loan predictor update

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+
+# Load the trained loan model
+model = joblib.load("loan_model.pkl")
+
+def predict_loan_status(
+    gender,
+    married,
+    dependents,
+    education,
+    self_employed,
+    applicant_income,
+    coapplicant_income,
+    loan_amount,
+    loan_amount_term,
+    credit_history,
+    property_area
+):
+    """
+    This function:
+    - Receives user inputs
+    - Converts categorical inputs to numeric values
+    - Uses the trained model to predict loan status
+    """
+
+    # Encoding categorical variables (must match training logic)
+    gender = 1 if gender == "Male" else 0
+    married = 1 if married == "Yes" else 0
+    education = 1 if education == "Graduate" else 0
+    self_employed = 1 if self_employed == "Yes" else 0
+
+    property_area_map = {
+        "Urban": 2,
+        "Semiurban": 1,
+        "Rural": 0
+    }
+    property_area = property_area_map[property_area]
+
+    # Combine inputs into model-ready format
+    features = np.array([[
+        gender,
+        married,
+        dependents,
+        education,
+        self_employed,
+        applicant_income,
+        coapplicant_income,
+        loan_amount,
+        loan_amount_term,
+        credit_history,
+        property_area
+    ]])
+
+    # Make prediction
+    prediction = model.predict(features)[0]
+
+    return "Loan Approved" if prediction == 1 else "Loan Rejected"
+
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_loan_status,
+    inputs=[
+        gr.Radio(["Male", "Female"], label="Gender"),
+        gr.Radio(["Yes", "No"], label="Married"),
+        gr.Number(label="Number of Dependents"),
+        gr.Radio(["Graduate", "Not Graduate"], label="Education"),
+        gr.Radio(["Yes", "No"], label="Self Employed"),
+        gr.Number(label="Applicant Income"),
+        gr.Number(label="Coapplicant Income"),
+        gr.Number(label="Loan Amount"),
+        gr.Number(label="Loan Amount Term"),
+        gr.Radio([1, 0], label="Credit History (1 = Good, 0 = Bad)"),
+        gr.Radio(["Urban", "Semiurban", "Rural"], label="Property Area"),
+    ],
+    outputs="text",
+    title="Loan Status Prediction System",
+    description="Predict whether a loan application will be approved or rejected using a trained machine learning model."
+)
+
+if __name__ == "__main__":
+    interface.launch()

loan_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3c8389a3a39806c6b8a9aa3a9aff0ae73a84a348721743c9ba8e75e18ba1fc72
+size 2414313

requirements.txt

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+scikit-learn
+pandas
+numpy
+joblib
+gradio