Update app.py

app.py

@@ -15,15 +15,15 @@ df.dropna(inplace=True)
 
 m = NeuralProphet(n_forecasts=3,
     n_lags=12, 
-    changepoints_range=1, num_hidden_layers=6, daily_seasonality= True, weekly_seasonality = True, yearly_seasonality = True, ar_reg=True,
-    n_changepoints=550, trend_reg_threshold=True, d_hidden=9, global_normalization=True, global_time_normalization=True, seasonality_reg=1, unknown_data_normalization=True,
+    changepoints_range=9, num_hidden_layers=6, daily_seasonality= True, weekly_seasonality = True, yearly_seasonality = True, ar_reg=True,
+    n_changepoints=250, trend_reg_threshold=True, d_hidden=9, global_normalization=True, global_time_normalization=True, seasonality_reg=1, unknown_data_normalization=True,
     seasonality_mode="multiplicative", drop_missing=True,
-    learning_rate=0.01
+    learning_rate=0.1
 )
 
 m.fit(df, freq='M')
 
-future = m.make_future_dataframe(df, periods=30, n_historic_predictions=True)
+future = m.make_future_dataframe(df, periods=3, n_historic_predictions=True)
 forecast = m.predict(future)
 
 def predict_vn_index(option=None):