Update app.py

app.py

@@ -164,7 +164,7 @@ def model_train(df,model, ds_col, freq):
     nf.fit(df)
     return model
 
-def forecast_time_series(df, model_type, horizon, max_steps=200, ds_col='ds'):
+def forecast_time_series(df, model_type, horizon, max_steps=200, ds_col='ds',y_col):
     start_time = time.time()  # Start timing
     freq = determine_frequency(df, ds_col)
     st.sidebar.write(f"Data frequency: {freq}")
@@ -177,11 +177,11 @@ def forecast_time_series(df, model_type, horizon, max_steps=200, ds_col='ds'):
     forecast_results[model_type] = generate_forecast(model, df, tag='retrain')
 
     for model_name, forecast_df in forecast_results.items():
-        plot_forecasts(forecast_df, df, f'{model_name} Forecast Comparison')
+        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.sidebar.success(f"Time taken for {model_type} forecast: {time_taken:.2f} seconds")
+    st.success(f"Time taken for {model_type} forecast: {time_taken:.2f} seconds")
 
 @st.cache_data
 def load_default():
@@ -218,6 +218,7 @@ def transfer_learning_forecasting():
 
     df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
     df['unique_id']=1
+    df = df[['unique_id','ds','y']]
     st.session_state.df = df
 
     # Determine frequency of data
@@ -239,11 +240,11 @@ def transfer_learning_forecasting():
         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')
+        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.sidebar.success(f"Time taken for {model_choice} forecast: {time_taken:.2f} seconds")
+    st.success(f"Time taken for {model_choice} forecast: {time_taken:.2f} seconds")
 
 def dynamic_forecasting():
     st.title("Dynamic Forecasting")
@@ -263,7 +264,11 @@ def dynamic_forecasting():
         y_col = st.selectbox("Select Target column", options=columns, index=columns.index('y') if 'y' in columns else 1)
         # unique_id_col = st.text_input("Unique ID column (default: '1')", value="1")
 
+    df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
     df['unique_id']=1
+    df = df[['unique_id','ds','y']]
+    st.session_state.df = df
+    
     st.session_state.ds_col = ds_col
     st.session_state.y_col = y_col
 
@@ -273,10 +278,8 @@ def dynamic_forecasting():
     dynamic_horizon = st.sidebar.number_input("Forecast horizon", value=18)
     dynamic_max_steps = st.sidebar.number_input('Max steps', value=200)
 
-    df = df.rename(columns={ds_col: 'ds', y_col: 'y'})
-    st.session_state.df = df
     
-    forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps, ds_col='ds')
+    forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps, ds_col='ds',y_col)
 
 pg = st.navigation({
     "Overview": [