Update app.py

app.py

@@ -251,12 +251,38 @@ def forecast_time_series(df, model_type, horizon, max_steps,y_col):
     forecast_results = {}
     forecast_results[model_type] = generate_forecast(model, df, tag='retrain')
 
+    st.session_state.forecast_results = forecast_results
+    
     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_type} forecast: {time_taken:.2f} seconds")
+    if 'forecast_results' in st.session_state:
+        forecast_results = st.session_state.forecast_results
+
+        st.markdown('You can download Input and Forecast Data below')
+        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:
+            if model_choice in forecast_results:
+                df_grid = forecast_results[model_choice]
+                st.write(df_grid)
+                # grid_table = AgGrid(
+                #                 df_grid,
+                #                 theme="alpine",
+                #             )
 
 @st.cache_data
 def load_default():
@@ -339,30 +365,30 @@ def transfer_learning_forecasting():
         time_taken = end_time - start_time
         st.success(f"Time taken for {model_choice} forecast: {time_taken:.2f} seconds")
 
-    if 'forecast_results' in st.session_state:
-        forecast_results = st.session_state.forecast_results
-
-        st.markdown('You can download Input and Forecast Data below')
-        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",
-            #             )
+        if 'forecast_results' in st.session_state:
+            forecast_results = st.session_state.forecast_results
     
-        with tab_forecast:
-            if model_choice in forecast_results:
-                df_grid = forecast_results[model_choice]
+            st.markdown('You can download Input and Forecast Data below')
+            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:
+                if model_choice in forecast_results:
+                    df_grid = forecast_results[model_choice]
+                    st.write(df_grid)
+                    # grid_table = AgGrid(
+                    #                 df_grid,
+                    #                 theme="alpine",
+                    #             )
 
 
 def dynamic_forecasting():