204 lines
7.1 KiB
Python
204 lines
7.1 KiB
Python
"""Dashboard API endpoints."""
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import uuid
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy import select, func, Integer
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.api.deps import get_current_user
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from app.database import get_db
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from app.models.user import User
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from app.models.brand import Brand
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from app.models.competitor import Competitor
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from app.models.query import Query as QueryModel
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from app.models.citation_record import CitationRecord
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from app.schemas.dashboard import (
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DashboardStatsResponse,
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DimensionScoreItem,
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PlatformScoreItem,
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RecentQueryItem,
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)
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from app.services.scoring.scoring_service import get_health_level
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from app.services.scoring.brand_scoring_data_service import (
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get_brand_scoring_data_service,
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REQUIRED_PLATFORMS,
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)
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from app.services.cache import get_cache_service, TTL_DASHBOARD
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router = APIRouter()
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@router.get("/stats", response_model=DashboardStatsResponse)
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async def get_dashboard_stats(
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brand_id: uuid.UUID | None = Query(None),
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current_user: User = Depends(get_current_user),
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db: AsyncSession = Depends(get_db),
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):
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"""
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获取看板统计数据
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包括：
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- 综合评分(V2)和较昨日变化
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- 健康等级
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- 五维度评分详情
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- 各平台评分列表（含竞品对比）
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- 竞品地位（领先/落后数量）
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- 最近查询记录
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"""
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cache = get_cache_service()
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if brand_id is None:
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brand_stmt = select(Brand).where(Brand.user_id == current_user.id).limit(1)
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brand_result = await db.execute(brand_stmt)
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brand = brand_result.scalar_one_or_none()
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if brand:
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brand_id = brand.id
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else:
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return DashboardStatsResponse(
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overall_score=0.0,
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health_level="danger",
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score_change=0.0,
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platform_scores=[],
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recent_queries=[],
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dimensions=[],
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competitors_ahead=0,
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competitors_behind=0,
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monitored_platforms=0,
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total_platforms=7,
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)
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cache_key = f"dashboard:stats:{current_user.id}:{brand_id}"
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cached = await cache.get_json(cache_key)
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if cached is not None:
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return cached
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brand_stmt = select(Brand).where(Brand.id == brand_id)
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brand_result = await db.execute(brand_stmt)
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brand = brand_result.scalar_one_or_none()
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brand_name = brand.name if brand else None
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scoring_data_service = get_brand_scoring_data_service()
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scoring_data = await scoring_data_service.get_brand_scoring_data(
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db, current_user.id, brand
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)
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overall_score = scoring_data.v2_result.overall_score
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score_change = scoring_data.change_from_yesterday
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dimensions = [
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DimensionScoreItem(
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name=scoring_data.v2_result.mention_rate.name,
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score=round(scoring_data.v2_result.mention_rate.score, 2),
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max_score=scoring_data.v2_result.mention_rate.max_score,
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percentage=round(scoring_data.v2_result.mention_rate.percentage, 2),
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),
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DimensionScoreItem(
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name=scoring_data.v2_result.recommendation_rank.name,
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score=round(scoring_data.v2_result.recommendation_rank.score, 2),
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max_score=scoring_data.v2_result.recommendation_rank.max_score,
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percentage=round(scoring_data.v2_result.recommendation_rank.percentage, 2),
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),
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DimensionScoreItem(
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name=scoring_data.v2_result.sentiment_score.name,
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score=round(scoring_data.v2_result.sentiment_score.score, 2),
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max_score=scoring_data.v2_result.sentiment_score.max_score,
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percentage=round(scoring_data.v2_result.sentiment_score.percentage, 2),
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),
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DimensionScoreItem(
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name=scoring_data.v2_result.citation_quality.name,
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score=round(scoring_data.v2_result.citation_quality.score, 2),
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max_score=scoring_data.v2_result.citation_quality.max_score,
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percentage=round(scoring_data.v2_result.citation_quality.percentage, 2),
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),
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DimensionScoreItem(
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name=scoring_data.v2_result.competitive_position.name,
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score=round(scoring_data.v2_result.competitive_position.score, 2),
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max_score=scoring_data.v2_result.competitive_position.max_score,
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percentage=round(scoring_data.v2_result.competitive_position.percentage, 2),
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),
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]
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ahead_count = scoring_data.competitor_data.get("ahead_count", 0)
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behind_count = scoring_data.competitor_data.get("behind_count", 0)
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platform_scores_dict = scoring_data.platform_scores
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competitor_scores_dict = await scoring_data_service.get_competitor_platform_scores(
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db, current_user.id, brand_id
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)
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competitor_stmt = select(Competitor).where(
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Competitor.brand_id == brand_id
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).limit(1)
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competitor_result = await db.execute(competitor_stmt)
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first_competitor = competitor_result.scalar_one_or_none()
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competitor_name = first_competitor.name if first_competitor else None
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platform_scores = [
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PlatformScoreItem(
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platform=platform,
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score=score,
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competitor_score=competitor_scores_dict.get(platform),
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competitor_name=competitor_name if competitor_scores_dict.get(platform, 0) > 0 else None,
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)
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for platform, score in platform_scores_dict.items()
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]
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monitored = sum(1 for s in platform_scores_dict.values() if s > 0)
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recent_queries_stmt = (
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select(QueryModel)
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.where(QueryModel.user_id == current_user.id)
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.order_by(QueryModel.created_at.desc())
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.limit(10)
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)
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recent_queries_result = await db.execute(recent_queries_stmt)
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recent_queries_list = list(recent_queries_result.scalars().all())
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recent_queries = []
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for query in recent_queries_list:
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citation_count_stmt = select(
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func.count().label("total"),
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func.sum(
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func.cast(
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func.case((CitationRecord.cited == True, 1), else_=0),
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Integer
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)
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).label("cited")
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).where(CitationRecord.query_id == query.id)
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count_result = await db.execute(citation_count_stmt)
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count_row = count_result.one()
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recent_queries.append(RecentQueryItem(
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id=str(query.id),
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keyword=query.keyword,
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target_brand=query.target_brand,
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citation_count=count_row.cited or 0,
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queried_at=query.last_queried_at or query.created_at,
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))
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health_level = get_health_level(overall_score)
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response = DashboardStatsResponse(
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overall_score=round(overall_score, 2),
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health_level=health_level,
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score_change=score_change,
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platform_scores=platform_scores,
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recent_queries=recent_queries,
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dimensions=dimensions,
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competitors_ahead=ahead_count,
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competitors_behind=behind_count,
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monitored_platforms=monitored,
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total_platforms=7,
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brand_name=brand_name,
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)
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await cache.set_json(
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cache_key,
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response.model_dump(mode="json"),
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expire=TTL_DASHBOARD,
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)
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return response
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