Upload gardio_forecast.py

gardio_forecast.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import pandas as pd
+from neuralprophet import NeuralProphet, set_log_level
+import io
+import warnings
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+set_log_level("ERROR")
+
+url = "VN Index Historical Data.csv"
+df = pd.read_csv(url)
+df = df[["Date", "Price"]]
+df = df.rename(columns={"Date": "ds", "Price": "y"})
+df.fillna(method='ffill', inplace=True)
+df.dropna(inplace=True)
+
+m = NeuralProphet(
+    n_forecasts=30,
+    n_lags=12,
+    changepoints_range=5,
+    num_hidden_layers=6,
+    yearly_seasonality=True,
+    n_changepoints=150,
+    trend_reg_threshold=False,  # Disable trend regularization threshold
+    d_hidden=9,
+    global_normalization=True,
+    seasonality_reg=1,
+    unknown_data_normalization=True,
+    seasonality_mode="multiplicative",
+    drop_missing=True,
+    learning_rate=0.1
+)
+
+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)
+    return path
+
+
+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(share=True)
+
+
+