Update app.py

app.py

@@ -251,17 +251,19 @@ def transfer_learning_forecasting():
         nhits_model, timesnet_model, lstm_model, tft_model = select_model_based_on_frequency(frequency, nhits_models, timesnet_models, lstm_models, tft_models)
         forecast_results = {}
     
-        start_time = time.time()  # Start timing
-        if model_choice == "NHITS":
-            forecast_results['NHITS'] = generate_forecast(nhits_model, df)
-        elif model_choice == "TimesNet":
-            forecast_results['TimesNet'] = generate_forecast(timesnet_model, df)
-        elif model_choice == "LSTM":
-            forecast_results['LSTM'] = generate_forecast(lstm_model, df)
-        elif model_choice == "TFT":
-            forecast_results['TFT'] = generate_forecast(tft_model, df)
+        
 
         if st.sidebar.button("Submit"):
+            start_time = time.time()  # Start timing
+            if model_choice == "NHITS":
+                forecast_results['NHITS'] = generate_forecast(nhits_model, df)
+            elif model_choice == "TimesNet":
+                forecast_results['TimesNet'] = generate_forecast(timesnet_model, df)
+            elif model_choice == "LSTM":
+                forecast_results['LSTM'] = generate_forecast(lstm_model, df)
+            elif model_choice == "TFT":
+                forecast_results['TFT'] = generate_forecast(tft_model, df)
+                
             for model_name, forecast_df in forecast_results.items():
                 plot_forecasts(forecast_df, df, f'{model_name} Forecast for {y_col}')