app.py

# 长文本总结
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain import OpenAI
from langchain import PromptTemplate
from langchain.docstore.document import Document as LangDoc


text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)


import openai
from openai.error import RateLimitError
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate
)
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.output_parsers import RegexParser


openai.api_key = ""

summary_prompt = (
"总结以下会议记录中所探讨的主要话题，忽略细节\n"
"会议记录：{text}\n"
"在输出时，请注意以下几点：\n"
"1. 输出内容中避免口语化内容\n"
"2. 每个话题用序号标注\n"
"3. 不输出无关信息"
)

qa_prompt = """
结合下面的信息，用中文回答最后的问题。如果你不知道答案，说“我不知道”，不可以编造答案。
除了回答问题外，还需要输出一个分数，表示你对这个问题的回答的自信程度。分数越高，你越自信。按照以下的格式输出：

回答：[回答内容]
分数：[0到100间的数字]

开始回答：
{context}

问题：{question}
"""


def get_chatgpt_reply(query, context=[]):
    context += [query]

    llm_chat = ChatOpenAI(model_name="gpt-3.5-turbo", max_tokens=2000, temperature=0.3)
    embeddings = OpenAIEmbeddings()
    docsearch = Chroma(persist_directory="./VectorDB", embedding_function=embeddings)

    output_parser = RegexParser(
        regex=r"(.*)\n*分数：([0-9]*).*",
        output_keys=["answer", "score"],
    )
    PROMPT = PromptTemplate(
        template=qa_prompt, input_variables=["context", "question"], output_parser=output_parser
    )
    chain_type_kwargs = {"prompt": PROMPT}

    qa = RetrievalQA.from_chain_type(llm_chat, chain_type="map_rerank", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)
    result = qa.run(query)

    context += [result]
    responses = [(u,b) for u,b in zip(context[::2], context[1::2])]
    return responses, context


def get_chatgpt_summary(content):
    texts = text_splitter.split_text(content)
    docs = [LangDoc(page_content=t) for t in texts]
    llm_summary = OpenAI(model_name="gpt-3.5-turbo", max_tokens=300, temperature=0.2)
    each_round_template = PromptTemplate(input_variables=["text"], template=summary_prompt)
    chain_summary = load_summarize_chain(llm_summary, chain_type="stuff", prompt=each_round_template)
    summary = "\n*******\n".join([chain_summary.run([doc]) for doc in docs])
    return summary


import gradio as gr
from docx import Document
import os


def upload_file(file):
    doc = Document(file.name)
    content = ""
    for para in doc.paragraphs:
        content += para.text
        content += '\n'
    texts = text_splitter.split_text(content)
    docs = [LangDoc(page_content=t) for t in texts]
    embeddings = OpenAIEmbeddings()
    docsearch = Chroma.from_documents(docs, embeddings, persist_directory="./VectorDB")
    docsearch.persist()
    return content


def set_api_key(api_key):
    openai.api_key = api_key
    os.environ["OPENAI_API_KEY"] = api_key
    return None


with gr.Blocks(theme=gr.themes.Default(text_size='lg', radius_size='sm')) as demo:
    with gr.Column():
        # 产品介绍
        title = gr.Markdown("# <center>ChatMeeting</center>")
        desc = gr.Markdown("<center>让AI帮你整理会议纪要\n\n支持.docx文件</center>")
    
    with gr.Column():
        # api key
        api_input = gr.Textbox(label="API Key", placeholder="请输入API Key", type="password")
        api_btn = gr.Button(value="设置")
        api_btn.click(fn=set_api_key, inputs=api_input, outputs=None)
    with gr.Row():
        with gr.Column():
            # 文件上传
            file_input = gr.File(file_types=[".docx"], label="原始文稿", interactive=True)
            upload_btn = gr.Button(value="上传")
            # 文字展示
            with gr.Tab("原文"):
                # 原文
                content_box = gr.Textbox(label="文稿内容")
            with gr.Tab("总结"):
                # 总结
                summary_box = gr.Textbox(label="总结内容")
        with gr.Column():
            # 对话交互
            chatbot = gr.Chatbot(label="对话内容").style(height=500)
            state = gr.State([])
            txt = gr.Textbox(label="用户", placeholder="请输入内容")
            with gr.Row():
                summary = gr.Button(value="一键总结")
                clear = gr.Button(value="清空")
            
            summary.click(fn=get_chatgpt_summary, inputs=content_box, outputs=summary_box)
            txt.submit(get_chatgpt_reply, [txt, state], [chatbot, state])
            clear.click(lambda: None, None, chatbot, queue=False)

    # 上传文件，langchain解析
    upload_btn.click(fn=upload_file, inputs=file_input, outputs=content_box)


demo.launch()