142 lines
5.5 KiB
Python
142 lines
5.5 KiB
Python
from __future__ import annotations
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from datetime import timedelta
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from typing import Any
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from django.conf import settings
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from django.core.cache import cache
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from django.db.models import Count, Q, Sum
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from django.utils import timezone
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from apps.jobs.models import JobPosting, ScoreRun
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from apps.jobs.services.relevance import it_relevance_query
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from apps.notifications.models import DigestOutbox, ReminderOutbox
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from apps.profiles.models import SearchProfile
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from apps.sources.models import Source, SourceRun
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from ..health import readiness
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from ..tasks import RUNTIME_HEARTBEAT_KEY
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def _runtime_heartbeat_state() -> dict[str, Any]:
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if settings.CELERY_TASK_ALWAYS_EAGER:
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return {"label": "Eager/lokaal", "healthy": True, "observed_at": None}
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raw_timestamp = cache.get(RUNTIME_HEARTBEAT_KEY)
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if not raw_timestamp:
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return {"label": "Geen recente heartbeat", "healthy": False, "observed_at": None}
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try:
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observed_at = timezone.datetime.fromisoformat(str(raw_timestamp))
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if timezone.is_naive(observed_at):
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observed_at = timezone.make_aware(observed_at)
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except (TypeError, ValueError):
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return {"label": "Ongeldige heartbeat", "healthy": False, "observed_at": None}
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age_seconds = max(0, int((timezone.now() - observed_at).total_seconds()))
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return {
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"label": f"Actief · {age_seconds}s geleden"
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if age_seconds <= 150
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else "Heartbeat verouderd",
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"healthy": age_seconds <= 150,
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"observed_at": observed_at,
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}
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def build_automation_cockpit() -> dict[str, Any]:
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"""Build an honest operational read model without probing unsafe hidden endpoints."""
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since = timezone.now() - timedelta(hours=24)
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recent_runs = SourceRun.objects.filter(started_at__gte=since)
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run_metrics = recent_runs.aggregate(
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discovered=Sum("discovered_count"),
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extracted=Sum("extracted_count"),
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created=Sum("created_count"),
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updated=Sum("updated_count"),
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duplicates=Sum("duplicate_count"),
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failures=Count("id", filter=Q(status=SourceRun.Status.FAILED)),
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)
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notification_counts = {
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"pending": DigestOutbox.objects.filter(status=DigestOutbox.Status.PENDING).count()
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+ ReminderOutbox.objects.filter(status=ReminderOutbox.Status.PENDING).count(),
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"failed": DigestOutbox.objects.filter(status=DigestOutbox.Status.FAILED).count()
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+ ReminderOutbox.objects.filter(status=ReminderOutbox.Status.FAILED).count(),
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"sent_24h": DigestOutbox.objects.filter(
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status=DigestOutbox.Status.SENT, sent_at__gte=since
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).count()
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+ ReminderOutbox.objects.filter(
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status=ReminderOutbox.Status.SENT, sent_at__gte=since
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).count(),
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}
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source_total = Source.objects.count()
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source_active = Source.objects.filter(status=Source.Status.ACTIVE).count()
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active_jobs = JobPosting.objects.filter(status=JobPosting.Status.ACTIVE)
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phase_rows = [
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{"key": "discover", "label": "Ontdekken", "value": source_total, "unit": "bronnen"},
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{"key": "fetch", "label": "Ophalen", "value": recent_runs.count(), "unit": "runs / 24u"},
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{
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"key": "extract",
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"label": "Extraheren",
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"value": run_metrics["extracted"] or 0,
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"unit": "items",
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},
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{
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"key": "normalize",
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"label": "Normaliseren",
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"value": (run_metrics["created"] or 0) + (run_metrics["updated"] or 0),
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"unit": "records",
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},
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{
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"key": "dedupe",
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"label": "Dedupliceren",
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"value": run_metrics["duplicates"] or 0,
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"unit": "herkend",
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},
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{
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"key": "filter",
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"label": "IT-filter",
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"value": active_jobs.exclude(it_relevance_query()).count(),
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"unit": "uit beeld",
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},
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{
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"key": "analyze",
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"label": "Analyseren",
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"value": ScoreRun.objects.filter(created_at__gte=since).count(),
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"unit": "scores / 24u",
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},
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{
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"key": "notify",
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"label": "Notificeren",
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"value": notification_counts["sent_24h"],
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"unit": "verzonden / 24u",
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},
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]
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runtime_heartbeat = _runtime_heartbeat_state()
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return {
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"health": readiness(),
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"source_total": source_total,
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"source_active": source_active,
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"run_metrics": run_metrics,
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"run_count": recent_runs.count(),
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"active_jobs": active_jobs.filter(it_relevance_query()).count(),
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"review_jobs": ScoreRun.objects.filter(
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recommendation__in=[ScoreRun.Recommendation.STRONG, ScoreRun.Recommendation.POSSIBLE],
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job__status=JobPosting.Status.ACTIVE,
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).count(),
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"notification_counts": notification_counts,
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"phase_rows": phase_rows,
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"recent_runs": list(
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SourceRun.objects.select_related("source").order_by("-started_at")[:10]
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),
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"source_rows": Source.objects.annotate(run_count=Count("runs")).order_by("name")[:12],
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"queue_state": "Eager/lokaal"
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if settings.CELERY_TASK_ALWAYS_EAGER
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else "Diepte niet beschikbaar",
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"runtime_heartbeat": runtime_heartbeat,
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"ai_state": "Geconfigureerd"
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if settings.OLLAMA_ENABLED and settings.OLLAMA_MODEL
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else "Niet geconfigureerd",
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"ai_profiles": SearchProfile.objects.filter(ai_scoring_enabled=True).count(),
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"browser_state": "Geen aparte browserextractor geconfigureerd",
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}
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