Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd | |
| from huggingface_hub import hf_hub_download | |
| import joblib | |
| import os | |
| # Get token from environment | |
| token = os.environ.get("HF_TOKEN") | |
| # Download the model | |
| model_path = hf_hub_download( | |
| repo_id="Hugo014/Tourism-Model", | |
| filename="best_tourism_model_v2.joblib", | |
| repo_type="model", | |
| token=token | |
| ) | |
| model = joblib.load(model_path) | |
| print("Model loaded successfully!") | |
| # Streamlit UI for Tourism Package Prediction | |
| st.title("🌴 Wellness Tourism Package Prediction") | |
| st.write(""" | |
| This application predicts whether a customer is likely to purchase the **Wellness Tourism Package** | |
| based on their demographic and interaction data. Enter the customer details below to get a prediction. | |
| """) | |
| st.sidebar.header("📋 Customer Information") | |
| # Customer Demographics | |
| st.sidebar.subheader("Demographics") | |
| age = st.sidebar.number_input("Age", min_value=18, max_value=100, value=35, step=1) | |
| gender = st.sidebar.selectbox("Gender", ["Female", "Male"]) | |
| marital_status = st.sidebar.selectbox("Marital Status", ["Single", "Married", "Divorced", "Unmarried"]) | |
| occupation = st.sidebar.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"]) | |
| monthly_income = st.sidebar.number_input("Monthly Income ($)", min_value=0, max_value=100000, value=25000, step=1000) | |
| # Family & Travel Details | |
| st.sidebar.subheader("Travel Preferences") | |
| number_of_person_visiting = st.sidebar.number_input("Number of People Traveling", min_value=1, max_value=10, value=2, step=1) | |
| number_of_children_visiting = st.sidebar.number_input("Number of Children (Below 5)", min_value=0, max_value=5, value=0, step=1) | |
| preferred_property_star = st.sidebar.slider("Preferred Hotel Star Rating", min_value=3.0, max_value=5.0, value=4.0, step=0.5) | |
| number_of_trips = st.sidebar.number_input("Average Trips Per Year", min_value=0, max_value=20, value=2, step=1) | |
| # Assets & Documentation | |
| st.sidebar.subheader("Assets") | |
| own_car = st.sidebar.selectbox("Own Car", ["No", "Yes"]) | |
| passport = st.sidebar.selectbox("Valid Passport", ["No", "Yes"]) | |
| # Professional Details | |
| st.sidebar.subheader("Professional Info") | |
| designation = st.sidebar.selectbox("Designation", ["Manager", "Senior Manager", "AVP", "VP", "Executive"]) | |
| city_tier = st.sidebar.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"]) | |
| # Interaction Data | |
| st.sidebar.subheader("Sales Interaction") | |
| type_of_contact = st.sidebar.selectbox("Type of Contact", ["Self Inquiry", "Company Invited"]) | |
| product_pitched = st.sidebar.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"]) | |
| pitch_satisfaction_score = st.sidebar.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3, step=1) | |
| number_of_followups = st.sidebar.number_input("Number of Follow-ups", min_value=0, max_value=10, value=2, step=1) | |
| duration_of_pitch = st.sidebar.number_input("Duration of Pitch (minutes)", min_value=0.0, max_value=60.0, value=15.0, step=1.0) | |
| # Encode inputs to match training data | |
| def encode_inputs(): | |
| # Gender encoding (Female=0, Male=1) | |
| gender_encoded = 0 if gender == "Female" else 1 | |
| # TypeofContact encoding (Company Invited=0, Self Inquiry=1) | |
| contact_encoded = 1 if type_of_contact == "Self Inquiry" else 0 | |
| # Occupation encoding | |
| occupation_map = {"Salaried": 2, "Small Business": 0, "Large Business": 3, "Free Lancer": 1} | |
| occupation_encoded = occupation_map[occupation] | |
| # MaritalStatus encoding (Single=2, Married=0, Divorced=1, Unmarried=3) | |
| marital_map = {"Single": 2, "Married": 0, "Divorced": 1, "Unmarried": 3} | |
| marital_encoded = marital_map[marital_status] | |
| # ProductPitched ordinal encoding (Basic=0, Standard=1, Deluxe=2, Super Deluxe=3, King=4) | |
| product_map = {"Basic": 0, "Standard": 1, "Deluxe": 2, "Super Deluxe": 3, "King": 4} | |
| product_encoded = product_map[product_pitched] | |
| # Designation ordinal encoding (Manager=0, Senior Manager=1, AVP=2, VP=3, Executive=4) | |
| designation_map = {"Manager": 0, "Senior Manager": 1, "AVP": 2, "VP": 3, "Executive": 4} | |
| designation_encoded = designation_map[designation] | |
| # CityTier (extract number) | |
| city_tier_num = int(city_tier.split()[-1]) | |
| # Binary encodings | |
| own_car_encoded = 1 if own_car == "Yes" else 0 | |
| passport_encoded = 1 if passport == "Yes" else 0 | |
| return { | |
| 'Unnamed: 0': 0, # Model expects this column (dummy index) | |
| 'Age': age, | |
| 'TypeofContact': contact_encoded, | |
| 'CityTier': city_tier_num, | |
| 'Occupation': occupation_encoded, | |
| 'Gender': gender_encoded, | |
| 'NumberOfPersonVisiting': number_of_person_visiting, | |
| 'PreferredPropertyStar': preferred_property_star, | |
| 'MaritalStatus': marital_encoded, | |
| 'NumberOfTrips': number_of_trips, | |
| 'Passport': passport_encoded, | |
| 'OwnCar': own_car_encoded, | |
| 'NumberOfChildrenVisiting': number_of_children_visiting, | |
| 'Designation': designation_encoded, | |
| 'MonthlyIncome': monthly_income, | |
| 'PitchSatisfactionScore': pitch_satisfaction_score, | |
| 'ProductPitched': product_encoded, | |
| 'NumberOfFollowups': number_of_followups, | |
| 'DurationOfPitch': duration_of_pitch | |
| } | |
| # Main content area | |
| st.markdown("---") | |
| col1, col2, col3 = st.columns([1, 2, 1]) | |
| with col2: | |
| if st.button("🔮 Predict Purchase Likelihood", use_container_width=True): | |
| # Prepare input data | |
| input_dict = encode_inputs() | |
| input_data = pd.DataFrame([input_dict]) | |
| # Make prediction | |
| with st.spinner("Analyzing customer profile..."): | |
| prediction = model.predict(input_data)[0] | |
| prediction_proba = model.predict_proba(input_data)[0] | |
| # Display results | |
| st.markdown("---") | |
| st.subheader("📊 Prediction Result") | |
| if prediction == 1: | |
| st.success("✅ **High Purchase Likelihood**") | |
| st.write(f"This customer is **likely to purchase** the Wellness Tourism Package.") | |
| st.metric("Confidence", f"{prediction_proba[1]*100:.1f}%") | |
| st.info("💡 **Recommendation**: Prioritize this lead for sales outreach!") | |
| else: | |
| st.warning("❌ **Low Purchase Likelihood**") | |
| st.write(f"This customer is **unlikely to purchase** the package at this time.") | |
| st.metric("Confidence", f"{prediction_proba[0]*100:.1f}%") | |
| st.info("💡 **Recommendation**: Consider nurturing this lead or offering alternative packages.") | |
| # Show probability breakdown | |
| st.markdown("---") | |
| st.subheader("📈 Probability Breakdown") | |
| col_a, col_b = st.columns(2) | |
| with col_a: | |
| st.metric("Will NOT Purchase", f"{prediction_proba[0]*100:.1f}%") | |
| with col_b: | |
| st.metric("Will Purchase", f"{prediction_proba[1]*100:.1f}%") | |
| # Footer | |
| st.markdown("---") | |
| st.caption("🏨 Visit with Us - Tourism Package Prediction System | Powered by XGBoost ML Model") | |