Mahdewa/grape-leaf-disease-classify

1from flask import Flask, request, jsonify
2from flask_cors import CORS
3import tensorflow as tf
4import numpy as np
5from PIL import Image, ImageOps
6import io
7import os
8
9app = Flask(__name__)
10CORS(app)
11
12try:
13    model = tf.keras.models.load_model('model_terbaik_klasifikasi_anggur.h5')
14    print("✅ Model berhasil dimuat!")
15except:
16    print("❌ Model tidak ditemukan.")
17    model = None
18
19# Definisi Kelas
20CLASS_NAMES = [
21    'Grape___Black_rot', 
22    'Grape___Esca_(Black_Measles)', 
23    'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 
24    'Grape___healthy'
25]
26
27@app.route('/predict', methods=['POST'])
28def predict():
29    if 'file' not in request.files:
30        return jsonify({'error': 'Tidak ada file diupload'}), 400
31    
32    file = request.files['file']
33    
34    try:
35        # 2. PREPROCESSING IMAGE
36        # Baca gambar langsung dari memori (tanpa save ke disk dulu)
37        image = Image.open(file.stream)
38        
39        # Resize ke 128x128 (Sesuai training di Kaggle)
40        image = ImageOps.fit(image, (128, 128), Image.Resampling.LANCZOS)
41        
42        # Convert ke Array & Normalisasi (Sesuai training 1./255)
43        img_array = np.asarray(image)
44        img_array = img_array / 255.0
45        
46        # Tambah dimensi batch (Jadi (1, 128, 128, 3))
47        img_array = np.expand_dims(img_array, axis=0)
48
49        # 3. PREDIKSI
50        if model is None:
51            return jsonify({'error': 'Model belum siap'}), 500
52
53        prediction = model.predict(img_array)
54        class_index = np.argmax(prediction)
55        confidence = float(np.max(prediction) * 100)
56        
57        result_class = CLASS_NAMES[class_index]
58
59        # Kirim respons JSON ke React
60        return jsonify({
61            'class': result_class,
62            'confidence': f"{confidence:.2f}",
63            'status': 'success'
64        })
65
66    except Exception as e:
67        print(e)
68        return jsonify({'error': str(e)}), 500
69
70if __name__ == '__main__':
71    app.run(host='0.0.0.0', port=7860)