app.py

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

st.set_page_config(page_title="Wellness Tourism Prediction", layout="centered")

st.title("Wellness Tourism Purchase Prediction")
st.write("Predict whether a customer will purchase the Wellness Tourism Package.")

# Load model from Hugging Face Model Hub
model_path = hf_hub_download(
    repo_id="AngadSi/wellness-purchase-prediction-model",
    filename="wellness_purchase_model.joblib",
    repo_type="model"
)

model = joblib.load(model_path)

# UI inputs
age = st.number_input("Age", min_value=18, max_value=100, value=30)
income = st.number_input("Monthly Income", min_value=0, value=6000)
trips = st.number_input("Number of Trips", min_value=0, value=2)
pitch_score = st.slider("Pitch Satisfaction Score", 1, 100, 80)

if st.button("Predict"):
    df = pd.DataFrame([{
        "Age": age,
        "MonthlyIncome": income,
        "NumberOfTrips": trips,
        "PitchSatisfactionScore": pitch_score
    }])

    pred = model.predict(df)[0]
    prob = model.predict_proba(df)[0][1]

    st.success(f"Prediction: {'Will Purchase' if pred == 1 else 'Will Not Purchase'}")
    st.info(f"Purchase Probability: {round(prob, 2)}")