Update app.py

app.py

@@ -30,9 +30,11 @@ age = st.number_input("Age", min_value=18, max_value=100, value=30)
 tenure = st.number_input("Tenure (in months)", min_value=0, max_value=120, value=12)
 usage_frequency = st.number_input("Usage Frequency", min_value=0, max_value=100, value=5)
 support_calls = st.number_input("Support Calls", min_value=0, max_value=50, value=2)
+payment_delay = st.number_input("Payment Delay (days)", min_value=0, max_value=365, value=5)
 total_spend = st.number_input("Total Spend", min_value=0.0, max_value=10000.0, value=100.0, step=10.0)
+last_interaction = st.number_input("Last Interaction (days ago)", min_value=0, max_value=365, value=15)
 
-# ✅ Updated: Match categories used during training
+# Match categories used during training
 gender = st.selectbox("Gender", options=["Male", "Female"])
 subscription_type = st.selectbox("Subscription Type", options=["Premium", "Standard"])
 contract_length = st.selectbox("Contract Length", options=["Monthly", "Quarterly"])
@@ -43,7 +45,9 @@ input_df = pd.DataFrame({
     "Tenure": [tenure],
     "Usage Frequency": [usage_frequency],
     "Support Calls": [support_calls],
+    "Payment Delay": [payment_delay],
     "Total Spend": [total_spend],
+    "Last Interaction": [last_interaction],
     "Gender": [gender],
     "Subscription Type": [subscription_type],
     "Contract Length": [contract_length]
@@ -65,7 +69,7 @@ input_scaled = scaler.transform(input_transformed)
 if st.button("Predict Churn"):
     prediction = ensemble.predict(input_scaled)
     prediction_proba = ensemble.predict_proba(input_scaled)[:, 1]
-    
+
     result = "Churned" if prediction[0] == 1 else "Not Churned"
     st.write("### Prediction Results")
     st.write(f"**Prediction:** {result}")