Update app.py

app.py

@@ -226,47 +226,41 @@ def transfer_learning_forecasting():
     frequency = determine_frequency(df)
     st.sidebar.write(f"Detected frequency: {frequency}")
 
-    tab_insample = st.tabs(
-                ["Input data"]
-            )
-    with tab_insample:
-        df_grid = df.drop(columns="unique_id")
-        grid_table = AgGrid(
-                        df_grid,
-                        editable=False,
-                        # theme="streamlit",
-                        fit_columns_on_grid_load=True,
-                        height=360,
-                    )
-
-    # 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 = {}
-
-    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)
-
-    df_grid = df.drop(columns="unique_id")
-    grid_table = AgGrid(
-                    df_grid,
-                    editable=False,
-                    theme="streamlit",
-                    fit_columns_on_grid_load=True,
-                    height=360,
+    col1, col2 = st.columns([2,4])
+    with col1:
+        tab_insample = st.tabs(
+                    ["Input data"]
                 )
-    for model_name, forecast_df in forecast_results.items():
-        plot_forecasts(forecast_df, df, f'{model_name} Forecast for {y_col}')
+        with tab_insample:
+            df_grid = df.drop(columns="unique_id")
+            grid_table = AgGrid(
+                            df_grid,
+                            theme="alpine",
+                            fit_columns_on_grid_load=True,
+                            height=360,
+                        )
 
-    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 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 = {}
+    
+        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")
 
 def dynamic_forecasting():
     st.title("Dynamic Forecasting")