feat(ml): normalize Camie suggestion names to human-readable

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"

backend/app/services/ml/suggestions.py

@@ -4,7 +4,7 @@ threshold-filtered, category-grouped, ranked suggestions for one image.
 
 from dataclasses import dataclass, field
 
-from sqlalchemy import select
+from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ...models import (
@@ -16,6 +16,7 @@ from ...models import (
 from ...models.tag import image_tag
 from .aliases import AliasService
 from .centroids import CentroidService
+from .tag_name import normalize as normalize_tag_name
 from .tagger import SURFACED_CATEGORIES
 
 
@@ -84,7 +85,12 @@ class SuggestionService:
         )
 
         # --- Camie predictions ---
-        candidates: list[tuple[str, str, float]] = []
+        # candidates carry (raw_name, display_name, category, confidence).
+        # raw_name = the booru-formatted vocab key, kept for alias_map
+        # lookup since alias rows are hand-curated against raw keys.
+        # display_name = normalize_tag_name(raw_name) — what the operator
+        # sees AND what gets written to tag.name on Accept.
+        candidates: list[tuple[str, str, str, float]] = []
         for name, p in predictions.items():
             category = p.get("category", "general")
             if category not in SURFACED_CATEGORIES:
@@ -92,10 +98,14 @@ class SuggestionService:
             conf = float(p.get("confidence", 0.0))
             if conf < self._threshold_for(settings, category):
                 continue
-            candidates.append((name, category, conf))
+            display = normalize_tag_name(name)
+            if display is None:
+                # emoticon / pure-punctuation vocab entry — drop entirely
+                continue
+            candidates.append((name, display, category, conf))
 
         alias_map = await self.aliases.resolve_many(
-            [(n, c) for n, c, _ in candidates]
+            [(raw, c) for raw, _disp, c, _conf in candidates]
         )
 
         merged: dict[object, Suggestion] = {}
@@ -116,8 +126,8 @@ class SuggestionService:
                     creates_new_tag=existing.creates_new_tag,
                 )
 
-        for name, category, conf in candidates:
-            canonical = alias_map.get((name, category))
+        for raw, display, category, conf in candidates:
+            canonical = alias_map.get((raw, category))
             if canonical is not None:
                 if canonical.id in applied or canonical.id in rejected:
                     continue
@@ -133,9 +143,17 @@ class SuggestionService:
                     ),
                 )
             else:
+                # Case-insensitive match on BOTH the raw camie key AND
+                # the normalized form — covers legacy underscore-named
+                # Tag rows accepted before normalization shipped, AND
+                # any tag the operator created with the human form.
                 existing_tag = (
                     await self.session.execute(
-                        select(Tag).where(Tag.name == name)
+                        select(Tag).where(
+                            func.lower(Tag.name).in_(
+                                [raw.lower(), display.lower()]
+                            )
+                        )
                     )
                 ).scalars().first()
                 if existing_tag is not None:
@@ -157,10 +175,10 @@ class SuggestionService:
                     )
                 else:
                     _merge(
-                        f"raw:{name}:{category}",
+                        f"raw:{display}:{category}",
                         Suggestion(
                             canonical_tag_id=None,
-                            display_name=name,
+                            display_name=display,
                             category=category,
                             score=conf,
                             source="tagger",