Update model.py

model.py

@@ -368,7 +368,7 @@ def retrain_model_with_feedback(user_id):
     print("Retrained model saved.")
 '''
     
-
+'''
 def retrain_from_feedback(user_id: str):
     """Retrains model using feedback for a specific user and updates saved model + scaler."""
     base_df = pd.read_csv(DATA_PATH)
@@ -398,7 +398,32 @@ def retrain_from_feedback(user_id: str):
     print(f"✅ Model retrained and saved for user {user_id}")
     return True
 
+ '''
+def retrain_model_with_feedback(user_id):
+    base_df = pd.read_csv(DATA_PATH)
+    feedback_df = fetch_feedback_for_user(user_id)
+    if feedback_df.empty:
+        print("No feedback found for user:", user_id)
+        return {"message": f"No feedback found for user {user_id}"}
     
+    combined_df = pd.concat([base_df, feedback_df], ignore_index=True)
+    X, y1, y2, y3, scaler_obj = prepare_data(combined_df, combined_df['product'].unique().tolist())
+    model = build_model((X.shape[1], X.shape[2]))
+    model.fit(
+        X,
+        {'daily_consumption_output': y1,
+         'finish_error_output': y2,
+         'finish_days_output': y3},
+        epochs=10,
+        batch_size=32,
+        validation_split=0.1,
+    )
+    model.save(MODEL_PATH)
+    print("Retrained model saved.")
+    return {"message": f"Retraining complete for user {user_id}"}
+
+
+
 
 # ===============================
 # 6. PREDICTION