bakery-ia/services/forecasting/app/api/forecasting_operations.py

# services/forecasting/app/api/forecasting_operations.py
"""
Forecasting Operations API - Business operations for forecast generation and predictions
"""

import structlog
from fastapi import APIRouter, Depends, HTTPException, status, Query, Path, Request
from typing import List, Dict, Any, Optional
from datetime import date, datetime, timezone
import uuid

from app.services.forecasting_service import EnhancedForecastingService
from app.services.prediction_service import PredictionService
from app.services.forecast_cache import get_forecast_cache_service
from app.schemas.forecasts import (
    ForecastRequest, ForecastResponse, BatchForecastRequest,
    BatchForecastResponse, MultiDayForecastResponse
)
from shared.auth.decorators import get_current_user_dep
from shared.database.base import create_database_manager
from shared.monitoring.decorators import track_execution_time
from shared.monitoring.metrics import get_metrics_collector
from app.core.config import settings
from app.models import AuditLog
from shared.routing import RouteBuilder
from shared.auth.access_control import require_user_role, service_only_access
from shared.security import create_audit_logger, create_rate_limiter, AuditSeverity, AuditAction
from shared.subscription.plans import get_forecast_quota, get_forecast_horizon_limit
from shared.redis_utils import get_redis_client

route_builder = RouteBuilder('forecasting')
logger = structlog.get_logger()
router = APIRouter(tags=["forecasting-operations"])

# Initialize audit logger
audit_logger = create_audit_logger("forecasting-service", AuditLog)

async def get_rate_limiter():
    """Dependency for rate limiter"""
    redis_client = await get_redis_client()
    return create_rate_limiter(redis_client)


def get_enhanced_forecasting_service():
    """Dependency injection for EnhancedForecastingService"""
    database_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")
    return EnhancedForecastingService(database_manager)


def get_enhanced_prediction_service():
    """Dependency injection for enhanced PredictionService"""
    database_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")
    return PredictionService(database_manager)


@router.post(
    route_builder.build_operations_route("single"),
    response_model=ForecastResponse
)
@require_user_role(['viewer', 'member', 'admin', 'owner'])
@track_execution_time("enhanced_single_forecast_duration_seconds", "forecasting-service")
async def generate_single_forecast(
    request: ForecastRequest,
    tenant_id: str = Path(..., description="Tenant ID"),
    request_obj: Request = None,
    current_user: dict = Depends(get_current_user_dep),
    enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
    """Generate a single product forecast with caching support"""
    metrics = get_metrics_collector(request_obj)

    try:
        logger.info("Generating single forecast",
                   tenant_id=tenant_id,
                   inventory_product_id=request.inventory_product_id,
                   forecast_date=request.forecast_date.isoformat())

        if metrics:
            metrics.increment_counter("single_forecasts_total")

        # Initialize cache service
        cache_service = get_forecast_cache_service(settings.REDIS_URL)

        # Check cache first
        cached_forecast = await cache_service.get_cached_forecast(
            tenant_id=uuid.UUID(tenant_id),
            product_id=uuid.UUID(request.inventory_product_id),
            forecast_date=request.forecast_date
        )

        if cached_forecast:
            if metrics:
                metrics.increment_counter("forecast_cache_hits_total")
            logger.info("Returning cached forecast",
                       tenant_id=tenant_id,
                       forecast_id=cached_forecast.get('id'))
            return ForecastResponse(**cached_forecast)

        # Cache miss - generate forecast
        if metrics:
            metrics.increment_counter("forecast_cache_misses_total")

        forecast = await enhanced_forecasting_service.generate_forecast(
            tenant_id=tenant_id,
            request=request
        )

        # Cache the result
        await cache_service.cache_forecast(
            tenant_id=uuid.UUID(tenant_id),
            product_id=uuid.UUID(request.inventory_product_id),
            forecast_date=request.forecast_date,
            forecast_data=forecast.dict()
        )

        if metrics:
            metrics.increment_counter("single_forecasts_success_total")

        logger.info("Single forecast generated successfully",
                   tenant_id=tenant_id,
                   forecast_id=forecast.id)

        return forecast

    except ValueError as e:
        if metrics:
            metrics.increment_counter("forecast_validation_errors_total")
        logger.error("Forecast validation error", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=str(e)
        )
    except Exception as e:
        if metrics:
            metrics.increment_counter("single_forecasts_errors_total")
        logger.error("Single forecast generation failed", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Forecast generation failed"
        )


@router.post(
    route_builder.build_operations_route("multi-day"),
    response_model=MultiDayForecastResponse
)
@require_user_role(['viewer', 'member', 'admin', 'owner'])
@track_execution_time("enhanced_multi_day_forecast_duration_seconds", "forecasting-service")
async def generate_multi_day_forecast(
    request: ForecastRequest,
    tenant_id: str = Path(..., description="Tenant ID"),
    request_obj: Request = None,
    current_user: dict = Depends(get_current_user_dep),
    enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
    """Generate multiple daily forecasts for the specified period"""
    metrics = get_metrics_collector(request_obj)

    try:
        logger.info("Generating multi-day forecast",
                   tenant_id=tenant_id,
                   inventory_product_id=request.inventory_product_id,
                   forecast_days=request.forecast_days,
                   forecast_date=request.forecast_date.isoformat())

        if metrics:
            metrics.increment_counter("multi_day_forecasts_total")

        if request.forecast_days <= 0 or request.forecast_days > 30:
            raise ValueError("forecast_days must be between 1 and 30")

        forecast_result = await enhanced_forecasting_service.generate_multi_day_forecast(
            tenant_id=tenant_id,
            request=request
        )

        if metrics:
            metrics.increment_counter("multi_day_forecasts_success_total")

        logger.info("Multi-day forecast generated successfully",
                   tenant_id=tenant_id,
                   inventory_product_id=request.inventory_product_id,
                   forecast_days=len(forecast_result.get("forecasts", [])))

        return forecast_result

    except ValueError as e:
        if metrics:
            metrics.increment_counter("forecast_validation_errors_total")
        logger.error("Multi-day forecast validation error", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=str(e)
        )
    except Exception as e:
        if metrics:
            metrics.increment_counter("multi_day_forecasts_errors_total")
        logger.error("Multi-day forecast generation failed", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Multi-day forecast generation failed"
        )


@router.post(
    route_builder.build_operations_route("batch"),
    response_model=BatchForecastResponse
)
@require_user_role(['admin', 'owner'])
@track_execution_time("enhanced_batch_forecast_duration_seconds", "forecasting-service")
async def generate_batch_forecast(
    request: BatchForecastRequest,
    tenant_id: str = Path(..., description="Tenant ID"),
    request_obj: Request = None,
    current_user: dict = Depends(get_current_user_dep),
    enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
    """Generate forecasts for multiple products in batch (Admin+ only, quota enforced)"""
    metrics = get_metrics_collector(request_obj)

    try:
        logger.info("Generating batch forecast",
                   tenant_id=tenant_id,
                   product_count=len(request.inventory_product_ids))

        if metrics:
            metrics.increment_counter("batch_forecasts_total")

        # Check if we need to get all products instead of specific ones
        inventory_product_ids = request.inventory_product_ids
        if inventory_product_ids is None or len(inventory_product_ids) == 0:
            # If no specific products requested, fetch all products for the tenant
            # from the inventory service to generate forecasts for all of them
            from shared.clients.inventory_client import InventoryServiceClient
            from app.core.config import settings
            
            inventory_client = InventoryServiceClient(settings)
            all_ingredients = await inventory_client.get_all_ingredients(tenant_id=tenant_id)
            inventory_product_ids = [str(ingredient['id']) for ingredient in all_ingredients] if all_ingredients else []
            
            # If still no products, return early with success response
            if not inventory_product_ids:
                logger.info("No products found for forecasting", tenant_id=tenant_id)
                from app.schemas.forecasts import BatchForecastResponse
                now = datetime.now(timezone.utc)
                return BatchForecastResponse(
                    id=str(uuid.uuid4()),
                    tenant_id=tenant_id,
                    batch_name=getattr(request, 'batch_name', f"orchestrator-batch-{datetime.now().strftime('%Y%m%d')}"),
                    status="completed",
                    total_products=0,
                    completed_products=0,
                    failed_products=0,
                    requested_at=now,
                    completed_at=now,
                    processing_time_ms=0,
                    forecasts=None,
                    error_message=None
                )

        # Skip rate limiting for service-to-service calls (orchestrator)
        # Rate limiting is handled at the gateway level for user requests

        # Create a copy of the request with the actual list of product IDs to forecast
        # (whether originally provided or fetched from inventory service)
        from app.schemas.forecasts import BatchForecastRequest
        updated_request = BatchForecastRequest(
            tenant_id=tenant_id,  # Use the tenant_id from the path parameter
            batch_name=getattr(request, 'batch_name', f"orchestrator-batch-{datetime.now().strftime('%Y%m%d')}"),
            inventory_product_ids=inventory_product_ids,
            forecast_days=getattr(request, 'forecast_days', 7)
        )
        
        batch_result = await enhanced_forecasting_service.generate_batch_forecasts(
            tenant_id=tenant_id,
            request=updated_request
        )

        if metrics:
            metrics.increment_counter("batch_forecasts_success_total")

        logger.info("Batch forecast generated successfully",
                   tenant_id=tenant_id,
                   total_forecasts=batch_result.get('total_forecasts', 0))

        # Convert the service result to BatchForecastResponse format
        from app.schemas.forecasts import BatchForecastResponse
        now = datetime.now(timezone.utc)
        return BatchForecastResponse(
            id=batch_result.get('id', str(uuid.uuid4())),  # Use 'id' field (UUID) instead of 'batch_id' (string)
            tenant_id=tenant_id,
            batch_name=updated_request.batch_name,
            status="completed",
            total_products=batch_result.get('total_forecasts', 0),
            completed_products=batch_result.get('successful_forecasts', 0),
            failed_products=batch_result.get('failed_forecasts', 0),
            requested_at=now,
            completed_at=now,
            processing_time_ms=0,
            forecasts=[],
            error_message=None
        )

    except ValueError as e:
        if metrics:
            metrics.increment_counter("forecast_validation_errors_total")
        logger.error("Batch forecast validation error", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=str(e)
        )
    except Exception as e:
        if metrics:
            metrics.increment_counter("batch_forecasts_errors_total")
        logger.error("Batch forecast generation failed", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Batch forecast generation failed"
        )


@router.post(
    route_builder.build_operations_route("realtime")
)
@require_user_role(['viewer', 'member', 'admin', 'owner'])
@track_execution_time("enhanced_realtime_prediction_duration_seconds", "forecasting-service")
async def generate_realtime_prediction(
    prediction_request: Dict[str, Any],
    tenant_id: str = Path(..., description="Tenant ID"),
    request_obj: Request = None,
    current_user: dict = Depends(get_current_user_dep),
    prediction_service: PredictionService = Depends(get_enhanced_prediction_service)
):
    """Generate real-time prediction"""
    metrics = get_metrics_collector(request_obj)

    try:
        logger.info("Generating real-time prediction",
                   tenant_id=tenant_id,
                   inventory_product_id=prediction_request.get("inventory_product_id"))

        if metrics:
            metrics.increment_counter("realtime_predictions_total")

        required_fields = ["inventory_product_id", "model_id", "features"]
        missing_fields = [field for field in required_fields if field not in prediction_request]
        if missing_fields:
            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail=f"Missing required fields: {missing_fields}"
            )

        prediction_result = await prediction_service.predict_with_weather_forecast(
            model_id=prediction_request["model_id"],
            model_path=prediction_request.get("model_path", ""),
            features=prediction_request["features"],
            tenant_id=tenant_id,
            days=prediction_request.get("days", 7),
            confidence_level=prediction_request.get("confidence_level", 0.8)
        )

        if metrics:
            metrics.increment_counter("realtime_predictions_success_total")

        logger.info("Real-time prediction generated successfully",
                   tenant_id=tenant_id,
                   days=len(prediction_result))

        return {
            "tenant_id": tenant_id,
            "inventory_product_id": prediction_request["inventory_product_id"],
            "model_id": prediction_request["model_id"],
            "predictions": prediction_result,
            "days": len(prediction_result),
            "timestamp": datetime.now(timezone.utc).isoformat()
        }

    except HTTPException:
        raise
    except ValueError as e:
        if metrics:
            metrics.increment_counter("prediction_validation_errors_total")
        logger.error("Prediction validation error", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=str(e)
        )
    except Exception as e:
        if metrics:
            metrics.increment_counter("realtime_predictions_errors_total")
        logger.error("Real-time prediction failed", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Real-time prediction failed"
        )


@router.post(
    route_builder.build_operations_route("batch-predictions")
)
@require_user_role(['viewer', 'member', 'admin', 'owner'])
async def generate_batch_predictions(
    predictions_request: List[Dict[str, Any]],
    tenant_id: str = Path(..., description="Tenant ID"),
    current_user: dict = Depends(get_current_user_dep),
    prediction_service: PredictionService = Depends(get_enhanced_prediction_service)
):
    """Generate batch predictions"""
    try:
        logger.info("Generating batch predictions", tenant_id=tenant_id, count=len(predictions_request))

        results = []
        for pred_request in predictions_request:
            try:
                prediction_result = await prediction_service.predict_with_weather_forecast(
                    model_id=pred_request["model_id"],
                    model_path=pred_request.get("model_path", ""),
                    features=pred_request["features"],
                    tenant_id=tenant_id,
                    days=pred_request.get("days", 7),
                    confidence_level=pred_request.get("confidence_level", 0.8)
                )
                results.append({
                    "inventory_product_id": pred_request.get("inventory_product_id"),
                    "predictions": prediction_result,
                    "success": True
                })
            except Exception as e:
                results.append({
                    "inventory_product_id": pred_request.get("inventory_product_id"),
                    "error": str(e),
                    "success": False
                })

        return {"predictions": results, "total": len(results)}

    except Exception as e:
        logger.error("Batch predictions failed", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Batch predictions failed"
        )


@router.post(
    route_builder.build_operations_route("validate-predictions")
)
async def validate_predictions(
    tenant_id: str = Path(..., description="Tenant ID"),
    start_date: date = Query(...),
    end_date: date = Query(...),
    enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
    """Validate predictions against actual sales data"""
    try:
        logger.info("Validating predictions", tenant_id=tenant_id)

        validation_results = await enhanced_forecasting_service.validate_predictions(
            tenant_id=tenant_id,
            start_date=start_date,
            end_date=end_date
        )

        return validation_results

    except Exception as e:
        logger.error("Prediction validation failed", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Prediction validation failed"
        )


@router.get(
    route_builder.build_operations_route("statistics")
)
async def get_forecast_statistics(
    tenant_id: str = Path(..., description="Tenant ID"),
    start_date: Optional[date] = Query(None),
    end_date: Optional[date] = Query(None),
    enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
    """Get forecast statistics"""
    try:
        logger.info("Getting forecast statistics", tenant_id=tenant_id)

        stats = await enhanced_forecasting_service.get_forecast_statistics(
            tenant_id=tenant_id,
            start_date=start_date,
            end_date=end_date
        )

        return stats

    except Exception as e:
        logger.error("Failed to get forecast statistics", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Failed to retrieve forecast statistics"
        )


@router.delete(
    route_builder.build_operations_route("cache")
)
async def clear_prediction_cache(
    tenant_id: str = Path(..., description="Tenant ID"),
    prediction_service: PredictionService = Depends(get_enhanced_prediction_service)
):
    """Clear prediction cache"""
    try:
        logger.info("Clearing prediction cache", tenant_id=tenant_id)

        await prediction_service.clear_cache(tenant_id=tenant_id)

        return {"message": "Prediction cache cleared successfully"}

    except Exception as e:
        logger.error("Failed to clear prediction cache", error=str(e), tenant_id=tenant_id)
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail="Failed to clear prediction cache"
        )


@router.post(
    route_builder.build_operations_route("validate-forecasts"),
    response_model=dict
)
@service_only_access
@track_execution_time("validate_forecasts_duration_seconds", "forecasting-service")
async def validate_forecasts(
    validation_date: date = Query(..., description="Date to validate forecasts for"),
    tenant_id: str = Path(..., description="Tenant ID"),
    request_obj: Request = None,
    current_user: dict = Depends(get_current_user_dep),
    enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
):
    """
    Validate forecasts for a specific date against actual sales.
    Calculates MAPE, RMSE, MAE and identifies products with poor accuracy.

    This endpoint is called by the orchestrator during Step 5 to validate
    yesterday's forecasts and trigger retraining if needed.

    Args:
        validation_date: Date to validate forecasts for
        tenant_id: Tenant ID

    Returns:
        Dict with overall metrics and poor accuracy products list:
        - overall_mape: Mean Absolute Percentage Error across all products
        - overall_rmse: Root Mean Squared Error across all products
        - overall_mae: Mean Absolute Error across all products
        - products_validated: Number of products validated
        - poor_accuracy_products: List of products with MAPE > 30%
    """
    metrics = get_metrics_collector(request_obj)

    try:
        logger.info("Validating forecasts for date",
                   tenant_id=tenant_id,
                   validation_date=validation_date.isoformat())

        if metrics:
            metrics.increment_counter("forecast_validations_total")

        # Get all forecasts for the validation date
        from app.repositories.forecast_repository import ForecastRepository
        from shared.clients.sales_client import SalesServiceClient

        db_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")

        async with db_manager.get_session() as session:
            forecast_repo = ForecastRepository(session)

            # Get forecasts for the validation date
            forecasts = await forecast_repo.get_forecasts_by_date(
                tenant_id=uuid.UUID(tenant_id),
                forecast_date=validation_date
            )

            if not forecasts:
                logger.warning("No forecasts found for validation date",
                             tenant_id=tenant_id,
                             validation_date=validation_date.isoformat())
                return {
                    "overall_mape": 0,
                    "overall_rmse": 0,
                    "overall_mae": 0,
                    "products_validated": 0,
                    "poor_accuracy_products": []
                }

            # Get actual sales for the validation date from sales service
            sales_client = SalesServiceClient(settings, "forecasting-service")
            actual_sales_response = await sales_client.get_sales_by_date_range(
                tenant_id=tenant_id,
                start_date=validation_date,
                end_date=validation_date
            )

            # Create sales lookup dict
            sales_dict = {}
            if actual_sales_response and 'sales' in actual_sales_response:
                for sale in actual_sales_response['sales']:
                    product_id = sale.get('inventory_product_id')
                    quantity = sale.get('quantity', 0)
                    if product_id:
                        # Aggregate quantities for the same product
                        sales_dict[product_id] = sales_dict.get(product_id, 0) + quantity

            # Calculate metrics for each product
            import numpy as np

            mape_list = []
            rmse_list = []
            mae_list = []
            poor_accuracy_products = []

            for forecast in forecasts:
                product_id = str(forecast.inventory_product_id)
                actual_quantity = sales_dict.get(product_id)

                # Skip if no actual sales data
                if actual_quantity is None:
                    continue

                predicted_quantity = forecast.predicted_demand

                # Calculate errors
                absolute_error = abs(predicted_quantity - actual_quantity)
                squared_error = (predicted_quantity - actual_quantity) ** 2

                # Calculate percentage error (avoid division by zero)
                if actual_quantity > 0:
                    percentage_error = (absolute_error / actual_quantity) * 100
                else:
                    # If actual is 0 but predicted is not, treat as 100% error
                    percentage_error = 100 if predicted_quantity > 0 else 0

                mape_list.append(percentage_error)
                rmse_list.append(squared_error)
                mae_list.append(absolute_error)

                # Track products with poor accuracy
                if percentage_error > 30:
                    poor_accuracy_products.append({
                        "product_id": product_id,
                        "mape": round(percentage_error, 2),
                        "predicted": round(predicted_quantity, 2),
                        "actual": round(actual_quantity, 2)
                    })

            # Calculate overall metrics
            overall_mape = np.mean(mape_list) if mape_list else 0
            overall_rmse = np.sqrt(np.mean(rmse_list)) if rmse_list else 0
            overall_mae = np.mean(mae_list) if mae_list else 0

            result = {
                "overall_mape": round(overall_mape, 2),
                "overall_rmse": round(overall_rmse, 2),
                "overall_mae": round(overall_mae, 2),
                "products_validated": len(mape_list),
                "poor_accuracy_products": poor_accuracy_products
            }

            logger.info("Forecast validation complete",
                       tenant_id=tenant_id,
                       validation_date=validation_date.isoformat(),
                       overall_mape=result["overall_mape"],
                       products_validated=result["products_validated"],
                       poor_accuracy_count=len(poor_accuracy_products))

            if metrics:
                metrics.increment_counter("forecast_validations_completed_total")
                metrics.observe_histogram("forecast_validation_mape", overall_mape)

            return result

    except Exception as e:
        logger.error("Failed to validate forecasts",
                    error=str(e),
                    tenant_id=tenant_id,
                    validation_date=validation_date.isoformat())
        if metrics:
            metrics.increment_counter("forecast_validations_failed_total")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to validate forecasts: {str(e)}"
        )


# ============================================================================
# Tenant Data Deletion Operations (Internal Service Only)
# ============================================================================

@router.delete(
    route_builder.build_base_route("tenant/{tenant_id}", include_tenant_prefix=False),
    response_model=dict
)
@service_only_access
async def delete_tenant_data(
    tenant_id: str = Path(..., description="Tenant ID to delete data for"),
    current_user: dict = Depends(get_current_user_dep)
):
    """
    Delete all forecasting data for a tenant (Internal service only)

    This endpoint is called by the orchestrator during tenant deletion.
    It permanently deletes all forecasting-related data including:
    - Forecasts (all time periods)
    - Prediction batches
    - Model performance metrics
    - Prediction cache
    - Audit logs

    **WARNING**: This operation is irreversible!

    Returns:
        Deletion summary with counts of deleted records
    """
    from app.services.tenant_deletion_service import ForecastingTenantDeletionService

    try:
        logger.info("forecasting.tenant_deletion.api_called", tenant_id=tenant_id)

        db_manager = create_database_manager(settings.DATABASE_URL, "forecasting")

        async with db_manager.get_session() as session:
            deletion_service = ForecastingTenantDeletionService(session)
            result = await deletion_service.safe_delete_tenant_data(tenant_id)

            if not result.success:
                raise HTTPException(
                    status_code=500,
                    detail=f"Tenant data deletion failed: {', '.join(result.errors)}"
                )

            return {
                "message": "Tenant data deletion completed successfully",
                "summary": result.to_dict()
            }

    except HTTPException:
        raise
    except Exception as e:
        logger.error("forecasting.tenant_deletion.api_error",
                    tenant_id=tenant_id,
                    error=str(e),
                    exc_info=True)
        raise HTTPException(
            status_code=500,
            detail=f"Failed to delete tenant data: {str(e)}"
        )


@router.get(
    route_builder.build_base_route("tenant/{tenant_id}/deletion-preview", include_tenant_prefix=False),
    response_model=dict
)
@service_only_access
async def preview_tenant_data_deletion(
    tenant_id: str = Path(..., description="Tenant ID to preview deletion for"),
    current_user: dict = Depends(get_current_user_dep)
):
    """
    Preview what data would be deleted for a tenant (dry-run)

    This endpoint shows counts of all data that would be deleted
    without actually deleting anything. Useful for:
    - Confirming deletion scope before execution
    - Auditing and compliance
    - Troubleshooting

    Returns:
        Dictionary with entity names and their counts
    """
    from app.services.tenant_deletion_service import ForecastingTenantDeletionService

    try:
        logger.info("forecasting.tenant_deletion.preview_called", tenant_id=tenant_id)

        db_manager = create_database_manager(settings.DATABASE_URL, "forecasting")

        async with db_manager.get_session() as session:
            deletion_service = ForecastingTenantDeletionService(session)
            preview = await deletion_service.get_tenant_data_preview(tenant_id)

            total_records = sum(preview.values())

            return {
                "tenant_id": tenant_id,
                "service": "forecasting",
                "preview": preview,
                "total_records": total_records,
                "warning": "These records will be permanently deleted and cannot be recovered"
            }

    except Exception as e:
        logger.error("forecasting.tenant_deletion.preview_error",
                    tenant_id=tenant_id,
                    error=str(e),
                    exc_info=True)
        raise HTTPException(
            status_code=500,
            detail=f"Failed to preview tenant data deletion: {str(e)}"
        )