This repository provides an API to interact with the trained model, allowing users to post images and receive classification results.
We have implemented two options for serving the API:
- FastAPI: Directly access the model to make predictions.
- TensorFlow Serving: Run TensorFlow Serving container with the chosen models and build a FastAPI app that connects to it.
Refer to the TensorFlow Serving API guide.
Whether running the api locally or in a docker container with docker compose, you will need to create the .env file to define the environment variables.
Do that by copying the content of sample.env into .env and modifying it as needed.