gardio_forecast.py

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)