TensorMap is a web application that allows users to create machine learning algorithms visually. TensorMap supports reverse engineering of the visual layout to a TensorFlow implementation in preferred languages. The goal of the project is to let beginners play with machine learning algorithms in TensorFlow without requiring extensive background knowledge about the library. For more details about the project, read our project wiki.

Follow these steps to set up and run TensorMap using Docker.

• Docker
• Docker Compose

1. Clone the Repository:

   git clone https://github.com/c2siorg/tensormap.git
   cd TensorMap

2. Set Up Environment:

   • Ensure Docker and Docker Compose are installed and running on your machine.

3. Build and Run the Application: Use Docker Compose to build and start the TensorMap services (database, server, and client):

   docker-compose up --build

   This will:

   • Start a PostgreSQL database.
   • Build and run the TensorMap server (Flask backend).
   • Build and run the TensorMap client (React frontend).

4. Access the Application:

   • Frontend (Client): Open your browser and go to http://localhost:5173.
   • Backend (Server): The Flask API will be available at http://localhost:5000.

5. Stop the Application: To stop the running services, press Ctrl+C in the terminal or run:

   docker-compose down

The docker-compose.yml file defines the following services:

• Database: PostgreSQL database for storing application data.
• Server: Flask backend for TensorMap.
• Client: React frontend for TensorMap.

You can modify the docker-compose.yml file to customize the setup (e.g., change ports or environment variables).

If you're developing TensorMap, you can use Docker to streamline your workflow:

• Rebuild and Restart the Client:

  docker-compose up --build client

• View Logs:

  docker-compose logs client

• Access the Container Shell:

  docker exec -it <client-container-id> /bin/sh

Please read the 'Note to Contributors' in the project wiki for more details.

This project is licensed under the MIT License - see the LICENSE.md file for details.

This updated README.md includes clear instructions for running TensorMap using Docker, making it easier for users to get started. Let me know if you need further adjustments!