model.py

import streamlit as st
import pandas as pd
import pickle


def get_input_data():
    gender_map = {"Male": 1, "Female": 2}
    edu_map = {"Graduate School": 1, "University": 2, "High School": 3, "Others": 4}
    marital_map = {"Married": 1, "Single": 2, "Others": 3}
    pay_option_map = {
        "-2: Unused": -2,
        "-1: Pay duly": -1,
        "0: Revolving credit": 0,
        "1: One month late payment": 1,
        "2: Two months late payment": 2,
        "3: Three months late payment": 3,
        "4: Four months late payment": 4,
        "5: Five months late payment": 5,
        "6: Six months late payment": 6,
        "7: Seven months late payment": 7,
        "8: Eight months late payment": 8,
        "9: Nine months or above late payment": 9
    }

    limit_balance = st.number_input(label="Input the account's limit balance", min_value=0.0)
    gender = gender_map[st.selectbox(label="Gender", options=list(gender_map.keys()))]
    education = edu_map[st.selectbox(label="Education level", options=list(edu_map.keys()))]
    marital = marital_map[st.selectbox(label="Marital status", options=list(marital_map.keys()))]
    age = st.number_input(label="Age", min_value=18, format='%d')

    pay_status, bill_amt, paid_amt = {}, {}, {}
    months = ["September", "August", "July", "June", "May", "April"]
    for month in months:
        pay_status[month] = pay_option_map[st.selectbox(label=f"Repayment status in {month}", options=list(pay_option_map.keys()))]
        bill_amt[month] = st.number_input(label=f"Bill amount in {month}")
        paid_amt[month] = st.number_input(label=f"Paid amount in {month}", min_value=0.0)

    return pd.DataFrame({
        "limit_balance": [limit_balance],
        "gender": [gender],
        "education_level": [education],
        "marital_status": [marital],
        "age": [age],
        **{f"pay_{i}": [pay_status[month]] for i, month in enumerate(months, start=1)},
        **{f"bill_amt_{i}": [bill_amt[month]] for i, month in enumerate(months, start=1)},
        **{f"pay_amt_{i}": [paid_amt[month]] for i, month in enumerate(months, start=1)}
    })

def display_prediction(data_inf):
    with open("model_svm.pkl", 'rb') as file:
        model = pickle.load(file)
    y_pred_inf = model.predict(data_inf)
    if y_pred_inf == 0:
        st.write("Not Default Payment")
    else:
        st.write("Default Payment")

def run():
    st.title("Predict the payment type")
    data_inf = get_input_data()
    st.header("Table Input")
    st.table(data_inf)
    if st.button(label="Predict"):
        display_prediction(data_inf)