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

1. Clone the repository:

   git clone https://github.com/DaniyalAhm/Crud.git
   cd Crud

2. 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.

• TensorFlow
• Amazon Rekognition
• Docker
• React
• Node.js
• Flask