app.py

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= 3,
    n_lags=12, 
    changepoints_range=1, num_hidden_layers=6, daily_seasonality= False, weekly_seasonality = False, yearly_seasonality = True, ar_reg=True,
    n_changepoints=150, trend_reg_threshold=True, d_hidden=9, global_normalization=True, global_time_normalization=True, seasonality_reg=1, unknown_data_normalization=True,
    seasonality_mode="multiplicative", drop_missing=True, 
    learning_rate=0.1
)

m.fit(df, freq='M')

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)
    description = "The predictions are conducted by a Deep Learning AI algorithm, and data augmentation is performed by the AI Consultant team. Data is updated after 5 PM GMT+7 on trading days."
    disclaimer = "Please consider this as a reference only; the company holds no responsibility for your investment status."
    

    return fig1_path, fig2_path, description, disclaimer


if __name__ == "__main__":
    dropdown = gr.inputs.Dropdown(["VNIndex"], label="Choose an option", default="VNIndex")
    outputs = [
        gr.outputs.Image(type="filepath", label="The VN Index price history and forecast"),
        gr.outputs.Image(type="filepath", label="Forecasting the VN Index for the next 90 days"),
        gr.outputs.Textbox(label="Description"),
        gr.outputs.Textbox(label="Disclaimer")
    ]
    interface = gr.Interface(fn=predict_vn_index, inputs=dropdown, outputs=outputs, title="Forecasting the VN Index for the next 90 days")
    interface.launch()