Update app.py

app.py

@@ -1,38 +1,37 @@
 import gradio as gr
-import numpy as np
 import joblib
 
 # Load the trained model
-model = joblib.load('loan_classifier.joblib')
+model = joblib.load("loan_classifier.joblib")
 
 
-
-
-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):
+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
+        "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())]
@@ -43,22 +42,31 @@ def predict_loan_status(int_rate,
     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"),
-         ]
+    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()
+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()