Add AI tag model and UI.

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.

app/models/post.rb

@@ -1307,6 +1307,14 @@ class Post < ApplicationRecord
         where(md5: metadata.select(:md5))
       end
 
+      def ai_tags_include(value)
+        tag = Tag.find_by_name_or_alias(value)
+        return none if tag.nil?
+
+        ai_tags = AITag.joins(:media_asset).where(tag: tag, score: (50..))
+        where(ai_tags.where("media_assets.md5 = posts.md5").arel.exists)
+      end
+
       def uploader_matches(username)
         case username.downcase
         when "any"