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import gradio as gr
import pandas as pd
from neuralprophet import NeuralProphet, set_log_level
import warnings
set_log_level("ERROR")
warnings.filterwarnings("ignore", category=UserWarning)
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=1,
num_hidden_layers=3,
daily_seasonality=False,
weekly_seasonality=True,
yearly_seasonality=True,
n_changepoints=150,
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
)
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):
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 fig1_path, fig2_path, disclaimer
if __name__ == "__main__":
dropdown = gr.inputs.Dropdown(["VNIndex"], label="Choose an option", default="VNIndex")
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()
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