Start fixing forecast service API 14

services/data/app/api/weather.py

@@ -72,7 +72,7 @@ async def get_current_weather(
         logger.error("Failed to get current weather", error=str(e))
         raise HTTPException(status_code=500, detail=f"Internal server error: {str(e)}")
 
-@router.get("/tenants/{tenant_id}/weather/forecast", response_model=List[WeatherForecastResponse])
+@router.post("/tenants/{tenant_id}/weather/forecast", response_model=List[WeatherForecastResponse])
 async def get_weather_forecast(
     latitude: float = Query(..., description="Latitude"),
     longitude: float = Query(..., description="Longitude"),

services/forecasting/app/services/prediction_service.py

@@ -17,6 +17,7 @@ import pandas as pd
 import httpx
 from pathlib import Path
 import os
+import joblib
 
 from app.core.config import settings
 from shared.monitoring.metrics import MetricsCollector
@@ -89,7 +90,11 @@ class PredictionService:
             raise
     
     async def _load_model(self, model_id: str, model_path: str):
-        """Load model from shared volume using API metadata"""
+        """Load model from shared volume using joblib"""
+        
+        if not await self._validate_model_file(model_path):
+            logger.error(f"Model file not valid: {model_path}")
+            return None
         
         # Check cache first
         if model_id in self.model_cache:
@@ -98,10 +103,9 @@ class PredictionService:
                 return cached_model
         
         try:
-            # Load model directly from shared volume (fast!)
             if os.path.exists(model_path):
-                with open(model_path, 'rb') as f:
-                    model = pickle.load(f)
+                # ✅ FIX: Use joblib.load instead of pickle.load
+                model = joblib.load(model_path)
                 
                 # Cache the model
                 self.model_cache[model_id] = (model, datetime.now())
@@ -115,6 +119,34 @@ class PredictionService:
             logger.error(f"Error loading model: {e}")
             return None
         
+    async def _validate_model_file(self, model_path: str) -> bool:
+        """Validate model file before loading"""
+        try:
+            if not os.path.exists(model_path):
+                logger.error(f"Model file not found: {model_path}")
+                return False
+                
+            # Check file size (should be > 1KB for a trained model)
+            file_size = os.path.getsize(model_path)
+            if file_size < 1024:
+                logger.warning(f"Model file too small ({file_size} bytes): {model_path}")
+                return False
+                
+            # Try to peek at file header to detect format
+            with open(model_path, 'rb') as f:
+                header = f.read(8)
+                
+            # Check for joblib signature
+            if header.startswith(b']\x93PICKLE') or header.startswith(b'\x80\x03'):
+                return True
+            else:
+                logger.warning(f"Unrecognized file format: {model_path}")
+                return False
+                
+        except Exception as e:
+            logger.error(f"Model validation error: {e}")
+            return False
+    
     def _prepare_prophet_features(self, features: Dict[str, Any]) -> pd.DataFrame:
         """Convert features to Prophet-compatible DataFrame"""
         
@@ -136,18 +168,67 @@ class PredictionService:
                 else:
                     df[feature] = 0.0
             
-            # Add categorical features
-            df['day_of_week'] = int(features.get('day_of_week', 0))
-            df['is_weekend'] = int(features.get('is_weekend', False))
+            # Extract date information for temporal features
+            forecast_date = pd.to_datetime(features['date'])
+            day_of_week = forecast_date.weekday()  # 0=Monday, 6=Sunday
+            
+            # Add temporal features (MUST match training service exactly!)
+            df['day_of_week'] = int(day_of_week)
+            df['day_of_month'] = int(forecast_date.day)
+            df['month'] = int(forecast_date.month)
+            df['quarter'] = int(forecast_date.quarter)
+            df['week_of_year'] = int(forecast_date.isocalendar().week)
+            
+            # Bakery-specific temporal features (match training exactly!)
+            df['is_weekend'] = int(day_of_week >= 5)  # Saturday=5, Sunday=6
+            df['is_monday'] = int(day_of_week == 0)   # ✅ FIX: Add missing is_monday
+            df['is_tuesday'] = int(day_of_week == 1)
+            df['is_wednesday'] = int(day_of_week == 2)
+            df['is_thursday'] = int(day_of_week == 3)
+            df['is_friday'] = int(day_of_week == 4)
+            df['is_saturday'] = int(day_of_week == 5)
+            df['is_sunday'] = int(day_of_week == 6)
+            
+            # Month-based features
+            df['is_january'] = int(forecast_date.month == 1)
+            df['is_february'] = int(forecast_date.month == 2)
+            df['is_march'] = int(forecast_date.month == 3)
+            df['is_april'] = int(forecast_date.month == 4)
+            df['is_may'] = int(forecast_date.month == 5)
+            df['is_june'] = int(forecast_date.month == 6)
+            df['is_july'] = int(forecast_date.month == 7)
+            df['is_august'] = int(forecast_date.month == 8)
+            df['is_september'] = int(forecast_date.month == 9)
+            df['is_october'] = int(forecast_date.month == 10)
+            df['is_november'] = int(forecast_date.month == 11)
+            df['is_december'] = int(forecast_date.month == 12)
+            
+            # Season-based features  
+            season = ((forecast_date.month % 12) + 3) // 3  # 1=spring, 2=summer, 3=autumn, 4=winter
+            df['is_spring'] = int(season == 1)
+            df['is_summer'] = int(season == 2)  
+            df['is_autumn'] = int(season == 3)
+            df['is_winter'] = int(season == 4)
+            
+            # Business context features
             df['is_holiday'] = int(features.get('is_holiday', False))
             
             # Business type encoding
             business_type = features.get('business_type', 'individual')
             df['is_central_workshop'] = int(business_type == 'central_workshop')
+            df['is_individual_bakery'] = int(business_type == 'individual')
+            
+            # Special day features (these might be in training data)
+            df['is_month_start'] = int(forecast_date.day <= 3)
+            df['is_month_end'] = int(forecast_date.day >= 28)
+            df['is_quarter_start'] = int(forecast_date.month in [1, 4, 7, 10] and forecast_date.day <= 7)
+            df['is_quarter_end'] = int(forecast_date.month in [3, 6, 9, 12] and forecast_date.day >= 25)
             
             logger.debug("Prepared Prophet features", 
                         features_count=len(df.columns),
-                        date=features['date'])
+                        date=features['date'],
+                        day_of_week=day_of_week,
+                        is_monday=df['is_monday'].iloc[0])
             
             return df
             

services/forecasting/requirements.txt

@@ -22,6 +22,7 @@ prophet==1.1.4
 scikit-learn==1.3.2
 pandas==2.1.4
 numpy==1.25.2
+joblib==1.3.2
 
 # Messaging
 aio-pika==9.3.1

services/training/app/models/training.py

@@ -169,6 +169,7 @@ class TrainedModel(Base):
     def to_dict(self):
         return {
             "id": self.id,
+            "model_id": self.id,
             "tenant_id": self.tenant_id,
             "product_name": self.product_name,
             "model_type": self.model_type,

shared/clients/data_client.py

@@ -241,9 +241,9 @@ class DataServiceClient(BaseServiceClient):
         """
         # Prepare request payload with proper date handling
         payload = {
-            "days": days,  # Already in ISO format from calling code
             "latitude": latitude or 40.4168,  # Default Madrid coordinates
-            "longitude": longitude or -3.7038
+            "longitude": longitude or -3.7038,
+            "days": days  # Already in ISO format from calling code
         }
         
         logger.info(f"Weather request payload: {payload}", tenant_id=tenant_id)