app.py

from flask import Flask, request, jsonify
from flask_cors import CORS
import joblib
import numpy as np 
import os 

# Inisialisasi aplikasi Flask
app = Flask(__name__)
CORS(app)

# --- TAHAP 1: KOSONGKAN AREA GLOBAL ---
# JANGAN LOAD MODEL DI SINI.
# Kita buat variabelnya saja, tapi isinya 'None' (kosong)
# Ini membuat server NYALA INSTAN.
model = None
scaler = None
jk_encoder = None
stunting_encoder = None

# Fungsi untuk memuat model (hanya akan dipanggil 1x)
def load_models():
    global model, scaler, jk_encoder, stunting_encoder
    try:
        if model is None: # Cek, jika masih kosong, baru di-load
            print("Mencoba memuat model untuk PERTAMA KALI...")
            model = joblib.load("best_model.joblib")
            scaler = joblib.load("scaler.joblib")
            jk_encoder = joblib.load("Jenis Kelamin_encoder.joblib")
            stunting_encoder = joblib.load("Stunting_encoder.joblib")
            print("Semua 4 model berhasil dimuat!")
        return True
    except Exception as e:
        print(f"Error saat memuat model: {e}")
        return False

# --- TAHAP 2: ENDPOINT PREDIKSI ---
@app.route('/') 
def home():
    return jsonify({
           "status": "OK",
           "info": "Welcome to the Stunting Prediction API!",
           "how_to_use": "Send POST to /predict with: jenis_kelamin, umur, tinggi, berat"
}) 

@app.route('/predict', methods=['POST'])
def predict():
    global model, scaler, jk_encoder, stunting_encoder

    # Panggil fungsi load_models.
    # Jika model belum di-load, dia akan di-load sekarang.
    # Jika sudah, dia akan dilewati.
    if not load_models():
        return jsonify({"error": "Model gagal dimuat di server. Cek logs."}), 500

    # ... (Sisa kodenya SAMA PERSIS) ...
    try:
        data = request.get_json()
        jk_string = data['jenis_kelamin']
        umur = data['umur']
        tinggi = data['tinggi']
        berat = data['berat']

        jk_encoded = jk_encoder.transform([jk_string])[0]
        numerical_features = [[umur, tinggi, berat]]
        scaled_features = scaler.transform(numerical_features)
        
        umur_scaled = scaled_features[0][0]
        tinggi_scaled = scaled_features[0][1]
        berat_scaled = scaled_features[0][2]

        final_features_list = [jk_encoded, umur_scaled, tinggi_scaled, berat_scaled]
        final_features = [np.array(final_features_list)]
        
        prediction_encoded = model.predict(final_features)
        prediction_string = stunting_encoder.inverse_transform(prediction_encoded)

        output = prediction_string[0]
        return jsonify({'prediction': output})

    except KeyError as e:
        return jsonify({"error": f"Key JSON tidak ditemukan: {str(e)}."}), 400
    except Exception as e:
        return jsonify({"error": f"Terjadi error saat prediksi: {str(e)}"}), 400

# --- TAHAP 3: SERVER RUN ---
# (Ini sudah benar, JANGAN DIUBAH)
if __name__ == '__main__':
    port = int(os.environ.get('PORT', 7860))
    app.run(host="0.0.0.0", port=port, debug=False)