Update app.py

app.py

@@ -616,40 +616,38 @@ def timegpt_anom():
                 # Convert the Plotly figure to a Matplotlib figure if needed
                 # Note: You may need to handle this conversion depending on your specific use case
                 # For now, this example assumes that you are using a Matplotlib figure
-                fig = nixtla_client.plot(df, forecast_df, level=[90], engine='matplotlib')
+                fig = nixtla_client.plot(df, anom_df, level=[90], engine='matplotlib')
                 st.pyplot(fig)
             elif plot_type == "Plotly":
                 # Plotly figure directly
-                fig = nixtla_client.plot(df, forecast_df, level=[90], engine='plotly')
+                fig = nixtla_client.plot(df, anom_df, level=[90], engine='plotly')
                 st.plotly_chart(fig)
     
             end_time = time.time()  # End timing
             time_taken = end_time - start_time
             st.success(f"Time taken for TimeGPT forecast: {time_taken:.2f} seconds")
 
-            if 'forecast_df' in st.session_state:
-                forecast_df = st.session_state.forecast_df
+    
+            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",
+                #             )
         
-                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:
-                    df_grid = forecast_df
-                    st.write(df_grid)
-                    # grid_table = AgGrid(
-                    #                 df_grid,
-                    #                 theme="alpine",
-                    #             )
+            with tab_forecast:
+                df_grid = anom_df
+                st.write(df_grid)
+                # grid_table = AgGrid(
+                #                 df_grid,
+                #                 theme="alpine",
+                #             )