QuickDraw API

Welcome to the QuickDraw API repository. This project encompasses the development of a machine learning model and its deployment through a user-friendly application. The complete project details are available here.

Project Workflow

1. Machine Learning Model Development

   • The initial phase involved developing the machine learning model. Detailed information about the model can be found here.

2. API Endpoint Deployment

   • We deployed the API endpoints using FastAPI in Python.

3. User Interface Development

   • A user-friendly application interface was created using HTML, CSS, and JavaScript.

4. Containerization

   • The final application was containerized using Docker.

Repository Structure

File/Folder	Description
app/	Contains the files related to application development.
endpoints/	Contains the files for API endpoints.
.gitignore	Specifies files and directories to be ignored by Git.
.dockerignore	Specifies files and directories to be ignored by Docker files.
docker-compose.yml	The docker compose yaml file for app launching.
Dockerfile.txt	The main docker file for the app.
README.md	Short Documentation overview.
requirements.txt	Lists all the dependencies required for the project.

Getting Started

1. Clone the repo or download the docker-compose.yml file

• To clone the repo :

git clone https://github.com/mlengineershub/QuickDraw-API.git
cd QuickDraw-API

2. Run the docker-compose.yml :

docker-compose up -d

• Note : You can modify some parameters such as the port and the device directly in the docker-compose.yml

3. Go to : localhost:PORT (Default PORT = 5500) in your browser

Contributing

We welcome contributions to enhance the QuickDraw API. To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

Contact

For any inquiries or feedback, please contact us.