app.py

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

app = Flask(__name__)
CORS(app)

# Load Decision Tree model
model = joblib.load('decision_tree_model.pkl')

@app.route('/')
def home():
    return "Iris Decision Tree API is running!"

@app.route('/predict', methods=['POST'])
def predict_iris():
    try:
        # Expecting JSON: {"data": [sepal_length, sepal_width, petal_length, petal_width]}
        data = request.json.get('data', None)
        if data is None or len(data) != 4:
            return jsonify({'error': 'Input harus berupa array dengan 4 fitur: sepal_length, sepal_width, petal_length, petal_width'}), 400

        # Convert to numpy array and reshape
        features = np.array(data).reshape(1, -1)
        pred = model.predict(features)[0]
        # Map target to class name
        target_names = ['setosa', 'versicolor', 'virginica']
        pred_class = target_names[pred]
        return jsonify({'prediksi_spesies': pred_class})
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/health', methods=['GET'])
def health():
    return jsonify({'status': 'OK', 'message': 'API is running'}), 200

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