# import libraries
import streamlit as st
import numpy as np
import pandas as pd
import joblib

def app():
    st.title('Prediction for Default Payment on the Next Month')

    df = pd.read_csv('eda_data.csv')
    st.subheader('Dataset Preview')
    st.write(df)

    st.subheader('Input Data')
    input = user_input(df)

    st.subheader('User Input')
    st.write(input)

    load_model = joblib.load("model.pkl")

    prediction = load_model.predict(input)

    if prediction == 1:
        prediction = 'Payment Defaulted (1)'
    else:
        prediction = 'Payment Not Defaulted (0)'

    st.subheader('Prediction Result:')
    st.write('Based on user input, the model predicted: ')
    st.write(prediction)

def user_input(df):
    limit_balance = st.number_input('limit_balance', value=0.0)
    education_level = st.selectbox('education_level', df['education_level'].unique())
    pay_1 = st.selectbox('pay_1', [i for i in range(-1, 13)])
    pay_2 = st.selectbox('pay_2', [i for i in range(-1, 13)])
    pay_3 = st.selectbox('pay_3', [i for i in range(-1, 13)])
    pay_4 = st.selectbox('pay_4', [i for i in range(-1, 13)])
    pay_5 = st.selectbox('pay_5', [i for i in range(-1, 13)])
    pay_6 = st.selectbox('pay_6', [i for i in range(-1, 13)])
    pay_amt_1 = st.number_input('pay_amt_1', value=0.0)
    pay_amt_2 = st.number_input('pay_amt_2', value=0.0)
    pay_amt_3 = st.number_input('pay_amt_3', value=0.0)
    pay_amt_4 = st.number_input('pay_amt_4', value=0.0)
    pay_amt_5 = st.number_input('pay_amt_5', value=0.0)
    pay_amt_6 = st.number_input('pay_amt_6', value=0.0)

    data = {
        'limit_balance' : limit_balance,
        'education_level' : education_level,
        'pay_1' : pay_1,
        'pay_2' : pay_2,
        'pay_3': pay_3,
        'pay_4' : pay_4,
        'pay_5' : pay_5,
        'pay_6' : pay_6,
        'pay_amt_1' : pay_amt_1,
        'pay_amt_2' : pay_amt_2,
        'pay_amt_3' : pay_amt_3,
        'pay_amt_4' : pay_amt_4,
        'pay_amt_5' : pay_amt_5,
        'pay_amt_6' : pay_amt_6
    }
    features = pd.DataFrame(data, index=[0])
    return features