Django REST Swagger VS doccano

Common frameworks for building APIs in Python are the Django Rest Framework and Flask-RESTPLUS. Django developers can add the django-rest-swagger module via pip, whereas Flask-RESTPLUS has Swagger support built in. Both are very powerful in terms of automation, so they do not require a lot of configuration.

Examples include Swagger-Core, SpringFox, NSwag / Swashbuckle, Swagger-express / HAPI-Swagger, Django-REST-Swagger / Flask-RESTplus, etc.

I've been going off of the documentation on the django-rest-swagger github page, more specifically the part called "How it works". It shows that you can define your own parameters for your rest api, and have those parameters show up in your swagger doc page.

I have a few URLs that I want to exclude from my REST API documentation. I'm using Django REST Swagger and the only documentation I can find (https://github.com/marcgibbons/django-rest-swagger) doesn't really tell me much. There is the "exclude_namespaces" part of SWAGGER_SETTINGS in settings.py, but there is no real explanation or example of how to use this.

Huh?

I wrote my own system for classifying a stream of texts in Python, I might Open Source it one of these days but I have to get it to the point where it is modular enough that I can customize it to do the particular things I want without subjecting people to my whims... I use it every day and I'm not afraid to demo it because it is rock solid.

My understanding is that my system would not be hard to adapt to work on images for certain kinds of tasks.

Pytorch is open source, Huggingface is open source. CUDA isn't. This is

https://labelstud.io/

and for annotating text spans there are so many open source tools

https://github.com/doccano/doccano

I worked for a company a few years back that built annotation tools for projects we sold to customers but never quite got to a polished general purpose annotator. Today there are an overwhelming number of companies in this space and products I never heard of, many of which are cloud based or paid. Looks like a gold rush to me.

I followed the guide to have doccano https://github.com/doccano/doccano setup in a notebook server in Kubeflow. It is running and the django connection is established, but it fails to connect with my localmachine so when I try to open the link, it does not respond. Is there a way to connect apps running on remove notebook servers to local in Kubeflow?

Why not just use the docker instructions on the GitHub page? https://github.com/doccano/doccano

doccano is built on a Django backend though I believe it uses extensive front end code for the annotation UI.

I worked at a place where we developed a system for doing this kind of tagging but it was for making training sets and there was no expectation that you could export the document from the system for normal use.

Quite a few NLP annotation systems are out there

https://prodi.gy/

https://brat.nlplab.org/

https://doccano.herokuapp.com/