app.py

import streamlit as st
import pandas as pd
import numpy as np
import joblib
from huggingface_hub import hf_hub_download

st.set_page_config(
    page_title="Tourism Package Predictor",
    page_icon="✈️",
    layout="wide"
)

st.title(" Wellness Tourism Package Purchase Predictor")
st.markdown("""
This application predicts whether a customer is likely to purchase the Wellness Tourism Package.
Enter customer details below to get a prediction.
""")

@st.cache_resource
def load_model():
    try:
        model_path = hf_hub_download(
            repo_id='SharleyK/TourismPackagePrediction-Model',
            filename='best_model.pkl'
        )
        model = joblib.load(model_path)
        return model
    except Exception as e:
        st.error(f"Error loading model: {e}")
        return None

model = load_model()

st.header("Customer Information")

col1, col2, col3 = st.columns(3)

with col1:
    age = st.number_input("Age", min_value=18, max_value=100, value=30)
    city_tier = st.selectbox("City Tier", [1, 2, 3])
    duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=0.0, value=15.0)
    occupation = st.selectbox("Occupation", ['Salaried', 'Small Business', 'Large Business', 'Free Lancer'])
    gender = st.selectbox("Gender", ['Male', 'Female'])

with col2:
    num_persons = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
    num_followups = st.number_input("Number of Followups", min_value=0.0, value=3.0)
    product_pitched = st.selectbox("Product Pitched", ['Basic', 'Standard', 'Deluxe', 'Super Deluxe', 'King'])
    preferred_star = st.selectbox("Preferred Property Star", [3.0, 4.0, 5.0])
    marital_status = st.selectbox("Marital Status", ['Single', 'Married', 'Divorced', 'Unmarried'])

with col3:
    num_trips = st.number_input("Number of Trips Per Year", min_value=0.0, value=2.0)
    passport = st.selectbox("Has Passport", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
    pitch_satisfaction = st.slider("Pitch Satisfaction Score", 1, 5, 3)
    own_car = st.selectbox("Owns Car", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
    num_children = st.number_input("Number of Children Visiting", min_value=0.0, value=0.0)

col4, col5 = st.columns(2)
with col4:
    designation = st.selectbox("Designation", ['Executive', 'Manager', 'Senior Manager', 'AVP', 'VP'])
with col5:
    monthly_income = st.number_input("Monthly Income", min_value=0.0, value=25000.0)

type_of_contact = st.selectbox("Type of Contact", ['Company Invited', 'Self Inquiry'])

occupation_map = {'Salaried': 0, 'Small Business': 1, 'Large Business': 2, 'Free Lancer': 3}
product_map = {'Basic': 0, 'Standard': 1, 'Deluxe': 2, 'Super Deluxe': 3, 'King': 4}
marital_map = {'Single': 0, 'Married': 1, 'Divorced': 2, 'Unmarried': 3}
designation_map = {'Executive': 0, 'Manager': 1, 'Senior Manager': 2, 'AVP': 3, 'VP': 4}

if st.button(" Predict Purchase Probability", type="primary"):
    if model is not None:
        try:
            input_data = pd.DataFrame({
                'Age': [age],
                'TypeofContact': [1 if type_of_contact == 'Company Invited' else 0],
                'CityTier': [city_tier],
                'DurationOfPitch': [duration_of_pitch],
                'Occupation': [occupation_map[occupation]],
                'Gender': [0 if gender == 'Male' else 1],
                'NumberOfPersonVisiting': [num_persons],
                'NumberOfFollowups': [num_followups],
                'ProductPitched': [product_map[product_pitched]],
                'PreferredPropertyStar': [preferred_star],
                'MaritalStatus': [marital_map[marital_status]],
                'NumberOfTrips': [num_trips],
                'Passport': [passport],
                'PitchSatisfactionScore': [pitch_satisfaction],
                'OwnCar': [own_car],
                'NumberOfChildrenVisiting': [num_children],
                'Designation': [designation_map[designation]],
                'MonthlyIncome': [monthly_income]
            })

            prediction = model.predict(input_data)[0]
            probability = model.predict_proba(input_data)[0]

            st.success(" Prediction Complete!")

            col_pred1, col_pred2 = st.columns(2)

            with col_pred1:
                if prediction == 1:
                    st.metric("Prediction", " Will Purchase", delta="High Priority")
                else:
                    st.metric("Prediction", " Will Not Purchase", delta="Low Priority")

            with col_pred2:
                confidence = max(probability) * 100
                st.metric("Confidence", f"{confidence:.2f}%")

            st.progress(confidence / 100)

            st.markdown("---")
            st.subheader(" Recommendation")

            if prediction == 1:
                st.balloons()
                st.success("""
                 **High Conversion Probability!**

                This customer shows strong indicators for purchase:
                - Consider prioritizing this lead
                - Assign to experienced sales representative
                - Offer personalized package options
                - Schedule follow-up within 24-48 hours
                """)
            else:
                st.warning("""
                 **Requires More Engagement**

                This customer may need additional nurturing:
                - Schedule more follow-up calls
                - Provide tailored promotional offers
                - Share customer testimonials and reviews
                - Highlight unique package benefits
                """)

            with st.expander(" View Input Summary"):
                st.dataframe(input_data, use_container_width=True)

        except Exception as e:
            st.error(f" Prediction error: {e}")
            st.info("Please ensure all fields are filled correctly.")
    else:
        st.error(" Model not loaded. Please check the configuration.")

with st.sidebar:
    st.header(" About")
    st.markdown("""
    This ML-powered application helps **Visit with Us** identify potential customers
    for the Wellness Tourism Package.

    **Features:**
    -  AI-powered predictions
    -  Confidence scores
    -  Actionable recommendations
    -  Real-time results

    **Model Info:**
    - Algorithm: Ensemble ML Models
    - Accuracy: 85%+
    - Training Data: 4,000+ customers
    """)

    st.markdown("---")
    st.markdown("**Need Help?**")
    st.markdown("Contact: support@visitwithus.com")

st.markdown("---")
st.markdown("""
<div style='text-align: center'>
    <p>Built with  using Streamlit | Powered by <b>Visit with Us</b></p>
    <p style='font-size: 12px; color: gray;'>MLOps Pipeline • Hugging Face Deployment • Real-time Predictions</p>
</div>
""", unsafe_allow_html=True)