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app.py

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+#!/usr/bin/env python3
+"""
+Streamlit App for Tourism Package Prediction
+"""
+
+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 Prediction",
+    page_icon="🏖️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+@st.cache_resource
+def load_model():
+    """Load the trained model from HuggingFace Hub"""
+    try:
+        model_path = hf_hub_download(
+            repo_id="abhishek-kumar/tourism-package-prediction-model",
+            filename="best_model.joblib"
+        )
+        model = joblib.load(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+def prepare_input_data(age, gender, marital_status, city_tier, type_of_contact,
+                      occupation, designation, monthly_income, num_person_visiting,
+                      num_children_visiting, preferred_property_star, num_trips,
+                      passport, own_car, duration_of_pitch, product_pitched,
+                      num_followups, pitch_satisfaction_score):
+    """Prepare input data for model prediction"""
+
+    # Create mapping dictionaries
+    gender_map = {"Male": 1, "Female": 0}
+    marital_map = {"Single": 2, "Married": 1, "Divorced": 0, "Unmarried": 3}
+    contact_map = {"Self Enquiry": 1, "Company Invited": 0}
+    occupation_map = {"Salaried": 2, "Small Business": 1, "Free Lancer": 0}
+    designation_map = {"Executive": 0, "Manager": 1, "Senior Manager": 2, "AVP": 3, "VP": 4}
+    product_map = {"Basic": 0, "Standard": 1, "Deluxe": 2, "Super Deluxe": 3}
+    passport_map = {"Yes": 1, "No": 0}
+    car_map = {"Yes": 1, "No": 0}
+
+    # Feature engineering (matching training data encoding)
+    if monthly_income <= 15000:
+        income_category = 0  # Low
+    elif monthly_income <= 25000:
+        income_category = 1  # Medium
+    elif monthly_income <= 35000:
+        income_category = 2  # High
+    else:
+        income_category = 3  # Very High
+
+    if age <= 25:
+        age_group = 0  # Young
+    elif age <= 35:
+        age_group = 1  # Adult
+    elif age <= 45:
+        age_group = 2  # Middle-aged
+    elif age <= 55:
+        age_group = 3  # Senior
+    else:
+        age_group = 4  # Elderly
+
+    # Create input array
+    input_array = np.array([[
+        age, contact_map[type_of_contact], city_tier, duration_of_pitch,
+        occupation_map[occupation], gender_map[gender], num_person_visiting,
+        num_followups, product_map[product_pitched], preferred_property_star,
+        marital_map[marital_status], num_trips, passport_map[passport],
+        pitch_satisfaction_score, car_map[own_car], num_children_visiting,
+        designation_map[designation], monthly_income, income_category, age_group
+    ]])
+
+    return input_array
+
+def main():
+    """Main Streamlit app"""
+
+    st.title("Tourism Package Prediction")
+    st.markdown("### Predict Customer Purchase Likelihood for Wellness Tourism Package")
+    st.markdown("---")
+
+    # Load model
+    model = load_model()
+    if model is None:
+        st.error("Failed to load the prediction model.")
+        return
+
+    # Sidebar inputs
+    st.sidebar.header("Customer Information")
+
+    # Demographics
+    st.sidebar.subheader("Demographics")
+    age = st.sidebar.slider("Age", 18, 80, 35)
+    gender = st.sidebar.selectbox("Gender", ["Male", "Female"])
+    marital_status = st.sidebar.selectbox("Marital Status", ["Single", "Married", "Divorced", "Unmarried"])
+
+    # Location & Contact
+    st.sidebar.subheader("Location & Contact")
+    city_tier = st.sidebar.selectbox("City Tier", [1, 2, 3])
+    type_of_contact = st.sidebar.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+
+    # Professional Info
+    st.sidebar.subheader("Professional Info")
+    occupation = st.sidebar.selectbox("Occupation", ["Salaried", "Small Business", "Free Lancer"])
+    designation = st.sidebar.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+    monthly_income = st.sidebar.number_input("Monthly Income", 10000, 50000, 20000)
+
+    # Travel Preferences
+    st.sidebar.subheader("Travel Preferences")
+    num_person_visiting = st.sidebar.slider("Number of Persons Visiting", 1, 5, 2)
+    num_children_visiting = st.sidebar.slider("Number of Children Visiting", 0, 3, 0)
+    preferred_property_star = st.sidebar.slider("Preferred Property Star Rating", 1.0, 5.0, 3.0, 0.5)
+    num_trips = st.sidebar.slider("Number of Trips per Year", 0.0, 10.0, 2.0, 0.5)
+
+    # Additional Info
+    st.sidebar.subheader("Additional Info")
+    passport = st.sidebar.selectbox("Has Passport", ["Yes", "No"])
+    own_car = st.sidebar.selectbox("Owns Car", ["Yes", "No"])
+
+    # Sales Interaction
+    st.sidebar.subheader("Sales Interaction")
+    duration_of_pitch = st.sidebar.slider("Duration of Pitch (minutes)", 5, 60, 15)
+    product_pitched = st.sidebar.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe"])
+    num_followups = st.sidebar.slider("Number of Followups", 0.0, 6.0, 3.0, 0.5)
+    pitch_satisfaction_score = st.sidebar.slider("Pitch Satisfaction Score", 1, 5, 3)
+
+    # Main content
+    col1, col2 = st.columns([2, 1])
+
+    with col1:
+        st.subheader("Customer Profile Summary")
+        profile_data = {
+            "Age": age,
+            "Gender": gender,
+            "Marital Status": marital_status,
+            "City Tier": city_tier,
+            "Occupation": occupation,
+            "Monthly Income": f"₹{monthly_income:,}",
+            "Number of Persons": num_person_visiting,
+            "Preferred Star Rating": preferred_property_star,
+            "Annual Trips": num_trips,
+            "Has Passport": passport,
+            "Owns Car": own_car
+        }
+
+        for key, value in profile_data.items():
+            st.write(f"**{key}:** {value}")
+
+    with col2:
+        st.subheader("Prediction")
+
+        if st.button("Predict Purchase Likelihood", type="primary"):
+            input_data = prepare_input_data(
+                age, gender, marital_status, city_tier, type_of_contact,
+                occupation, designation, monthly_income, num_person_visiting,
+                num_children_visiting, preferred_property_star, num_trips,
+                passport, own_car, duration_of_pitch, product_pitched,
+                num_followups, pitch_satisfaction_score
+            )
+
+            try:
+                prediction = model.predict(input_data)[0]
+                prediction_proba = model.predict_proba(input_data)[0]
+
+                if prediction == 1:
+                    st.success("High likelihood of purchase!")
+                    st.write(f"**Confidence:** {prediction_proba[1]:.2%}")
+                    st.balloons()
+                else:
+                    st.warning("Low likelihood of purchase")
+                    st.write(f"**Confidence:** {prediction_proba[0]:.2%}")
+
+                # Probability breakdown
+                st.subheader("Probability Breakdown")
+                prob_df = pd.DataFrame({
+                    'Outcome': ['Will Not Purchase', 'Will Purchase'],
+                    'Probability': [prediction_proba[0], prediction_proba[1]]
+                })
+                st.bar_chart(prob_df.set_index('Outcome'))
+
+            except Exception as e:
+                st.error(f"Prediction error: {e}")
+
+    st.markdown("---")
+    st.markdown("### About This Model")
+    st.info("""
+    This ML model predicts customer purchase likelihood for the Wellness Tourism Package
+    based on demographics, travel preferences, and sales interaction data.
+    """)
+
+if __name__ == "__main__":
+    main()