File size: 8,700 Bytes
89bf662 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
#!/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 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")
EMAIL_KEY = os.getenv("EMAIL_KEY")
client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
# 存储用户会话信息
user_sessions = {}
# 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": "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, functions=None, function_call=None):
try:
response = client.chat.completions.create(
model="gpt-4o-mini",
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 split_message(message, max_length=500):
return [message[i:i+max_length] for i in range(0, len(message), max_length)]
def search_duckduckgo(keywords):
search_term = " ".join(keywords)
with DDGS() as ddgs:
return list(ddgs.text(keywords=search_term, region="cn-zh", safesearch="on", max_results=5))
def send_email(to, subject, content):
try:
with smtplib.SMTP('106.15.184.28', 8025) as smtp:
smtp.login("jwt", EMAIL_KEY)
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
def process_function_call(response_message, session):
function_name = response_message.function_call.name
function_args = json.loads(response_message.function_call.arguments)
print(f"\n模型选择调用函数: {function_name}")
if function_name == "search_duckduckgo":
keywords = function_args.get('keywords', [])
if not keywords:
print("错误:模型没有提供搜索关键词")
return None
print(f"关键词: {', '.join(keywords)}")
return search_duckduckgo(keywords)
elif function_name == "send_email":
to = function_args.get('to')
subject = function_args.get('subject')
content = function_args.get('content')
if not session.get('email_sent', False):
if send_email(to, subject, content):
session['email_sent'] = True
return "邮件发送成功"
else:
return "邮件发送失败"
else:
return "邮件已经发送过,不再重复发送。"
else:
print(f"未知的函数名称: {function_name}")
return None
@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 from_user_name not in user_sessions:
user_sessions[from_user_name] = {'messages': [], 'pending_response': [], 'email_sent': False}
session = user_sessions[from_user_name]
if 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。"
else:
session['messages'].append({"role": "user", "content": user_message_content})
response_message = get_openai_response(session['messages'], functions=FUNCTIONS, function_call="auto")
if response_message.function_call:
function_response = process_function_call(response_message, session)
if function_response:
session['messages'].extend([
response_message.model_dump(),
{
"role": "function",
"name": response_message.function_call.name,
"content": json.dumps(function_response, ensure_ascii=False)
}
])
final_response = get_openai_response(session['messages'])
if final_response:
gpt_response = final_response.content
else:
gpt_response = "抱歉,我遇到了一些问题,无法回答您的问题。"
else:
gpt_response = "抱歉,我在执行任务时遇到了问题。"
else:
gpt_response = response_message.content
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) |