1aeb52186e
Add a database model for storing AI-predicted tags, and add a UI for browsing and searching these tags. AI tags are generated by the Danbooru Autotagger (https://github.com/danbooru/autotagger). See that repo for details about the model. The database schema is `ai_tags (media_asset_id integer, tag_id integer, score smallint)`. This is designed to be as space-efficient as possible, since in production we have over 300 million AI-generated tags (6 million images and 50 tags per post). This amounts to over 10GB in size, plus indexes. You can search for AI tags using e.g. `ai:scenery`. You can do `ai:scenery -scenery` to find posts where the scenery tag is potentially missing, or `scenery -ai:scenery` to find posts that are potentially mistagged (or more likely where the AI missed the tag). You can browse AI tags at https://danbooru.donmai.us/ai_tags. On this page you can filter by confidence level. You can also search unposted media assets by AI tag. To generate tags, use the `autotag` script from the Autotagger repo, something like this: docker run --rm -v ~/danbooru/public/data/360x360:/images ghcr.io/danbooru/autotagger ./autotag -c -f /images | gzip > tags.csv.gz To import tags, use the fix script in script/fixes/. Expect a Danbooru-size dataset to take hours to days to generate tags, then 20-30 minutes to import. Currently this all has to be done by hand.
Logical
This directory contains library code used through Danbooru. This includes things like defining API clients, dealing with sources, parsing tag searches, storing and resizing images, and so on.
Many of the files here use the Service Object pattern. Instead of putting complex code in models or controllers, it goes here, in plain old Ruby objects (POROs). This keeps models and controllers simpler, and keeps domain logic isolated and independent from the database and the HTTP request cycle.