Folders and files
| Name | Name | Last commit date | ||
|---|---|---|---|---|
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.
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Machine Learning Model Development
- The initial phase involved developing the machine learning model. Detailed information about the model can be found here.
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API Endpoint Deployment
- We deployed the API endpoints using FastAPI in Python.
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User Interface Development
- A user-friendly application interface was created using HTML, CSS, and JavaScript.
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Containerization
- The final application was containerized using Docker.
| 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. |
- 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- 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
- Go to : localhost:PORT (Default PORT = 5500) in your browser
We welcome contributions to enhance the QuickDraw API. To contribute:
- Fork the repository.
- Create a new branch (git checkout -b feature-branch).
- Commit your changes (git commit -am 'Add new feature').
- Push to the branch (git push origin feature-branch).
- Create a new Pull Request.
For any inquiries or feedback, please contact us.