app.py

import streamlit as st
import pandas as pd

from autoML import autoML

st.set_page_config(layout="wide")

with st.sidebar:

    st.subheader('Demo Datasets')
    demo_but_class = st.button(label="Demo Classification on Wine Rate Dataset")
    demo_but_reg = st.button(label="Demo Regression on California House Dataset")

    st.subheader('AutoML your Dataset')

    csv = st.file_uploader(label='CSV file', type='csv')
    
    task = st.selectbox(label='Task', options=['Classification', 'Regression'])

    if task == 'Classification':
        metric_to_minimize_class = st.selectbox(label='Metric to minimize', options=['accuracy'])
        metric_to_minimize_reg = None
    if task == 'Regression':
        metric_to_minimize_reg = st.selectbox(label='Metric to minimize', options=['r2'])
        metric_to_minimize_class = None
        
    if csv:
        df = pd.read_csv(csv)
        df.to_csv('datasets/temp_file.csv', index=False)
        lst_features = df.columns
        label = st.selectbox(label='Label', options=lst_features)
    
    budget = st.text_area(label='Budget Time', value="5")
    start_but = st.button(label='AutoML')


if start_but:

    autoML('datasets/temp_file.csv', task, budget, label, metric_to_minimize_class, metric_to_minimize_reg)


if demo_but_class:

    autoML(csv='datasets/WineRate.csv', 
           task='Classification', 
           budget=budget, 
           label='quality', 
           metric_to_minimize_class='accuracy', 
           metric_to_minimize_reg=None)


if demo_but_reg:

    autoML(csv='datasets/house_california.csv', 
           task='Regression', 
           budget=budget, 
           label='median_house_value', 
           metric_to_minimize_class=None, 
           metric_to_minimize_reg='r2')