The repository contains the code associated with our capstone project. The project is a web application which integrates OpenAI's CLIP to generate labels for images.

Checkout the Tip Sub-Module for access to TIP Python classes:

git submodule update --recursive --remote

Boot your virtual environment and install the required packages:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

cp .env.template .env
source .env

This sets the REDIS_URL environment variable to redis://localhost:6379 by default. If you are using a different Redis instance, update the .env file accordingly.

Start the server:

uvicorn app.main:app --reload

Interact with the application at http://localhost:8000/{path}

1. /clip/cache

   • This endpoint will generate the TIP Cache for used in inference. This is required before using the /clip endpoint.

2. /clip

   • This endpoint will generate the label for the image provided in the request body. This endpoint requires the clip/cache endpoint to be called first and the received cache_id to be passed in the body.

3. /world

   • This endpoint exists as a simple health check for the application. It will return a simple JSON response.

For detailed endpoint documentation check out the documentation section.

FastAPI provides an interactive documentation page at http://localhost:8000/docs

Pre-build linux/amd64 images for this repository are published to our public docker hub repository.

There is a test suite setup using Pytest. To run the tests, run the following command:

pytest

Ruff is setup to lint the code. To run the linter and fix issues, run the following command:

ruff check app --fix