app-纯单模型.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from flask import Flask, request, make_response
import hashlib
import time
import xml.etree.ElementTree as ET
import os
from openai import OpenAI
from dotenv import load_dotenv

# 加载环境变量
load_dotenv()

app = Flask(__name__)

# 配置
TOKEN = os.getenv('TOKEN')
API_KEY = os.getenv("API_KEY")
BASE_URL = os.getenv("OPENAI_BASE_URL")
client = OpenAI(api_key=API_KEY, base_url=BASE_URL)

# 存储用户会话信息
user_sessions = {}

def verify_wechat(request):
    # 获取微信服务器发送过来的参数
    data = request.args
    signature = data.get('signature')
    timestamp = data.get('timestamp')
    nonce = data.get('nonce')
    echostr = data.get('echostr')
    
    # 对参数进行字典排序，拼接字符串
    temp = [timestamp, nonce, TOKEN]
    temp.sort()
    temp = ''.join(temp)
    
    # 加密
    if (hashlib.sha1(temp.encode('utf8')).hexdigest() == signature):
        return echostr
    else:
        return 'error', 403

def getUserMessageContentFromXML(xml_content):
    # 解析XML字符串
    root = ET.fromstring(xml_content)
    # 提取数据
    content = root.find('Content').text
    from_user_name = root.find('FromUserName').text
    to_user_name = root.find('ToUserName').text
    return content, from_user_name, to_user_name

def generate_response_xml(from_user_name, to_user_name, output_content):
    output_xml = '''
    <xml>
        <ToUserName><![CDATA[%s]]></ToUserName>
        <FromUserName><![CDATA[%s]]></FromUserName>
        <CreateTime>%s</CreateTime>
        <MsgType><![CDATA[text]]></MsgType>
        <Content><![CDATA[%s]]></Content>
    </xml>'''
    
    response = make_response(output_xml % (from_user_name, to_user_name, str(int(time.time())), output_content))
    response.content_type = 'application/xml'
    return response

def get_openai_response(messages):
    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=messages
        )
        return response.choices[0].message.content
    except Exception as e:
        print(f"调用OpenAI API时出错: {str(e)}")
        return "抱歉，我遇到了一些问题，无法回答您的问题。"

def split_message(message, max_length=500):
    return [message[i:i+max_length] for i in range(0, len(message), max_length)]

@app.route('/api/wx', methods=['GET', 'POST'])
def wechatai():
    if request.method == 'GET':
        return verify_wechat(request)
    else:
        # 处理POST请求
        print("user request data: ", request.data)
        user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(request.data)
        print("user message content: ", user_message_content)

        if user_message_content.lower() == '继续':
            if from_user_name in user_sessions and user_sessions[from_user_name]['pending_response']:
                response_content = user_sessions[from_user_name]['pending_response'].pop(0)
                if user_sessions[from_user_name]['pending_response']:
                    response_content += '\n\n回复"继续"获取下一部分。'
                else:
                    response_content += '\n\n回复结束。'
            else:
                response_content = "没有待发送的消息。"
        else:
            if from_user_name not in user_sessions:
                user_sessions[from_user_name] = {'messages': [], 'pending_response': []}

            session = user_sessions[from_user_name]
            session['messages'].append({"role": "user", "content": user_message_content})
            
            gpt_response = get_openai_response(session['messages'])
            session['messages'].append({"role": "assistant", "content": gpt_response})

            response_parts = split_message(gpt_response)
            
            if len(response_parts) > 1:
                response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
                session['pending_response'] = response_parts[1:]
            else:
                response_content = response_parts[0]

        return generate_response_xml(from_user_name, to_user_name, response_content)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, debug=True)