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app.py

@@ -1,237 +1,488 @@
 """
 Streamlit App for Wellness Tourism Package Prediction
-This application allows users to input customer data and predict
-whether they will purchase the Wellness Tourism Package.
+======================================================
+
+This application provides a user-friendly web interface for predicting
+whether a customer will purchase the Wellness Tourism Package.
+
+Features:
+- Interactive input forms for customer data
+- Real-time prediction with confidence scores
+- Professional UI with clear visualizations
+
+Author: Baskaran Radhakrishnan
+Date: 2026
 """
 
+# ============================================================================
+# SECTION 1: IMPORTS AND DEPENDENCIES
+# ============================================================================
+
+# Streamlit for web application framework
 import streamlit as st
+
+# Data manipulation
 import pandas as pd
+
+# Model loading and prediction
 from huggingface_hub import hf_hub_download
 import joblib
 
+
+# ============================================================================
+# SECTION 2: CONFIGURATION AND CONSTANTS
+# ============================================================================
+
+# Hugging Face Configuration
 HF_USERNAME = "BaskaranAIExpert"
+MODEL_REPO = "wellness-tourism-model"
+MODEL_FILENAME = "wellness_tourism_model_v1.joblib"
+
+# Page Configuration
+PAGE_TITLE = "Wellness Tourism Package Prediction"
+PAGE_ICON = "✈️"
+LAYOUT = "wide"
+
+
+# ============================================================================
+# SECTION 3: CATEGORICAL ENCODING MAPPINGS
+# ============================================================================
+
+# Categorical value encodings (must match training data preprocessing)
+CATEGORICAL_ENCODINGS = {
+    'Gender': {'Male': 0, 'Female': 1},
+    'MaritalStatus': {'Single': 0, 'Married': 1, 'Divorced': 2},
+    'TypeofContact': {'Company Invited': 0, 'Self Inquiry': 1},
+    'CityTier': {'Tier 1': 0, 'Tier 2': 1, 'Tier 3': 2},
+    'Occupation': {
+        'Salaried': 0, 'Freelancer': 1, 'Small Business': 2,
+        'Large Business': 3, 'Other': 4
+    },
+    'Designation': {
+        'Executive': 0, 'Manager': 1, 'Senior Manager': 2,
+        'AVP': 3, 'VP': 4, 'Other': 5
+    },
+    'ProductPitched': {
+        'Basic': 0, 'Standard': 1, 'Deluxe': 2,
+        'Super Deluxe': 3, 'King': 4
+    }
+}
+
+
+# ============================================================================
+# SECTION 4: PAGE CONFIGURATION
+# ============================================================================
+
+def configure_page():
+    """
+    Configures Streamlit page settings.
+    """
+    st.set_page_config(
+        page_title=PAGE_TITLE,
+        page_icon=PAGE_ICON,
+        layout=LAYOUT,
+        initial_sidebar_state="expanded"
+    )
+
 
-# Page configuration
-st.set_page_config(
-    page_title="Wellness Tourism Package Prediction",
-    page_icon="✈️",
-    layout="wide"
-)
+# ============================================================================
+# SECTION 5: MODEL LOADING
+# ============================================================================
 
-# Download and load the model
 @st.cache_resource
-def load_model():
-    """Load the trained model from Hugging Face Hub"""
+def load_model(hf_username, model_repo, model_filename):
+    """
+    Loads the trained model from Hugging Face Hub.
+    Uses caching to avoid reloading on every interaction.
+    
+    Args:
+        hf_username (str): Hugging Face username
+        model_repo (str): Model repository name
+        model_filename (str): Name of the model file
+        
+    Returns:
+        tuple: (model, error_message) - Model object and error message (if any)
+    """
     try:
-        model_path = hf_hub_download(
-            repo_id=f"{HF_USERNAME}/wellness-tourism-model",
-            filename="wellness_tourism_model_v1.joblib"
-        )
-        model = joblib.load(model_path)
-        return model
+        with st.spinner("Loading model from Hugging Face Hub..."):
+            model_path = hf_hub_download(
+                repo_id=f"{hf_username}/{model_repo}",
+                filename=model_filename
+            )
+            model = joblib.load(model_path)
+        return model, None
     except Exception as e:
-        st.error(f"Error loading model: {str(e)}")
-        st.info("Please ensure the model is uploaded to Hugging Face Hub and the username is correct.")
-        return None
+        error_msg = f"Error loading model: {str(e)}"
+        return None, error_msg
 
-# Load model
-model = load_model()
 
-# Streamlit UI
-st.title("✈️ Wellness Tourism Package Prediction App")
-st.markdown("""
-This application predicts whether a customer will purchase the **Wellness Tourism Package** 
-based on their profile and interaction data. Enter the customer information below to get a prediction.
-""")
+# ============================================================================
+# SECTION 6: CATEGORICAL ENCODING
+# ============================================================================
+
+def encode_categorical(value, category_type):
+    """
+    Encodes categorical values to match training data encoding.
+    
+    Args:
+        value (str): Categorical value to encode
+        category_type (str): Type of category (e.g., 'Gender', 'CityTier')
+        
+    Returns:
+        int: Encoded value (defaults to 0 if not found)
+    """
+    return CATEGORICAL_ENCODINGS.get(category_type, {}).get(value, 0)
 
-if model is None:
-    st.stop()
 
-# Create two columns for better layout
-col1, col2 = st.columns(2)
+# ============================================================================
+# SECTION 7: USER INPUT COLLECTION
+# ============================================================================
 
-with col1:
+def collect_customer_details():
+    """
+    Collects customer demographic and profile information.
+    
+    Returns:
+        dict: Dictionary containing customer details
+    """
     st.subheader("📋 Customer Details")
     
-    age = st.number_input("Age", min_value=18, max_value=100, value=35, step=1)
-    gender = st.selectbox("Gender", ["Male", "Female"])
-    marital_status = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
-    occupation = st.selectbox("Occupation", [
-        "Salaried", "Freelancer", "Small Business", "Large Business", "Other"
-    ])
-    designation = st.selectbox("Designation", [
-        "Executive", "Manager", "Senior Manager", "AVP", "VP", "Other"
-    ])
-    monthly_income = st.number_input(
-        "Monthly Income (₹)", 
-        min_value=0, 
-        max_value=1000000, 
-        value=50000, 
-        step=1000
-    )
+    customer_data = {
+        'age': st.number_input("Age", min_value=18, max_value=100, value=35, step=1),
+        'gender': st.selectbox("Gender", ["Male", "Female"]),
+        'marital_status': st.selectbox("Marital Status", ["Single", "Married", "Divorced"]),
+        'occupation': st.selectbox("Occupation", [
+            "Salaried", "Freelancer", "Small Business", "Large Business", "Other"
+        ]),
+        'designation': st.selectbox("Designation", [
+            "Executive", "Manager", "Senior Manager", "AVP", "VP", "Other"
+        ]),
+        'monthly_income': st.number_input(
+            "Monthly Income (₹)",
+            min_value=0,
+            max_value=1000000,
+            value=50000,
+            step=1000
+        ),
+        'city_tier': st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"]),
+        'number_of_trips': st.number_input(
+            "Number of Trips (Annual Average)",
+            min_value=0,
+            max_value=20,
+            value=2,
+            step=1
+        ),
+        'passport': st.selectbox("Has Passport", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No"),
+        'own_car': st.selectbox("Owns Car", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+    }
     
-    city_tier = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
-    number_of_trips = st.number_input(
-        "Number of Trips (Annual Average)", 
-        min_value=0, 
-        max_value=20, 
-        value=2, 
-        step=1
-    )
-    passport = st.selectbox("Has Passport", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
-    own_car = st.selectbox("Owns Car", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+    return customer_data
+
 
-with col2:
+def collect_travel_details():
+    """
+    Collects travel-related information.
+    
+    Returns:
+        dict: Dictionary containing travel details
+    """
     st.subheader("👨‍👩‍👧‍👦 Travel Details")
     
-    number_of_persons = st.number_input(
-        "Number of Persons Visiting", 
-        min_value=1, 
-        max_value=10, 
-        value=2, 
-        step=1
-    )
-    number_of_children = st.number_input(
-        "Number of Children Visiting (Below 5 years)", 
-        min_value=0, 
-        max_value=5, 
-        value=0, 
-        step=1
-    )
-    preferred_property_star = st.selectbox(
-        "Preferred Property Star Rating", 
-        [3, 4, 5], 
-        index=1
-    )
+    travel_data = {
+        'number_of_persons': st.number_input(
+            "Number of Persons Visiting",
+            min_value=1,
+            max_value=10,
+            value=2,
+            step=1
+        ),
+        'number_of_children': st.number_input(
+            "Number of Children Visiting (Below 5 years)",
+            min_value=0,
+            max_value=5,
+            value=0,
+            step=1
+        ),
+        'preferred_property_star': st.selectbox(
+            "Preferred Property Star Rating",
+            [3, 4, 5],
+            index=1
+        )
+    }
     
+    return travel_data
+
+
+def collect_interaction_details():
+    """
+    Collects customer interaction and sales pitch information.
+    
+    Returns:
+        dict: Dictionary containing interaction details
+    """
     st.subheader("📞 Interaction Details")
     
-    type_of_contact = st.selectbox(
-        "Type of Contact", 
-        ["Company Invited", "Self Inquiry"]
-    )
-    product_pitched = st.selectbox(
-        "Product Pitched", 
-        ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"]
-    )
-    pitch_satisfaction_score = st.slider(
-        "Pitch Satisfaction Score", 
-        min_value=1, 
-        max_value=5, 
-        value=3, 
-        step=1
-    )
-    number_of_followups = st.number_input(
-        "Number of Follow-ups", 
-        min_value=0, 
-        max_value=10, 
-        value=2, 
-        step=1
-    )
-    duration_of_pitch = st.number_input(
-        "Duration of Pitch (minutes)", 
-        min_value=0.0, 
-        max_value=60.0, 
-        value=10.0, 
-        step=0.5
-    )
-
-# Encode categorical variables (matching the preprocessing in prep.py)
-def encode_categorical(value, category_type):
-    """Encode categorical values to match training data encoding"""
-    encodings = {
-        'Gender': {'Male': 0, 'Female': 1},
-        'MaritalStatus': {'Single': 0, 'Married': 1, 'Divorced': 2},
-        'TypeofContact': {'Company Invited': 0, 'Self Inquiry': 1},
-        'CityTier': {'Tier 1': 0, 'Tier 2': 1, 'Tier 3': 2},
-        'Occupation': {
-            'Salaried': 0, 'Freelancer': 1, 'Small Business': 2, 
-            'Large Business': 3, 'Other': 4
-        },
-        'Designation': {
-            'Executive': 0, 'Manager': 1, 'Senior Manager': 2,
-            'AVP': 3, 'VP': 4, 'Other': 5
-        },
-        'ProductPitched': {
-            'Basic': 0, 'Standard': 1, 'Deluxe': 2,
-            'Super Deluxe': 3, 'King': 4
-        }
+    interaction_data = {
+        'type_of_contact': st.selectbox(
+            "Type of Contact",
+            ["Company Invited", "Self Inquiry"]
+        ),
+        'product_pitched': st.selectbox(
+            "Product Pitched",
+            ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"]
+        ),
+        'pitch_satisfaction_score': st.slider(
+            "Pitch Satisfaction Score",
+            min_value=1,
+            max_value=5,
+            value=3,
+            step=1
+        ),
+        'number_of_followups': st.number_input(
+            "Number of Follow-ups",
+            min_value=0,
+            max_value=10,
+            value=2,
+            step=1
+        ),
+        'duration_of_pitch': st.number_input(
+            "Duration of Pitch (minutes)",
+            min_value=0.0,
+            max_value=60.0,
+            value=10.0,
+            step=0.5
+        )
     }
-    return encodings.get(category_type, {}).get(value, 0)
+    
+    return interaction_data
 
-# Assemble input into DataFrame
-if st.button("🔮 Predict Purchase Likelihood", type="primary"):
+
+# ============================================================================
+# SECTION 8: DATA PREPARATION FOR PREDICTION
+# ============================================================================
+
+def prepare_input_data(customer_data, travel_data, interaction_data):
+    """
+    Prepares input data in the format expected by the model.
+    
+    Args:
+        customer_data (dict): Customer demographic information
+        travel_data (dict): Travel-related information
+        interaction_data (dict): Interaction details
+        
+    Returns:
+        pd.DataFrame: Prepared input data
+    """
     input_data = pd.DataFrame([{
-        'Age': age,
-        'TypeofContact': encode_categorical(type_of_contact, 'TypeofContact'),
-        'CityTier': encode_categorical(city_tier, 'CityTier'),
-        'Occupation': encode_categorical(occupation, 'Occupation'),
-        'Gender': encode_categorical(gender, 'Gender'),
-        'NumberOfPersonVisiting': number_of_persons,
-        'PreferredPropertyStar': preferred_property_star,
-        'MaritalStatus': encode_categorical(marital_status, 'MaritalStatus'),
-        'NumberOfTrips': number_of_trips,
-        'Passport': passport,
-        'OwnCar': own_car,
-        'NumberOfChildrenVisiting': number_of_children,
-        'Designation': encode_categorical(designation, 'Designation'),
-        'MonthlyIncome': monthly_income,
-        'PitchSatisfactionScore': pitch_satisfaction_score,
-        'ProductPitched': encode_categorical(product_pitched, 'ProductPitched'),
-        'NumberOfFollowups': number_of_followups,
-        'DurationOfPitch': duration_of_pitch
+        'Age': customer_data['age'],
+        'TypeofContact': encode_categorical(interaction_data['type_of_contact'], 'TypeofContact'),
+        'CityTier': encode_categorical(customer_data['city_tier'], 'CityTier'),
+        'Occupation': encode_categorical(customer_data['occupation'], 'Occupation'),
+        'Gender': encode_categorical(customer_data['gender'], 'Gender'),
+        'NumberOfPersonVisiting': travel_data['number_of_persons'],
+        'PreferredPropertyStar': travel_data['preferred_property_star'],
+        'MaritalStatus': encode_categorical(customer_data['marital_status'], 'MaritalStatus'),
+        'NumberOfTrips': customer_data['number_of_trips'],
+        'Passport': customer_data['passport'],
+        'OwnCar': customer_data['own_car'],
+        'NumberOfChildrenVisiting': travel_data['number_of_children'],
+        'Designation': encode_categorical(customer_data['designation'], 'Designation'),
+        'MonthlyIncome': customer_data['monthly_income'],
+        'PitchSatisfactionScore': interaction_data['pitch_satisfaction_score'],
+        'ProductPitched': encode_categorical(interaction_data['product_pitched'], 'ProductPitched'),
+        'NumberOfFollowups': interaction_data['number_of_followups'],
+        'DurationOfPitch': interaction_data['duration_of_pitch']
     }])
     
-    try:
-        # Get expected columns from the preprocessing step in the pipeline
-        # The model is a Pipeline with a ColumnTransformer as the first step
-        expected_cols = None
-        if hasattr(model, 'steps') and len(model.steps) > 0:
-            preprocessor = model.steps[0][1]  # Get the ColumnTransformer
-            if hasattr(preprocessor, 'feature_names_in_'):
-                expected_cols = list(preprocessor.feature_names_in_)
-        
-        # If model expects 'Unnamed: 0', add it (workaround for current model)
-        # This will be fixed when the model is retrained without this column
-        if expected_cols and 'Unnamed: 0' in expected_cols:
-            if 'Unnamed: 0' not in input_data.columns:
-                input_data['Unnamed: 0'] = 0
-        
-        # Reorder columns to match expected order if available
-        if expected_cols:
-            # Ensure all expected columns are present
-            for col in expected_cols:
-                if col not in input_data.columns:
-                    input_data[col] = 0
-            # Select columns in the expected order
-            input_data = input_data[expected_cols]
-        
-        prediction = model.predict(input_data)[0]
-        prediction_proba = model.predict_proba(input_data)[0]
-        
-        st.markdown("---")
-        st.subheader("📊 Prediction Result")
+    return input_data
+
+
+def align_input_with_model(input_data, model):
+    """
+    Aligns input data columns with model's expected feature order.
+    
+    Args:
+        input_data (pd.DataFrame): Input data
+        model: Trained model pipeline
         
-        if prediction == 1:
-            st.success(f"✅ **The customer is LIKELY to purchase the Wellness Tourism Package!**")
-            st.info(f"Confidence: {prediction_proba[1]*100:.2f}%")
-        else:
-            st.warning(f"❌ **The customer is NOT LIKELY to purchase the Wellness Tourism Package.**")
-            st.info(f"Confidence: {prediction_proba[0]*100:.2f}%")
+    Returns:
+        pd.DataFrame: Aligned input data
+    """
+    # Get expected columns from the preprocessing step in the pipeline
+    expected_cols = None
+    if hasattr(model, 'steps') and len(model.steps) > 0:
+        preprocessor = model.steps[0][1]  # Get the ColumnTransformer
+        if hasattr(preprocessor, 'feature_names_in_'):
+            expected_cols = list(preprocessor.feature_names_in_)
+    
+    # Handle 'Unnamed: 0' column if model expects it
+    if expected_cols and 'Unnamed: 0' in expected_cols:
+        if 'Unnamed: 0' not in input_data.columns:
+            input_data['Unnamed: 0'] = 0
+    
+    # Reorder columns to match expected order
+    if expected_cols:
+        # Ensure all expected columns are present
+        for col in expected_cols:
+            if col not in input_data.columns:
+                input_data[col] = 0
+        # Select columns in the expected order
+        input_data = input_data[expected_cols]
+    
+    return input_data
+
+
+# ============================================================================
+# SECTION 9: PREDICTION AND DISPLAY
+# ============================================================================
+
+def make_prediction(model, input_data):
+    """
+    Makes prediction using the trained model.
+    
+    Args:
+        model: Trained model
+        input_data (pd.DataFrame): Prepared input data
         
-        col_prob1, col_prob2 = st.columns(2)
-        with col_prob1:
-            st.metric("Probability of Purchase", f"{prediction_proba[1]*100:.2f}%")
-        with col_prob2:
-            st.metric("Probability of No Purchase", f"{prediction_proba[0]*100:.2f}%")
+    Returns:
+        tuple: (prediction, prediction_proba) - Prediction and probabilities
+    """
+    prediction = model.predict(input_data)[0]
+    prediction_proba = model.predict_proba(input_data)[0]
+    return prediction, prediction_proba
+
+
+def display_prediction_results(prediction, prediction_proba):
+    """
+    Displays prediction results with visualizations.
+    
+    Args:
+        prediction (int): Predicted class (0 or 1)
+        prediction_proba (np.array): Prediction probabilities
+    """
+    st.markdown("---")
+    st.subheader("📊 Prediction Result")
+    
+    # Display main prediction
+    if prediction == 1:
+        st.success(f"✅ **The customer is LIKELY to purchase the Wellness Tourism Package!**")
+        st.info(f"**Confidence Level:** {prediction_proba[1]*100:.2f}%")
+    else:
+        st.warning(f"❌ **The customer is NOT LIKELY to purchase the Wellness Tourism Package.**")
+        st.info(f"**Confidence Level:** {prediction_proba[0]*100:.2f}%")
+    
+    # Display probability metrics
+    col_prob1, col_prob2 = st.columns(2)
+    with col_prob1:
+        st.metric(
+            "Probability of Purchase",
+            f"{prediction_proba[1]*100:.2f}%",
+            delta=f"{prediction_proba[1]*100 - 50:.2f}%"
+        )
+    with col_prob2:
+        st.metric(
+            "Probability of No Purchase",
+            f"{prediction_proba[0]*100:.2f}%",
+            delta=f"{prediction_proba[0]*100 - 50:.2f}%"
+        )
+    
+    # Display recommendation
+    if prediction == 1:
+        st.info("💡 **Recommendation:** This customer shows high purchase likelihood. Consider prioritizing follow-up communication.")
+    else:
+        st.info("💡 **Recommendation:** This customer shows low purchase likelihood. Consider alternative marketing strategies.")
+
+
+# ============================================================================
+# SECTION 10: MAIN APPLICATION UI
+# ============================================================================
+
+def render_header():
+    """
+    Renders the application header and description.
+    """
+    st.title(f"{PAGE_ICON} {PAGE_TITLE}")
+    st.markdown("""
+    This application predicts whether a customer will purchase the **Wellness Tourism Package**
+    based on their profile and interaction data. Enter the customer information below to get a prediction.
+    """)
+
+
+def render_footer():
+    """
+    Renders the application footer.
+    """
+    st.markdown("---")
+    st.markdown("""
+    <div style='text-align: center; color: gray; padding: 20px;'>
+        <p><strong>Built with ❤️ for Visit with Us</strong></p>
+        <p>MLOps Pipeline | Production Ready</p>
+        <p style='font-size: 0.8em;'>Model Version: v1.0 | Last Updated: 2024</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+
+def main():
+    """
+    Main application function that orchestrates the Streamlit UI.
+    """
+    # Configure page
+    configure_page()
+    
+    # Render header
+    render_header()
+    
+    # Load model
+    model, error = load_model(HF_USERNAME, MODEL_REPO, MODEL_FILENAME)
+    
+    # Handle model loading error
+    if model is None:
+        st.error(f"⚠️ {error}")
+        st.info("💡 Please ensure:")
+        st.info("1. The model is uploaded to Hugging Face Hub")
+        st.info("2. The username is correct in the configuration")
+        st.info("3. You have internet connectivity")
+        st.stop()
+    
+    # Display success message
+    st.success("✓ Model loaded successfully!")
+    
+    # Create input form layout
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        customer_data = collect_customer_details()
+    
+    with col2:
+        travel_data = collect_travel_details()
+        interaction_data = collect_interaction_details()
+    
+    # Prediction button
+    if st.button("🔮 Predict Purchase Likelihood", type="primary", use_container_width=True):
+        try:
+            # Prepare input data
+            input_data = prepare_input_data(customer_data, travel_data, interaction_data)
             
-    except Exception as e:
-        st.error(f"Error making prediction: {str(e)}")
+            # Align with model expectations
+            input_data = align_input_with_model(input_data, model)
+            
+            # Make prediction
+            prediction, prediction_proba = make_prediction(model, input_data)
+            
+            # Display results
+            display_prediction_results(prediction, prediction_proba)
+            
+        except Exception as e:
+            st.error(f"❌ Error making prediction: {str(e)}")
+            st.info("Please check the input values and try again.")
+    
+    # Render footer
+    render_footer()
+
 
-st.markdown("---")
-st.markdown("""
-<div style='text-align: center; color: gray;'>
-    <p>Built with ❤️ for Visit with Us | MLOps Pipeline</p>
-</div>
-""", unsafe_allow_html=True)
+# ============================================================================
+# SECTION 11: SCRIPT ENTRY POINT
+# ============================================================================
 
+if __name__ == "__main__":
+    main()