Update app.py

app.py

@@ -1 +1,44 @@
+import gradio as gr
+import joblib
+import pandas as pd
 
+
+model = joblib.load('loan_RFmodel.joblib')
+
+
+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'])
+    
+    prediction = model.predict(features)[0]
+    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"),
+        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")
+    ],
+    outputs="text",
+    title="Loan Approval Predictor",
+    description="Enter applicant details to predict loan eligibility."
+)
+
+# 4. Launch with the correct settings for Hugging Face
+demo.launch(server_name="0.0.0.0", server_port=7860)