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| import os | |
| import json | |
| import numpy as np | |
| # Using ai-edge-litert (Google's new TFLite runtime) | |
| from ai_edge_litert.interpreter import Interpreter | |
| # Import MobileNetV2 preprocessing | |
| try: | |
| from tensorflow.keras.applications.mobilenet_v2 import preprocess_input | |
| USE_MOBILENET_PREPROCESSING = True | |
| except ImportError: | |
| USE_MOBILENET_PREPROCESSING = False | |
| print("⚠️ TensorFlow not available, using standard normalization") | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from PIL import Image | |
| import io | |
| app = Flask(__name__) | |
| CORS(app) | |
| # --- KONFIGURASI --- | |
| BASE_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| # Pastikan path ini sesuai struktur folder kamu | |
| MODEL_PATH = os.path.join(BASE_DIR, 'models', 'batik_model.tflite') | |
| CLASSES_PATH = os.path.join(BASE_DIR, 'models', 'batik_classes_mobilenet_ultimate.json') | |
| print("==================================================") | |
| print("🚀 MEMULAI BATIK CLASSIFIER (TFLITE ENGINE V2)") | |
| print("⚡ Mode: ai-edge-litert Runtime (38 Batik Classes)") | |
| print("==================================================") | |
| # --- 1. LOAD MODEL TFLITE --- | |
| if not os.path.exists(MODEL_PATH): | |
| print(f"❌ ERROR: File model TFLite tidak ditemukan: {MODEL_PATH}") | |
| exit() | |
| try: | |
| # Using ai-edge-litert Interpreter | |
| interpreter = Interpreter(model_path=MODEL_PATH) | |
| interpreter.allocate_tensors() | |
| # Dapat info input/output | |
| input_details = interpreter.get_input_details() | |
| output_details = interpreter.get_output_details() | |
| input_shape = input_details[0]['shape'] | |
| output_shape = output_details[0]['shape'] | |
| print(f"✅ Model TFLite V2 berhasil dimuat!") | |
| print(f"📊 Input Shape: {input_shape}, Output Classes: {output_shape[1]}") | |
| except Exception as e: | |
| print(f"❌ Gagal load TFLite: {e}") | |
| exit() | |
| # --- 2. LOAD CLASSES --- | |
| if not os.path.exists(CLASSES_PATH): | |
| print(f"❌ ERROR: File label tidak ditemukan di {CLASSES_PATH}") | |
| exit() | |
| try: | |
| with open(CLASSES_PATH, 'r') as f: | |
| class_data = json.load(f) | |
| # Logika parsing JSON kamu sudah bagus! | |
| if isinstance(class_data, dict) and "classes" in class_data: | |
| class_names = class_data["classes"] | |
| elif isinstance(class_data, dict): | |
| try: | |
| sorted_keys = sorted(class_data.keys(), key=lambda x: int(x)) | |
| class_names = [class_data[k] for k in sorted_keys] | |
| except ValueError: | |
| class_names = list(class_data.values()) | |
| elif isinstance(class_data, list): | |
| class_names = class_data | |
| else: | |
| raise ValueError("Format JSON tidak dikenali.") | |
| print(f"✅ Berhasil memuat {len(class_names)} nama motif batik.") | |
| # print(f"📝 Contoh: {class_names[:3]}...") | |
| except Exception as e: | |
| print(f"❌ Gagal membaca file JSON Classes: {e}") | |
| exit() | |
| # --- PREPROCESSING --- | |
| def prepare_image(image, target_size=(224, 224)): | |
| if image.mode != "RGB": | |
| image = image.convert("RGB") | |
| image = image.resize(target_size) | |
| img_array = np.array(image, dtype=np.float32) | |
| # Use MobileNetV2 preprocessing if available (range -1 to 1) | |
| # Otherwise use standard normalization (range 0 to 1) | |
| if USE_MOBILENET_PREPROCESSING: | |
| # MobileNetV2 expects values in range -1 to 1 | |
| img_array = np.expand_dims(img_array, axis=0) | |
| img_array = preprocess_input(img_array) | |
| else: | |
| # Standard normalization to 0-1 range | |
| img_array = img_array / 255.0 | |
| img_array = np.expand_dims(img_array, axis=0) | |
| return img_array | |
| def home(): | |
| return jsonify({ | |
| "status": "Online", | |
| "mode": "ai-edge-litert Runtime (BatikLens V2)", | |
| "model_version": "v2", | |
| "classes_loaded": len(class_names) | |
| }) | |
| def predict(): | |
| # Support both 'file' and 'image' field names | |
| file = request.files.get('file') or request.files.get('image') | |
| if not file: | |
| return jsonify({"error": "No file part"}), 400 | |
| if file.filename == '': | |
| return jsonify({"error": "No selected file"}), 400 | |
| try: | |
| # 1. Baca & Proses Gambar | |
| image = Image.open(io.BytesIO(file.read())) | |
| input_data = prepare_image(image) | |
| # 2. Masukkan data ke Interpreter | |
| interpreter.set_tensor(input_details[0]['index'], input_data) | |
| # 3. Jalankan Prediksi | |
| interpreter.invoke() | |
| # 4. Ambil Hasil | |
| output_data = interpreter.get_tensor(output_details[0]['index']) | |
| predictions = output_data[0] | |
| # 5. Cari skor tertinggi | |
| predicted_index = np.argmax(predictions) | |
| predicted_label = class_names[predicted_index] | |
| confidence = float(predictions[predicted_index]) | |
| # Get top 5 predictions | |
| top_5_indices = np.argsort(predictions)[-5:][::-1] | |
| top_5_predictions = [ | |
| { | |
| "class": class_names[idx], | |
| "confidence": float(predictions[idx]), | |
| "percentage": f"{float(predictions[idx]):.2%}" | |
| } | |
| for idx in top_5_indices | |
| ] | |
| return jsonify({ | |
| "success": True, | |
| "prediction": predicted_label, | |
| "confidence": confidence, | |
| "percentage": f"{confidence:.2%}", | |
| "top_5_predictions": top_5_predictions | |
| }) | |
| except Exception as e: | |
| return jsonify({"success": False, "error": str(e)}), 500 | |
| if __name__ == '__main__': | |
| app.run(host='0.0.0.0', port=7860) |