first commit

app.py

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+import gradio as gr
+import numpy as np
+import joblib
+
+# Load the trained model
+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):
+    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())]
+    prediction = model.predict(input_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()

loan_classifier.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:991d8d301202fce392585331b05b533480816e46cd4a7fe289d61531a17660af
+size 22576512