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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
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