Update app.py

app.py

@@ -449,8 +449,10 @@ elif option == "Financial Recommender Engine":
 elif option == "SML Classification":
     st.title("SML Classification - Financial Product Prediction")
 
+    # Collect user inputs for prediction
+    st.markdown("### Provide the details to predict the financial product:")
+
     # User inputs
-    age = st.slider("Your Age", 18, 70, 30)
     income_bracket = st.selectbox("Income Bracket (1 = Lowest, 5 = Highest)", [1, 2, 3, 4, 5])
     has_internet_access = st.radio("Do you have Internet Access?", ["Yes", "No"])
     employed = st.radio("Are you employed?", ["Yes", "No"])
@@ -460,7 +462,7 @@ elif option == "SML Classification":
     education_level = st.selectbox("Education Level", ["Primary", "Secondary", "Tertiary"])
     age_group = st.selectbox("Age Group", ["Teen", "Young Adult", "Adult", "Middle Age", "Older Adult", "Elder", "Senior"])
 
-    # Prepare categorical features
+    # Prepare input data to match the model's expected features
     input_data = pd.DataFrame({
         'Income Bracket': [income_bracket],
         'Has Internet Access': [1 if has_internet_access == "Yes" else 0],
@@ -482,11 +484,12 @@ elif option == "SML Classification":
         'Age Group_Senior': [1 if age_group == "Senior" else 0]
     })
 
-    # One-hot encode the categorical features
+    # One-hot encode the input data
     input_data_encoded = pd.DataFrame(ohe.transform(input_data).todense(), columns=ohe.get_feature_names_out())
 
     # Prediction
     if st.button("Predict"):
+        # Predict using the loaded model
         prediction = xgb_model.predict(input_data_encoded)[0]
         st.write(f"Prediction: {prediction}")