from __future__ import annotations

from typing import Any

from django.contrib.auth import get_user_model
from django.db.models import (
    CharField,
    Count,
    DateTimeField,
    DecimalField,
    F,
    JSONField,
    OuterRef,
    Q,
    QuerySet,
    Subquery,
)
from django.utils import timezone

from apps.profiles.models import SearchProfile
from apps.sources.models import Source, SourceRun

from ..models import Application, ApplicationTimelineEvent, JobPosting, ScoreRun
from .relevance import it_relevance_query

User = get_user_model()


def annotate_latest_profile_score(
    queryset: QuerySet[JobPosting], profile: SearchProfile | None
) -> QuerySet[JobPosting]:
    """Annotate jobs with one deterministic latest score for the active profile."""

    if profile is None:
        return queryset
    latest = ScoreRun.objects.filter(job=OuterRef("pk"), profile=profile).order_by("-created_at")
    return queryset.annotate(
        match_score=Subquery(
            latest.values("score")[:1],
            output_field=DecimalField(max_digits=5, decimal_places=2),
        ),
        match_confidence=Subquery(
            latest.values("confidence")[:1],
            output_field=DecimalField(max_digits=4, decimal_places=3),
        ),
        match_recommendation=Subquery(
            latest.values("recommendation")[:1], output_field=CharField()
        ),
        match_positives=Subquery(latest.values("positives")[:1], output_field=JSONField()),
        match_concerns=Subquery(latest.values("concerns")[:1], output_field=JSONField()),
        match_hard_exclusions=Subquery(
            latest.values("hard_exclusions")[:1], output_field=JSONField()
        ),
        match_scored_at=Subquery(latest.values("created_at")[:1], output_field=DateTimeField()),
    )


def _recent_dashboard_activity(user: User) -> list[dict[str, Any]]:
    activity: list[dict[str, Any]] = []
    source_runs = SourceRun.objects.exclude(source__source_type=Source.Type.EMAIL)
    for run in source_runs.select_related("source").order_by("-started_at")[:4]:
        activity.append(
            {
                "occurred_at": run.started_at,
                "kind": "source",
                "title": f"Bronrun {run.get_status_display().lower()}",
                "detail": run.source.name,
                "tone": "positive" if run.status == SourceRun.Status.SUCCESS else "attention",
            }
        )
    for event in (
        ApplicationTimelineEvent.objects.filter(user=user)
        .select_related("application__job")
        .order_by("-created_at")[:4]
    ):
        activity.append(
            {
                "occurred_at": event.created_at,
                "kind": "application",
                "title": event.get_event_type_display(),
                "detail": event.application.job.original_title,
                "tone": "neutral",
            }
        )
    return sorted(activity, key=lambda item: item["occurred_at"], reverse=True)[:5]


def build_dashboard_cockpit(user: User, profile: SearchProfile | None) -> dict[str, Any]:
    """Build truthful dashboard metrics and bounded read lists."""

    active_jobs = JobPosting.objects.filter(status=JobPosting.Status.ACTIVE)
    active_it_jobs = active_jobs.filter(it_relevance_query())
    today = timezone.localdate()
    latest_scores = ScoreRun.objects.none()
    if profile is not None:
        latest_score_id = (
            ScoreRun.objects.filter(job_id=OuterRef("job_id"), profile=profile)
            .order_by("-created_at")
            .values("pk")[:1]
        )
        latest_scores = (
            ScoreRun.objects.select_related("job", "job__employer")
            .filter(
                profile=profile,
                pk=Subquery(latest_score_id),
                job__status=JobPosting.Status.ACTIVE,
                recommendation__in=[
                    ScoreRun.Recommendation.STRONG,
                    ScoreRun.Recommendation.POSSIBLE,
                ],
                hard_exclusions=[],
            )
            .filter(it_relevance_query("job__"))
        )

    sources = Source.objects.exclude(domain="jobs.example.org", status=Source.Status.DISABLED)
    source_counts = sources.aggregate(
        total=Count("id"),
        active=Count("id", filter=Q(status=Source.Status.ACTIVE)),
        attention=Count("id", filter=~Q(status=Source.Status.ACTIVE)),
    )
    open_applications = Application.objects.filter(user=user).exclude(
        status__in=[Application.Status.REJECTED, Application.Status.WITHDRAWN]
    )
    new_today = active_it_jobs.filter(first_seen__date=today).count()
    strong_today = latest_scores.filter(
        recommendation=ScoreRun.Recommendation.STRONG,
        job__first_seen__date=today,
    ).count()
    filtered_count = active_jobs.exclude(it_relevance_query()).count()
    duplicate_count = JobPosting.objects.filter(status=JobPosting.Status.DUPLICATE).count()

    if strong_today:
        day_conclusion = f"{strong_today} sterke match(es) verdienen vandaag eerst je aandacht."
    elif new_today:
        day_conclusion = f"De radar vond vandaag {new_today} nieuwe relevante vacature(s)."
    else:
        day_conclusion = "Geen nieuw sterk signaal; je bestaande opvolging blijft prioritair."

    return {
        # Het dagoverzicht blijft een beslissingsscherm; de volledige rangschikking
        # hoort in de verkenner. Vijf kaarten houden de primaire taak scanbaar.
        "score_cards": list(latest_scores.order_by("-score", "-created_at")[:5]),
        "priority_applications": list(
            open_applications.select_related("job", "job__employer").order_by(
                F("follow_up_date").asc(nulls_last=True), "-updated_at"
            )[:4]
        ),
        "active_jobs": active_it_jobs.count(),
        "filtered_non_it_jobs": filtered_count,
        "duplicate_jobs": duplicate_count,
        "new_today": new_today,
        "strong_today": strong_today,
        "applications_open": open_applications.count(),
        "source_counts": source_counts,
        "recent_activity": _recent_dashboard_activity(user),
        "day_conclusion": day_conclusion,
        "latest_scan": SourceRun.objects.exclude(source__source_type=Source.Type.EMAIL)
        .order_by("-started_at")
        .first(),
    }


def build_job_intelligence(
    *, job: JobPosting, profile: SearchProfile | None, user: User
) -> dict[str, Any]:
    """Return the bounded, user-scoped read model for explorer and detail."""

    score = (
        ScoreRun.objects.filter(job=job, profile=profile).order_by("-created_at").first()
        if profile
        else None
    )
    return {
        "score": score,
        "application": Application.objects.filter(user=user, job=job).first(),
        "duplicate_count": job.duplicates.count(),
        "source_count": job.source_aliases.count(),
        "version_count": job.versions.count(),
        "latest_versions": list(job.versions.all()[:5]),
        "provenance_rows": list(job.provenance.select_related("source_alias__source")[:12]),
    }