Update app.py

app.py

@@ -183,14 +183,14 @@ class StockPredictor:
 
             elif self.model_type == 'SARIMA':
                 forecast = self.model.get_forecast(steps=days)
-                return forecast.predicted_mean
-
+                return forecast.predicted_mean.values
+    
             elif self.model_type == 'Prophet':
                 future = self.model.make_future_dataframe(periods=days)
                 for feature in ['SMA_20', 'EMA_20', 'RSI', 'BB_High', 'BB_Low']:
                     future[feature] = self.data[feature].iloc[-1]  # Use last known value
                 forecast = self.model.predict(future)
-                return forecast['yhat'][-days:].values
+                return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']][-days:]
 
             elif self.model_type in ['XGBoost', 'RandomForest']:
                 last_data = self.data[self.features].iloc[-1:].values
@@ -201,18 +201,18 @@ class StockPredictor:
                     # Update last_data for next prediction
                     last_data = np.roll(last_data, -1, axis=1)
                     last_data[0, -5] = pred[0]  # Assuming 'Close' is the 5th from last feature
-                return predictions
+                return np.array(predictions)
 
         except Exception as e:
             print(f"Error predicting with {self.model_type} model: {str(e)}")
             return None
 
-    def evaluate_model(self, test_data):
-        predictions = self.predict(len(test_data))
-        mse = mean_squared_error(test_data['Close'], predictions)
-        mape = mean_absolute_percentage_error(test_data['Close'], predictions)
-        rmse = np.sqrt(mse)
-        return mse, mape, rmse
+        def evaluate_model(self, test_data):
+            predictions = self.predict(len(test_data))
+            mse = mean_squared_error(test_data['Close'], predictions)
+            mape = mean_absolute_percentage_error(test_data['Close'], predictions)
+            rmse = np.sqrt(mse)
+            return mse, mape, rmse
 
 def fetch_stock_data(ticker):
     try: