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- 后端API添加Redis缓存，有效期4小时 - 支持force_refresh参数强制刷新缓存 - 前端自动加载使用缓存，手动刷新强制更新 - 返回from_cache字段标识数据来源 Co-authored-by: Cursor <cursoragent@cursor.com>
243 lines
7.9 KiB
Python
243 lines
7.9 KiB
Python
"""
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能力评估API接口
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用于智能工牌数据分析、能力评估报告生成等
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"""
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import json
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from fastapi import APIRouter, Depends, HTTPException, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from typing import List, Optional
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from app.core.deps import get_current_user, get_db
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from app.core.redis import get_redis_client
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from app.models.user import User
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from app.schemas.base import ResponseModel
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from app.schemas.ability import AbilityAssessmentResponse, AbilityAssessmentHistory
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from app.services.yanji_service import YanjiService
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from app.services.ability_assessment_service import get_ability_assessment_service
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import logging
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logger = logging.getLogger(__name__)
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router = APIRouter()
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# Redis 缓存配置
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ABILITY_CACHE_KEY_PREFIX = "ability:assessment:"
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ABILITY_CACHE_TTL = 4 * 60 * 60 # 4小时缓存
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@router.post("/analyze-yanji", response_model=ResponseModel)
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async def analyze_yanji_badge_data(
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force_refresh: bool = Query(False, description="强制刷新缓存"),
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db: AsyncSession = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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分析智能工牌数据生成能力评估和课程推荐
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使用 Python 原生 AI 服务实现，结果缓存4小时。
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功能说明：
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1. 从言迹智能工牌获取员工的最近10条录音记录
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2. 分析对话数据，进行能力评估（6个维度）
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3. 基于能力短板生成课程推荐（3-5门）
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4. 保存评估记录到数据库
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5. 缓存结果到Redis（4小时有效期）
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要求：
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- 用户必须已绑定手机号（用于匹配言迹数据）
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参数：
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- force_refresh: 是否强制刷新缓存（默认False）
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返回：
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- assessment_id: 评估记录ID
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- total_score: 综合评分（0-100）
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- dimensions: 能力维度列表（6个维度）
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- recommended_courses: 推荐课程列表（3-5门）
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- conversation_count: 分析的对话数量
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- from_cache: 是否来自缓存
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"""
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# 检查用户是否绑定手机号
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if not current_user.phone:
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logger.warning(f"用户未绑定手机号: user_id={current_user.id}")
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raise HTTPException(
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status_code=400,
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detail="用户未绑定手机号，无法匹配言迹数据"
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)
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# Redis 缓存键
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cache_key = f"{ABILITY_CACHE_KEY_PREFIX}{current_user.id}"
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# 尝试从缓存获取（除非强制刷新）
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if not force_refresh:
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try:
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redis = get_redis_client()
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cached_data = await redis.get(cache_key)
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if cached_data:
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result = json.loads(cached_data)
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result['from_cache'] = True
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logger.info(
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f"从缓存返回能力评估结果: user_id={current_user.id}, "
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f"assessment_id={result.get('assessment_id')}"
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)
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return ResponseModel(
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code=200,
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message="智能工牌数据分析完成（缓存）",
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data=result
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)
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except Exception as e:
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logger.warning(f"读取缓存失败，将重新分析: {e}")
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# 获取服务实例
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yanji_service = YanjiService()
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assessment_service = get_ability_assessment_service()
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try:
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logger.info(
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f"开始分析智能工牌数据: user_id={current_user.id}, "
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f"phone={current_user.phone}, force_refresh={force_refresh}"
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)
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# 调用能力评估服务（使用 Python 原生实现）
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result = await assessment_service.analyze_yanji_conversations(
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user_id=current_user.id,
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phone=current_user.phone,
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db=db,
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yanji_service=yanji_service,
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engine="v2" # 固定使用 V2
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)
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# 缓存结果到 Redis
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try:
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redis = get_redis_client()
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# 序列化时处理 datetime 对象
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cache_data = {
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"assessment_id": result["assessment_id"],
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"total_score": result["total_score"],
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"dimensions": result["dimensions"],
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"recommended_courses": result["recommended_courses"],
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"conversation_count": result["conversation_count"],
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"analyzed_at": result["analyzed_at"].isoformat() if result.get("analyzed_at") else None,
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"engine": result.get("engine"),
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}
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await redis.setex(cache_key, ABILITY_CACHE_TTL, json.dumps(cache_data, ensure_ascii=False))
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logger.info(f"能力评估结果已缓存: user_id={current_user.id}, ttl={ABILITY_CACHE_TTL}s")
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except Exception as e:
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logger.warning(f"缓存结果失败: {e}")
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result['from_cache'] = False
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logger.info(
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f"智能工牌数据分析完成: user_id={current_user.id}, "
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f"assessment_id={result['assessment_id']}, "
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f"total_score={result['total_score']}"
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)
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return ResponseModel(
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code=200,
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message="智能工牌数据分析完成",
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data=result
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)
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except ValueError as e:
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# 业务逻辑错误（如未找到录音记录）
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logger.warning(f"智能工牌数据分析失败: {e}")
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raise HTTPException(status_code=404, detail=str(e))
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except Exception as e:
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# 系统错误
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logger.error(f"分析智能工牌数据失败: {e}", exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"分析失败: {str(e)}"
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)
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@router.get("/history", response_model=ResponseModel)
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async def get_assessment_history(
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limit: int = Query(default=10, ge=1, le=50, description="返回记录数量"),
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db: AsyncSession = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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获取用户的能力评估历史记录
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参数：
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- limit: 返回记录数量（默认10，最大50）
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返回：
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- 评估历史记录列表
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"""
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assessment_service = get_ability_assessment_service()
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try:
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history = await assessment_service.get_user_assessment_history(
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user_id=current_user.id,
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db=db,
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limit=limit
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)
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return ResponseModel(
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code=200,
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message=f"获取评估历史成功，共{len(history)}条",
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data={"history": history, "total": len(history)}
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)
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except Exception as e:
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logger.error(f"获取评估历史失败: {e}", exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"获取评估历史失败: {str(e)}"
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)
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@router.get("/{assessment_id}", response_model=ResponseModel)
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async def get_assessment_detail(
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assessment_id: int,
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db: AsyncSession = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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获取单个评估记录的详细信息
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参数：
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- assessment_id: 评估记录ID
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返回：
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- 评估详细信息
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"""
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assessment_service = get_ability_assessment_service()
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try:
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detail = await assessment_service.get_assessment_detail(
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assessment_id=assessment_id,
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db=db
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)
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# 权限检查：只能查看自己的评估记录
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if detail['user_id'] != current_user.id:
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raise HTTPException(
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status_code=403,
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detail="无权访问该评估记录"
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)
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return ResponseModel(
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code=200,
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message="获取评估详情成功",
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data=detail
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)
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except ValueError as e:
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raise HTTPException(status_code=404, detail=str(e))
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"获取评估详情失败: {e}", exc_info=True)
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raise HTTPException(
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status_code=500,
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detail=f"获取评估详情失败: {str(e)}"
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)
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