app.py

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

# Download and load the model
model_path = hf_hub_download(repo_id="Santhu976/Tourism-Package-Prediction", filename="best_machine_failure_model_v1.joblib")
model = joblib.load(model_path)

# 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 demographic and interaction details.
""")

st.header("Customer Details")

# User input
age = st.number_input("Age", min_value=18, max_value=100, value=35)
typeof_contact = st.selectbox(
    "Type of Contact",
    ["Company Invited", "Self Inquiry"]
)
city_tier = st.selectbox(
    "City Tier",
    [1, 2, 3]
)
occupation = st.selectbox(
    "Occupation",
    ["Salaried", "Small Business", "Large Business", "Freelancer"]
)
gender = st.selectbox(
    "Gender",
    ["Male", "Female"]
)
num_person_visiting = st.number_input(
    "Number of People Visiting",
    min_value=1, max_value=10, value=2
)
preferred_property_star = st.selectbox(
    "Preferred Property Star",
    [1, 2, 3, 4, 5]
)
marital_status = st.selectbox(
    "Marital Status",
    ["Single", "Married", "Divorced"]
)
num_trips = st.number_input(
    "Number of Trips (per year)",
    min_value=0, max_value=50, value=2
)
passport = st.selectbox(
    "Passport Available",
    [0, 1],
    format_func=lambda x: "Yes" if x == 1 else "No"
)
own_car = st.selectbox(
    "Owns a Car",
    [0, 1],
    format_func=lambda x: "Yes" if x == 1 else "No"
)
num_children = st.number_input(
    "Number of Children Visiting (below 5)",
    min_value=0, max_value=5, value=0
)
designation = st.selectbox(
    "Designation",
    ["Executive", "Manager", "Senior Manager", "AVP", "VP"]
)
monthly_income = st.number_input(
    "Monthly Income",
    min_value=0, value=50000, step=1000
)

# --------------------------------------------------
# Interaction Data
# --------------------------------------------------

st.header("Customer Interaction Details")

pitch_score = st.slider(
    "Pitch Satisfaction Score",
    min_value=1, max_value=5, value=3
)
product_pitched = st.selectbox(
    "Product Pitched",
    ["Basic", "Standard", "Deluxe", "Super Deluxe"]
)
num_followups = st.number_input(
    "Number of Follow-ups",
    min_value=0, max_value=20, value=2
)
duration_of_pitch = st.number_input(
    "Duration of Pitch (minutes)",
    min_value=0, max_value=120, value=20
)

# Assemble input into DataFrame
input_data = pd.DataFrame([{
    "Age": age,
    "TypeofContact": typeof_contact,
    "CityTier": city_tier,
    "Occupation": occupation,
    "Gender": gender,
    "NumberOfPersonVisiting": num_person_visiting,
    "PreferredPropertyStar": preferred_property_star,
    "MaritalStatus": marital_status,
    "NumberOfTrips": num_trips,
    "Passport": passport,
    "OwnCar": own_car,
    "NumberOfChildrenVisiting": num_children,
    "Designation": designation,
    "MonthlyIncome": monthly_income,
    "PitchSatisfactionScore": pitch_score,
    "ProductPitched": product_pitched,
    "NumberOfFollowups": num_followups,
    "DurationOfPitch": duration_of_pitch
}])


if st.button("Predict Purchase"):
    prediction = model.predict(input_data)[0]
    result = "Customer WILL Purchase the Package" if prediction == 1 else "Customer will NOT Purchase the Package"

    st.subheader("Prediction Result")
    st.success(result)