Upload app.py with huggingface_hub

app.py

@@ -4,25 +4,23 @@ 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.
+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',
+            repo_id='SharleyK/TourismPackagePrediction-GradientBoosting',
             filename='best_model.pkl'
         )
         model = joblib.load(model_path)
@@ -33,7 +31,6 @@ def load_model():
 
 model = load_model()
 
-# Create input form
 st.header("Customer Information")
 
 col1, col2, col3 = st.columns(3)
@@ -42,7 +39,7 @@ 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'])
+    occupation = st.selectbox("Occupation", ['Salaried', 'Small Business', 'Large Business', 'Free Lancer'])
     gender = st.selectbox("Gender", ['Male', 'Female'])
 
 with col2:
@@ -54,9 +51,9 @@ with col2:
 
 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'])
+    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", ['Yes', 'No'])
+    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)
@@ -67,59 +64,114 @@ with col5:
 
 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]
-        })
+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}
 
-        # Make prediction
+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!")
-
+            
+            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")
-
+                if prediction == 1:
+                    st.metric("Prediction", "✅ Will Purchase", delta="High Priority")
+                else:
+                    st.metric("Prediction", "❌ Will Not Purchase", delta="Low Priority")
+            
             with col_pred2:
-                st.metric("Confidence", f"{max(probability)*100:.2f}%")
-
-            # Recommendation
+                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.info("🎯 **Recommendation:** This customer has a high likelihood of purchasing. Consider prioritizing this lead!")
+                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("💡 **Recommendation:** This customer may need more engagement. Consider additional followups or tailored offers.")
-
+                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.error(f"❌ Prediction error: {e}")
+            st.info("Please ensure all fields are filled correctly.")
     else:
-        st.error("Model not loaded. Please check the configuration.")
+        st.error("❌ Model not loaded. Please check the configuration.")
+
+with st.sidebar:
+    st.header("ℹ️ 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")
 
-# Footer
 st.markdown("---")
-st.markdown("Built with ❤️ using Streamlit | Powered by Visit with Us")
+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)