README.md

metadata

title: Batik Classifier
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit

🎯 Batik Classifier API

REST API untuk klasifikasi motif batik menggunakan MobileNetV2 + TFLite dengan akurasi tinggi.

📊 Model Info

• Model: MobileNetV2 (TFLite)
• Classes: 42 motif batik Indonesia
• Input Size: 224x224 RGB

🚀 Quick Start

1. Setup Environment

# Buat virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# atau
venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

2. Model Files

Model files sudah tersedia di folder models/:

• batik_model.tflite
• batik_classes_mobilenet_ultimate.json
• batik_model_metadata.pkl

batik-classifier/
└── api/
    ├── app.py
    ├── requirements.txt
    ├── test_api.py
    └── models/
        ├── batik_knn_model_95acc.pkl
        ├── batik_classes.pkl
        └── batik_model_metadata.pkl

3. Run Server

python app.py

Server akan berjalan di: http://localhost:5000

📋 API Endpoints

GET /

Get API information

Response:

{
  "message": "Batik Classifier API",
  "version": "1.0",
  "model": "InceptionV3 + KNN",
  "accuracy": "95.00%",
  "classes": 20
}

POST /predict

Predict batik motif from image

Request:

• Method: POST
• Content-Type: multipart/form-data
• Body: image (file)

Example using curl:

curl -X POST http://localhost:5000/predict \
  -F "image=@path/to/batik.jpg"

Example using Python:

import requests

with open('batik.jpg', 'rb') as f:
    files = {'image': f}
    response = requests.post('http://localhost:5000/predict', files=files)
    print(response.json())

Response:

{
  "success": true,
  "prediction": "batik-parang",
  "confidence": 0.95,
  "percentage": "95.00%",
  "top_5_predictions": [
    {
      "class": "batik-parang",
      "confidence": 0.95,
      "percentage": "95.00%"
    },
    {
      "class": "batik-kawung",
      "confidence": 0.03,
      "percentage": "3.00%"
    }
  ]
}

GET /classes

Get list of all batik classes

Response:

{
  "success": true,
  "total": 20,
  "classes": [
    "batik-bali",
    "batik-betawi",
    "batik-celup",
    ...
  ]
}

GET /info

Get model information

Response:

{
  "success": true,
  "model_info": {
    "accuracy": "95.00%",
    "model_type": "InceptionV3 + KNN",
    "n_classes": 20,
    "total_training_data": 17000,
    "trained_date": "2025-12-07 11:41:28"
  }
}

GET /health

Health check endpoint

Response:

{
  "status": "healthy",
  "model_loaded": true
}

🧪 Testing

Run test script:

# Test endpoints
python test_api.py

# Test with image prediction
python test_api.py path/to/batik.jpg

📦 Deployment

Docker (Recommended)

FROM python:3.10-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 5000

CMD ["python", "app.py"]

Build and run:

docker build -t batik-classifier-api .
docker run -p 5000:5000 batik-classifier-api

Heroku

# Login
heroku login

# Create app
heroku create batik-classifier-api

# Deploy
git push heroku main

Railway / Render

1. Connect GitHub repository
2. Set build command: pip install -r requirements.txt
3. Set start command: python app.py
4. Deploy

🔧 Configuration

Environment variables:

FLASK_ENV=production  # production or development
PORT=5000             # Server port

📝 20 Batik Classes

1. batik-bali
2. batik-betawi
3. batik-celup
4. batik-cendrawasih
5. batik-ceplok
6. batik-ciamis
7. batik-garutan
8. batik-gentongan
9. batik-kawung
10. batik-keraton
11. batik-lasem
12. batik-megamendung
13. batik-parang
14. batik-pekalongan
15. batik-priangan
16. batik-sekar
17. batik-sidoluhur
18. batik-sidomukti
19. batik-sogan
20. batik-tambal

🤝 Support

For issues or questions:

• GitHub: https://github.com/Maftuuh1922/warisan-digital
• Email: rizkiuya12@gmail.com

📄 License

MIT License - feel free to use for commercial or personal projects.

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Made with ❤️ by Maftuuh1922