Loan

app.py

@@ -0,0 +1,76 @@
+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()