012-kaopeilian/backend/app/api/v1/endpoints/recommendation.py

"""
智能学习推荐 API
"""
from typing import List, Optional
from fastapi import APIRouter, Depends, Query
from sqlalchemy.ext.asyncio import AsyncSession
from pydantic import BaseModel

from app.core.deps import get_db, get_current_user
from app.models.user import User
from app.services.recommendation_service import RecommendationService

router = APIRouter()


# ============ Schemas ============

class CourseRecommendation(BaseModel):
    """课程推荐响应"""
    course_id: int
    course_name: str
    category: Optional[str] = None
    cover_image: Optional[str] = None
    description: Optional[str] = None
    progress_percent: Optional[float] = None
    student_count: Optional[int] = None
    source: Optional[str] = None
    reason: Optional[str] = None


class KnowledgePointRecommendation(BaseModel):
    """知识点推荐响应"""
    knowledge_point_id: int
    name: str
    description: Optional[str] = None
    type: Optional[str] = None
    course_id: int
    mistake_count: Optional[int] = None
    reason: Optional[str] = None


class RecommendationResponse(BaseModel):
    """推荐响应"""
    code: int = 200
    message: str = "success"
    data: dict


# ============ API Endpoints ============

@router.get("/courses", response_model=RecommendationResponse)
async def get_course_recommendations(
    limit: int = Query(10, ge=1, le=50, description="推荐数量"),
    include_reasons: bool = Query(True, description="是否包含推荐理由"),
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    """
    获取个性化课程推荐
    
    推荐策略：
    - 基于错题分析推荐相关课程
    - 基于能力评估推荐弱项课程
    - 基于学习进度推荐未完成课程
    - 基于热门程度推荐高人气课程
    """
    service = RecommendationService(db)
    recommendations = await service.get_recommendations(
        user_id=current_user.id,
        limit=limit,
        include_reasons=include_reasons,
    )
    
    return RecommendationResponse(
        code=200,
        message="获取推荐成功",
        data={
            "recommendations": recommendations,
            "total": len(recommendations),
        }
    )


@router.get("/knowledge-points", response_model=RecommendationResponse)
async def get_knowledge_point_recommendations(
    limit: int = Query(5, ge=1, le=20, description="推荐数量"),
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    """
    获取知识点复习推荐
    
    基于错题记录推荐需要重点复习的知识点
    """
    service = RecommendationService(db)
    recommendations = await service.get_knowledge_point_recommendations(
        user_id=current_user.id,
        limit=limit,
    )
    
    return RecommendationResponse(
        code=200,
        message="获取推荐成功",
        data={
            "recommendations": recommendations,
            "total": len(recommendations),
        }
    )


@router.get("/summary")
async def get_recommendation_summary(
    db: AsyncSession = Depends(get_db),
    current_user: User = Depends(get_current_user),
):
    """
    获取推荐摘要
    
    返回各类推荐的概要信息
    """
    service = RecommendationService(db)
    
    # 获取各类推荐
    all_recs = await service.get_recommendations(
        user_id=current_user.id,
        limit=20,
        include_reasons=True,
    )
    
    # 按来源分类统计
    source_counts = {}
    for rec in all_recs:
        source = rec.get("source", "other")
        source_counts[source] = source_counts.get(source, 0) + 1
    
    # 获取知识点推荐
    kp_recs = await service.get_knowledge_point_recommendations(
        user_id=current_user.id,
        limit=5,
    )
    
    return {
        "code": 200,
        "message": "success",
        "data": {
            "total_recommendations": len(all_recs),
            "source_breakdown": {
                "mistake_based": source_counts.get("mistake", 0),
                "ability_based": source_counts.get("ability", 0),
                "progress_based": source_counts.get("progress", 0),
                "popular": source_counts.get("popular", 0),
            },
            "weak_knowledge_points": len(kp_recs),
            "top_recommendation": all_recs[0] if all_recs else None,
        }
    }