from flask import Flask, request, jsonify
from flask_cors import CORS
import os
from datetime import datetime
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model

app = Flask(__name__)
CORS(app)

UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), 'uploads')
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

# 1. Sesuaikan nama file model!
MODEL_PATH = os.path.join(os.path.dirname(__file__), 'model_padi_blast.h5')

# 2. Sesuaikan dengan urutan class di Colab kamu (4 class)
CLASS_NAMES = ['Healthy', 'Blast', 'Blight', 'Tungro']

model = None
try:
    model = load_model(MODEL_PATH)
    print('Model berhasil dimuat dari', MODEL_PATH)
except Exception as e:
    print('Gagal memuat model:', e)

def preprocess_image(path):
    image = Image.open(path).convert('RGB')
    image = image.resize((150, 150))
    image_array = np.array(image).astype('float32') / 255.0
    return np.expand_dims(image_array, axis=0)

@app.route('/prediksi', methods=['POST'])
def prediksi():
    # 3. Menerima request dengan key 'gambar'
    if 'gambar' not in request.files:
        return jsonify({'error': 'Tidak ada file gambar'}), 400

    file = request.files['gambar']

    if file.filename == '':
        return jsonify({'error': 'Nama file kosong'}), 400

    filename = datetime.now().strftime('%Y%m%d%H%M%S_') + file.filename
    filepath = os.path.join(UPLOAD_FOLDER, filename)
    file.save(filepath)

    print('GAMBAR DITERIMA:', filename)

    if model is None:
        return jsonify({'error': 'Model tidak tersedia. Pastikan file .h5 ada.'}), 500

    try:
        image_array = preprocess_image(filepath)
        predictions = model.predict(image_array, verbose=0)
        predicted_index = int(np.argmax(predictions, axis=1)[0])
        confidence = float(np.max(predictions, axis=1)[0]) * 100.0
        label = CLASS_NAMES[predicted_index] if predicted_index < len(CLASS_NAMES) else f'Kelas {predicted_index + 1}'

        return jsonify({
            'penyakit': label,
            'confidence': round(confidence, 2)
        })
    except Exception as e:
        print('Gagal prediksi:', e)
        return jsonify({'error': 'Terjadi kesalahan saat memprediksi gambar.'}), 500

if __name__ == '__main__':
    print('=' * 50)
    print('Klasifikasi Penyakit Daun Padi')
    print('=' * 50)
    app.run(host='127.0.0.1', port=5000, debug=False)