Create app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load your saved model
+with open('xgb_credit_score_model.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Define the prediction function
+def predict_credit_score(interest_rate, num_credit_inquiries, delay_from_due_date, 
+                         num_credit_card, num_bank_accounts, outstanding_debt, 
+                         num_of_delayed_payment, num_of_loan):
+    # Arrange inputs into a format that the model expects
+    features = np.array([[interest_rate, num_credit_inquiries, delay_from_due_date, 
+                          num_credit_card, num_bank_accounts, outstanding_debt, 
+                          num_of_delayed_payment, num_of_loan]])
+    prediction = model.predict(features)
+    return f"Predicted Credit Score Category: {int(prediction[0])}"
+
+# Set up Gradio input interface with labeled inputs
+inputs = [
+    gr.Number(label="Interest Rate"),
+    gr.Number(label="Number of Credit Inquiries"),
+    gr.Number(label="Days Delayed from Due Date"),
+    gr.Number(label="Number of Credit Cards"),
+    gr.Number(label="Number of Bank Accounts"),
+    gr.Number(label="Outstanding Debt"),
+    gr.Number(label="Number of Delayed Payments"),
+    gr.Number(label="Number of Loans")
+]
+
+# Define the Gradio interface
+gr.Interface(fn=predict_credit_score, inputs=inputs, outputs="text",
+             title="Credit Score Predictor",
+             description="Enter your details to get a prediction of your credit score category.")\
+    .launch()