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app.py

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+
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Page configuration
+st.set_page_config(
+    page_title="Tourism Package Predictor",
+    page_icon="✈️",
+    layout="wide"
+)
+
+# Title and description
+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.
+"")
+
+# Load model
+@st.cache_resource
+def load_model():
+    try:
+        model_path = hf_hub_download(
+            repo_id='SharleyK/tourism-package-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()
+
+# Create input form
+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', 'Freelancer', 'Small Business', 'Large Business'])
+    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", ['Yes', 'No'])
+    pitch_satisfaction = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+    own_car = st.selectbox("Owns Car", ['Yes', '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'])
+
+# Prediction button
+if st.button("Predict Purchase Probability", type="primary"):
+    if model is not None:
+        # Create input dataframe
+        # Note: Adjust feature order and encoding based on your actual model
+        input_data = pd.DataFrame({
+            'Age': [age],
+            'TypeofContact': [1 if type_of_contact == 'Company Invited' else 0],
+            'CityTier': [city_tier],
+            'DurationOfPitch': [duration_of_pitch],
+            'Occupation': [['Salaried', 'Freelancer', 'Small Business', 'Large Business'].index(occupation)],
+            'Gender': [0 if gender == 'Male' else 1],
+            'NumberOfPersonVisiting': [num_persons],
+            'NumberOfFollowups': [num_followups],
+            'ProductPitched': [['Basic', 'Standard', 'Deluxe', 'Super Deluxe', 'King'].index(product_pitched)],
+            'PreferredPropertyStar': [preferred_star],
+            'MaritalStatus': [['Single', 'Married', 'Divorced', 'Unmarried'].index(marital_status)],
+            'NumberOfTrips': [num_trips],
+            'Passport': [1 if passport == 'Yes' else 0],
+            'PitchSatisfactionScore': [pitch_satisfaction],
+            'OwnCar': [1 if own_car == 'Yes' else 0],
+            'NumberOfChildrenVisiting': [num_children],
+            'Designation': [['Executive', 'Manager', 'Senior Manager', 'AVP', 'VP'].index(designation)],
+            'MonthlyIncome': [monthly_income]
+        })
+
+        # Make prediction
+        try:
+            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:
+                st.metric("Prediction", "Will Purchase" if prediction == 1 else "Will Not Purchase")
+
+            with col_pred2:
+                st.metric("Confidence", f"{max(probability)*100:.2f}%")
+
+            # Recommendation
+            if prediction == 1:
+                st.balloons()
+                st.info("🎯 **Recommendation:** This customer has a high likelihood of purchasing. Consider prioritizing this lead!")
+            else:
+                st.warning("💡 **Recommendation:** This customer may need more engagement. Consider additional followups or tailored offers.")
+
+        except Exception as e:
+            st.error(f"Prediction error: {e}")
+    else:
+        st.error("Model not loaded. Please check the configuration.")
+
+# Footer
+st.markdown("---")
+st.markdown("Built with ❤️ using Streamlit | Powered by Visit with Us")