import streamlit as st
import pandas as pd
import os
from model import load_ILINetDataset, preprocess_data, train_val_split, scale_train, train, predict, save_model, load_model, inverse_scale_predictions, plot_results, compute_mape

st.title('Exponential Smoothing on ILINetDataset')

if st.button(label='Get result chart'):

    path = f'./models'
    model_name = 'ExponentialSmoothing.pkl'

    if not os.path.exists(path=path):
        os.makedirs(path)

    if not model_name in os.listdir(path=path):
        dataset = load_ILINetDataset()
        prep_data = preprocess_data(dataset)
        train_ili, val_ili = train_val_split(prep_data)
        scaled_train_ili, scaler = scale_train(train_ili=train_ili)
        model = train(scaled_train_ili)
        save_model(model=model, path=path)
    else:
        model = load_model(path=os.path.join(path, model_name))

    preds = predict(model=model, val_ili=val_ili)
    unscaled_preds = inverse_scale_predictions(preds, scaler)
    fig = plot_results(train_ili=train_ili, val_ili=val_ili, preds=unscaled_preds)
    st.pyplot(fig=fig)  # figura contenente il grafico
    st.metric(label='MAPE', value='{:.2f}%'.format(compute_mape(preds=unscaled_preds, val=val_ili)))  # mape
    # TODO add others metrics