main.py

import streamlit as st
from prediction_helper import predict

st.title('Credit Risk Modelling')

row1 = st.columns(3)
row2 = st.columns(3)
row3 = st.columns(3)
row4 = st.columns(3)

with row1[0]:
    age = st.number_input('Age', min_value= 18, max_value= 100, step = 1)
with row1[1]:
    income = st.number_input('Income', min_value=0, max_value=10000000, step=1)
with row1[2]:
    loan_amount = st.number_input('Loan Amount', min_value = 0, max_value = 10000000)

loan_to_income_ratio = loan_amount / income if income > 0 else 0
with row2[0]:
    st.text('Loan to Income Ratio')
    st.text(f'{loan_to_income_ratio:.2f}')
with row2[1]:
    loan_tenure_months = st.number_input('Loan Tenure Months', min_value = 0, max_value = 36)
with row2[2]:
    avg_dpd_per_delinquency = st.number_input('Average DPD', min_value = 0, max_value = 20)

with row3[0]:
    delinquency_ratio = st.number_input('Delinquency Ratio', min_value = 0, max_value = 30, step = 1)
with row3[1]:
    credit_utilization_ratio = st.number_input('Credit Utilization Ratio', min_value = 0, max_value = 100, step = 1)
with row3[2]:
    number_of_open_accounts = st.number_input('Loan Accounts', min_value = 0, max_value = 4, step = 1)

with row4[0]:
    residence_type = st.selectbox('Residence Type', ['Owned', 'Rented', 'Mortgage'])
with row4[1]:
    loan_purpose = st.selectbox('Loan Purpose', ['Education', 'Home', 'Auto', 'Personal'])
with row4[2]:
    loan_type = st.selectbox('Loan Type', ['Secured', 'Unsecured'])


if st.button('Calculate Risk'):
    probability, credit_score, rating = predict(age, income, loan_amount, loan_tenure_months, avg_dpd_per_delinquency, delinquency_ratio,
                                            credit_utilization_ratio,number_of_open_accounts,residence_type,loan_purpose,loan_type)

    st.write(f'Probability : {probability:.2f}')
    st.write(f'Credit Score: {credit_score}')
    st.write(f'Rating : {rating}')