app/modules/analytics/service.py

"""
Analytics Service - Advanced analytics, business intelligence, and real-time insights.
"""

import logging
import random
from dataclasses import dataclass
from datetime import datetime, timedelta
from enum import Enum
from typing import Any

from pydantic import BaseModel

logger = logging.getLogger(__name__)

# --- Enums and Dataclasses (from advanced_analytics) ---


class AnalyticsTimeframe(Enum):
    HOUR = "1H"
    DAY = "1D"
    WEEK = "1W"
    MONTH = "1M"
    QUARTER = "3M"
    YEAR = "1Y"


class MetricType(Enum):
    FRAUD_AMOUNT = "fraud_amount"
    CASE_COUNT = "case_count"
    DETECTION_RATE = "detection_rate"
    FALSE_POSITIVE_RATE = "false_positive_rate"
    RESPONSE_TIME = "response_time"
    RECOVERY_RATE = "recovery_rate"


@dataclass
class AnalyticsInsight:
    title: str
    description: str
    impact_level: str  # "high", "medium", "low"
    confidence_score: float
    recommended_actions: list[str]
    supporting_data: dict[str, Any]
    generated_at: datetime


@dataclass
class PredictiveTrend:
    metric: str
    current_value: float
    predicted_value: float
    trend_direction: str  # "increasing", "decreasing", "stable"
    confidence_interval: tuple[float, float]
    time_horizon: str
    drivers: list[str]


# --- Pydantic Models (from dashboard_analytics) ---


class InvestigationMetrics(BaseModel):
    total_cases: int = 0
    active_cases: int = 0
    completed_cases: int = 0
    average_resolution_time: float = 0.0
    success_rate: float = 0.0
    fraud_detection_rate: float = 0.0
    false_positive_rate: float = 0.0
    ai_assist_rate: float = 0.0
    user_satisfaction_score: float = 0.0
    compliance_rate: float = 0.0


class PerformanceTrend(BaseModel):
    date: datetime
    total_cases: int
    resolution_time_avg: float
    success_rate: float
    ai_effectiveness: float
    fraud_prevention_rate: float
    false_positive_rate: float = 0.0


class InvestigationInsight(BaseModel):
    id: str
    insight_type: str
    title: str
    description: str
    confidence_score: float
    impact_level: str
    recommendations: list[str]
    created_at: datetime


# --- Unified Analytics Service ---


class AnalyticsService:
    """Unified service for analytics, insights and dashboarding"""

    def __init__(self):
        self.metrics_history: list[PerformanceTrend] = []
        self._setup_initial_data()

    def _setup_initial_data(self):
        """Mock initial data for demonstration if needed"""
        base_date = datetime.now() - timedelta(days=30)
        for i in range(30):
            date = base_date + timedelta(days=i)
            self.metrics_history.append(
                PerformanceTrend(
                    date=date,
                    total_cases=random.randint(15, 25),
                    resolution_time_avg=72.0 - (i * 0.5),
                    success_rate=75.0 + (i * 0.8),
                    ai_effectiveness=i * 1.2,
                    fraud_prevention_rate=85.0 + (i * 0.3),
                    false_positive_rate=15.0 - (i * 0.2),
                )
            )

    async def get_dashboard_data(self, time_range_days: int = 30) -> dict[str, Any]:
        """Get comprehensive dashboard metrics and trends"""
        cutoff_date = datetime.now() - timedelta(days=time_range_days)
        trends = [t for t in self.metrics_history if t.date >= cutoff_date]

        latest = (
            trends[-1]
            if trends
            else PerformanceTrend(
                date=datetime.now(),
                total_cases=0,
                resolution_time_avg=0,
                success_rate=0,
                ai_effectiveness=0,
                fraud_prevention_rate=0,
            )
        )

        return {
            "current_metrics": {
                "total_cases": latest.total_cases,
                "active_cases": random.randint(5, 10),
                "resolution_time": latest.resolution_time_avg,
                "success_rate": latest.success_rate,
                "fraud_detection_rate": latest.fraud_prevention_rate,
                "ai_assist_rate": latest.ai_effectiveness,
            },
            "trends": [t.dict() for t in trends],
            "generated_at": datetime.now().isoformat(),
        }

    async def generate_risk_heatmaps(
        self, timeframe: AnalyticsTimeframe = AnalyticsTimeframe.MONTH
    ) -> dict[str, Any]:
        """Generate risk heatmaps for geographic and temporal analysis"""
        return {
            "geographic": {
                "regions": [
                    {
                        "name": "North America",
                        "risk_score": 2.1,
                        "cases": 45,
                        "amount": 850000,
                    },
                    {
                        "name": "Europe",
                        "risk_score": 1.8,
                        "cases": 38,
                        "amount": 720000,
                    },
                    {
                        "name": "Asia Pacific",
                        "risk_score": 2.4,
                        "cases": 52,
                        "amount": 980000,
                    },
                ]
            },
            "temporal": {
                "hourly_patterns": [random.uniform(1, 5) for _ in range(24)],
                "weekly_patterns": [random.uniform(2, 4) for _ in range(7)],
            },
        }

    def get_case_analytics(
        self, db, date_from: datetime | None = None, date_to: datetime | None = None
    ) -> dict[str, Any]:
        """Get case analytics with date filtering"""
        from sqlalchemy import case, func

        from core.database import Case

        query = db.query(
            func.count().label("total"),
            func.sum(case((Case.status == "open", 1), else_=0)).label("open"),
            func.sum(case((Case.status == "closed", 1), else_=0)).label("closed"),
            func.sum(case((Case.priority == "critical", 1), else_=0)).label("critical"),
        )

        if date_from:
            query = query.filter(Case.created_at >= date_from)
        if date_to:
            query = query.filter(Case.created_at <= date_to)

        result = query.one()

        return {
            "total_cases": result.total or 0,
            "open_cases": result.open or 0,
            "closed_cases": result.closed or 0,
            "critical_cases": result.critical or 0,
            "date_from": date_from.isoformat() if date_from else None,
            "date_to": date_to.isoformat() if date_to else None,
        }

    def get_transaction_aggregates(
        self,
        db,
        case_id: str | None = None,
        date_from: datetime | None = None,
        date_to: datetime | None = None,
    ) -> dict[str, Any]:
        """Get transaction aggregates with filtering"""
        from sqlalchemy import func

        from core.database import Transaction

        query = db.query(
            func.count(Transaction.id).label("count"),
            func.sum(Transaction.amount).label("total_amount"),
            func.avg(Transaction.amount).label("avg_amount"),
            func.max(Transaction.amount).label("max_amount"),
        )

        if case_id:
            query = query.filter(Transaction.case_id == case_id)
        if date_from:
            query = query.filter(Transaction.date >= date_from)
        if date_to:
            query = query.filter(Transaction.date <= date_to)

        result = query.one()

        return {
            "transaction_count": result.count or 0,
            "total_amount": float(result.total_amount or 0),
            "average_amount": float(result.avg_amount or 0),
            "max_amount": float(result.max_amount or 0),
            "case_id": case_id,
            "date_from": date_from.isoformat() if date_from else None,
            "date_to": date_to.isoformat() if date_to else None,
        }


# Singleton
analytics_service = AnalyticsService()
# For backward compatibility with advanced_analytics.py imports
advanced_analytics = analytics_service