HH/apps/analytics/ui_views.py

"""
Analytics Console UI views
"""

from datetime import datetime

from django.contrib.auth.decorators import login_required
from django.core.paginator import Paginator
from django.db.models import Avg, Count, F, Q, Value
from django.db.models.functions import Concat
from django.http import JsonResponse
from django.shortcuts import render

from apps.complaints.models import Complaint
from apps.organizations.models import Department, Hospital
from apps.px_action_center.models import PXAction
from apps.surveys.models import SurveyInstance
from apps.physicians.models import PhysicianMonthlyRating

from .models import KPI, KPIValue
from .services import UnifiedAnalyticsService, ExportService
from apps.core.decorators import block_source_user
import json


def serialize_queryset_values(queryset):
    """Properly serialize QuerySet values to JSON string."""
    if queryset is None:
        return "[]"
    data = list(queryset)
    result = []
    for item in data:
        row = {}
        for key, value in item.items():
            # Convert UUID to string
            if hasattr(value, "hex"):  # UUID object
                row[key] = str(value)
            # Convert Python None to JavaScript null
            elif value is None:
                row[key] = None
            else:
                row[key] = value
        result.append(row)
    return json.dumps(result, default=str)


@block_source_user
@login_required
def analytics_dashboard(request):
    """
    Analytics dashboard with KPIs and charts.

    Comprehensive dashboard showing:
    - KPI cards with current values for Complaints, Actions, Surveys, Feedback
    - Trend charts
    - Department rankings
    - Source distribution
    - Status breakdown
    """
    from apps.feedback.models import Feedback
    from django.utils import timezone
    from datetime import timedelta
    from django.db.models.functions import TruncDate, TruncMonth

    user = request.user

    # Get hospital filter
    hospital_filter = request.GET.get("hospital")
    if hospital_filter:
        hospital = Hospital.objects.filter(id=hospital_filter).first()
    elif user.hospital:
        hospital = user.hospital
    else:
        hospital = None

    # Base querysets
    complaints_queryset = Complaint.objects.all()
    actions_queryset = PXAction.objects.all()
    surveys_queryset = SurveyInstance.objects.filter(status="completed")
    feedback_queryset = Feedback.objects.all()

    if hospital:
        complaints_queryset = complaints_queryset.filter(hospital=hospital)
        actions_queryset = actions_queryset.filter(hospital=hospital)
        surveys_queryset = surveys_queryset.filter(survey_template__hospital=hospital)
        feedback_queryset = feedback_queryset.filter(hospital=hospital)

    # ============ COMPLAINTS KPIs ============
    total_complaints = complaints_queryset.count()
    open_complaints = complaints_queryset.filter(status="open").count()
    in_progress_complaints = complaints_queryset.filter(status="in_progress").count()
    resolved_complaints = complaints_queryset.filter(status="resolved").count()
    closed_complaints = complaints_queryset.filter(status="closed").count()
    overdue_complaints = complaints_queryset.filter(is_overdue=True).count()

    # Complaint sources
    complaint_sources = complaints_queryset.values("source").annotate(count=Count("id")).order_by("-count")[:6]

    # Complaint domains (Level 1)
    top_domains = (
        complaints_queryset.filter(domain__isnull=False)
        .values("domain__name_en")
        .annotate(count=Count("id"))
        .order_by("-count")[:5]
    )

    # Complaint categories (Level 2)
    top_categories = (
        complaints_queryset.filter(category__isnull=False)
        .values("category__name_en")
        .annotate(count=Count("id"))
        .order_by("-count")[:5]
    )

    # Complaint severity
    severity_breakdown = complaints_queryset.values("severity").annotate(count=Count("id")).order_by("-count")

    # Status breakdown
    status_breakdown = complaints_queryset.values("status").annotate(count=Count("id")).order_by("-count")

    # ============ ACTIONS KPIs ============
    total_actions = actions_queryset.count()
    open_actions = actions_queryset.filter(status="open").count()
    in_progress_actions = actions_queryset.filter(status="in_progress").count()
    approved_actions = actions_queryset.filter(status="approved").count()
    closed_actions = actions_queryset.filter(status="closed").count()
    overdue_actions = actions_queryset.filter(is_overdue=True).count()

    # Action sources
    action_sources = actions_queryset.values("source_type").annotate(count=Count("id")).order_by("-count")[:6]

    # Action categories
    action_categories = (
        actions_queryset.exclude(category="").values("category").annotate(count=Count("id")).order_by("-count")[:5]
    )

    # ============ SURVEYS KPIs ============
    total_surveys = surveys_queryset.count()
    avg_survey_score = surveys_queryset.aggregate(avg=Avg("total_score"))["avg"] or 0
    negative_surveys = surveys_queryset.filter(is_negative=True).count()

    # Survey completion rate
    all_surveys = SurveyInstance.objects.all()
    if hospital:
        all_surveys = all_surveys.filter(survey_template__hospital=hospital)
    total_sent = all_surveys.count()
    completed_surveys = all_surveys.filter(status="completed").count()
    completion_rate = (completed_surveys / total_sent * 100) if total_sent > 0 else 0

    # Survey types
    survey_types = all_surveys.values("survey_template__survey_type").annotate(count=Count("id")).order_by("-count")[:5]

    # ============ FEEDBACK KPIs ============
    total_feedback = feedback_queryset.count()
    compliments = feedback_queryset.filter(feedback_type="compliment").count()
    suggestions = feedback_queryset.filter(feedback_type="suggestion").count()

    # Sentiment analysis
    sentiment_breakdown = feedback_queryset.values("sentiment").annotate(count=Count("id")).order_by("-count")

    # Feedback categories
    feedback_categories = feedback_queryset.values("category").annotate(count=Count("id")).order_by("-count")[:5]

    # Average rating
    avg_rating = feedback_queryset.filter(rating__isnull=False).aggregate(avg=Avg("rating"))["avg"] or 0

    # ============ TRENDS (Last 30 days) ============
    thirty_days_ago = timezone.now() - timedelta(days=30)

    # Complaint trends
    complaint_trend = (
        complaints_queryset.filter(created_at__gte=thirty_days_ago)
        .annotate(day=TruncDate("created_at"))
        .values("day")
        .annotate(count=Count("id"))
        .order_by("day")
    )

    # Survey score trend
    survey_score_trend = (
        surveys_queryset.filter(completed_at__gte=thirty_days_ago)
        .annotate(day=TruncDate("completed_at"))
        .values("day")
        .annotate(avg_score=Avg("total_score"))
        .order_by("day")
    )

    # ============ DEPARTMENT RANKINGS ============
    department_rankings = (
        Department.objects.filter(status="active")
        .annotate(
            avg_score=Avg(
                "journey_instances__surveys__total_score", filter=Q(journey_instances__surveys__status="completed")
            ),
            survey_count=Count("journey_instances__surveys", filter=Q(journey_instances__surveys__status="completed")),
            complaint_count=Count("complaints"),
            action_count=Count("px_actions"),
        )
        .filter(survey_count__gt=0)
        .order_by("-avg_score")[:7]
    )

    # ============ TIME-BASED CALCULATIONS ============
    # Average resolution time (complaints)
    resolved_with_time = complaints_queryset.filter(
        status__in=["resolved", "closed"], resolved_at__isnull=False, created_at__isnull=False
    )
    if resolved_with_time.exists():
        avg_resolution_hours = resolved_with_time.annotate(
            resolution_time=F("resolved_at") - F("created_at")
        ).aggregate(avg=Avg("resolution_time"))["avg"]
        if avg_resolution_hours:
            avg_resolution_hours = avg_resolution_hours.total_seconds() / 3600
        else:
            avg_resolution_hours = 0
    else:
        avg_resolution_hours = 0

    # Average action completion time
    closed_actions_with_time = actions_queryset.filter(
        status="closed", closed_at__isnull=False, created_at__isnull=False
    )
    if closed_actions_with_time.exists():
        avg_action_days = closed_actions_with_time.annotate(completion_time=F("closed_at") - F("created_at")).aggregate(
            avg=Avg("completion_time")
        )["avg"]
        if avg_action_days:
            avg_action_days = avg_action_days.days
        else:
            avg_action_days = 0
    else:
        avg_action_days = 0

    # ============ SLA COMPLIANCE ============
    total_with_sla = complaints_queryset.filter(due_at__isnull=False).count()
    resolved_within_sla = complaints_queryset.filter(
        status__in=["resolved", "closed"], resolved_at__lte=F("due_at")
    ).count()
    sla_compliance = (resolved_within_sla / total_with_sla * 100) if total_with_sla > 0 else 0

    # ============ NPS CALCULATION ============
    # NPS = % Promoters (9-10) - % Detractors (0-6)
    nps_surveys = surveys_queryset.filter(survey_template__survey_type="nps", total_score__isnull=False)
    if nps_surveys.exists():
        promoters = nps_surveys.filter(total_score__gte=9).count()
        detractors = nps_surveys.filter(total_score__lte=6).count()
        total_nps = nps_surveys.count()
        nps_score = ((promoters - detractors) / total_nps * 100) if total_nps > 0 else 0
    else:
        nps_score = 0

    kpis = {
        "total_complaints": total_complaints,
        "open_complaints": open_complaints,
        "in_progress_complaints": in_progress_complaints,
        "resolved_complaints": resolved_complaints,
        "closed_complaints": closed_complaints,
        "overdue_complaints": overdue_complaints,
        "avg_resolution_hours": round(avg_resolution_hours, 1),
        "sla_compliance": round(sla_compliance, 1),
        "total_actions": total_actions,
        "open_actions": open_actions,
        "in_progress_actions": in_progress_actions,
        "approved_actions": approved_actions,
        "closed_actions": closed_actions,
        "overdue_actions": overdue_actions,
        "avg_action_days": round(avg_action_days, 1),
        "total_surveys": total_surveys,
        "avg_survey_score": round(avg_survey_score, 2),
        "nps_score": round(nps_score, 1),
        "negative_surveys": negative_surveys,
        "completion_rate": round(completion_rate, 1),
        "total_feedback": total_feedback,
        "compliments": compliments,
        "suggestions": suggestions,
        "avg_rating": round(avg_rating, 2),
    }

    context = {
        "kpis": kpis,
        "selected_hospital": hospital,
        "complaint_sources": serialize_queryset_values(complaint_sources),
        "top_domains": serialize_queryset_values(top_domains),
        "top_categories": serialize_queryset_values(top_categories),
        "severity_breakdown": serialize_queryset_values(severity_breakdown),
        "status_breakdown": serialize_queryset_values(status_breakdown),
        "complaint_trend": serialize_queryset_values(complaint_trend),
        "action_sources": serialize_queryset_values(action_sources),
        "action_categories": serialize_queryset_values(action_categories),
        "survey_types": serialize_queryset_values(survey_types),
        "survey_score_trend": serialize_queryset_values(survey_score_trend),
        "sentiment_breakdown": serialize_queryset_values(sentiment_breakdown),
        "feedback_categories": serialize_queryset_values(feedback_categories),
        "department_rankings": department_rankings,
    }

    return render(request, "analytics/dashboard.html", context)


@block_source_user
@login_required
def kpi_list(request):
    """KPI definitions list view"""
    queryset = KPI.objects.all()

    # Apply filters
    category_filter = request.GET.get("category")
    if category_filter:
        queryset = queryset.filter(category=category_filter)

    is_active = request.GET.get("is_active")
    if is_active == "true":
        queryset = queryset.filter(is_active=True)
    elif is_active == "false":
        queryset = queryset.filter(is_active=False)

    # Ordering
    queryset = queryset.order_by("category", "name")

    # Pagination
    page_size = int(request.GET.get("page_size", 25))
    paginator = Paginator(queryset, page_size)
    page_number = request.GET.get("page", 1)
    page_obj = paginator.get_page(page_number)

    context = {
        "page_obj": page_obj,
        "kpis": page_obj.object_list,
        "filters": request.GET,
    }

    return render(request, "analytics/kpi_list.html", context)


@block_source_user
@login_required
def command_center(request):
    """
    PX Command Center - Unified Dashboard

    Comprehensive dashboard showing all PX360 metrics:
    - Complaints, Surveys, Actions KPIs
    - Interactive charts with ApexCharts
    - Department and Physician rankings
    - Export to Excel/PDF
    """
    user = request.user

    # Get filter parameters
    filters = {
        "date_range": request.GET.get("date_range", "30d"),
        "hospital": request.GET.get("hospital", ""),
        "department": request.GET.get("department", ""),
        "kpi_category": request.GET.get("kpi_category", ""),
        "custom_start": request.GET.get("custom_start", ""),
        "custom_end": request.GET.get("custom_end", ""),
    }

    # Get hospitals for filter
    hospitals = Hospital.objects.filter(status="active")
    if not user.is_px_admin() and user.hospital:
        hospitals = hospitals.filter(id=user.hospital.id)

    # Get departments for filter
    departments = Department.objects.filter(status="active")
    if filters.get("hospital"):
        departments = departments.filter(hospital_id=filters["hospital"])
    elif not user.is_px_admin() and user.hospital:
        departments = departments.filter(hospital=user.hospital)

    # Get initial KPIs
    custom_start = None
    custom_end = None
    if filters["custom_start"] and filters["custom_end"]:
        custom_start = datetime.strptime(filters["custom_start"], "%Y-%m-%d")
        custom_end = datetime.strptime(filters["custom_end"], "%Y-%m-%d")

    kpis = UnifiedAnalyticsService.get_all_kpis(
        user=user,
        date_range=filters["date_range"],
        hospital_id=filters["hospital"] if filters["hospital"] else None,
        department_id=filters["department"] if filters["department"] else None,
        custom_start=custom_start,
        custom_end=custom_end,
    )

    context = {
        "filters": filters,
        "departments": departments,
        "kpis": kpis,
    }

    return render(request, "analytics/command_center.html", context)


@block_source_user
@login_required
def command_center_api(request):
    """
    API endpoint for Command Center data

    Returns JSON data for KPIs, charts, and tables based on filters.
    Used by JavaScript to dynamically update dashboard.
    """
    if request.method != "GET":
        return JsonResponse({"error": "Only GET requests allowed"}, status=405)

    user = request.user

    # Get filter parameters
    date_range = request.GET.get("date_range", "30d")
    hospital_id = request.GET.get("hospital")
    department_id = request.GET.get("department")
    kpi_category = request.GET.get("kpi_category")
    custom_start_str = request.GET.get("custom_start")
    custom_end_str = request.GET.get("custom_end")

    # Parse custom dates
    custom_start = None
    custom_end = None
    if custom_start_str and custom_end_str:
        try:
            custom_start = datetime.strptime(custom_start_str, "%Y-%m-%d")
            custom_end = datetime.strptime(custom_end_str, "%Y-%m-%d")
        except ValueError:
            pass

    # Handle hospital_id (can be integer or UUID string)
    hospital_id = hospital_id if hospital_id else None

    # Handle department_id (UUID string)
    department_id = department_id if department_id else None

    # Get KPIs
    kpis = UnifiedAnalyticsService.get_all_kpis(
        user=user,
        date_range=date_range,
        hospital_id=hospital_id,
        department_id=department_id,
        kpi_category=kpi_category,
        custom_start=custom_start,
        custom_end=custom_end,
    )

    # Ensure numeric KPIs are proper Python types for JSON serialization
    numeric_kpis = [
        "total_complaints",
        "open_complaints",
        "overdue_complaints",
        "high_severity_complaints",
        "resolved_complaints",
        "total_actions",
        "open_actions",
        "overdue_actions",
        "escalated_actions",
        "resolved_actions",
        "total_surveys",
        "negative_surveys",
        "avg_survey_score",
        "negative_social_mentions",
        "low_call_ratings",
        "total_sentiment_analyses",
    ]

    for key in numeric_kpis:
        if key in kpis:
            value = kpis[key]
            if value is None:
                kpis[key] = 0.0 if key == "avg_survey_score" else 0
            elif isinstance(value, (int, float)):
                # Already a number - ensure floats for specific fields
                if key == "avg_survey_score":
                    kpis[key] = float(value)
            else:
                # Try to convert to number
                try:
                    kpis[key] = float(value)
                except (ValueError, TypeError):
                    kpis[key] = 0.0 if key == "avg_survey_score" else 0

    # Handle nested trend data
    if "complaints_trend" in kpis and isinstance(kpis["complaints_trend"], dict):
        trend = kpis["complaints_trend"]
        trend["current"] = int(trend.get("current", 0))
        trend["previous"] = int(trend.get("previous", 0))
        trend["percentage_change"] = float(trend.get("percentage_change", 0))

    # Get chart data
    chart_types = [
        "complaints_trend",
        "complaints_by_category",
        "survey_satisfaction_trend",
        "survey_distribution",
        "department_performance",
        "physician_leaderboard",
    ]

    charts = {}
    for chart_type in chart_types:
        charts[chart_type] = UnifiedAnalyticsService.get_chart_data(
            user=user,
            chart_type=chart_type,
            date_range=date_range,
            hospital_id=hospital_id,
            department_id=department_id,
            custom_start=custom_start,
            custom_end=custom_end,
        )

    # Get table data
    tables = {}

    # Overdue complaints table
    complaints_qs = Complaint.objects.filter(is_overdue=True)
    if hospital_id:
        complaints_qs = complaints_qs.filter(hospital_id=hospital_id)
    if department_id:
        complaints_qs = complaints_qs.filter(department_id=department_id)

    # Apply role-based filtering
    if not user.is_px_admin() and user.hospital:
        complaints_qs = complaints_qs.filter(hospital=user.hospital)
    if user.is_department_manager() and user.department:
        complaints_qs = complaints_qs.filter(department=user.department)

    tables["overdue_complaints"] = list(
        complaints_qs.select_related("hospital", "department", "patient", "source")
        .order_by("due_at")[:20]
        .values(
            "id",
            "title",
            "severity",
            "due_at",
            "complaint_source_type",
            hospital_name=F("hospital__name"),
            department_name=F("department__name"),
            patient_full_name=Concat("patient__first_name", Value(" "), "patient__last_name"),
            source_name=F("source__name_en"),
            assigned_to_full_name=Concat("assigned_to__first_name", Value(" "), "assigned_to__last_name"),
        )
    )

    # Physician leaderboard table
    physician_data = charts.get("physician_leaderboard", {}).get("metadata", [])
    tables["physician_leaderboard"] = [
        {
            "physician_id": p["physician_id"],
            "name": p["name"],
            "specialization": p["specialization"],
            "department": p["department"],
            "rating": float(p["rating"]) if p["rating"] is not None else 0.0,
            "surveys": int(p["surveys"]) if p["surveys"] is not None else 0,
            "positive": int(p["positive"]) if p["positive"] is not None else 0,
            "neutral": int(p["neutral"]) if p["neutral"] is not None else 0,
            "negative": int(p["negative"]) if p["negative"] is not None else 0,
        }
        for p in physician_data
    ]

    return JsonResponse({"kpis": kpis, "charts": charts, "tables": tables})


@block_source_user
@login_required
def export_command_center(request, export_format):
    """
    Export Command Center data to Excel or PDF

    Args:
        export_format: 'excel' or 'pdf'

    Returns:
        HttpResponse with file download
    """
    if export_format not in ["excel", "pdf"]:
        return JsonResponse({"error": "Invalid export format"}, status=400)

    user = request.user

    # Get filter parameters
    date_range = request.GET.get("date_range", "30d")
    hospital_id = request.GET.get("hospital")
    department_id = request.GET.get("department")
    kpi_category = request.GET.get("kpi_category")
    custom_start_str = request.GET.get("custom_start")
    custom_end_str = request.GET.get("custom_end")

    # Parse custom dates
    custom_start = None
    custom_end = None
    if custom_start_str and custom_end_str:
        try:
            custom_start = datetime.strptime(custom_start_str, "%Y-%m-%d")
            custom_end = datetime.strptime(custom_end_str, "%Y-%m-%d")
        except ValueError:
            pass

    # Handle hospital_id and department_id (can be integer or UUID string)
    hospital_id = hospital_id if hospital_id else None
    department_id = department_id if department_id else None

    # Get all data
    kpis = UnifiedAnalyticsService.get_all_kpis(
        user=user,
        date_range=date_range,
        hospital_id=hospital_id,
        department_id=department_id,
        kpi_category=kpi_category,
        custom_start=custom_start,
        custom_end=custom_end,
    )

    chart_types = [
        "complaints_trend",
        "complaints_by_category",
        "survey_satisfaction_trend",
        "survey_distribution",
        "department_performance",
        "physician_leaderboard",
    ]

    charts = {}
    for chart_type in chart_types:
        charts[chart_type] = UnifiedAnalyticsService.get_chart_data(
            user=user,
            chart_type=chart_type,
            date_range=date_range,
            hospital_id=hospital_id,
            department_id=department_id,
            custom_start=custom_start,
            custom_end=custom_end,
        )

    # Get table data
    tables = {}

    # Overdue complaints
    complaints_qs = Complaint.objects.filter(is_overdue=True)
    if hospital_id:
        complaints_qs = complaints_qs.filter(hospital_id=hospital_id)
    if department_id:
        complaints_qs = complaints_qs.filter(department_id=department_id)

    if not user.is_px_admin() and user.hospital:
        complaints_qs = complaints_qs.filter(hospital=user.hospital)
    if user.is_department_manager() and user.department:
        complaints_qs = complaints_qs.filter(department=user.department)

    tables["overdue_complaints"] = {
        "headers": ["ID", "Title", "Patient", "Severity", "Hospital", "Department", "Due Date"],
        "rows": list(
            complaints_qs.select_related("hospital", "department", "patient")
            .order_by("due_at")[:100]
            .annotate(
                patient_full_name=Concat("patient__first_name", Value(" "), "patient__last_name"),
                hospital_name=F("hospital__name"),
                department_name=F("department__name"),
            )
            .values_list("id", "title", "patient_full_name", "severity", "hospital_name", "department_name", "due_at")
        ),
    }

    # Physician leaderboard
    physician_data = charts.get("physician_leaderboard", {}).get("metadata", [])
    tables["physician_leaderboard"] = {
        "headers": ["Name", "Specialization", "Department", "Rating", "Surveys", "Positive", "Neutral", "Negative"],
        "rows": [
            [
                p["name"],
                p["specialization"],
                p["department"],
                str(p["rating"]),
                str(p["surveys"]),
                str(p["positive"]),
                str(p["neutral"]),
                str(p["negative"]),
            ]
            for p in physician_data
        ],
    }

    # Prepare export data
    export_data = ExportService.prepare_dashboard_data(user=user, kpis=kpis, charts=charts, tables=tables)

    # Export based on format
    if export_format == "excel":
        return ExportService.export_to_excel(export_data)
    elif export_format == "pdf":
        return ExportService.export_to_pdf(export_data)

    return JsonResponse({"error": "Export failed"}, status=500)