Update app.py

app.py

@@ -155,9 +155,10 @@ def select_model(horizon, model_type, max_steps=200):
     else:
         raise ValueError(f"Unsupported model type: {model_type}")
 
-def model_train(df,model, ds_col):
+def model_train(df,model, ds_col, freq):
+    nf = NeuralForecast(models=[model], freq=freq)
     df[ds_col] = pd.to_datetime(df[ds_col])
-    model.fit(df)
+    nf.fit(df)
     return model
 
 def forecast_time_series(df, model_type, horizon, max_steps=200, ds_col='ds'):
@@ -166,7 +167,7 @@ def forecast_time_series(df, model_type, horizon, max_steps=200, ds_col='ds'):
     st.sidebar.write(f"Data frequency: {freq}")
     
     selected_model = select_model(horizon, model_type, max_steps)
-    model = model_train(df, selected_model, ds_col)
+    model = model_train(df, selected_model, ds_col,freq)
     
     forecast_results = {}
     st.sidebar.write(f"Generating forecast using {model_type} model...")
@@ -187,7 +188,8 @@ def load_default():
 def transfer_learning_forecasting():
     st.title("Transfer Learning Forecasting")
 
-    nhits_model, timesnet_model, lstm_model, tft_model = load_all_models()
+    nhits_models, timesnet_models, lstm_models, tft_models = load_all_models()
+    
     with st.sidebar.expander("Upload and Configure Dataset", expanded=True):
         uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
         if uploaded_file: