from fastapi import APIRouter, Depends
from sqlalchemy.ext.asyncio import AsyncSession

from app.database import get_db
from app.schemas.query import QueryRequest, QueryResponse, VideoData
from app.services.query_service import query_videos
from app.services.calculator import calculate_metrics
from app.services.brand_api import get_brand_names
from app.api.v1.export import set_export_data

router = APIRouter()


@router.post("/query", response_model=QueryResponse)
async def query(
    request: QueryRequest,
    db: AsyncSession = Depends(get_db),
) -> QueryResponse:
    """
    批量查询 KOL 视频数据.

    支持三种查询方式:
    - star_id: 按星图ID精准匹配
    - unique_id: 按达人unique_id精准匹配
    - nickname: 按达人昵称模糊匹配
    """
    try:
        # 1. 查询数据库
        videos = await query_videos(db, request.type, request.values)

        if not videos:
            return QueryResponse(success=True, data=[], total=0)

        # 2. 提取品牌ID并批量获取品牌名称
        brand_ids = [v.brand_id for v in videos if v.brand_id]
        brand_map = await get_brand_names(brand_ids) if brand_ids else {}

        # 3. 转换为响应模型并计算指标
        data = []
        for video in videos:
            video_data = VideoData.model_validate(video)

            # 填充品牌名称
            if video.brand_id:
                video_data.brand_name = brand_map.get(video.brand_id, video.brand_id)

            # 计算预估指标
            metrics = calculate_metrics(
                estimated_video_cost=video.estimated_video_cost,
                natural_play_cnt=video.natural_play_cnt,
                total_play_cnt=video.total_play_cnt,
                after_view_search_uv=video.after_view_search_uv,
            )
            video_data.estimated_natural_cpm = metrics["estimated_natural_cpm"]
            video_data.estimated_natural_search_uv = metrics["estimated_natural_search_uv"]
            video_data.estimated_natural_search_cost = metrics["estimated_natural_search_cost"]

            data.append(video_data)

        # 缓存数据供导出使用
        set_export_data([d.model_dump() for d in data])

        return QueryResponse(success=True, data=data, total=len(data))

    except Exception as e:
        return QueryResponse(success=False, data=[], total=0, error=str(e))