Update app.py

app.py

@@ -158,16 +158,17 @@ def select_model(horizon, model_type, max_steps=200):
     else:
         raise ValueError(f"Unsupported model type: {model_type}")
 
-def forecast_time_series(df, model_type, freq, horizon, max_steps=200):
+def model_train(model):
+    model.fit(df)
+    return model
+def forecast_time_series(df, model_type, horizon, max_steps=200):
     start_time = time.time()  # Start timing
-    if freq:
-        df['ds'] = pd.date_range(start='1970-01-01', periods=len(df), freq=freq)
-    else:
-        freq = determine_frequency(df)
-        st.write(f"Determined frequency: {freq}")
+    freq = determine_frequency(df)
+    st.write(f"Determined frequency: {freq}")
     df['ds'] = pd.to_datetime(df['ds'], errors='coerce')
     df = df.dropna(subset=['ds'])
-    model = select_model(horizon, model_type, max_steps)
+    selected_model = select_model(horizon, model_type, max_steps)
+    model = model_train(selected_model)
     forecast_results = {}
     st.write(f"Generating forecast using {model_type} model...")
     forecast_results[model_type] = generate_forecast(model, df)
@@ -242,12 +243,8 @@ def dynamic_forecasting():
     dynamic_horizon = st.number_input("Forecast horizon", value=18)
     dynamic_max_steps = st.number_input('Max steps', value=200)
 
-
-    # Determine frequency of data
-    frequency = determine_frequency(df)
-    st.write(f"Detected frequency: {frequency}")
     
-    forecast_time_series(df, dynamic_model_choice, frequency, dynamic_horizon, dynamic_max_steps)
+    forecast_time_series(df, dynamic_model_choice, dynamic_horizon, dynamic_max_steps)
     
 pg = st.navigation({
     "Overview": [