import json
import joblib
import pandas as pd
import streamlit as st
from pathlib import Path

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

MODEL_PATH = Path("model.pkl")
META_PATH  = Path("model_meta.json")

@st.cache_resource
def load_artifacts():
    if not MODEL_PATH.exists():
        raise FileNotFoundError("model.pkl not found in Space repo root.")
    if not META_PATH.exists():
        raise FileNotFoundError("model_meta.json not found in Space repo root.")

    model = joblib.load(MODEL_PATH)
    meta = json.loads(META_PATH.read_text())
    return model, meta

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

st.subheader("Enter customer details")

inputs = {}
with st.form("predict_form"):
    for col in features:
        if col in cat_cols:
            # Text input is safest without hardcoding categories
            inputs[col] = st.text_input(col, value="Unknown")
        else:
            # Use integer style for count-like fields, else float
            if any(k in col.lower() for k in ["number", "count", "passport", "children", "car", "trips"]):
                inputs[col] = st.number_input(col, min_value=0, value=0, step=1)
            else:
                inputs[col] = st.number_input(col, value=0.0)

    submitted = st.form_submit_button("Predict")

if submitted:
    X = pd.DataFrame([inputs], columns=features)

    # If user typed commas like "2,03" in text fields, we leave them as is.
    # Numeric inputs above are already numeric.
    proba = float(model.predict_proba(X)[0][1])
    pred = int(proba >= 0.5)

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