63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
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from datetime import datetime, timedelta
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from sqlalchemy import func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.activity import Activity
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from app.models.match import Match
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from app.models.user import User
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async def get_dashboard_data(session: AsyncSession) -> dict:
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total_users = int((await session.execute(select(func.count(User.id)))).scalar() or 0)
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total_matches = int((await session.execute(select(func.count(Match.id)))).scalar() or 0)
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total_activities = int((await session.execute(select(func.count(Activity.id)))).scalar() or 0)
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pending_audit = int(
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(await session.execute(select(func.count(User.id)).where(User.audit_status == 1))).scalar() or 0
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)
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ongoing_activities = int(
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(await session.execute(select(func.count(Activity.id)).where(Activity.status.in_([1, 3])))).scalar() or 0
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)
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seven_days_ago = datetime.now() - timedelta(days=7)
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active_users_7d = int(
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(await session.execute(select(func.count(User.id)).where(User.updated_at >= seven_days_ago))).scalar() or 0
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)
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match_rate = round(total_matches / total_users, 3) if total_users else 0
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user_trend = []
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match_trend = []
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for offset in range(6, -1, -1):
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day_start = (datetime.now() - timedelta(days=offset)).replace(hour=0, minute=0, second=0, microsecond=0)
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day_end = day_start + timedelta(days=1)
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user_count = int(
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(await session.execute(
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select(func.count(User.id)).where(User.created_at >= day_start, User.created_at < day_end)
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)).scalar()
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or 0
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)
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match_count = int(
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(await session.execute(
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select(func.count(Match.id)).where(Match.matched_at >= day_start, Match.matched_at < day_end)
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)).scalar()
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or 0
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)
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day_label = day_start.strftime('%Y-%m-%d')
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user_trend.append({"date": day_label, "count": user_count})
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match_trend.append({"date": day_label, "count": match_count})
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return {
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"total_users": total_users,
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"active_users_7d": active_users_7d,
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"pending_audit": pending_audit,
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"total_activities": total_activities,
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"ongoing_activities": ongoing_activities,
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"total_matches": total_matches,
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"match_rate": match_rate,
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"user_trend": user_trend,
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"match_trend": match_trend,
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}
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