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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 |