Deploy Flask Batik Classifier

Dockerfile

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+# Gunakan Python versi stabil
+FROM python:3.10
+
+# Set folder kerja di dalam container
+WORKDIR /app
+
+# Copy requirements.txt ke container
+COPY requirements.txt .
+
+# Install semua dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy semua file project (app.py, utils.py, model, dll)
+COPY . .
+
+# Expose port HuggingFace
+EXPOSE 7860
+
+# Jalankan Flask API
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import os
+from werkzeug.utils import secure_filename
+from utils import predict_image
+
+app = Flask(__name__)
+CORS(app)  # Allow cross-origin (Next.js ke Flask)
+
+UPLOAD_FOLDER = 'uploads'
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file uploaded'}), 400
+
+    file = request.files['file']
+    filename = secure_filename(file.filename)
+    filepath = os.path.join(UPLOAD_FOLDER, filename)
+    file.save(filepath)
+
+    result = predict_image(filepath)
+
+    return jsonify(result)
+
+if __name__ == '__main__':
+    app.run(debug=True)

mobilenetv3_batik.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4ae89f2869d74e5335d53c7329d42ba51164cd733e5e7977d0648844e500e95f
+size 4032392

requirements.txt

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+flask
+flask-cors
+tensorflow==2.13.0
+pillow
+gunicorn

utils.py

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+import tensorflow as tf
+from tensorflow.keras.utils import img_to_array, load_img
+import numpy as np
+import os
+
+# Path model TFLite
+model_path = r"D:\KULIAH UNS\KLASIFIKASI BATIK\training\mobilenetv3_batik.tflite"
+
+# Load interpreter TFLite
+interpreter = tf.lite.Interpreter(model_path=model_path)
+interpreter.allocate_tensors()
+
+# Ambil detail input & output
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Label kelas
+class_names = [
+    'batik-bali', 'batik-betawi', 'batik-celup', 'batik-cendrawasih',
+    'batik-ceplok', 'batik-ciamis', 'batik-garutan', 'batik-gentongan',
+    'batik-kawung', 'batik-keraton', 'batik-lasem', 'batik-megamendung',
+    'batik-parang', 'batik-pekalongan', 'batik-priangan', 'batik-sekar',
+    'batik-sidoluhur', 'batik-sidomukti', 'batik-sogan', 'batik-tambal'
+]
+
+def predict_image(image_path):
+    # Load & preprocess image
+    image = load_img(image_path, target_size=(224, 224))
+    image = img_to_array(image) / 255.0
+    image = np.expand_dims(image, axis=0).astype(np.float32)
+
+    # Set tensor input
+    interpreter.set_tensor(input_details[0]['index'], image)
+
+    # Jalankan inference
+    interpreter.invoke()
+
+    # Ambil tensor output
+    predictions = interpreter.get_tensor(output_details[0]['index'])[0]
+    max_index = np.argmax(predictions)
+    predicted_class = class_names[max_index]
+    confidence = float(predictions[max_index]) * 100
+
+    return {
+        "class": predicted_class,
+        "confidence": round(confidence, 2)
+    }