Update app.py

app.py

@@ -13,12 +13,13 @@ df = df.rename(columns={"Date": "ds", "Price": "y"})
 df.fillna(method='ffill', inplace=True)
 df.dropna(inplace=True)
 
-def lr_scheduler_step(self, epoch, batch_idx, optimizer):
-    # Get the OneCycleLR scheduler
-    scheduler = self.optimizers[0].scheduler
+class CustomNeuralProphet(NeuralProphet):
+    def lr_scheduler_step(self, epoch, batch_idx, optimizer):
+        # Get the OneCycleLR scheduler
+        scheduler = self.optimizers[0].scheduler
 
-    # Call the `step` method on the scheduler
-    scheduler.step()
+        # Call the `step` method on the scheduler
+        scheduler.step()
 
 m = CustomNeuralProphet(
     n_forecasts=30,
@@ -37,7 +38,7 @@ m = CustomNeuralProphet(
     learning_rate=0.03,
 )
 
-m.fit(df, freq='D')
+m.fit(df, freq='D', epochs=10, validate_each_epoch=True, valid_p=0.2)
 
 future = m.make_future_dataframe(df, periods=30, n_historic_predictions=True)
 forecast = m.predict(future)
@@ -53,7 +54,7 @@ def predict_vn_index(option=None):
 
 if __name__ == "__main__":
     dropdown = gr.inputs.Dropdown(["VNIndex"], label="Choose an option", default="VNIndex")
-    image_output = gr.outputs.Image(type="filepath", label="Forecast Plot")
+    image_output = gr.outputs.Image(type="file", label="Forecast Plot")
     disclaimer_output = gr.outputs.Textbox(label="Disclaimer")
     interface = gr.Interface(fn=predict_vn_index, inputs=dropdown, outputs=[image_output, disclaimer_output], title="Dự báo VN Index 30 ngày tới")
     interface.launch()