saksham
1a4c80958f
FastAPI + Celery + Next.js + Postgres/Redis app with company monitoring, source collection, LLM-based change analysis, enrichment, and account security (Turnstile, escalating lockout, email verification).
106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
"""Dashboard analytics: aggregate counts across every company a user owns,
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scoped by joining through Company.user_id (none of the source tables carry
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user_id directly except Alert). Every bucketed dict is pre-seeded with every
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enum member at 0 so the frontend never has to guess which keys might be
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missing.
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"""
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from __future__ import annotations
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import uuid
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from datetime import UTC, 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.alert import Alert
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from app.models.company import Company
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from app.models.detected_change import DetectedChange
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from app.models.enums import ChangeType, MonitoringRunStatus, SeverityLevel, SourceStatus
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from app.models.monitoring_run import MonitoringRun
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from app.models.source import Source
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from app.schemas.dashboard import DashboardAnalytics, RecentSignal, RunsByDayPoint
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async def get_dashboard_analytics(
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db: AsyncSession, user_id: uuid.UUID, *, days: int = 30, recent_limit: int = 10
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) -> DashboardAnalytics:
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since = datetime.now(UTC) - timedelta(days=days)
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changes_by_type = {ct.value: 0 for ct in ChangeType}
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changes_result = await db.execute(
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select(DetectedChange.change_type, func.count())
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.join(Company, DetectedChange.company_id == Company.id)
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.where(Company.user_id == user_id, DetectedChange.created_at >= since)
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.group_by(DetectedChange.change_type)
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)
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for change_type, count in changes_result.all():
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changes_by_type[change_type.value] = count
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alerts_by_severity = {s.value: 0 for s in SeverityLevel}
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alerts_result = await db.execute(
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select(Alert.severity, func.count())
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.where(Alert.user_id == user_id, Alert.created_at >= since)
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.group_by(Alert.severity)
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)
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for severity, count in alerts_result.all():
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alerts_by_severity[severity.value] = count
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sources_by_status = {s.value: 0 for s in SourceStatus}
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sources_result = await db.execute(
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select(Source.status, func.count())
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.join(Company, Source.company_id == Company.id)
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.where(Company.user_id == user_id)
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.group_by(Source.status)
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)
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for source_status, count in sources_result.all():
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sources_by_status[source_status.value] = count
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runs_result = await db.execute(
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select(MonitoringRun.created_at, MonitoringRun.status)
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.join(Company, MonitoringRun.company_id == Company.id)
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.where(Company.user_id == user_id, MonitoringRun.created_at >= since)
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)
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day_buckets: dict[str, dict[str, int]] = {}
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for created_at, run_status in runs_result.all():
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day = created_at.date().isoformat()
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bucket = day_buckets.setdefault(day, {"successful": 0, "failed": 0, "other": 0})
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if run_status == MonitoringRunStatus.SUCCESSFUL:
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bucket["successful"] += 1
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elif run_status == MonitoringRunStatus.FAILED:
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bucket["failed"] += 1
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else:
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bucket["other"] += 1
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runs_by_day = [
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RunsByDayPoint(date=day, **counts) for day, counts in sorted(day_buckets.items())
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]
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signals_result = await db.execute(
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select(DetectedChange, Company.name)
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.join(Company, DetectedChange.company_id == Company.id)
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.where(Company.user_id == user_id)
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.order_by(DetectedChange.created_at.desc())
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.limit(recent_limit)
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)
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recent_signals = [
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RecentSignal(
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id=change.id,
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company_id=change.company_id,
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company_name=company_name,
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change_type=change.change_type.value,
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severity=change.severity.value,
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confidence_score=change.confidence_score,
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summary=change.summary,
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created_at=change.created_at,
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)
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for change, company_name in signals_result.all()
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]
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return DashboardAnalytics(
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changes_by_type=changes_by_type,
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alerts_by_severity=alerts_by_severity,
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sources_by_status=sources_by_status,
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runs_by_day=runs_by_day,
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recent_signals=recent_signals,
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)
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