbot |
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) |
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prompt_set_name.select(change_prompt_set, prompt_set_name, prompt_name) |
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prompt_name.select(change_prompt, [prompt_set_name, prompt_name], system_msg) |
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response.then(lambda: gr.update(interactive=True), None, [msg], queue=False) |
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clear.click(empty_chat, None, [chatbot], queue=False) |
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demo.title = "AI Chat" |
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demo.queue() |
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demo.launch() |
