Spacy-fastapi

This project provides REST API interface (thanks to FastAPI) to Spacy V3 with GPU support reducing the latency for a fast, complete and customizable NLP self-contained pipeline part.

Getting Started

Docker

If you want to run the Docker image, simply run

docker run -p 8000:80 intrical/spacy-fastapi:latest

If you want to enable the GPU support and reduce the inferente time, you first need to install CUDA support for the Docker Runtime. You can follow a nice guide here.

From source

If you want to run the code from your machine not in docker image, first pull the code

git clone https://github.com/Intrical-AI/spacy-fastapi.git
cd spacy-fastapi

Create now the virtualenvironment

virtualenv --python=/usr/bin/python3.8 env
source ./env/bin/activate

Now install Pipenv and consequently all the requirements

pip install pipenv
pipenv sync

Then, download the language models from spacy that you need, in this case I will download the English model, you can follow the instructions here if you need other languages.

python -m spacy download en_core_web_trf

Now you are set! You can finally run the web server using uvicorn (FastAPI Documentation)

uvicorn main:app --port 8000 --reload

To test if everything is up and running, use the /predict endpoint. You can do that using Postman or more quickly CURL

curl --location --request POST 'http://localhost:8100/predict' \
--header 'Content-Type: application/json' \
--header 'Cookie: csrftoken=dENUPbzrsmkx1cEVH93cLJhNHKj22AUWEjqE8WOOyy5dqvC0RhzgkUtdJzUiRFCS' \
--data-raw '{
    "texts": [
        "Microsoft is a big company."
    ],
    "language": "en"
}'

The answer should be like this

{
    "documents": [
        [
            {
                "text": "Microsoft is a big company.",
                "tense": "VBZ",
                "entities": [
                    {
                        "name": "Microsoft",
                        "start": 0,
                        "end": 9,
                        "label": "ORG",
                        "syntactic_pos": "PROPN",
                        "syntactic_dep": "nsubj",
                        "tense": "NNP",
                        "morph": {
                            "NounType": "Prop",
                            "Number": "Sing"
                        }
                    }
                ]
            }
        ]
    ]
}

Running the tests

TODO

Authors

• Alvise Sembenico as Intrical AI - Initial work - Alvise Sembenico

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details