{"id":26337753,"url":"https://github.com/donartkins/sycx-api","last_synced_at":"2026-07-28T13:31:42.644Z","repository":{"id":318727929,"uuid":"944911750","full_name":"DonArtkins/SycX-API","owner":"DonArtkins","description":"A minimalist, high-performance Flask REST API template with built-in rate limiting and best practices. 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This is a great starting point for building robust APIs.\n\n## Features\n\n- 🚄 High-performance REST API setup\n- 🔒 Built-in rate limiting (configurable)\n- 🌐 CORS enabled (for cross-origin requests)\n- 📝 Clear project structure\n- 🔄 Version control ready (Git pre-initialized)\n- 📦 Minimal dependencies\n- 🤖 ML model integration ready\n\n## Quick Start\n\n1. **Enter Project Directory:**\n\n    ```bash\n    cd SycX-API\n    ```\n\n2. **Activate the Virtual Environment:**\n\n    ```bash\n    # Linux/Mac\n    source venv/bin/activate\n    ```\n\n    ```bash\n    # Windows\n    .\\venv\\Scripts\\activate\n    ```\n\n3. **Install Dependencies:**\n\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n4. **Run the API:**\n\n    ```bash\n    python3 run.py\n    ```\n\n    The API will start in debug mode. You'll see output in your terminal.\n\n## API Structure\n\nThe project follows a clear structure:\n\n```\nSycX-API/\n├── app/\n│   ├── api/\n│   │   └── v1/              # API version 1\n│   │       ├── __init__.py  # Initializes the v1 API\n│   │       └── routes.py    # Defines API endpoints\n│   ├── config/\n│   │   └── config.py        # Configuration settings\n│   ├── models/             # Store your ML models here\n│   │   ├── __init__.py\n│   │   └── trained_models/ # Directory for saved models\n│   ├── services/          # Business logic and model inference\n│   │   └── __init__.py\n│   ├── utils/\n│   │   └── helpers.py      # Utility functions (e.g., rate limiting)\n│   └── __init__.py         # Initializes the app package\n├── tests/                # Add your unit tests here\n├── docs/                 # API documentation\n├── venv/                 # Virtual environment\n├── .env                  # Environment variables\n├── .gitignore           # Git ignore rules\n├── LICENSE              # License information\n├── CONTRIBUTING.md      # Contribution guidelines\n├── README.md            # This file!\n├── requirements.txt     # Python package dependencies\n└── run.py              # Main application entry point\n```\n\n## Using the API\n\n### Making Requests with Postman\n\n1. **Install Postman:**\n   Download and install from [postman.com](https://www.postman.com/downloads/)\n\n2. **Basic Endpoints:**\n   - Health Check:\n     - Method: GET\n     - URL: `http://localhost:5000/api/v1/health`\n   \n   - Hello World:\n     - Method: GET\n     - URL: `http://localhost:5000/api/v1/hello`\n     \n   - Hello World (POST):\n     - Method: POST\n     - URL: `http://localhost:5000/api/v1/hello`\n     - Headers: `Content-Type: application/json`\n     - Body:\n       ```json\n       {\n           \"message\": \"Hello from Postman!\"\n       }\n       ```\n\n### Adding Custom Endpoints\n\n1. **Create a New Route:**\n   In `app/api/v1/routes.py`, add your new endpoint:\n\n   ```python\n   class MyNewEndpoint(Resource):\n       @rate_limit\n       def get(self):\n           return {\"message\": \"My new endpoint\"}, 200\n       \n       @rate_limit\n       def post(self):\n           data = request.get_json()\n           # Process your data here\n           return {\"result\": \"Processing complete\"}, 201\n\n   # Register your new endpoint\n   api.add_resource(MyNewEndpoint, '/my-endpoint')\n   ```\n\n2. **Test Your Endpoint:**\n   - URL: `http://localhost:5000/api/v1/my-endpoint`\n   - Methods: GET, POST\n   - Headers: `Content-Type: application/json`\n\n### Integrating ML Models\n\n1. **Project Structure for ML:**\n   - Place model classes in `app/models/`\n   - Store trained models in `app/models/trained_models/`\n   - Put inference logic in `app/services/`\n\n2. **Example Model Integration:**\n\n   ```python\n   # app/models/custom_model.py\n   from transformers import Pipeline  # or your preferred ML library\n\n   class MyModel:\n       def __init__(self):\n           self.model = None\n           \n       def load_model(self, model_path):\n           # Load your model here\n           self.model = Pipeline.from_pretrained(model_path)\n           \n       def predict(self, input_data):\n           # Make predictions\n           return self.model(input_data)\n   ```\n\n3. **Create a Service:**\n\n   ```python\n   # app/services/model_service.py\n   from app.models.custom_model import MyModel\n   \n   class ModelService:\n       def __init__(self):\n           self.model = MyModel()\n           self.model.load_model('app/models/trained_models/my_model')\n           \n       def get_prediction(self, input_data):\n           return self.model.predict(input_data)\n   ```\n\n4. **Create an Endpoint:**\n\n   ```python\n   # app/api/v1/routes.py\n   from app.services.model_service import ModelService\n   \n   class PredictionEndpoint(Resource):\n       def __init__(self):\n           self.model_service = ModelService()\n   \n       @rate_limit\n       def post(self):\n           data = request.get_json()\n           prediction = self.model_service.get_prediction(data['input'])\n           return {'prediction': prediction}, 200\n   \n   # Register endpoint\n   api.add_resource(PredictionEndpoint, '/predict')\n   ```\n\n5. **Make Prediction Request:**\n   - Method: POST\n   - URL: `http://localhost:5000/api/v1/predict`\n   - Headers: `Content-Type: application/json`\n   - Body:\n     ```json\n     {\n         \"input\": \"your input data here\"\n     }\n     ```\n\n### Training Models\n\n1. **Create Training Script:**\n   Place your training scripts in `app/models/training/`:\n\n   ```python\n   # app/models/training/train_model.py\n   def train_model(data_path, save_path):\n       # Load your data\n       # Train your model\n       # Save the model\n       model.save(save_path)\n   \n   if __name__ == '__main__':\n       train_model('path/to/data', 'app/models/trained_models/my_model')\n   ```\n\n2. **Run Training:**\n   ```bash\n   python -m app.models.training.train_model\n   ```\n\n## Best Practices\n\n1. **API Versioning:**\n   - Keep different versions in separate directories (`app/api/v1/`, `app/api/v2/`)\n   - Use version prefix in URLs (`/api/v1/`, `/api/v2/`)\n\n2. **Rate Limiting:**\n   - Configure in `.env`:\n     ```\n     RATE_LIMIT=1000\n     RATE_LIMIT_PERIOD=15\n     ```\n\n3. **Error Handling:**\n   - Use appropriate HTTP status codes\n   - Return descriptive error messages\n   - Log errors properly\n\n4. **Model Management:**\n   - Version your models\n   - Keep model weights in `app/models/trained_models/`\n   - Use environment variables for model paths\n   - Document model requirements and dependencies\n\n5. **Testing:**\n   - Write unit tests in `tests/`\n   - Test API endpoints\n   - Test model inference\n   - Run tests before deployment\n\n## Security Best Practices\n\n1. **API Security:**\n   - Use HTTPS in production\n   - Implement authentication if needed\n   - Validate all input data\n   - Set appropriate CORS policies\n\n2. **Model Security:**\n   - Validate model inputs\n   - Set resource limits\n   - Monitor model performance\n   - Regular security updates\n\n## Contributing\n\nSee `CONTRIBUTING.md` for details on how to contribute to this project.\n\n## License\n\nMIT License. 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