app.py

import streamlit as st
st.write("✅ NEW APP VERSION LOADED - COMMIT TEST")

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

# ================= CONFIG =================
MODEL_REPO = "Prashantbhat1607/wellness-tourism-model"
MODEL_FILE = "model.joblib"
META_FILE  = "model_meta.json"
# ==========================================

st.set_page_config(page_title="Wellness Tourism Predictor", layout="centered")
st.title("🏝️ Wellness Tourism Purchase Predictor")
st.write("Predict whether a customer will purchase the Wellness Tourism Package.")

@st.cache_resource
def load_model_and_meta():
    model_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename=MODEL_FILE,
        repo_type="model"
    )
    meta_path = hf_hub_download(
        repo_id=MODEL_REPO,
        filename=META_FILE,
        repo_type="model"
    )

    model = joblib.load(model_path)
    with open(meta_path, "r") as f:
        meta = json.load(f)

    return model, meta

model, meta = load_model_and_meta()

features = meta["features"]
cat_cols = set(meta["categorical_cols"])
num_cols = set(meta["numeric_cols"])

st.subheader("Enter customer details")

inputs = {}
for col in features:
    if col in cat_cols:
        inputs[col] = st.text_input(col, value="Unknown")
    else:
        inputs[col] = st.number_input(col, value=0.0)

if st.button("Predict"):
    X = pd.DataFrame([inputs], columns=features)
    proba = model.predict_proba(X)[0][1]
    pred = int(proba >= 0.5)

    st.success(
        f"Prediction: {'✅ Will Purchase' if pred==1 else '❌ Will NOT Purchase'}"
    )
    st.write(f"Probability of purchase: **{proba:.3f}**")