Camie's booru-style vocab strings (`uchiha_sasuke_(naruto)`,
`#unicus_(idolmaster)`, `1000-nen_ikiteru_(vocaloid)`, `:/`) were
surfacing raw in SuggestionsPanel — and worse, the SAME raw string was
written to tag.name on Accept, polluting the DB with `underscored_lowercase`
names that don't match the operator's "Title Case" tag convention.
Add backend/app/services/ml/tag_name.py with a single normalize()
applying nine rules (strip leading junk #/./+/;/~/_/ws, drop trailing
_(disambiguator) blocks iteratively, strip wrapping quotes, underscores
to spaces, space after colon, title-case each word's first char,
preserve hyphens/apostrophes/digits, drop entries with no letters).
Wire into SuggestionService.for_image:
- raw Camie key kept for alias_map lookup (alias rows are hand-curated
against raw keys; don't disturb)
- display_name = normalize(raw); None means drop the candidate
- existing-tag lookup widened to case-insensitive match against BOTH
raw and normalized forms so legacy underscore-named Tag rows accepted
before this change still surface as "existing" not "+ new"
The read path: load tagger_predictions, drop unsurfaced categories
(rating/meta/year), apply per-category thresholds, batch-resolve aliases,
skip applied + rejected, augment with centroid hits above the similarity
threshold, merge duplicate signals (take max score, mark source 'both'),
group by category, sort by score DESC. Tests marked integration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen