Update app.py

app.py

@@ -6,28 +6,26 @@ import pandas as pd
 model = joblib.load('loan_RFmodel.joblib')
 
 
-def predict_loan(gender, married, dependents, education, self_employed, 
+def predict_loan(married, dependents, education, 
                  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', 
+    ]], columns=['Married', 'Dependents', 'Education', 
                  'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 
                  'Loan_Amount_Term', 'Credit_History', 'Property_Area'])
     
     prediction = model.predict(features)[0]
-    return "✅ Loan Approved" if prediction == 1 else "❌ Loan Rejected"
+    return "Loan Approved" if prediction == 1 else "Loan Rejected"
 
 demo = gr.Interface(
     fn=predict_loan,
     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.Number(label="Applicant Income"),
         gr.Number(label="Co-applicant Income"),
         gr.Number(label="Loan Amount"),