app.py

import gradio as gr
import pandas as pd
import numpy as np
from joblib import load

# Load the trained model
model = load("credit_classifier.joblib")

# Prediction function
def predict_credit_score(outstanding_debt, credit_mix, credit_history_age, monthly_balance,
                         payment_behaviour, annual_income, delayed_payments):
    input_data = [[
        outstanding_debt, credit_mix, credit_history_age,
        monthly_balance, payment_behaviour, annual_income,
        delayed_payments
    ]]
    prediction = model.predict(input_data)[0]

    target_names = {0: "Good", 1: "Poor", 2: "Standard"}
    return target_names.get(prediction, "Unknown")

# Gradio interface
demo = gr.Interface(
    fn=predict_credit_score,
    inputs=[
        gr.Number(label="Outstanding Debt"),
        gr.Number(label="Credit Mix"),
        gr.Number(label="Credit History Age"),
        gr.Number(label="Monthly Balance"),
        gr.Number(label="Payment Behaviour"),
        gr.Number(label="Annual Income"),
        gr.Number(label="Delayed Payments"),
    ],
    outputs="text",
    title="Credit Scoring Classifier",
    description="Enter your credit-related information to classify your credit score as Good, Standard, or Poor.",
)

demo.launch()