Update app.py

app.py

@@ -1,14 +1,12 @@
 import gradio as gr
 import pandas as pd
-from neuralprophet import NeuralProphet, set_log_level
+from neuralprophet import NeuralProphet
 import io
 import warnings
-import pytorch_lightning
+import torch
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
-set_log_level("ERROR")
-
 url = "VN Index Historical Data.csv"
 df = pd.read_csv(url)
 df = df[["Date", "Price"]]
@@ -16,24 +14,30 @@ df = df.rename(columns={"Date": "ds", "Price": "y"})
 df.fillna(method='ffill', inplace=True)
 df.dropna(inplace=True)
 
-m = NeuralProphet(
+class CustomNeuralProphet(NeuralProphet):
+    def lr_scheduler_step(self, epoch: int = None) -> None:
+        # Override the lr_scheduler_step method to avoid the MisconfigurationException
+        if self.lr_scheduler is not None and isinstance(self.lr_scheduler, torch.optim.lr_scheduler.OneCycleLR):
+            self.lr_scheduler.step()
+
+m = CustomNeuralProphet(
     n_forecasts=30,
     n_lags=12,
-    changepoints_range=5,
-    num_hidden_layers=6,
+    changepoints_range=1,
+    num_hidden_layers=3,
     yearly_seasonality=True,
     n_changepoints=150,
     trend_reg_threshold=False,  # Disable trend regularization threshold
-    d_hidden=9,
+    d_hidden=3,
     global_normalization=True,
     seasonality_reg=1,
     unknown_data_normalization=True,
     seasonality_mode="multiplicative",
     drop_missing=True,
-    learning_rate=0.1
+    learning_rate=0.1,
 )
 
-m.fit(df, freq='D')
+m.fit(df, freq='D', epochs=10, validate_each_epoch=True, valid_p=0.2)  # Specify number of epochs and validation parameters
 
 future = m.make_future_dataframe(df, periods=30, n_historic_predictions=True)
 forecast = m.predict(future)
@@ -43,13 +47,17 @@ def predict_vn_index(option=None):
     fig = m.plot(forecast)
     path = "forecast_plot.png"
     fig.savefig(path)
-    return path
+    disclaimer = "Quý khách chỉ xem đây là tham khảo, công ty không chịu bất cứ trách nhiệm nào về tình trạng đầu tư của quý khách."
+    return path, disclaimer
 
 
 if __name__ == "__main__":
     dropdown = gr.inputs.Dropdown(["VNIndex"], label="Choose an option", default="VNIndex")
-    interface = gr.Interface(fn=predict_vn_index, inputs=dropdown, outputs="image", title="Dự báo VN Index 30 ngày tới")
-    interface.launch()
+    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(share=True)
+