Update app for HF Spaces

app.py

@@ -0,0 +1,155 @@
+import os
+import json
+import numpy as np
+# Using ai-edge-litert (Google's new TFLite runtime)
+from ai_edge_litert.interpreter import Interpreter
+
+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) 
+    
+    # Normalisasi (Pastikan saat training kamu juga dibagi 255.0)
+    img_array = img_array / 255.0
+    
+    # Tambah dimensi batch
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+@app.route('/', methods=['GET'])
+def home():
+    return jsonify({
+        "status": "Online", 
+        "mode": "ai-edge-litert Runtime (BatikLens V2)",
+        "model_version": "v2",
+        "classes_loaded": len(class_names)
+    })
+
+@app.route('/predict', methods=['POST'])
+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=5000)