ImageProAPI

ImageProAPI is a scalable image processing API that allows clients to upload images and apply multiple transformations through RESTful endpoints.

The API supports operations such as resizing, compression, format conversion, and filtering. Image processing tasks are handled asynchronously to ensure the API remains responsive even during heavy workloads.

Built with Django and Django REST Framework, ImageProAPI focuses on performance, security, and scalability, making it suitable for integration with web applications, mobile apps, or data processing pipelines.

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Features

• Image transformations using Pillow
  • Resize
  • Compression
  • Filters
  • Format conversion
• Asynchronous image processing using Celery workers
• Redis-backed task queue
• AWS S3 storage for media files and static assets
• Dockerized environment for consistent development and deployment
• API rate limiting to prevent abuse and improve service stability
• JWT authentication for secure API access
• Automatic image cleanup after expiry
• Modular API design suitable for integration into web or mobile applications
• API rate limiting implemented using Django REST Framework throttling

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Architecture Overview

The system is designed to prevent heavy image operations from blocking API requests.

1. Client uploads an image and specifies operations
2. API stores the image and operation instructions
3. A Celery worker processes the image asynchronously
4. A Celery worker retrieves the task from Redis and processes the image
5. The processed image is stored back in S3
6. Clients can retrieve the processing status and download the image
7. Celery Beat schedules periodic cleanup tasks

Components

• Django REST API
• Celery workers for background processing
• Redis as message broker
• AWS S3 for media and static files storage
• Docker for containerized development and deployment
• Pillow for image transformations

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Setup Instructions

1. Clone the repository:

   git clone https://github.com/JhayceeCodes/ImageProAPI
   cd ImageProAPI 

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # Linux/macOS
   venv\Scripts\activate     # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables (if any, e.g., Redis URL, Django secret key).

5. Run migrations:

   python manage.py migrate

6. Start the development server

   python manage.py runserver

7. Start Celery worker (ensure Redis is running):

   celery -A config worker -l info

8. Start Celery beat (for scheduled tasks like auto-deletion):

   celery -A config beat -l info

Running with Docker

To run the project using Docker:

docker compose up --build

This will start:

• Django API
• Redis
• Celery Worker
• Celery Beat

Once running, the API will be available at:

http://localhost:8000

API Endpoints

Authentication

Method	Endpoint	Description
POST	/accounts/register/	Register a new user
POST	/accounts/login/	Login and obtain access & refresh token
POST	/accounts/refresh/	Refresh access token
POST	/account/logout/	Logout and blacklist refresh token

Image Operations

Method	Endpoint	Description
POST	/api/images/	Upload a new image with operations (as JSON).
GET	/api/images/	List user images.
GET	/api/images/{id}/	Retrieve image details including status and download URL (if ready).
GET	/api/images/{id}download/	Download the processed image. Only available if status = completed.

Usage Flow

• Upload an image with processing operations.

• The API queues the request for background processing.

• A Celery worker processes the image.

• Client polls the image status endpoint.

• When processing is complete, a download link becomes available.

Background Processing

• Image Processing: All image transformations are handled asynchronously by Celery workers to prevent blocking the API.

• Automatic Cleanup: Expired images are automatically deleted via a scheduled Celery task.

• Redis is used as the Celery broker.

Technologies Used

• Python
• Django
• Django REST Framework
• Celery
• Redis
• Pillow
• Docker
• AWS S3

Future Improvements

• Webhook support for processing completion
• Batch image processing
• API request analytics and usage metrics
• API key authentication and client access management

License

This project is licensed under the MIT License.