app/services/analytics.py

from datetime import datetime, timedelta
from sqlalchemy import func, and_, select, extract
from ..db.database import db
from ..utils.cache import cache
from ..db.models import StaffActivity, PerformanceMetric, User, ActivityType
from typing import Dict, List, Any, Optional
import numpy as np
from collections import defaultdict

class AnalyticsService:
    @staticmethod
    async def get_sales_analytics(start_date: datetime, end_date: datetime) -> Dict[str, Any]:
        cache_key = f"sales_analytics:{start_date.date()}:{end_date.date()}"
        cached_data = await cache.get_cache(cache_key)
        if cached_data:
            return cached_data

        pipeline = [
            {
                "$match": {
                    "created_at": {
                        "$gte": start_date,
                        "$lte": end_date
                    },
                    "status": {"$in": ["completed", "delivered"]}
                }
            },
            {
                "$group": {
                    "_id": {"$dateToString": {"format": "%Y-%m-%d", "date": "$created_at"}},
                    "total_sales": {"$sum": "$total_amount"},
                    "order_count": {"$sum": 1}
                }
            },
            {"$sort": {"_id": 1}}
        ]

        sales_data = await db.db["orders"].aggregate(pipeline).to_list(None)
        result = {
            "daily_sales": sales_data,
            "total_revenue": sum(day["total_sales"] for day in sales_data),
            "total_orders": sum(day["order_count"] for day in sales_data),
            "average_order_value": sum(day["total_sales"] for day in sales_data) / 
                                 (sum(day["order_count"] for day in sales_data) or 1)
        }

        await cache.set_cache(cache_key, result, expire=3600)  # Cache for 1 hour
        return result

    @staticmethod
    async def get_product_analytics() -> Dict[str, Any]:
        cache_key = "product_analytics"
        cached_data = await cache.get_cache(cache_key)
        if cached_data:
            return cached_data

        pipeline = [
            {
                "$unwind": "$products"
            },
            {
                "$group": {
                    "_id": "$products.product_id",
                    "total_quantity": {"$sum": "$products.quantity"},
                    "total_revenue": {
                        "$sum": {
                            "$multiply": ["$products.price", "$products.quantity"]
                        }
                    }
                }
            },
            {
                "$sort": {"total_revenue": -1}
            },
            {
                "$limit": 10
            }
        ]

        top_products = await db.db["orders"].aggregate(pipeline).to_list(None)
        
        # Get product details
        for product in top_products:
            product_detail = await db.db["products"].find_one({"_id": product["_id"]})
            if product_detail:
                product["name"] = product_detail["name"]
                product["category"] = product_detail["category"]

        result = {
            "top_products": top_products,
            "total_products": await db.db["products"].count_documents({}),
            "low_stock_products": await db.db["products"].count_documents({"inventory_count": {"$lt": 10}})
        }

        await cache.set_cache(cache_key, result, expire=3600)  # Cache for 1 hour
        return result

    @staticmethod
    async def get_customer_analytics() -> Dict[str, Any]:
        cache_key = "customer_analytics"
        cached_data = await cache.get_cache(cache_key)
        if cached_data:
            return cached_data

        pipeline = [
            {
                "$group": {
                    "_id": "$customer_id",
                    "total_orders": {"$sum": 1},
                    "total_spent": {"$sum": "$total_amount"},
                    "last_order": {"$max": "$created_at"}
                }
            },
            {
                "$sort": {"total_spent": -1}
            }
        ]

        customer_data = await db.db["orders"].aggregate(pipeline).to_list(None)
        
        result = {
            "total_customers": len(customer_data),
            "top_customers": customer_data[:10],
            "average_customer_value": sum(c["total_spent"] for c in customer_data) / (len(customer_data) or 1),
            "customer_segments": {
                "high_value": len([c for c in customer_data if c["total_spent"] > 1000]),
                "medium_value": len([c for c in customer_data if 500 <= c["total_spent"] <= 1000]),
                "low_value": len([c for c in customer_data if c["total_spent"] < 500])
            }
        }

        await cache.set_cache(cache_key, result, expire=3600)  # Cache for 1 hour
        return result

class StaffAnalyticsService:
    @staticmethod
    async def record_activity(

        user_id: int,

        branch_id: int,

        activity_type: ActivityType,

        details: dict,

        duration: Optional[float] = None

    ) -> StaffActivity:
        """Record a staff activity with performance scoring and notifications"""
        
        # Calculate performance score based on activity type and details
        score = None
        if activity_type == ActivityType.SALE:
            # Score based on sale amount and speed
            amount = details.get('amount', 0)
            duration = details.get('duration', 0)  # duration in minutes
            if duration > 0:
                score = min((amount / 100) * (5 / duration), 10)  # Max score of 10
        elif activity_type == ActivityType.CUSTOMER_SERVICE:
            # Score based on interaction quality
            satisfaction = details.get('customer_satisfaction', 0)
            resolution_time = details.get('resolution_time', 0)
            if resolution_time > 0:
                score = min((satisfaction * 2) * (10 / resolution_time), 10)

        async with db() as session:
            activity = StaffActivity(
                user_id=user_id,
                branch_id=branch_id,
                activity_type=activity_type,
                details=details,
                duration=duration,
                performance_score=score
            )
            session.add(activity)
            await session.commit()
            await session.refresh(activity)
            
            # Get current metrics before update
            prev_metrics = await StaffAnalyticsService._get_current_metrics(user_id, branch_id)
            
            # Update daily performance metrics
            new_metrics = await StaffAnalyticsService._update_performance_metrics(
                user_id, branch_id, activity
            )
            
            return activity, prev_metrics, new_metrics

    @staticmethod
    async def _get_current_metrics(user_id: int, branch_id: int) -> Optional[Dict[str, Any]]:
        """Get current day's metrics for a user"""
        today = datetime.utcnow().date()
        
        async with db() as session:
            stmt = select(PerformanceMetric).where(
                and_(
                    PerformanceMetric.user_id == user_id,
                    PerformanceMetric.branch_id == branch_id,
                    func.date(PerformanceMetric.metric_date) == today
                )
            )
            result = await session.execute(stmt)
            metric = result.scalar_one_or_none()
            
            if metric:
                return {
                    "total_sales": metric.total_sales,
                    "transaction_count": metric.transaction_count,
                    "void_count": metric.void_count,
                    "efficiency_score": metric.efficiency_score,
                    "customer_interaction_count": metric.customer_interaction_count
                }
            return None

    @staticmethod
    async def _update_performance_metrics(

        user_id: int,

        branch_id: int,

        activity: StaffActivity

    ) -> Dict[str, Any]:
        """Update daily performance metrics based on new activity"""
        today = datetime.utcnow().date()
        
        async with db() as session:
            # Get or create today's metrics
            stmt = select(PerformanceMetric).where(
                and_(
                    PerformanceMetric.user_id == user_id,
                    PerformanceMetric.branch_id == branch_id,
                    func.date(PerformanceMetric.metric_date) == today
                )
            )
            result = await session.execute(stmt)
            metric = result.scalar_one_or_none()
            
            if not metric:
                metric = PerformanceMetric(
                    user_id=user_id,
                    branch_id=branch_id,
                    metric_date=datetime.utcnow()
                )
                session.add(metric)

            # Update metrics based on activity type
            if activity.activity_type == ActivityType.SALE:
                metric.total_sales += activity.details.get('amount', 0)
                metric.transaction_count += 1
                metric.average_transaction_value = metric.total_sales / metric.transaction_count
            elif activity.activity_type == ActivityType.VOID:
                metric.void_count += 1
            elif activity.activity_type == ActivityType.CUSTOMER_SERVICE:
                metric.customer_interaction_count += 1
            elif activity.activity_type in [ActivityType.LOGIN, ActivityType.LOGOUT]:
                if activity.duration:
                    metric.login_time += activity.duration

            # Calculate efficiency score
            # Weight different factors in the score
            weights = {
                'sales': 0.4,
                'speed': 0.2,
                'accuracy': 0.2,
                'customer_service': 0.2
            }
            
            # Calculate component scores
            sales_score = min((metric.total_sales / 1000) * 10, 10)  # Scale sales to 0-10
            speed_score = 10 * (1 - (metric.login_time / (8 * 60)))  # Assuming 8-hour day
            accuracy_score = 10 * (1 - (metric.void_count / max(metric.transaction_count, 1)))
            cs_score = min((metric.customer_interaction_count / 10) * 10, 10)
            
            metric.efficiency_score = (
                (sales_score * weights['sales']) +
                (speed_score * weights['speed']) +
                (accuracy_score * weights['accuracy']) +
                (cs_score * weights['customer_service'])
            )

            await session.commit()
            await session.refresh(metric)
            
            return {
                "total_sales": metric.total_sales,
                "transaction_count": metric.transaction_count,
                "average_transaction_value": metric.average_transaction_value,
                "void_count": metric.void_count,
                "customer_interaction_count": metric.customer_interaction_count,
                "login_time": metric.login_time,
                "efficiency_score": metric.efficiency_score
            }

    @staticmethod
    async def get_staff_performance(

        branch_id: Optional[int] = None,

        user_id: Optional[int] = None,

        start_date: Optional[datetime] = None,

        end_date: Optional[datetime] = None

    ) -> Dict[str, Any]:
        """Get comprehensive staff performance metrics"""
        if not start_date:
            start_date = datetime.utcnow() - timedelta(days=30)
        if not end_date:
            end_date = datetime.utcnow()

        cache_key = f"staff_performance:{branch_id}:{user_id}:{start_date.date()}:{end_date.date()}"
        cached_data = await cache.get_cache(cache_key)
        if cached_data:
            return cached_data

        async with db() as session:
            # Base query conditions
            conditions = [
                PerformanceMetric.metric_date.between(start_date, end_date)
            ]
            if branch_id:
                conditions.append(PerformanceMetric.branch_id == branch_id)
            if user_id:
                conditions.append(PerformanceMetric.user_id == user_id)

            # Get aggregated metrics
            metrics_stmt = select(
                PerformanceMetric.user_id,
                func.sum(PerformanceMetric.total_sales).label('total_sales'),
                func.sum(PerformanceMetric.transaction_count).label('transaction_count'),
                func.avg(PerformanceMetric.average_transaction_value).label('avg_transaction_value'),
                func.sum(PerformanceMetric.void_count).label('void_count'),
                func.sum(PerformanceMetric.customer_interaction_count).label('customer_interactions'),
                func.sum(PerformanceMetric.login_time).label('total_login_time'),
                func.avg(PerformanceMetric.efficiency_score).label('avg_efficiency_score')
            ).where(
                and_(*conditions)
            ).group_by(
                PerformanceMetric.user_id
            )

            result = await session.execute(metrics_stmt)
            metrics_data = result.all()

            # Get user details
            user_ids = [m.user_id for m in metrics_data]
            users_stmt = select(User).where(User.id.in_(user_ids))
            users = (await session.execute(users_stmt)).scalars().all()
            users_dict = {u.id: u for u in users}

            # Format response data
            performance_data = []
            for metric in metrics_data:
                user = users_dict.get(metric.user_id)
                if user:
                    performance_data.append({
                        "user_id": user.id,
                        "username": user.username,
                        "full_name": user.full_name,
                        "metrics": {
                            "total_sales": metric.total_sales,
                            "transaction_count": metric.transaction_count,
                            "average_transaction_value": metric.avg_transaction_value,
                            "void_count": metric.void_count,
                            "customer_interactions": metric.customer_interactions,
                            "total_login_time": metric.total_login_time,
                            "efficiency_score": metric.avg_efficiency_score
                        }
                    })

            # Calculate branch averages if branch_id is specified
            branch_averages = None
            if branch_id:
                avg_stmt = select(
                    func.avg(PerformanceMetric.total_sales).label('avg_sales'),
                    func.avg(PerformanceMetric.transaction_count).label('avg_transactions'),
                    func.avg(PerformanceMetric.average_transaction_value).label('avg_transaction_value'),
                    func.avg(PerformanceMetric.efficiency_score).label('avg_efficiency')
                ).where(
                    and_(
                        PerformanceMetric.branch_id == branch_id,
                        PerformanceMetric.metric_date.between(start_date, end_date)
                    )
                )
                
                avg_result = await session.execute(avg_stmt)
                branch_avg = avg_result.one()
                branch_averages = {
                    "average_daily_sales": branch_avg.avg_sales,
                    "average_daily_transactions": branch_avg.avg_transactions,
                    "average_transaction_value": branch_avg.avg_transaction_value,
                    "average_efficiency_score": branch_avg.avg_efficiency
                }

            response = {
                "staff_performance": performance_data,
                "date_range": {
                    "start_date": start_date.isoformat(),
                    "end_date": end_date.isoformat()
                }
            }

            if branch_averages:
                response["branch_averages"] = branch_averages

            # Cache the response for 1 hour
            await cache.set_cache(cache_key, response, expire=3600)
            return response

analytics = AnalyticsService()
staff_analytics = StaffAnalyticsService()