370 lines
13 KiB
Python
370 lines
13 KiB
Python
import logging
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import sys
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logging.basicConfig(
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level=logging.DEBUG,
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format='%(asctime)s - %(levelname)s - %(message)s',
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handlers=[
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logging.FileHandler('app_debug.log'),
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logging.StreamHandler(sys.stdout)
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]
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)
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logger = logging.getLogger(__name__)
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from flask import Flask, render_template, request, jsonify, session
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from services.deepseek_service import deepseek_service
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from config import Config
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import uuid
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import datetime
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app = Flask(__name__)
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app.secret_key = "interviewer-secret-key-change-in-production"
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# 设置session过期时间为1小时
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app.permanent_session_lifetime = datetime.timedelta(hours=1)
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# 内存数据库存储面试数据，避免session过期导致的"面试不存在"错误
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interviews_db = {}
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INTERVIEW_PHASES = {
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"intro": "自我介绍",
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"professional": "专业能力",
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"scenario": "情景假设",
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"career": "职业规划",
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"closing": "面试结束"
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}
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QUESTION_COUNTS = {
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"intro": 2,
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"professional": 4,
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"scenario": 2,
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"career": 1
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}
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@app.route("/")
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def index():
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return render_template("index.html")
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@app.route("/api/chat", methods=["POST"])
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def chat():
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data = request.json
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user_input = data.get("message", "").strip()
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system_type = data.get("system_type", "general_assistant")
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conversation_key = data.get("conversation_key", "default")
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if not user_input:
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return jsonify({"error": "请输入内容"}), 400
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if conversation_key not in session:
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session[conversation_key] = []
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conversation_history = session[conversation_key]
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try:
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result = deepseek_service.chat(
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user_input=user_input,
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conversation_history=conversation_history,
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system_type=system_type
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)
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conversation_history.append({"role": "user", "content": user_input})
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conversation_history.append(result)
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session[conversation_key] = conversation_history[-20:]
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return jsonify({"response": result["content"]})
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route("/api/resume/optimize", methods=["POST"])
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def optimize_resume():
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data = request.json
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resume_content = data.get("resume_content", "").strip()
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target_position = data.get("target_position", "").strip()
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if not resume_content:
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return jsonify({"error": "请提供简历内容"}), 400
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try:
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result = deepseek_service.optimize_resume(resume_content, target_position)
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return jsonify(result)
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route("/api/interview/start", methods=["POST"])
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def start_interview():
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data = request.json
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job_position = data.get("job_position", "").strip()
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difficulty = data.get("difficulty", "intermediate")
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if not job_position:
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return jsonify({"error": "请选择目标岗位"}), 400
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interview_id = str(uuid.uuid4())
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interview_data = {
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"job_position": job_position,
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"difficulty": difficulty,
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"current_phase": "intro",
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"question_count": 0,
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"conversation_history": [],
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"is_active": True
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}
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session[f"interview_{interview_id}"] = interview_data
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interviews_db[interview_id] = interview_data
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first_question = deepseek_service.generate_interview_question(
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job_position=job_position,
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difficulty=difficulty,
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phase="intro"
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)
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session[f"interview_{interview_id}"]["conversation_history"].append({
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"role": "assistant",
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"content": f"你好！我是面试官，现在开始针对{job_position}岗位的面试。\n\n{first_question}"
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})
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return jsonify({
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"interview_id": interview_id,
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"job_position": job_position,
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"difficulty": difficulty,
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"question": first_question,
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"phase": "intro"
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})
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@app.route("/api/interview/answer", methods=["POST"])
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def answer_question():
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data = request.json
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interview_id = data.get("interview_id", "").strip()
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user_answer = data.get("answer", "").strip()
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request_feedback = data.get("request_feedback", False)
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if not interview_id:
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return jsonify({"error": "无效的面试ID"}), 400
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# 首先从内存数据库查找面试数据
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if interview_id not in interviews_db:
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# 如果内存数据库中没有，再检查session
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interview_key = f"interview_{interview_id}"
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if interview_key not in session:
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return jsonify({"error": "面试不存在或已结束"}), 400
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# 如果session中有，同步到内存数据库
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interviews_db[interview_id] = session[interview_key]
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interview_data = interviews_db[interview_id]
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if not interview_data["is_active"]:
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return jsonify({"error": "面试已结束"}), 400
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if not user_answer:
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return jsonify({"error": "请输入你的回答"}), 400
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conversation_history = interview_data["conversation_history"]
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if request_feedback:
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last_question = ""
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logger.debug(f"查找最后一个问题，对话历史长度：{len(conversation_history)}")
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for msg in reversed(conversation_history):
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logger.debug(f"检查消息：角色={msg['role']}, 内容={msg['content'][:50]}...")
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if msg["role"] == "assistant" and ("？" in msg["content"] or "?" in msg["content"]):
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last_question = msg["content"]
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logger.debug(f"找到最后一个问题：{last_question[:50]}...")
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break
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if last_question:
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try:
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feedback = deepseek_service.chat_with_feedback(
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user_input=last_question,
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user_answer=user_answer,
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conversation_history=conversation_history[:-1]
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)
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conversation_history.append({"role": "user", "content": user_answer})
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conversation_history.append(feedback)
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return jsonify({
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"feedback": feedback["content"],
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"ended": False
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})
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except Exception as e:
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logger.error(f"生成反馈失败：{str(e)}", exc_info=True)
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return jsonify({"error": f"生成反馈失败：{str(e)}"}), 500
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else:
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logger.warning("没有找到最后一个问题")
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return jsonify({"error": "没有找到相关问题"}), 400
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conversation_history.append({"role": "user", "content": user_answer})
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interview_data["question_count"] += 1
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current_phase = interview_data["current_phase"]
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phase_order = ["intro", "professional", "scenario", "career", "closing"]
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current_index = phase_order.index(current_phase)
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questions_in_phase = QUESTION_COUNTS.get(current_phase, 1)
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next_question = None
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if interview_data["question_count"] >= questions_in_phase:
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if current_index < len(phase_order) - 1:
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next_phase = phase_order[current_index + 1]
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interview_data["current_phase"] = next_phase
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interview_data["question_count"] = 0
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if next_phase == "closing":
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conversation_history.append({
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"role": "assistant",
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"content": "面试结束！感谢你的参与。点击下方按钮获取本次面试的详细反馈。"
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})
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interview_data["is_active"] = False
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return jsonify({
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"question": None,
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"feedback": "面试结束",
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"ended": True,
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"conversation_history": conversation_history[-10:]
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})
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else:
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phase_names = {
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"intro": "自我介绍",
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"professional": "专业能力",
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"scenario": "情景假设",
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"career": "职业规划"
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}
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try:
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next_question = deepseek_service.generate_interview_question(
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job_position=interview_data["job_position"],
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difficulty=interview_data["difficulty"],
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conversation_history=conversation_history,
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phase=next_phase
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)
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conversation_history.append({
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"role": "assistant",
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"content": f"（{phase_names.get(next_phase, next_phase)}阶段）\n\n{next_question}"
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})
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except Exception as e:
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return jsonify({"error": f"生成问题失败：{str(e)}"}), 500
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else:
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try:
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next_question = deepseek_service.generate_interview_question(
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job_position=interview_data["job_position"],
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difficulty=interview_data["difficulty"],
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conversation_history=conversation_history,
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phase=current_phase
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)
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conversation_history.append({"role": "assistant", "content": next_question})
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except Exception as e:
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return jsonify({"error": f"生成问题失败：{str(e)}"}), 500
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# 同时更新内存数据库和session中的数据
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interviews_db[interview_id] = interview_data
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session[f"interview_{interview_id}"] = interview_data
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return jsonify({
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"question": next_question,
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"feedback": None,
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"ended": False,
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"phase": interview_data["current_phase"]
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})
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@app.route("/api/interview/feedback", methods=["POST"])
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def get_interview_feedback():
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logger.info("接收到面试反馈请求")
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try:
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# 首先确保能够获取请求数据
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if not request.is_json:
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logger.warning("请求数据不是JSON格式")
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return jsonify({"error": "请求数据必须是JSON格式"}), 400
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data = request.json
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logger.debug(f"请求数据：{data}")
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# 检查必要参数
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interview_id = data.get("interview_id", "").strip()
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conversation_history = data.get("conversation_history", [])
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if not interview_id:
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logger.warning("面试ID无效")
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return jsonify({"error": "无效的面试ID"}), 400
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if not conversation_history:
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logger.warning("没有提供面试对话历史")
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return jsonify({"error": "没有提供面试对话历史"}), 400
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# 获取岗位信息
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job_position = ""
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if interview_id in interviews_db:
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job_position = interviews_db[interview_id].get("job_position", "")
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logger.debug(f"岗位信息：{job_position}")
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logger.debug(f"对话历史长度：{len(conversation_history)}")
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# 构建系统提示
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system_prompt = """作为一位专业的面试评估专家，请对整场面试进行全面评估。
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请提供：
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1. 整体表现评分（0-100分）和评级（优秀/良好/一般/需改进）
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2. 各轮回答的详细分析（针对每个问题和回答给出具体评价）
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3. strengths（优势）- 列出至少3点
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4. areas_for_improvement（需要改进的方面）- 列出至少3点
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5. 具体的准备建议（针对改进点给出可操作的建议）
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请用中文回复，格式清晰、结构化，避免过于笼统的描述。"""
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# 构建对话文本
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conversation_text = "\n\n".join([
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f"{'面试官' if msg['role'] == 'assistant' else '候选人'}：{msg['content']}"
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for msg in conversation_history
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])
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# 构建用户提示
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if job_position:
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user_prompt = f"请分析以下针对{job_position}岗位的面试对话并给出综合反馈：\n\n{conversation_text}"
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else:
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user_prompt = f"请分析以下面试对话并给出综合反馈：\n\n{conversation_text}"
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# 构建完整的消息
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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logger.debug("准备调用DeepSeek API生成反馈")
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logger.debug(f"API请求消息数量：{len(messages)}")
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# 调用DeepSeek API
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response = deepseek_service._call_api(messages)
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logger.debug("DeepSeek API调用成功")
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# 处理API响应
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if not response or "choices" not in response or not response["choices"]:
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logger.error("API响应格式错误：缺少choices字段")
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return jsonify({"error": "生成反馈失败：API响应格式错误"}), 500
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feedback = response["choices"][0]["message"]["content"]
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logger.info("反馈生成成功")
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logger.debug(f"生成的反馈内容长度：{len(feedback)}字符")
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logger.debug(f"生成的反馈内容开头：{feedback[:100]}...")
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return jsonify({"feedback": feedback})
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except Exception as e:
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logger.error(f"生成面试反馈失败：{str(e)}", exc_info=True)
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# 返回更具体的错误信息
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return jsonify({"error": f"生成面试反馈失败：{str(e)}", "details": str(type(e).__name__)}), 500
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if __name__ == "__main__":
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app.run(
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host=Config.APP_HOST,
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port=Config.APP_PORT,
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debug=False
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)
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