LPC/app.py

import logging
import sys

logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('app_debug.log'),
        logging.StreamHandler(sys.stdout)
    ]
)

logger = logging.getLogger(__name__)

from flask import Flask, render_template, request, jsonify, session
from services.deepseek_service import deepseek_service
from config import Config
import uuid
import datetime

app = Flask(__name__)
app.secret_key = "interviewer-secret-key-change-in-production"
# 设置session过期时间为1小时
app.permanent_session_lifetime = datetime.timedelta(hours=1)

# 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
interviews_db = {}

INTERVIEW_PHASES = {
    "intro": "自我介绍",
    "professional": "专业能力",
    "scenario": "情景假设",
    "career": "职业规划",
    "closing": "面试结束"
}

QUESTION_COUNTS = {
    "intro": 2,
    "professional": 4,
    "scenario": 2,
    "career": 1
}

@app.route("/")
def index():
    return render_template("index.html")


@app.route("/api/chat", methods=["POST"])
def chat():
    data = request.json
    user_input = data.get("message", "").strip()
    system_type = data.get("system_type", "general_assistant")
    conversation_key = data.get("conversation_key", "default")
    
    if not user_input:
        return jsonify({"error": "请输入内容"}), 400
    
    if conversation_key not in session:
        session[conversation_key] = []
    
    conversation_history = session[conversation_key]
    
    try:
        result = deepseek_service.chat(
            user_input=user_input,
            conversation_history=conversation_history,
            system_type=system_type
        )
        
        conversation_history.append({"role": "user", "content": user_input})
        conversation_history.append(result)
        session[conversation_key] = conversation_history[-20:]
        
        return jsonify({"response": result["content"]})
    
    except Exception as e:
        return jsonify({"error": str(e)}), 500


@app.route("/api/resume/optimize", methods=["POST"])
def optimize_resume():
    data = request.json
    resume_content = data.get("resume_content", "").strip()
    target_position = data.get("target_position", "").strip()
    
    if not resume_content:
        return jsonify({"error": "请提供简历内容"}), 400
    
    try:
        result = deepseek_service.optimize_resume(resume_content, target_position)
        return jsonify(result)
    
    except Exception as e:
        return jsonify({"error": str(e)}), 500


@app.route("/api/interview/start", methods=["POST"])
def start_interview():
    data = request.json
    job_position = data.get("job_position", "").strip()
    difficulty = data.get("difficulty", "intermediate")
    
    if not job_position:
        return jsonify({"error": "请选择目标岗位"}), 400
    
    interview_id = str(uuid.uuid4())
    
    interview_data = {
        "job_position": job_position,
        "difficulty": difficulty,
        "current_phase": "intro",
        "question_count": 0,
        "conversation_history": [],
        "is_active": True
    }
    
    session[f"interview_{interview_id}"] = interview_data
    interviews_db[interview_id] = interview_data
    
    first_question = deepseek_service.generate_interview_question(
        job_position=job_position,
        difficulty=difficulty,
        phase="intro"
    )
    
    session[f"interview_{interview_id}"]["conversation_history"].append({
        "role": "assistant",
        "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}"
    })
    
    return jsonify({
        "interview_id": interview_id,
        "job_position": job_position,
        "difficulty": difficulty,
        "question": first_question,
        "phase": "intro"
    })


@app.route("/api/interview/answer", methods=["POST"])
def answer_question():
    data = request.json
    interview_id = data.get("interview_id", "").strip()
    user_answer = data.get("answer", "").strip()
    request_feedback = data.get("request_feedback", False)
    
    if not interview_id:
        return jsonify({"error": "无效的面试ID"}), 400
    
    # 首先从内存数据库查找面试数据
    if interview_id not in interviews_db:
        # 如果内存数据库中没有，再检查session
        interview_key = f"interview_{interview_id}"
        if interview_key not in session:
            return jsonify({"error": "面试不存在或已结束"}), 400
        # 如果session中有，同步到内存数据库
        interviews_db[interview_id] = session[interview_key]
    
    interview_data = interviews_db[interview_id]
    
    if not interview_data["is_active"]:
        return jsonify({"error": "面试已结束"}), 400
    
    if not user_answer:
        return jsonify({"error": "请输入你的回答"}), 400
    
    conversation_history = interview_data["conversation_history"]
    
    if request_feedback:
        last_question = ""
        logger.debug(f"查找最后一个问题，对话历史长度：{len(conversation_history)}")
        for msg in reversed(conversation_history):
            logger.debug(f"检查消息：角色={msg['role']}, 内容={msg['content'][:50]}...")
            if msg["role"] == "assistant" and ("？" in msg["content"] or "?" in msg["content"]):
                last_question = msg["content"]
                logger.debug(f"找到最后一个问题：{last_question[:50]}...")
                break
        
        if last_question:
            try:
                feedback = deepseek_service.chat_with_feedback(
                    user_input=last_question,
                    user_answer=user_answer,
                    conversation_history=conversation_history[:-1]
                )
                conversation_history.append({"role": "user", "content": user_answer})
                conversation_history.append(feedback)
                
                return jsonify({
                    "feedback": feedback["content"],
                    "ended": False
                })
            except Exception as e:
                logger.error(f"生成反馈失败：{str(e)}", exc_info=True)
                return jsonify({"error": f"生成反馈失败：{str(e)}"}), 500
        else:
            logger.warning("没有找到最后一个问题")
            return jsonify({"error": "没有找到相关问题"}), 400
    
    conversation_history.append({"role": "user", "content": user_answer})
    
    interview_data["question_count"] += 1
    current_phase = interview_data["current_phase"]
    phase_order = ["intro", "professional", "scenario", "career", "closing"]
    
    current_index = phase_order.index(current_phase)
    questions_in_phase = QUESTION_COUNTS.get(current_phase, 1)
    
    next_question = None
    
    if interview_data["question_count"] >= questions_in_phase:
        if current_index < len(phase_order) - 1:
            next_phase = phase_order[current_index + 1]
            interview_data["current_phase"] = next_phase
            interview_data["question_count"] = 0
            
            if next_phase == "closing":
                conversation_history.append({
                    "role": "assistant",
                    "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。"
                })
                interview_data["is_active"] = False
                
                return jsonify({
                    "question": None,
                    "feedback": "面试结束",
                    "ended": True,
                    "conversation_history": conversation_history[-10:]
                })
            else:
                phase_names = {
                    "intro": "自我介绍",
                    "professional": "专业能力",
                    "scenario": "情景假设",
                    "career": "职业规划"
                }
                
                try:
                    next_question = deepseek_service.generate_interview_question(
                        job_position=interview_data["job_position"],
                        difficulty=interview_data["difficulty"],
                        conversation_history=conversation_history,
                        phase=next_phase
                    )
                    conversation_history.append({
                        "role": "assistant",
                        "content": f"（{phase_names.get(next_phase, next_phase)}阶段）\n\n{next_question}"
                    })
                except Exception as e:
                    return jsonify({"error": f"生成问题失败：{str(e)}"}), 500
    else:
        try:
            next_question = deepseek_service.generate_interview_question(
                job_position=interview_data["job_position"],
                difficulty=interview_data["difficulty"],
                conversation_history=conversation_history,
                phase=current_phase
            )
            conversation_history.append({"role": "assistant", "content": next_question})
        except Exception as e:
            return jsonify({"error": f"生成问题失败：{str(e)}"}), 500
    
    # 同时更新内存数据库和session中的数据
    interviews_db[interview_id] = interview_data
    session[f"interview_{interview_id}"] = interview_data
    
    return jsonify({
        "question": next_question,
        "feedback": None,
        "ended": False,
        "phase": interview_data["current_phase"]
    })


@app.route("/api/interview/feedback", methods=["POST"])
def get_interview_feedback():
    logger.info("接收到面试反馈请求")
    
    try:
        # 首先确保能够获取请求数据
        if not request.is_json:
            logger.warning("请求数据不是JSON格式")
            return jsonify({"error": "请求数据必须是JSON格式"}), 400
        
        data = request.json
        logger.debug(f"请求数据：{data}")
        
        # 检查必要参数
        interview_id = data.get("interview_id", "").strip()
        conversation_history = data.get("conversation_history", [])
        
        if not interview_id:
            logger.warning("面试ID无效")
            return jsonify({"error": "无效的面试ID"}), 400
        
        if not conversation_history:
            logger.warning("没有提供面试对话历史")
            return jsonify({"error": "没有提供面试对话历史"}), 400
        
        # 获取岗位信息
        job_position = ""
        if interview_id in interviews_db:
            job_position = interviews_db[interview_id].get("job_position", "")
        
        logger.debug(f"岗位信息：{job_position}")
        logger.debug(f"对话历史长度：{len(conversation_history)}")
        
        # 构建系统提示
        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。

请提供：
1. 整体表现评分（0-100分）和评级（优秀/良好/一般/需改进）
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）
3. strengths（优势）- 列出至少3点
4. areas_for_improvement（需要改进的方面）- 列出至少3点
5. 具体的准备建议（针对改进点给出可操作的建议）

请用中文回复，格式清晰、结构化，避免过于笼统的描述。"""
    
        # 构建对话文本
        conversation_text = "\n\n".join([
            f"{'面试官' if msg['role'] == 'assistant' else '候选人'}：{msg['content']}" 
            for msg in conversation_history
        ])
        
        # 构建用户提示
        if job_position:
            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}"
        else:
            user_prompt = f"请分析以下面试对话并给出综合反馈：\n\n{conversation_text}"
        
        # 构建完整的消息
        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ]
        
        logger.debug("准备调用DeepSeek API生成反馈")
        logger.debug(f"API请求消息数量：{len(messages)}")
        
        # 调用DeepSeek API
        response = deepseek_service._call_api(messages)
        logger.debug("DeepSeek API调用成功")
        
        # 处理API响应
        if not response or "choices" not in response or not response["choices"]:
            logger.error("API响应格式错误：缺少choices字段")
            return jsonify({"error": "生成反馈失败：API响应格式错误"}), 500
        
        feedback = response["choices"][0]["message"]["content"]
        logger.info("反馈生成成功")
        logger.debug(f"生成的反馈内容长度：{len(feedback)}字符")
        logger.debug(f"生成的反馈内容开头：{feedback[:100]}...")
        
        return jsonify({"feedback": feedback})
    
    except Exception as e:
        logger.error(f"生成面试反馈失败：{str(e)}", exc_info=True)
        # 返回更具体的错误信息
        return jsonify({"error": f"生成面试反馈失败：{str(e)}", "details": str(type(e).__name__)}), 500


if __name__ == "__main__":
    app.run(
        host=Config.APP_HOST,
        port=Config.APP_PORT,
        debug=False
    )