72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
import os
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import io
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import numpy as np
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import tensorflow as tf
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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from PIL import Image
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app = Flask(__name__)
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CORS(app)
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# 1. SETUP PATH MODEL
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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MODEL_PATH = os.path.join(BASE_DIR, 'model_efficientNet.keras')
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# 2. DUMMY PREPROCESS (Agar tidak error saat load Lambda layer)
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def preprocess_input(x):
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return x
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print("⏳ Sedang memuat 'Otak AI'...")
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try:
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# Menggunakan parameter Keras 3 untuk memuat model lama
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model = tf.keras.models.load_model(
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MODEL_PATH,
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custom_objects={'preprocess_input': preprocess_input},
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compile=False,
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safe_mode=False # Kunci agar Keras 3 mau menerima config Keras lama
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)
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print("BERHASIL: Model AI readyy!")
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except Exception as e:
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print(f"GAGAL: {str(e)}")
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# Label klasifikasi kopi kamu
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labels = ['Honey', 'Natural', 'Washed']
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@app.route('/predict', methods=['POST'])
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def predict():
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try:
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file = request.files['image']
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# 1. Load Gambar & Resize ke 224x224
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img = Image.open(file.stream).convert('RGB')
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img = img.resize((224, 224))
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# 2. Konversi ke Array
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img_array = np.array(img).astype('float32')
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# 3. JURUS SAKTI: Gunakan preprocessing asli EfficientNet
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# Ini akan menangani scaling warna agar sama persis dengan saat training
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img_array = tf.keras.applications.efficientnet.preprocess_input(img_array)
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img_array = np.expand_dims(img_array, axis=0)
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# 4. Prediksi
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preds = model.predict(img_array, verbose=0)
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class_idx = np.argmax(preds[0])
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confidence = float(np.max(preds[0]))
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print(f"📥 Prediksi: {labels[class_idx]} ({confidence*100:.2f}%)")
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return jsonify({
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'label': labels[class_idx],
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'confidence': f"{confidence * 100:.2f}%",
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'status': 'success'
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})
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except Exception as e:
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print(f"❌ ERROR PREDIKSI: {str(e)}")
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return jsonify({'error': str(e)}), 500
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if __name__ == '__main__':
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# Jalankan di port 5001 agar tidak diblokir Windows AirPlay
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app.run(host='127.0.0.1', port=5001, debug=True) |