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| import gradio as gr | |
| import joblib | |
| # Load the trained model | |
| model = joblib.load("loan_classifier.joblib") | |
| # Load Standared Scaler | |
| scalar = joblib.load("std_scaler.bin") | |
| def predict_loan_status( | |
| int_rate, | |
| installment, | |
| log_annual_inc, | |
| dti, | |
| fico, | |
| revol_bal, | |
| revol_util, | |
| inq_last_6mths, | |
| delinq_2yrs, | |
| pub_rec, | |
| installment_to_income_ratio, | |
| credit_history, | |
| ): | |
| input_dict = { | |
| "int.rate": int_rate, | |
| "installment": installment, | |
| "log.annual.inc": log_annual_inc, | |
| "dti": dti, | |
| "fico": fico, | |
| "revol.bal": revol_bal, | |
| "revol.util": revol_util, | |
| "inq.last.6mths": inq_last_6mths, | |
| "delinq.2yrs": delinq_2yrs, | |
| "pub.rec": pub_rec, | |
| "installment_to_income_ratio": installment_to_income_ratio, | |
| "credit_history": credit_history, | |
| } | |
| # Convert the dictionary to a 2D array | |
| input_array = [list(input_dict.values())] | |
| scaled_array = scalar.transform(input_array) | |
| prediction = model.predict(scaled_array)[0] | |
| if prediction == 0: | |
| return "Loan fully paid" | |
| else: | |
| return "Loan not fully paid" | |
| inputs = [ | |
| gr.Slider(0.06, 0.23, step=0.01, label="Interest Rate"), | |
| gr.Slider(100, 950, step=10, label="Installment"), | |
| gr.Slider(7, 15, step=0.1, label="Log Annual Income"), | |
| gr.Slider(0, 40, step=1, label="DTI Ratio"), | |
| gr.Slider(600, 850, step=1, label="FICO Score"), | |
| gr.Slider(0, 120000, step=1000, label="Revolving Balance"), | |
| gr.Slider(0, 120, step=1, label="Revolving Utilization"), | |
| gr.Slider(0, 10, step=1, label="Inquiries in Last 6 Months"), | |
| gr.Slider(0, 20, step=1, label="Delinquencies in Last 2 Years"), | |
| gr.Slider(0, 10, step=1, label="Public Records"), | |
| gr.Slider(0, 5, step=0.1, label="Installment to Income Ratio"), | |
| gr.Slider(0, 1, step=0.01, label="Credit History"), | |
| ] | |
| outputs = [gr.Label(num_top_classes=2)] | |
| title = "Loan Approval Classifier" | |
| description = ( | |
| "Enter the details of the loan applicant to check if the loan is approved or not." | |
| ) | |
| gr.Interface( | |
| fn=predict_loan_status, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| description=description, | |
| ).launch() | |