app.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
import json
from openai import OpenAI
from dotenv import load_dotenv
from duckduckgo_search import DDGS
import requests
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

# 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")
emailkey = os.getenv("EMAIL_KEY")
client = OpenAI(api_key=API_KEY, base_url=BASE_URL)

# 定义可用的模型列表
AVAILABLE_MODELS = {
    'gpt-3.5-turbo': 'GPT-3.5 Turbo',
    'gpt-4o': 'GPT-4o',
    'gpt-4o-mini': 'GPT-4o-mini',
}

# 存储用户会话信息
user_sessions = {}

# 定义函数
def search_duckduckgo(keywords):
    search_term = " ".join(keywords)
    with DDGS() as ddgs:
        results = list(ddgs.text(keywords=search_term, region="cn-zh", safesearch="on", max_results=5))
        return [{"title": result['title'], "body": result['body'].replace('\n', ' ')} for result in results]

def search_papers(query):
    url = f"https://api.crossref.org/works?query={query}"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        papers = data['message']['items']
        processed_papers = []
        for paper in papers:
            processed_paper = {
                "标题": paper.get('title', [''])[0],
                "作者": ", ".join([f"{author.get('given', '')} {author.get('family', '')}" for author in paper.get('author', [])]),
                "DOI": paper.get('DOI', ''),
                "摘要": paper.get('abstract', '').replace('<p>', '').replace('</p>', '').replace('<italic>', '*').replace('</italic>', '*')
            }
            processed_papers.append(processed_paper)
        return processed_papers
    else:
        return []

def send_email(to, subject, content):
    try:
        with smtplib.SMTP('106.15.184.28', 8025) as smtp:
            smtp.login("jwt", emailkey)
            message = MIMEMultipart()
            message['From'] = "Me <[email protected]>"
            message['To'] = to
            message['Subject'] = subject
            message.attach(MIMEText(content, 'html'))
            smtp.sendmail("[email protected]", to, message.as_string())
        return True
    except Exception as e:
        print(f"发送邮件时出错: {str(e)}")
        return False

# 定义函数列表
FUNCTIONS = [
    {
        "name": "search_duckduckgo",
        "description": "使用DuckDuckGo搜索引擎查询信息。可以搜索最新新闻、文章、博客等内容。",
        "parameters": {
            "type": "object",
            "properties": {
                "keywords": {
                    "type": "array",
                    "items": {"type": "string"},
                    "description": "搜索的关键词列表。例如：['Python', '机器学习', '最新进展']。"
                }
            },
            "required": ["keywords"]
        }
    },
    {
        "name": "search_papers",
        "description": "使用Crossref API搜索学术论文。",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "搜索查询字符串。例如：'climate change'。"
                }
            },
            "required": ["query"]
        }
    },
    {
        "name": "send_email",
        "description": "发送电子邮件。",
        "parameters": {
            "type": "object",
            "properties": {
                "to": {
                    "type": "string",
                    "description": "收件人邮箱地址"
                },
                "subject": {
                    "type": "string",
                    "description": "邮件主题"
                },
                "content": {
                    "type": "string",
                    "description": "邮件内容"
                }
            },
            "required": ["to", "subject", "content"]
        }
    }
]

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, model="gpt-4o-mini", functions=None, function_call=None):
    try:
        response = client.chat.completions.create(
            model=model,
            messages=messages,
            functions=functions,
            function_call=function_call
        )
        return response.choices[0].message
    except Exception as e:
        print(f"调用OpenAI API时出错: {str(e)}")
        return None

def process_function_call(function_name, function_args):
    if function_name == "search_duckduckgo":
        keywords = function_args.get('keywords', [])
        if not keywords:
            return "搜索关键词为空，无法执行搜索。"
        return search_duckduckgo(keywords)
    elif function_name == "search_papers":
        query = function_args.get('query', '')
        if not query:
            return "搜索查询为空，无法执行论文搜索。"
        return search_papers(query)
    elif function_name == "send_email":
        to = function_args.get('to', '')
        subject = function_args.get('subject', '')
        content = function_args.get('content', '')
        if not to or not subject or not content:
            return "邮件信息不完整，无法发送邮件。"
        success = send_email(to, subject, content)
        return {
            "success": success,
            "message": "邮件发送成功" if success else "邮件发送失败",
            "to": to,
            "subject": subject,
            "content": content,
            "is_email": True
        }
    else:
        return "未知的函数调用。"

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
#         xml_str = request.data
#         if not xml_str:
#             return ""
        
#         user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(xml_str)
        
#         if from_user_name not in user_sessions:
#             user_sessions[from_user_name] = {'model': 'gpt-4o-mini', 'messages': [], 'pending_response': []}

#         session = user_sessions[from_user_name]
        
#         if user_message_content.lower() == '/models':
#             response_content = f"可用的模型列表：\n{list_available_models()}\n\n使用 /model 模型名称 来切换模型"
#             return generate_response_xml(from_user_name, to_user_name, response_content)
#         elif user_message_content.lower().startswith('/model'):
#             model = user_message_content.split(' ')[1]
#             if model in AVAILABLE_MODELS:
#                 session['model'] = model
#                 response_content = f'模型已切换为 {AVAILABLE_MODELS[model]}'
#             else:
#                 response_content = f'无效的模型名称。可用的模型有：\n{list_available_models()}'
#             return generate_response_xml(from_user_name, to_user_name, response_content)
#         elif user_message_content.lower() == '继续':
#             if session['pending_response']:
#                 response_content = session['pending_response'].pop(0)
#                 if session['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。"
#             return generate_response_xml(from_user_name, to_user_name, response_content)
        
#         session['messages'].append({"role": "user", "content": user_message_content})
        
#         # 调用OpenAI API
#         ai_response = get_openai_response(session['messages'], model=session['model'], functions=FUNCTIONS, function_call="auto")
        
#         if ai_response.function_call:
#             function_name = ai_response.function_call.name
#             function_args = json.loads(ai_response.function_call.arguments)
#             function_result = process_function_call(function_name, function_args)
            
#             session['messages'].append(ai_response.model_dump())
#             session['messages'].append({
#                 "role": "function",
#                 "name": function_name,
#                 "content": json.dumps(function_result, ensure_ascii=False)
#             })
            
#             final_response = get_openai_response(session['messages'], model=session['model'])
#             response_content = final_response.content
#         else:
#             response_content = ai_response.content
        
#         session['messages'].append({"role": "assistant", "content": response_content})
        
#         # 处理长消息
#         response_parts = split_message(response_content)
#         if len(response_parts) > 1:
#             session['pending_response'] = response_parts[1:]
#             response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
        
#         return generate_response_xml(from_user_name, to_user_name, response_content)
# @app.route('/api/wx', methods=['GET', 'POST'])
# def wechatai():
#     if request.method == 'GET':
#         return verify_wechat(request)
#     else:
#         # 处理POST请求
#         xml_str = request.data
#         if not xml_str:
#             return ""
        
#         user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(xml_str)
        
#         if from_user_name not in user_sessions:
#             user_sessions[from_user_name] = {'model': 'gpt-4o-mini', 'messages': [], 'pending_response': []}

#         session = user_sessions[from_user_name]
        
#         if user_message_content.lower() == '/models':
#             response_content = f"可用的模型列表：\n{list_available_models()}\n\n使用 /model 模型名称 来切换模型"
#             return generate_response_xml(from_user_name, to_user_name, response_content)
#         elif user_message_content.lower().startswith('/model'):
#             model = user_message_content.split(' ')[1]
#             if model in AVAILABLE_MODELS:
#                 session['model'] = model
#                 response_content = f'模型已切换为 {AVAILABLE_MODELS[model]}'
#             else:
#                 response_content = f'无效的模型名称。可用的模型有：\n{list_available_models()}'
#             return generate_response_xml(from_user_name, to_user_name, response_content)
#         elif user_message_content.lower() == '继续':
#             if session['pending_response']:
#                 response_content = session['pending_response'].pop(0)
#                 if session['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。"
#             return generate_response_xml(from_user_name, to_user_name, response_content)
        
#         session['messages'].append({"role": "user", "content": user_message_content})
        
#         # 调用OpenAI API
#         ai_response = get_openai_response(session['messages'], model=session['model'], functions=FUNCTIONS, function_call="auto")
        
#         if ai_response.function_call:
#             function_name = ai_response.function_call.name
#             function_args = json.loads(ai_response.function_call.arguments)
#             function_result = process_function_call(function_name, function_args)
            
#             session['messages'].append(ai_response.model_dump())
#             session['messages'].append({
#                 "role": "function",
#                 "name": function_name,
#                 "content": json.dumps(function_result, ensure_ascii=False)
#             })
            
#             # 再次调用OpenAI API，将函数执行结果作为上下文
#             final_response = get_openai_response(session['messages'], model=session['model'])
#             response_content = final_response.content
#         else:
#             response_content = ai_response.content
        
#         session['messages'].append({"role": "assistant", "content": response_content})
        
#         # 处理长消息
#         response_parts = split_message(response_content)
#         if len(response_parts) > 1:
#             session['pending_response'] = response_parts[1:]
#             response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
        
#         return generate_response_xml(from_user_name, to_user_name, response_content)

@app.route('/api/wx', methods=['GET', 'POST'])
def wechatai():
    if request.method == 'GET':
        return verify_wechat(request)
    else:
        xml_str = request.data
        if not xml_str:
            return ""
        
        user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(xml_str)
        
        if from_user_name not in user_sessions:
            user_sessions[from_user_name] = {'model': 'gpt-3.5-turbo', 'messages': [], 'pending_response': []}

        session = user_sessions[from_user_name]
        
        # 处理特殊命令
        if user_message_content.lower() == '/models':
            response_content = f"可用的模型列表：\n{list_available_models()}\n\n使用 /model 模型名称 来切换模型"
            return generate_response_xml(from_user_name, to_user_name, response_content)
        elif user_message_content.lower().startswith('/model'):
            model = user_message_content.split(' ')[1]
            if model in AVAILABLE_MODELS:
                session['model'] = model
                response_content = f'模型已切换为 {AVAILABLE_MODELS[model]}'
            else:
                response_content = f'无效的模型名称。可用的模型有：\n{list_available_models()}'
            return generate_response_xml(from_user_name, to_user_name, response_content)
        elif user_message_content.lower() == '继续':
            if session['pending_response']:
                response_content = session['pending_response'].pop(0)
                if session['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。"
            return generate_response_xml(from_user_name, to_user_name, response_content)

        session['messages'].append({"role": "user", "content": user_message_content})
        messages = session['messages']
        
        # 次级模型1: 处理搜索相关函数
        sub_model_1_response = get_openai_response(messages, model=session['model'], functions=FUNCTIONS_GROUP_1, function_call="auto")
        
        # 次级模型2: 处理邮件发送相关函数
        sub_model_2_response = get_openai_response(messages, model=session['model'], functions=FUNCTIONS_GROUP_2, function_call="auto")
        
        function_call_1 = sub_model_1_response.function_call if sub_model_1_response and sub_model_1_response.function_call else None
        function_call_2 = sub_model_2_response.function_call if sub_model_2_response and sub_model_2_response.function_call else None
        
        final_function_call = None
        
        if function_call_1 and function_call_2:
            # 裁决模型: 决定使用哪个函数调用
            arbitration_messages = messages + [
                {"role": "system", "content": "两个次级模型都建议使用函数。请决定使用哪个函数更合适。"},
                {"role": "assistant", "content": f"次级模型1建议使用函数：{function_call_1.name}"},
                {"role": "assistant", "content": f"次级模型2建议使用函数：{function_call_2.name}"}
            ]
            arbitration_response = get_openai_response(arbitration_messages, model=session['model'])
            if arbitration_response and ("模型1" in arbitration_response.content or function_call_1.name in arbitration_response.content):
                final_function_call = function_call_1
            else:
                final_function_call = function_call_2
        elif function_call_1:
            final_function_call = function_call_1
        elif function_call_2:
            final_function_call = function_call_2
        
        if final_function_call:
            function_name = final_function_call.name
            function_args = json.loads(final_function_call.arguments)
            function_result = process_function_call(function_name, function_args)
            
            messages.append({"role": "function", "name": function_name, "content": json.dumps(function_result, ensure_ascii=False)})
        
        # 主模型: 生成最终回复
        final_response = get_openai_response(messages, model=session['model'])
        response_content = final_response.content if final_response else "Error occurred"
        
        session['messages'].append({"role": "assistant", "content": response_content})
        
        # 处理长消息
        response_parts = split_message(response_content)
        if len(response_parts) > 1:
            session['pending_response'] = response_parts[1:]
            response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
        
        return generate_response_xml(from_user_name, to_user_name, response_content)
        
def list_available_models():
    return "\n".join([f"{key}: {value}" for key, value in AVAILABLE_MODELS.items()])

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