Update app.py

app.py

@@ -1,10 +1,9 @@
 import gradio as gr
 import pandas as pd
-from neuralprophet import NeuralProphet
+from neuralprophet import NeuralProphet, set_log_level
 import warnings
-import torch.optim as optim
-from torch.optim.lr_scheduler import OneCycleLR
 
+set_log_level("ERROR")
 warnings.filterwarnings("ignore", category=UserWarning)
 
 url = "VN Index Historical Data.csv"
@@ -14,58 +13,55 @@ df = df.rename(columns={"Date": "ds", "Price": "y"})
 df.fillna(method='ffill', inplace=True)
 df.dropna(inplace=True)
 
-
-class CustomNeuralProphet(NeuralProphet):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.optimizer = None
-
-m = CustomNeuralProphet(
+m = NeuralProphet(
     n_forecasts=30,
     n_lags=12,
     changepoints_range=1,
     num_hidden_layers=3,
+    daily_seasonality=False,
+    weekly_seasonality=True,
     yearly_seasonality=True,
     n_changepoints=150,
-    trend_reg_threshold=False,
+    trend_reg_threshold=False,  # Disable trend regularization threshold
     d_hidden=3,
     global_normalization=True,
     seasonality_reg=1,
     unknown_data_normalization=True,
     seasonality_mode="multiplicative",
     drop_missing=True,
-    learning_rate=0.03,
+    learning_rate=0.03
 )
 
-# Set the custom LR scheduler
-m.fit(df, freq='D')  # Fit the model first before accessing the optimizer
-m.optimizer = optim.Adam(m.model.parameters(), lr=0.03)  # Example optimizer, adjust as needed
-
-lr_scheduler = OneCycleLR(
-    m.optimizer,
-    max_lr=0.1,
-    total_steps=100,
-    pct_start=0.3,
-    anneal_strategy='cos',
-)  # Example LR scheduler, adjust as needed
-
-m.trainer.lr_schedulers = [lr_scheduler]  # Set the LR scheduler to the trainer
+m.fit(df, freq='D')
 
 future = m.make_future_dataframe(df, periods=30, n_historic_predictions=True)
 forecast = m.predict(future)
 
-
 def predict_vn_index(option=None):
-    fig = m.plot(forecast)
-    path = "forecast_plot.png"
-    fig.savefig(path)
+    fig1 = m.plot(forecast)
+    fig1_path = "forecast_plot1.png"
+    fig1.savefig(fig1_path)
+
+    # Add code to generate the second image (fig2)
+    fig2 = m.plot_latest_forecast(forecast)  # Replace this line with code to generate the second image
+    fig2_path = "forecast_plot2.png"
+    fig2.savefig(fig2_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
+
+    return fig1_path, fig2_path, disclaimer
 
 
 if __name__ == "__main__":
     dropdown = gr.inputs.Dropdown(["VNIndex"], label="Choose an option", default="VNIndex")
-    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()
+    outputs = [
+        gr.outputs.Image(type="filepath", label="First Image"),
+        gr.outputs.Image(type="filepath", label="Second Image"),
+        gr.outputs.Textbox(label="Disclaimer")
+    ]
+    interface = gr.Interface(fn=predict_vn_index, inputs=dropdown, outputs=outputs, title="Dự báo VN Index 30 ngày tới")
+    interface.launch(share=True)
+
+
+
+
+