app.py

from flask import Flask, request, jsonify
import pandas as pd
from model import WellnessRecommendationModel
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = Flask(__name__)

model = WellnessRecommendationModel()

@app.route('/', methods=['GET'])
def home():
    return jsonify({
        "message": "Health & Wellness Activity Category Recommendation API",
        "description": "API untuk prediksi rekomendasi aktivitas health & wellness berdasarkan profil pengguna",
        "endpoints": {
            "/predict": "POST - Prediksi label aktivitas yang cocok berdasarkan data pengguna",
            "/health": "GET - Health check",
            "/model_info": "GET - Informasi tentang model"
        }
    })

@app.route('/health', methods=['GET'])
def health_check():
    return jsonify({
        "status": "Running",
        "model_loaded": model.is_loaded()
    })

@app.route('/model_info', methods=['GET'])
def model_info():
    return jsonify({
        "model_type": "Random Forest Classifier",
        "target_classes": model.get_target_classes(),
        "features": model.get_feature_names(),
        "description": "Model untuk prediksi aktivitas wellness favorit pengguna"
    })

@app.route('/predict', methods=['POST'])
def predict():
    try:
        data = request.get_json()
        
        if not data:
            return jsonify({"error": "No data provided"}), 400
        
        required_fields = ['Umur', 'Jenis Kelamin', 'Kota', 'Frekuensi', 
                          'Anggaran/Kunjungan (IDR)', 'Kemauan Bepergian', 
                          'Metode Pemesanan', 'Tujuan Utama', 'Yang Dicari']
        
        missing_fields = [field for field in required_fields if field not in data]
        if missing_fields:
            return jsonify({
                "error": f"Missing required fields: {missing_fields}",
                "required_fields": required_fields
            }), 400
        
        df_input = pd.DataFrame([data])
        
        prediction, probability = model.predict(df_input)
        
        response = {
            "prediction": prediction,
            "confidence": float(probability),
            "input_data": data,
            "all_probabilities": model.predict_proba(df_input)
        }
        
        return jsonify(response)
        
    except Exception as e:
        logger.error(f"Error in prediction: {str(e)}")
        return jsonify({"error": str(e)}), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, debug=False)