app.py

import streamlit as st
import pandas as pd
import joblib

st.header('FTDS Model Deployment')
st.write("""
Created by FTDS Curriculum Team
Use the sidebar to select input features.
""")

st.sidebar.header('User Input Features')

def user_input():
    limit_balance = st.sidebar.selectbox('limit_balance', [ 80000., 200000.,  20000., 260000., 150000., 300000., 130000.,
       500000., 230000., 460000., 780000., 170000., 320000., 290000.,
       240000., 340000., 360000., 380000., 180000., 100000.,  90000.,
        50000., 160000.,  70000., 280000., 220000.,  30000., 120000.,
        10000., 470000., 310000., 140000.,  60000., 110000., 430000.,
       210000., 490000., 330000., 250000., 400000., 370000., 440000.,
       700000., 530000., 390000., 410000., 270000., 560000.,  40000.,
       680000., 480000., 190000., 350000., 420000., 510000., 800000.,
       450000., 750000., 620000.])
    pay_1 = st.sidebar.slider('pay_1', min_value=-12, max_value=12)
    pay_2 = st.sidebar.slider('pay_2', min_value=-12, max_value=12)
    pay_3 = st.sidebar.slider('pay_3', min_value=-12, max_value=12)
    pay_4 = st.sidebar.slider('pay_4', min_value=-12, max_value=12)
    pay_5 = st.sidebar.slider('pay_5', min_value=-12, max_value=12)
    pay_6 = st.sidebar.slider('pay_6', min_value=-12, max_value=12)

    data = {
        'limit_balance': limit_balance,
        'pay_0': pay_1,
        'pay_2': pay_2,
        'pay_3': pay_3,
        'pay_4': pay_4,
        'pay_5': pay_5,
        'pay_6': pay_6
    }
    features = pd.DataFrame(data, index=[0])
    return features


input = user_input()

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

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

if st.button("Predict"):
    prediction = load_model.predict(input)

    if prediction == 1:
        prediction = 'Defaulted Payment'
    else:
        prediction = 'Not Defaulted'

    st.subheader('Based on user input, the placement model predicted: ')
    st.header(prediction)