Add forecasting service

services/forecasting/app/api/predictions.py

@@ -0,0 +1,141 @@
+# ================================================================
+# services/forecasting/app/api/predictions.py
+# ================================================================
+"""
+Prediction API endpoints - Real-time prediction capabilities
+"""
+
+import structlog
+from fastapi import APIRouter, Depends, HTTPException, status, Query
+from sqlalchemy.ext.asyncio import AsyncSession
+from typing import List, Dict, Any
+from datetime import date, datetime, timedelta
+
+from app.core.database import get_db
+from app.core.auth import get_current_user_from_headers
+from app.services.prediction_service import PredictionService
+from app.schemas.forecasts import ForecastRequest
+
+logger = structlog.get_logger()
+router = APIRouter()
+
+# Initialize service
+prediction_service = PredictionService()
+
+@router.post("/realtime")
+async def get_realtime_prediction(
+    product_name: str,
+    location: str,
+    forecast_date: date,
+    features: Dict[str, Any],
+    current_user: dict = Depends(get_current_user_from_headers)
+):
+    """Get real-time prediction without storing in database"""
+    
+    try:
+        tenant_id = str(current_user.get("tenant_id"))
+        
+        # Get latest model
+        from app.services.forecasting_service import ForecastingService
+        forecasting_service = ForecastingService()
+        
+        model_info = await forecasting_service._get_latest_model(
+            tenant_id, product_name, location
+        )
+        
+        if not model_info:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail=f"No trained model found for {product_name}"
+            )
+        
+        # Generate prediction
+        prediction = await prediction_service.predict(
+            model_id=model_info["model_id"],
+            features=features,
+            confidence_level=0.8
+        )
+        
+        return {
+            "product_name": product_name,
+            "location": location,
+            "forecast_date": forecast_date,
+            "predicted_demand": prediction["demand"],
+            "confidence_lower": prediction["lower_bound"],
+            "confidence_upper": prediction["upper_bound"],
+            "model_id": model_info["model_id"],
+            "model_version": model_info["version"],
+            "generated_at": datetime.now(),
+            "features_used": features
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error("Error getting realtime prediction", error=str(e))
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="Internal server error"
+        )
+
+@router.get("/quick/{product_name}")
+async def get_quick_prediction(
+    product_name: str,
+    location: str = Query(...),
+    days_ahead: int = Query(1, ge=1, le=7),
+    current_user: dict = Depends(get_current_user_from_headers)
+):
+    """Get quick prediction for next few days"""
+    
+    try:
+        tenant_id = str(current_user.get("tenant_id"))
+        
+        # Generate predictions for the next N days
+        predictions = []
+        
+        for day in range(1, days_ahead + 1):
+            forecast_date = date.today() + timedelta(days=day)
+            
+            # Prepare basic features
+            features = {
+                "date": forecast_date.isoformat(),
+                "day_of_week": forecast_date.weekday(),
+                "is_weekend": forecast_date.weekday() >= 5,
+                "business_type": "individual"
+            }
+            
+            # Get model and predict
+            from app.services.forecasting_service import ForecastingService
+            forecasting_service = ForecastingService()
+            
+            model_info = await forecasting_service._get_latest_model(
+                tenant_id, product_name, location
+            )
+            
+            if model_info:
+                prediction = await prediction_service.predict(
+                    model_id=model_info["model_id"],
+                    features=features
+                )
+                
+                predictions.append({
+                    "date": forecast_date,
+                    "predicted_demand": prediction["demand"],
+                    "confidence_lower": prediction["lower_bound"],
+                    "confidence_upper": prediction["upper_bound"]
+                })
+        
+        return {
+            "product_name": product_name,
+            "location": location,
+            "predictions": predictions,
+            "generated_at": datetime.now()
+        }
+        
+    except Exception as e:
+        logger.error("Error getting quick prediction", error=str(e))
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="Internal server error"
+        )
+