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()