Improve the frontend modals

services/training/app/repositories/model_repository.py

@@ -249,13 +249,35 @@ class ModelRepository(TrainingBaseRepository):
             )
             recent_models = recent_result.scalar() or 0
             
+            # Calculate average accuracy from model metrics
+            accuracy_query = text("""
+                SELECT AVG(mape) as average_mape, COUNT(*) as total_models_with_metrics
+                FROM trained_models 
+                WHERE tenant_id = :tenant_id
+                AND mape IS NOT NULL
+                AND is_active = true
+            """)
+            
+            accuracy_result = await self.session.execute(accuracy_query, {"tenant_id": tenant_id})
+            accuracy_row = accuracy_result.fetchone()
+            
+            average_mape = accuracy_row.average_mape if accuracy_row and accuracy_row.average_mape else 0
+            total_models_with_metrics = accuracy_row.total_models_with_metrics if accuracy_row else 0
+            
+            # Convert MAPE to accuracy percentage (lower MAPE = higher accuracy)
+            # Use 100 - MAPE as a simple conversion, but cap it at reasonable bounds
+            average_accuracy = max(0, min(100, 100 - float(average_mape))) if average_mape > 0 else 0
+            
             return {
                 "total_models": total_models,
                 "active_models": active_models,
                 "inactive_models": total_models - active_models,
                 "production_models": production_models,
                 "models_by_product": product_stats,
-                "recent_models_30d": recent_models
+                "recent_models_30d": recent_models,
+                "average_accuracy": average_accuracy,
+                "total_models_with_metrics": total_models_with_metrics,
+                "average_mape": float(average_mape) if average_mape > 0 else 0
             }
             
         except Exception as e:
@@ -268,7 +290,10 @@ class ModelRepository(TrainingBaseRepository):
                 "inactive_models": 0,
                 "production_models": 0,
                 "models_by_product": {},
-                "recent_models_30d": 0
+                "recent_models_30d": 0,
+                "average_accuracy": 0,
+                "total_models_with_metrics": 0,
+                "average_mape": 0
             }
     
     async def _deactivate_other_production_models(
@@ -343,4 +368,4 @@ class ModelRepository(TrainingBaseRepository):
             logger.error("Failed to get model performance summary",
                         model_id=model_id,
                         error=str(e))
-            return {}
+            return {}

services/training/requirements.txt

@@ -11,7 +11,7 @@ alembic==1.17.0
 psycopg2-binary==2.9.10
 
 # ML libraries
-prophet==1.1.6
+prophet==1.2.1
 scikit-learn==1.6.1
 pandas==2.2.3
 numpy==2.2.2