Update app.py

app.py

@@ -1,44 +1,76 @@
 import gradio as gr
 import joblib
-import pandas as pd
+import numpy as np
 
+# Load the trained loan model
+model = joblib.load("loan_model.pkl")
 
-model = joblib.load('loan_RFmodel.joblib')
+def predict_loan_status(
+    married,
+    dependents,
+    education,
+    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)
+    married = 1 if married == "Yes" else 0
+    education = 1 if education == "Graduate" else 0
 
-def predict_loan(gender, married, dependents, education, self_employed, 
-                 income, co_income, amount, term, history, area):
-    
-  
-    features = pd.DataFrame([[
-        gender, married, dependents, education, self_employed, 
-        income, co_income, amount, term, history, area
-    ]], columns=['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 
-                 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 
-                 'Loan_Amount_Term', 'Credit_History', 'Property_Area'])
-    
+    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([[
+        married,
+        dependents,
+        education,
+        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"
 
-runapp = gr.Interface(
-    fn=predict_loan,
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_loan_status,
     inputs=[
-        gr.Dropdown(choices=[(1, "Male"), (0, "Female")], label="Gender"),
-        gr.Dropdown(choices=[(1, "Yes"), (0, "No")], label="Married"),
-        gr.Number(label="Dependents (0, 1, 2, or 3)"),
-        gr.Dropdown(choices=[(0, "Graduate"), (1, "Not Graduate")], label="Education"),
-        gr.Dropdown(choices=[(1, "Yes"), (0, "No")], label="Self Employed"),
+        gr.Radio(["Yes", "No"], label="Married"),
+        gr.Number(label="Number of Dependents"),
+        gr.Radio(["Graduate", "Not Graduate"], label="Education"),
         gr.Number(label="Applicant Income"),
-        gr.Number(label="Co-applicant Income"),
+        gr.Number(label="Coapplicant Income"),
         gr.Number(label="Loan Amount"),
-        gr.Number(label="Term (e.g. 360)"),
-        gr.Dropdown(choices=[(1, "Good"), (0, "Bad")], label="Credit History"),
-        gr.Dropdown(choices=[(0, "Rural"), (1, "Semiurban"), (2, "Urban")], label="Property Area")
+        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 Approval Predictor",
-    description="Enter applicant details to predict loan eligibility."
+    title="Loan Status Prediction System",
+    description="Predict whether a loan application will be approved or rejected using a trained machine learning model."
 )
 
-# 4. Launch with the correct settings for Hugging Face
-runapp.launch(server_name="0.0.0.0", server_port=7860)
+if __name__ == "__main__":
+    interface.launch()