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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)
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