Update app.py

app.py

@@ -174,10 +174,10 @@ def forecast_time_series(df, model_type, horizon, max_steps,y_col):
     st.sidebar.write(f"Data frequency: {freq}")
     
     selected_model = select_model(horizon, model_type, max_steps)
+    st.spinner(f"Training {model_type} model...")
     model = model_train(df, selected_model,freq)
     
     forecast_results = {}
-    st.sidebar.write(f"Generating forecast using {model_type} model...")
     forecast_results[model_type] = generate_forecast(model, df, tag='retrain')
 
     for model_name, forecast_df in forecast_results.items():
@@ -221,7 +221,7 @@ def transfer_learning_forecasting():
     # Model selection and forecasting
     st.sidebar.subheader("Model Selection and Forecasting")
     model_choice = st.sidebar.selectbox("Select model", ["NHITS", "TimesNet", "LSTM", "TFT"])
-    horizon = st.sidebar.number_input("Forecast horizon", value=18)
+    horizon = st.sidebar.number_input("Forecast horizon", value=12)
 
     df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
     df['unique_id']=1
@@ -312,8 +312,8 @@ def dynamic_forecasting():
     # Dynamic forecasting
     st.sidebar.subheader("Dynamic Model Selection and Forecasting")
     dynamic_model_choice = st.sidebar.selectbox("Select model for dynamic forecasting", ["NHITS", "TimesNet", "LSTM", "TFT"], key="dynamic_model_choice")
-    dynamic_horizon = st.sidebar.number_input("Forecast horizon", value=18)
-    dynamic_max_steps = st.sidebar.number_input('Max steps', value=10)
+    dynamic_horizon = st.sidebar.number_input("Forecast horizon", value=12)
+    dynamic_max_steps = st.sidebar.number_input('Max steps', value=20)
 
     if st.sidebar.button("Submit"):
         forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps,y_col)
@@ -349,13 +349,14 @@ def timegpt_fcst():
         id_col = 'ts_test'
         df['unique_id']=id_col
         freq = determine_frequency(df)
-        
-        forecast_df = nixtla_client.forecast(
-            df=df,
-            h=7,
-            freq=freq,
-            level=[90]
-        )
+
+        if st.sidebar.button("Submit"):
+            forecast_df = nixtla_client.forecast(
+                df=df,
+                h=7,
+                freq=freq,
+                level=[90]
+            )
 
         nixtla_client.plot(
             forecast_df,
@@ -394,15 +395,16 @@ def timegpt_anom():
         id_col = 'ts_test'
         df['unique_id']=id_col
         freq = determine_frequency(df)
-        
-        anom_df = nixtla_client.detect_anomalies(
-            df=df,
-            freq=freq,
-            level=90
-        )
+
+        if st.sidebar.button("Submit"):
+            anom_df = nixtla_client.detect_anomalies(
+                df=df,
+                freq=freq,
+                level=90
+            )
 
         nixtla_client.plot(
-            forecast_df,
+            df,
             anom_df
         )