This repository contains a CRUD (Create, Read, Update, Delete) application developed using a variety of technologies to ensure isolation, security, and efficiency.
This project was instrumental in introducing me to TensorFlow. However, for this particular application, I opted to use Amazon Rekognition due to its advanced features and ease of integration.
- Dockerized Environment: The project is structured to run the backend and frontend in separate Docker containers. This approach ensures isolation between services, enhancing security and maintainability.
- Backend: Developed using Node.js and Flask, providing a robust and scalable server-side solution.
- Frontend: Built with React, offering a dynamic and responsive user interface.
- Amazon Rekognition Integration: Utilized for image analysis and recognition, providing powerful and accurate insights.
- Docker
- Docker Compose
-
Clone the repository:
git clone https://github.com/DaniyalAhm/Crud.git cd Crud
-
Build and run the containers:
docker-compose up --build
The application can be accessed via http://localhost:3000
for the frontend and http://localhost:5000
for the backend.
- Frontend: React
- Backend: Node.js, Flask
- Image Analysis: Amazon Rekognition
- Containerization: Docker
Contributions are welcome! Please fork the repository and create a pull request with your changes.
This project is licensed under the MIT License - see the LICENSE file for details.