Lazy Analysis System (LAS)

Deploy using Docker

To deploy this project using docker :

$ git clone https://github.com/salahalaoui/fastapi-celery-rabbitmq

Now move to the project root directory. The .env is committed to git history just for the convenience of the developer reading this code: sensible environment variables should never be published in repositories.

$ mv fastapi-celery-rabbitmq
$ docker compose build
$ docker compose up

In order to use the analysis routes you should first authenticate at the top right of the application docs (0.0.0.0:8080/docs) in the button that reads Authorize. Before that you need to create a new user with the POST register

Authentification

The API uses Bearer token from OAuth2PasswordBearer instead of a static api key, it's more secure because even it's compromised the token will expire and the user will have to ask for a new token.

Stack

The docker-compose.yml is configured to create :

• an image of the application named app,
• an image of PostgreSQL where we only store the user,
• an image of a celery worker,
• an image of RabbitMQ –rabbit– as celery broker
• an image of Redis v6.2 –redis for celery backend
• an image of flower to monitor celery tasks at localhost:5556.
• an image of pgadmin for PostgreSQL database administration at localhost:5050

To see the application working sound and safe, please visit the URI localhost:8080/docs

Accepted files

The api uses the mime type to determine the nature and format of a file.

Current files accepted:

• application/vnd.openxmlformats-officedocument.wordprocessingml.document for .docx files
• text/plain for text-only data

Asynchronous task performed

The celery task executed is word counter, because this task doesn't properly simulate a long task, the developper can add an environnement variable ADD_DELAY_TASK in secondes to make the task takes more time to execute.