refactor(ml): DRY pass — shared sweep helpers + table-driven settings (#161)

Consolidate duplication accrued across the ML tagging + settings backend,
behavior-preserving (over-DRY guard applied — the three auto-apply sweep
BODIES stay separate; only their shared inner helpers are extracted).

- _sigmoid / _conflict_scores / _insert_presentation_review (heads.py): the
  score→prob transform (6 inlined sites), the presentation conflict signal
  (2 sites), and the ring-loud PresentationReview insert (2 sites, single-
  sourced so the mode column can't drift on the shared composite PK).
- _applied_or_rejected (training_data.py): the per-tag "applied ∪ rejected"
  skip-set, byte-identical at 3 sweep sites (heads.py x2, tasks/ml.py ccip).
- ccip sweep divergence fixes: import ccip._FIGURE_KINDS + training_data._l2norm
  instead of local copies that silently drift when the canonical changes.
- MLSettings.load / .load_sync classmethods (mirror ImportSettings); route all
  8 scalar_one singleton reads through them (the session.get None-path stays).
- GET serializers for MLSettings + ImportSettings are now table-driven off the
  same _EDITABLE tuples PATCH writes, so a new field can't be silently absent
  from GET (the split that historically dropped fields).
- AUTO_APPLY_THRESHOLD_MIN/MAX constant single-sources the [0.5,0.999] operating
  range across the service clamp + the 5 API validators.
- test_ml_dry_helpers.py pins _applied_or_rejected + _sigmoid.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi

backend/app/tasks/ml.py

@@ -105,9 +105,7 @@ def embed_image(self, image_id: int) -> dict:
             record = session.get(ImageRecord, image_id)
             if record is None:
                 return {"status": "missing", "image_id": image_id}
-            settings = session.execute(
-                select(MLSettings).where(MLSettings.id == 1)
-            ).scalar_one()
+            settings = MLSettings.load_sync(session)
 
             src = Path(record.path)
             is_vid = _is_video(src)
@@ -488,15 +486,10 @@ def scheduled_ccip_auto_apply() -> str:
     from sqlalchemy import select as sa_select
     from sqlalchemy.dialects.postgresql import insert as pg_insert
 
-    from ..models import ImageRegion, MLSettings, Tag, TagKind, TagSuggestionRejection
+    from ..models import ImageRegion, MLSettings, Tag, TagKind
     from ..models.tag import image_tag
-
-    fig = ("face", "figure")
-
-    def _l2(m):
-        n = np.linalg.norm(m, axis=1, keepdims=True)
-        n[n == 0] = 1.0
-        return m / n
+    from ..services.ml.ccip import _FIGURE_KINDS
+    from ..services.ml.training_data import _applied_or_rejected, _l2norm
 
     SessionLocal = _sync_session_factory()
     with SessionLocal() as session:
@@ -521,7 +514,7 @@ def scheduled_ccip_auto_apply() -> str:
             )
             .join(Tag, Tag.id == image_tag.c.tag_id)
             .where(Tag.kind == TagKind.character)
-            .where(ImageRegion.kind.in_(fig))
+            .where(ImageRegion.kind.in_(_FIGURE_KINDS))
             .where(ImageRegion.ccip_embedding.is_not(None))
             .where(ImageRegion.image_record_id.in_(single))
         ).all()
@@ -532,29 +525,16 @@ def scheduled_ccip_auto_apply() -> str:
         for tid, vec in ref_rows:
             by_char.setdefault(tid, []).append(vec)
         ref_tags = list(by_char)
-        mats = [_l2(np.asarray(by_char[t], dtype=np.float32)) for t in ref_tags]
+        mats = [_l2norm(np.asarray(by_char[t], dtype=np.float32), np) for t in ref_tags]
         allref = np.vstack(mats)                                  # (total, 768)
         seg = np.cumsum([0] + [len(m) for m in mats])[:-1]        # per-char start
 
         # Per character: images that already carry OR rejected the tag — skip.
-        skip = {t: set() for t in ref_tags}
-        for t in ref_tags:
-            for (iid,) in session.execute(
-                sa_select(image_tag.c.image_record_id).where(
-                    image_tag.c.tag_id == t
-                )
-            ):
-                skip[t].add(iid)
-            for (iid,) in session.execute(
-                sa_select(TagSuggestionRejection.image_record_id).where(
-                    TagSuggestionRejection.tag_id == t
-                )
-            ):
-                skip[t].add(iid)
+        skip = _applied_or_rejected(session, ref_tags)
 
         img_ids = list(session.execute(
             sa_select(ImageRegion.image_record_id)
-            .where(ImageRegion.kind.in_(fig), ImageRegion.ccip_embedding.is_not(None))
+            .where(ImageRegion.kind.in_(_FIGURE_KINDS), ImageRegion.ccip_embedding.is_not(None))
             .distinct()
         ).scalars())
 
@@ -566,7 +546,7 @@ def scheduled_ccip_auto_apply() -> str:
                 sa_select(ImageRegion.image_record_id, ImageRegion.ccip_embedding)
                 .where(
                     ImageRegion.image_record_id.in_(chunk),
-                    ImageRegion.kind.in_(fig),
+                    ImageRegion.kind.in_(_FIGURE_KINDS),
                     ImageRegion.ccip_embedding.is_not(None),
                 )
             ).all()
@@ -574,7 +554,7 @@ def scheduled_ccip_auto_apply() -> str:
             for iid, vec in rows:
                 by_img.setdefault(iid, []).append(vec)
             for iid, vecs in by_img.items():
-                q = _l2(np.asarray(vecs, dtype=np.float32))      # (nq, 768)
+                q = _l2norm(np.asarray(vecs, dtype=np.float32), np)  # (nq, 768)
                 colmax = (q @ allref.T).max(axis=0)              # (total,)
                 charmax = np.maximum.reduceat(colmax, seg)       # (n_chars,)
                 for ci in np.where(charmax >= thr)[0]: