Update app.py

app.py

@@ -248,50 +248,49 @@ def transfer_learning_forecasting():
     frequency = determine_frequency(df)
     st.sidebar.write(f"Detected frequency: {frequency}")
 
-    col1, col2 = st.columns([2,4])
-    with col1:
-        tab_insample, tab_forecast  = st.tabs(
+
+    nhits_model, timesnet_model, lstm_model, tft_model = select_model_based_on_frequency(frequency, nhits_models, timesnet_models, lstm_models, tft_models)
+    forecast_results = {}
+
+    
+
+    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}')
+
+        end_time = time.time()  # End timing
+        time_taken = end_time - start_time
+        st.success(f"Time taken for {model_choice} forecast: {time_taken:.2f} seconds")
+
+    tab_insample, tab_forecast  = st.tabs(
                     ["Input data", "Forecast"]
                 )
-        with tab_insample:
-            df_grid = df.drop(columns="unique_id")
-            st.write(df_grid)
-            # grid_table = AgGrid(
-            #                 df_grid,
-            #                 theme="alpine",
-            #             )
-
-        with tab_forecast:
-            df_grid = df.drop(columns="unique_id")
-            # grid_table = AgGrid(
-            #                 df_grid,
-            #                 theme="alpine",
-            #             )
-            
-    with col2:
-    # Load pre-trained models
-        nhits_model, timesnet_model, lstm_model, tft_model = select_model_based_on_frequency(frequency, nhits_models, timesnet_models, lstm_models, tft_models)
-        forecast_results = {}
-    
         
-
-        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}')
-
-            end_time = time.time()  # End timing
-            time_taken = end_time - start_time
-            st.success(f"Time taken for {model_choice} forecast: {time_taken:.2f} seconds")
+    with tab_insample:
+        df_grid = df.drop(columns="unique_id")
+        st.write(df_grid)
+        # grid_table = AgGrid(
+        #                 df_grid,
+        #                 theme="alpine",
+        #             )
+
+    with tab_forecast:
+        df_grid = forecast_results[model_choice].drop(columns="unique_id")
+        st.write(df_grid)
+        # grid_table = AgGrid(
+        #                 df_grid,
+        #                 theme="alpine",
+        #             )
 
 
 def dynamic_forecasting():