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Create app.py
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app.py
ADDED
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1 |
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Hugging Face's logo
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Hugging Face
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Search models, datasets, users...
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Models
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Datasets
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Spaces
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Posts
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Docs
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Pricing
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Spaces:
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mistpe
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/
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functioncall1
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private
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App
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Files
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Community
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Settings
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functioncall1
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/
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app.py
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mistpe's picture
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mistpe
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Update app.py
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0bd5c33
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verified
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8 minutes ago
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raw
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Copy download link
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history
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blame
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edit
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delete
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11.9 kB
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import os
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import json
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import requests
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import smtplib
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from email.mime.text import MIMEText
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from email.mime.multipart import MIMEMultipart
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from flask import Flask, request, jsonify, send_from_directory
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from openai import OpenAI
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from bs4 import BeautifulSoup
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import random
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from functions import FUNCTIONS_GROUP_1, FUNCTIONS_GROUP_2, get_function_descriptions
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app = Flask(__name__)
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API_KEY = os.getenv("OPENAI_API_KEY")
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BASE_URL = os.getenv("OPENAI_BASE_URL")
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emailkey = os.getenv("EMAIL_KEY")
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client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
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def search_duckduckgo(keywords):
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search_term = " ".join(keywords)
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url = "https://www.bing.com/search"
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user_agents = [
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"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
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"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:89.0) Gecko/20100101 Firefox/89.0",
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"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.1 Safari/605.1.15",
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]
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headers = {
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"User-Agent": random.choice(user_agents)
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}
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params = {
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"q": search_term,
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"setlang": "zh-CN"
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}
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response = requests.get(url, params=params, headers=headers)
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results = []
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if response.status_code == 200:
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soup = BeautifulSoup(response.text, 'html.parser')
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for item in soup.select('.b_algo')[:5]: # Limit to 5 results
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title_elem = item.select_one('h2 a')
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snippet_elem = item.select_one('.b_caption p')
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if title_elem and snippet_elem:
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results.append({
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"title": title_elem.text,
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"href": title_elem['href'],
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"body": snippet_elem.text
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})
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return results
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def search_papers(query):
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url = f"https://api.crossref.org/works?query={query}"
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response = requests.get(url)
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if response.status_code == 200:
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data = response.json()
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papers = data['message']['items']
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processed_papers = []
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for paper in papers:
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processed_paper = {
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"标题": paper.get('title', [''])[0],
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"作者": ", ".join([f"{author.get('given', '')} {author.get('family', '')}" for author in paper.get('author', [])]),
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"DOI": paper.get('DOI', ''),
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"ISBN": ", ".join(paper.get('ISBN', [])),
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"摘要": paper.get('abstract', '').replace('<p>', '').replace('</p>', '').replace('<italic>', '').replace('</italic>', '')
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}
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processed_papers.append(processed_paper)
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return processed_papers
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else:
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return []
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def send_email(to, subject, content):
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try:
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with smtplib.SMTP('106.15.184.28', 8025) as smtp:
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smtp.login("jwt", emailkey)
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message = MIMEMultipart()
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message['From'] = "Me <[email protected]>"
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message['To'] = to
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message['Subject'] = subject
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message.attach(MIMEText(content, 'html'))
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smtp.sendmail("[email protected]", to, message.as_string())
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return True
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except Exception as e:
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print(f"发送邮件时出错: {str(e)}")
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return False
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def get_openai_response(messages, model="gpt-4o-mini", functions=None, function_call=None):
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try:
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response = client.chat.completions.create(
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model=model,
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messages=messages,
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functions=functions,
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function_call=function_call
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)
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return response.choices[0].message
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except Exception as e:
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print(f"调用OpenAI API时出错: {str(e)}")
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return None
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def process_function_call(function_name, function_args):
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if function_name == "search_duckduckgo":
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keywords = function_args.get('keywords', [])
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148 |
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if not keywords:
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return "搜索关键词为空,无法执行搜索。"
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150 |
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return search_duckduckgo(keywords)
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151 |
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elif function_name == "search_papers":
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query = function_args.get('query', '')
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153 |
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if not query:
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return "搜索查询为空,无法执行论文搜索。"
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155 |
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return search_papers(query)
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156 |
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elif function_name == "send_email":
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to = function_args.get('to', '')
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158 |
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subject = function_args.get('subject', '')
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159 |
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content = function_args.get('content', '')
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if not to or not subject or not content:
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return "邮件信息不完整,无法发送邮件。"
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162 |
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success = send_email(to, subject, content)
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return {
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164 |
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"success": success,
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"message": "邮件发送成功" if success else "邮件发送失败",
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"to": to,
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"subject": subject,
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"content": content,
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"is_email": True
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170 |
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}
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171 |
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else:
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172 |
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return "未知的函数调用。"
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173 |
+
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174 |
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@app.route('/')
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175 |
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def index():
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176 |
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return send_from_directory('.', 'index.html')
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177 |
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178 |
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@app.route('/chat', methods=['POST'])
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179 |
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def chat():
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180 |
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data = request.json
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181 |
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question = data['question']
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182 |
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history = data.get('history', [])
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183 |
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messages = history + [{"role": "user", "content": question}]
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184 |
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185 |
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status_log = []
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186 |
+
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187 |
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# 次级模型1: 处理搜索相关函数
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status_log.append("次级模型1:正在判断是否需要选调第一组函数")
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189 |
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sub_model_1_response = get_openai_response(messages, model="gpt-4o-mini", functions=FUNCTIONS_GROUP_1, function_call="auto")
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190 |
+
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191 |
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# 次级模型2: 处理邮件发送相关函数
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status_log.append("次级模型2:正在判断是否需要选调第二组函数")
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sub_model_2_response = get_openai_response(messages, model="gpt-4o-mini", functions=FUNCTIONS_GROUP_2, function_call="auto")
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+
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function_call_1 = sub_model_1_response.function_call if sub_model_1_response and sub_model_1_response.function_call else None
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function_call_2 = sub_model_2_response.function_call if sub_model_2_response and sub_model_2_response.function_call else None
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if not function_call_1:
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status_log.append("次级模型1:判断不需要选调第一组函数")
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if not function_call_2:
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status_log.append("次级模型2:判断不需要选调第二组函数")
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final_function_call = None
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response = None
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search_results = None
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email_sent = False
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208 |
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if function_call_1 and function_call_2:
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# 裁决模型: 决定使用哪个函数调用
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status_log.append("裁决模型:正在决定使用哪个函数调用")
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arbitration_messages = messages + [
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212 |
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{"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。"},
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{"role": "assistant", "content": f"次级模型1建议使用函数:{function_call_1.name}"},
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{"role": "assistant", "content": f"次级模型2建议使用函数:{function_call_2.name}"}
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]
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arbitration_response = get_openai_response(arbitration_messages, model="gpt-4o-mini")
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217 |
+
if "模型1" in arbitration_response.content or function_call_1.name in arbitration_response.content:
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final_function_call = function_call_1
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219 |
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status_log.append(f"裁决模型:决定使用函数 {function_call_1.name}")
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+
else:
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final_function_call = function_call_2
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status_log.append(f"裁决模型:决定使用函数 {function_call_2.name}")
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+
elif function_call_1:
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final_function_call = function_call_1
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status_log.append(f"次级模型1:决定使用函数 {function_call_1.name}")
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elif function_call_2:
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final_function_call = function_call_2
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status_log.append(f"次级模型2:决定使用函数 {function_call_2.name}")
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else:
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status_log.append("所有次级模型:判断不需要进行任何函数调用")
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+
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if final_function_call:
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function_name = final_function_call.name
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function_args = json.loads(final_function_call.arguments)
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status_log.append(f"正在执行函数 {function_name}")
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result = process_function_call(function_name, function_args)
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status_log.append(f"函数 {function_name} 执行完成")
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+
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+
if isinstance(result, dict) and result.get("is_email", False):
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response = f"邮件{'已成功' if result['success'] else '未能成功'}发送到 {result['to']}。\n\n主题:{result['subject']}\n\n内容:\n{result['content']}"
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241 |
+
email_sent = result['success']
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242 |
+
elif isinstance(result, list):
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+
search_results = result
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+
messages.append({
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+
"role": "function",
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+
"name": function_name,
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+
"content": json.dumps(result, ensure_ascii=False)
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+
})
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+
else:
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+
messages.append({
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+
"role": "function",
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"name": function_name,
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+
"content": str(result)
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})
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+
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+
# 只有在没有邮件发送结果时才调用主模型
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+
if not response:
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+
status_log.append("主模型:正在生成回答")
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259 |
+
final_response = get_openai_response(messages, model="gpt-4o-mini")
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260 |
+
response = final_response.content if final_response else "Error occurred"
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261 |
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status_log.append("主模型:回答生成完成")
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+
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return jsonify({
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264 |
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"response": response,
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"status_log": status_log,
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+
"search_results": search_results,
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"search_used": bool(search_results),
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"email_sent": email_sent
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+
})
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+
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+
@app.route('/settings', methods=['POST'])
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+
def update_settings():
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+
data = request.json
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+
max_history = data.get('max_history', 10)
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+
return jsonify({"status": "success", "max_history": max_history})
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+
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=7860, debug=True)
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+
# from flask import Flask, request, jsonify, send_from_directory
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# import requests
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281 |
+
# from bs4 import BeautifulSoup
|
282 |
+
# import random
|
283 |
+
# import time
|
284 |
+
|
285 |
+
# app = Flask(__name__)
|
286 |
+
|
287 |
+
# def perform_bing_search(keywords):
|
288 |
+
# search_term = " ".join(keywords)
|
289 |
+
# url = "https://www.bing.com/search"
|
290 |
+
# user_agents = [
|
291 |
+
# "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
|
292 |
+
# "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:89.0) Gecko/20100101 Firefox/89.0",
|
293 |
+
# "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.1 Safari/605.1.15",
|
294 |
+
# ]
|
295 |
+
# headers = {"User-Agent": random.choice(user_agents)}
|
296 |
+
# params = {"q": search_term, "setlang": "zh-CN"}
|
297 |
+
|
298 |
+
# response = requests.get(url, params=params, headers=headers)
|
299 |
+
# if response.status_code == 200:
|
300 |
+
# soup = BeautifulSoup(response.text, 'html.parser')
|
301 |
+
# results = soup.select('.b_algo')
|
302 |
+
# search_results = []
|
303 |
+
# for result in results[:5]: # 只取前5个结果
|
304 |
+
# title = result.select_one('h2 a')
|
305 |
+
# snippet = result.select_one('.b_caption p')
|
306 |
+
# if title and snippet:
|
307 |
+
# search_results.append({
|
308 |
+
# "title": title.text,
|
309 |
+
# "url": title['href'],
|
310 |
+
# "snippet": snippet.text
|
311 |
+
# })
|
312 |
+
# return search_results
|
313 |
+
# return []
|
314 |
+
|
315 |
+
# @app.route('/')
|
316 |
+
# def index():
|
317 |
+
# return send_from_directory('.', 'we.html')
|
318 |
+
|
319 |
+
# @app.route('/start_test', methods=['POST'])
|
320 |
+
# def start_test():
|
321 |
+
# data = request.json
|
322 |
+
# keywords = data['keywords'].split()
|
323 |
+
# interval = int(data['interval'])
|
324 |
+
|
325 |
+
# first_search = perform_bing_search(keywords)
|
326 |
+
# time.sleep(interval)
|
327 |
+
# second_search = perform_bing_search(keywords)
|
328 |
+
|
329 |
+
# success = len(first_search) > 0 and len(second_search) > 0
|
330 |
+
# return jsonify({
|
331 |
+
# "success": success,
|
332 |
+
# "first_search": first_search,
|
333 |
+
# "second_search": second_search
|
334 |
+
# })
|
335 |
+
|
336 |
+
# if __name__ == '__main__':
|
337 |
+
# app.run(host='0.0.0.0', port=7860, debug=True)
|
338 |
+
|