app.py

import streamlit as st
import pandas as pd
import joblib

model = joblib.load("extra_trees_credit_model.pkl")
encoder = {
    col: joblib.load(f"{col}_le.pkl")
    for col in ["Sex", "Housing", "Saving accounts", "Checking account", "Purpose"]
}

st.title("Credit Risk Prediction App")
st.write("Enter applicant information to predict if the credit risk is good or bad")

age = st.number_input("Age", min_value=18, max_value=100, value=30)
sex = st.selectbox("Sex", ["male", "female"])
job = st.number_input("jobs(0-3)", min_value=0, max_value=3, value=1)
housing = st.selectbox("Housing", ["own", "rent", "free"])
saving_accounts = st.selectbox(
    "Saving accounts", ["little", "moderate", "rich", "quite rich"]
)
checking_account = st.selectbox(
    "Checking account", ["little", "moderate", "rich", "quite rich"]
)
credit_amount = st.number_input("Credit amount", min_value=0, value=1000)
duration = st.number_input("Duration (months)", min_value=1, value=12)

input_df = pd.DataFrame({
    "Age": [age],
    "Sex": [encoder["Sex"].transform([sex])[0]],
    "Job": [job],
    "Housing": [encoder["Housing"].transform([housing])[0]],
    "Saving accounts": [encoder["Saving accounts"].transform([saving_accounts])[0]],
    "Checking account": [encoder["Checking account"].transform([checking_account])[0]],
    "Credit amount": [credit_amount],
    "Duration": [duration]
})

if st.button("Predict Risk"):
    pred = model.predict(input_df)[0]

    if pred == 1:
        st.success("The predicted credit risk is: Good Credit Risk")
    else:
        st.error("The predicted credit risk is: Bad Credit Risk")