import streamlit as st
import pandas as pd
from flaml import AutoML
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

from utils import csv_to_featuers_list, pre_process_df, pre_process_features

st.set_page_config(layout="wide")

st.title("Auto ML")

with st.sidebar:
    demo_but = st.button(label="Demo with Wine Rate Dataset")
    csv = st.file_uploader(label='CSV file')
    type_ = st.selectbox(label='Type', options=['Classification', 'Regression'])
    lst_features = csv_to_featuers_list(csv)
    label = st.selectbox(label='label', options=lst_features)
    automl_type = st.selectbox(label='Type of AutoML', options=['AutoML (Flmal)', 'Sklearn AutoML'])
    budget = st.text_area(label='Budget Time', value="10")
    start_but = st.button(label='AutoML')


if demo_but == True:
    df = pd.read_csv('WineRate.csv')
    df = pre_process_df(df)
    label = 'quality'
    X = df[df.columns.difference([label])]
    y = df[label]
    X = pre_process_features(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.2, random_state=89)
    automl = AutoML()
    automl.fit(X_train, y_train, task="classification", time_budget=int(budget))

    acc = accuracy_score(automl.predict(X_test), y_test)
    st.text(f'accuracy = {acc}')