Create app.py

app.py

@@ -0,0 +1,204 @@
+import os
+import json
+import requests
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from flask import Flask, request, jsonify, send_from_directory
+from openai import OpenAI
+from duckduckgo_search import DDGS
+from functions import FUNCTIONS_GROUP_1, FUNCTIONS_GROUP_2, get_function_descriptions
+
+app = Flask(__name__)
+API_KEY = os.getenv("OPENAI_API_KEY")
+BASE_URL = os.getenv("OPENAI_BASE_URL")
+emailkey = os.getenv("EMAIL_KEY")
+client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+
+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 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', ''),
+                "ISBN": ", ".join(paper.get('ISBN', [])),
+                "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
+
+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。"
+
+@app.route('/')
+def index():
+    return send_from_directory('.', 'index.html')
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    data = request.json
+    question = data['question']
+    history = data.get('history', [])
+    messages = history + [{"role": "user", "content": question}]
+    
+    status_log = []
+    
+    # 次级模型1: 处理搜索相关函数
+    status_log.append("次级模型1：正在判断是否需要选调第一组函数")
+    sub_model_1_response = get_openai_response(messages, model="gpt-4o-mini", functions=FUNCTIONS_GROUP_1, function_call="auto")
+    
+    # 次级模型2: 处理邮件发送相关函数
+    status_log.append("次级模型2：正在判断是否需要选调第二组函数")
+    sub_model_2_response = get_openai_response(messages, model="gpt-4o-mini", 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
+    
+    if not function_call_1:
+        status_log.append("次级模型1：判断不需要选调第一组函数")
+    if not function_call_2:
+        status_log.append("次级模型2：判断不需要选调第二组函数")
+    
+    final_function_call = None
+    response = None
+    search_results = None
+    email_sent = False
+    
+    if function_call_1 and function_call_2:
+        # 裁决模型: 决定使用哪个函数调用
+        status_log.append("裁决模型：正在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="gpt-4o-mini")
+        if "模型1" in arbitration_response.content or function_call_1.name in arbitration_response.content:
+            final_function_call = function_call_1
+            status_log.append(f"裁决模型：决定使用函数 {function_call_1.name}")
+        else:
+            final_function_call = function_call_2
+            status_log.append(f"裁决模型：决定使用函数 {function_call_2.name}")
+    elif function_call_1:
+        final_function_call = function_call_1
+        status_log.append(f"次级模型1：决定使用函数 {function_call_1.name}")
+    elif function_call_2:
+        final_function_call = function_call_2
+        status_log.append(f"次级模型2：决定使用函数 {function_call_2.name}")
+    else:
+        status_log.append("所有次级模型：判断不需要进行任何函数调用")
+    
+    if final_function_call:
+        function_name = final_function_call.name
+        function_args = json.loads(final_function_call.arguments)
+        status_log.append(f"正在执行函数 {function_name}")
+        result = process_function_call(function_name, function_args)
+        status_log.append(f"函数 {function_name} 执行完成")
+        
+        if isinstance(result, dict) and result.get("is_email", False):
+            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']}"
+            email_sent = result['success']
+        elif isinstance(result, list):
+            search_results = result
+            messages.append({
+                "role": "function",
+                "name": function_name,
+                "content": json.dumps(result, ensure_ascii=False)
+            })
+        else:
+            messages.append({
+                "role": "function",
+                "name": function_name,
+                "content": str(result)
+            })
+    
+    # 只有在没有邮件发送结果时才调用主模型
+    if not response:
+        status_log.append("主模型：正在生成回答")
+        final_response = get_openai_response(messages, model="gpt-4o-mini")
+        response = final_response.content if final_response else "Error occurred"
+        status_log.append("主模型：回答生成完成")
+    
+    return jsonify({
+        "response": response,
+        "status_log": status_log,
+        "search_results": search_results,
+        "search_used": bool(search_results),
+        "email_sent": email_sent
+    })
+    
+@app.route('/settings', methods=['POST'])
+def update_settings():
+    data = request.json
+    max_history = data.get('max_history', 10)
+    return jsonify({"status": "success", "max_history": max_history})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860, debug=True)