HH/apps/analytics/tasks.py

import logging

from celery import shared_task
from django.utils import timezone

from apps.organizations.models import Hospital

from .kpi_models import KPIReportType
from .kpi_service import KPICalculationService

logger = logging.getLogger(__name__)


@shared_task(bind=True, ignore_result=True)
def calculate_daily_kpis(self):
    today = timezone.now()
    year = today.year
    month = today.month

    hospitals = Hospital.objects.filter(status="active")
    report_types = [rt[0] for rt in KPIReportType.choices]

    for hospital in hospitals:
        for report_type in report_types:
            try:
                KPICalculationService.generate_monthly_report(
                    report_type=report_type,
                    hospital=hospital,
                    year=year,
                    month=month,
                )
                logger.info(f"Daily KPI calculated: {report_type} for {hospital.name} ({year}-{month:02d})")
            except Exception as e:
                logger.exception(f"Failed daily KPI: {report_type} for {hospital.name}: {e}")


# =============================================================================
# AI-Powered Analytics — Async Celery Tasks
# =============================================================================


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def generate_executive_summary_task(
    self, user_id=None, hospital_id=None, department_id=None, period="30d", force_refresh=False
):
    """
    Async task: Generate AI executive summary and cache it.
    Called by Celery beat daily, or manually when user forces refresh.
    """
    from apps.analytics.services.ai_analytics import ExecutiveSummaryGenerator

    # Build a mock user-like object for role scoping (admin sees all)
    class _User:
        def __init__(self, uid):
            self.id = uid
            self.hospital = None
            self.department = None

        def is_px_admin(self):
            return True

    user = _User(user_id) if user_id else _User("system")

    try:
        result = ExecutiveSummaryGenerator.generate(
            user,
            hospital_id=hospital_id,
            department_id=department_id,
            period=period,
            force_refresh=force_refresh,
        )
        logger.info(
            f"Executive summary generated: {period} for hospital={hospital_id}, "
            f"risk={result.get('risk_level', 'unknown')}"
        )
    except Exception as e:
        logger.exception(f"Failed to generate executive summary: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def generate_action_recommendations_task(self, user_id=None, hospital_id=None, department_id=None):
    """
    Async task: Generate AI action recommendations from complaint clusters.
    """
    from apps.analytics.services.ai_analytics import ActionRecommendationEngine

    class _User:
        def __init__(self, uid):
            self.id = uid
            self.hospital = None
            self.department = None

        def is_px_admin(self):
            return True

    user = _User(user_id) if user_id else _User("system")

    try:
        results = ActionRecommendationEngine.generate_recommendations(
            user, hospital_id=hospital_id, department_id=department_id, limit=5
        )
        logger.info(f"Action recommendations generated: {len(results)} recommendations")
    except Exception as e:
        logger.exception(f"Failed to generate action recommendations: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True)
def precompute_dashboard_cache_task(self):
    """
    Async task: Pre-compute all cacheable analytics data for all active hospitals.
    Run daily at 3 AM. Users can trigger on-demand refresh via dashboard button.
    """
    from apps.analytics.services.ai_analytics import (
        ExecutiveSummaryGenerator,
        EarlyWarningSystem,
        ComplaintVolumeForecaster,
        SLABreachPredictor,
        ActionRecommendationEngine,
        VisitEfficiencyAnalyzer,
    )
    from django.contrib.auth import get_user_model

    User = get_user_model()
    admin_users = User.objects.filter(is_active=True, groups__name="PX Admin")

    if not admin_users.exists():
        # Fallback: use first superuser
        admin_users = User.objects.filter(is_active=True, is_superuser=True)

    hospitals = list(Hospital.objects.filter(status="active"))

    for admin in admin_users[:3]:  # limit to first 3 admins
        for hospital in hospitals:
            try:
                ExecutiveSummaryGenerator.generate(admin, hospital_id=str(hospital.id), period="30d")
                EarlyWarningSystem.detect(admin, hospital_id=str(hospital.id), limit=5)
                ComplaintVolumeForecaster.forecast(admin, hospital_id=str(hospital.id), forecast_days=30)
                SLABreachPredictor.predict(admin, hospital_id=str(hospital.id), limit=10)
                ActionRecommendationEngine.generate_recommendations(admin, hospital_id=str(hospital.id), limit=5)
                VisitEfficiencyAnalyzer.analyze(admin, hospital_id=str(hospital.id))
                logger.info(f"Precomputed analytics for admin={admin.id}, hospital={hospital.id}")
            except Exception as e:
                logger.exception(f"Failed to precompute for admin={admin.id}, hospital={hospital.id}: {e}")


# =============================================================================
# Hourly AI Analytics Pre-computation Tasks
# =============================================================================


def _get_system_admin():
    """Return a mock admin user for background tasks."""

    class _User:
        def __init__(self, uid):
            self.id = uid
            self.hospital = None
            self.department = None

        def is_px_admin(self):
            return True

    return _User("system")


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def precompute_executive_summary_task(self, hospital_id=None, period="30d"):
    """Pre-compute executive summary for a single hospital."""
    from apps.analytics.services.ai_analytics import ExecutiveSummaryGenerator

    user = _get_system_admin()
    try:
        result = ExecutiveSummaryGenerator.generate(user, hospital_id=hospital_id, period=period, force_refresh=True)
        logger.info(
            f"Precomputed executive summary: period={period} hospital={hospital_id} "
            f"risk={result.get('risk_level', 'unknown')}"
        )
    except Exception as e:
        logger.exception(f"Failed precompute_executive_summary_task: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def precompute_early_warning_task(self, hospital_id=None, limit=5):
    """Pre-compute early warning system for a single hospital."""
    from apps.analytics.services.ai_analytics import EarlyWarningSystem

    user = _get_system_admin()
    try:
        results = EarlyWarningSystem.detect(user, hospital_id=hospital_id, limit=limit)
        logger.info(f"Precomputed early warnings: hospital={hospital_id} count={len(results)}")
    except Exception as e:
        logger.exception(f"Failed precompute_early_warning_task: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def precompute_complaint_forecast_task(self, hospital_id=None, forecast_days=30):
    """Pre-compute complaint volume forecast for a single hospital."""
    from apps.analytics.services.ai_analytics import ComplaintVolumeForecaster

    user = _get_system_admin()
    try:
        result = ComplaintVolumeForecaster.forecast(user, hospital_id=hospital_id, forecast_days=forecast_days)
        logger.info(f"Precomputed complaint forecast: hospital={hospital_id}")
    except Exception as e:
        logger.exception(f"Failed precompute_complaint_forecast_task: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def precompute_sla_breach_prediction_task(self, hospital_id=None, limit=10):
    """Pre-compute SLA breach predictions for a single hospital."""
    from apps.analytics.services.ai_analytics import SLABreachPredictor

    user = _get_system_admin()
    try:
        results = SLABreachPredictor.predict(user, hospital_id=hospital_id, limit=limit)
        logger.info(f"Precomputed SLA breach predictions: hospital={hospital_id} count={len(results)}")
    except Exception as e:
        logger.exception(f"Failed precompute_sla_breach_prediction_task: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def precompute_action_recommendations_task(self, hospital_id=None, limit=5):
    """Pre-compute action recommendations for a single hospital."""
    from apps.analytics.services.ai_analytics import ActionRecommendationEngine

    user = _get_system_admin()
    try:
        results = ActionRecommendationEngine.generate_recommendations(user, hospital_id=hospital_id, limit=limit)
        logger.info(f"Precomputed action recommendations: hospital={hospital_id} count={len(results)}")
    except Exception as e:
        logger.exception(f"Failed precompute_action_recommendations_task: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True, max_retries=2, default_retry_delay=60)
def precompute_visit_efficiency_task(self, hospital_id=None):
    """Pre-compute visit efficiency analysis for a single hospital."""
    from apps.analytics.services.ai_analytics import VisitEfficiencyAnalyzer

    user = _get_system_admin()
    try:
        result = VisitEfficiencyAnalyzer.analyze(user, hospital_id=hospital_id)
        logger.info(
            f"Precomputed visit efficiency: hospital={hospital_id} "
            f"score={result.get('efficiency_score', 'N/A')}"
        )
    except Exception as e:
        logger.exception(f"Failed precompute_visit_efficiency_task: {e}")
        self.retry(exc=e)


@shared_task(bind=True, ignore_result=True)
def precompute_all_ai_analytics_task(self):
    """
    Master task: pre-compute ALL AI analytics for every active hospital every hour.
    Dispatches sub-tasks per hospital so failures are isolated.
    """
    hospitals = list(Hospital.objects.filter(status="active").values_list("id", flat=True))

    for hid in hospitals:
        hospital_id = str(hid)
        precompute_executive_summary_task.delay(hospital_id=hospital_id, period="30d")
        precompute_early_warning_task.delay(hospital_id=hospital_id, limit=5)
        precompute_complaint_forecast_task.delay(hospital_id=hospital_id, forecast_days=30)
        precompute_sla_breach_prediction_task.delay(hospital_id=hospital_id, limit=10)
        precompute_action_recommendations_task.delay(hospital_id=hospital_id, limit=5)
        precompute_visit_efficiency_task.delay(hospital_id=hospital_id)
        logger.info(f"Dispatched hourly AI precompute tasks for hospital={hospital_id}")

    # Also run for global (no hospital filter)
    precompute_executive_summary_task.delay(hospital_id=None, period="30d")
    precompute_early_warning_task.delay(hospital_id=None, limit=5)
    precompute_complaint_forecast_task.delay(hospital_id=None, forecast_days=30)
    precompute_sla_breach_prediction_task.delay(hospital_id=None, limit=10)
    precompute_action_recommendations_task.delay(hospital_id=None, limit=5)
    precompute_visit_efficiency_task.delay(hospital_id=None)
    logger.info("Dispatched hourly AI precompute tasks for global scope")