refactor(ml): drop dead tagger/suggestion settings + columns (#1199)

Hygiene follow-up to the Camie retirement (#1189) — these were left inert to
bound that change; nothing reads them now. Migration 0068 drops:
- ml_settings: tagger_store_floor, tagger_model_version, suggestion_threshold_
  character/general (already dead pre-retirement — scoring uses per-head
  thresholds), video_min_tag_frames (only the deleted video-prediction
  aggregator used it).
- image_record: tagger_model_version (no writer), centroid_scores (dead JSON
  cache, no reader).

Also: ml_admin _EDITABLE/GET/_validate pruned (dropped the store-floor invariant
+ video_min_tag_frames check); MLThresholdSliders trimmed to a video-embedding
card (interval + max frames only); importer no longer resets the dropped cols;
download_models drops the Camie fetch; stale CASCADE comments in cleanup_service
no longer name the removed tables. Tests updated.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

backend/app/api/ml_admin.py

@@ -9,12 +9,8 @@ ml_admin_bp = Blueprint("ml_admin", __name__, url_prefix="/api/ml")
 
 
 _EDITABLE = (
-    "suggestion_threshold_character",
-    "suggestion_threshold_general",
-    "tagger_store_floor",
     "video_frame_interval_seconds",
     "video_max_frames",
-    "video_min_tag_frames",
     "head_min_positives",
     "head_auto_apply_precision",
     "head_auto_apply_enabled",
@@ -37,13 +33,8 @@ async def get_settings():
         ).scalar_one()
     return jsonify(
         {
-            "suggestion_threshold_character": s.suggestion_threshold_character,
-            "suggestion_threshold_general": s.suggestion_threshold_general,
-            "tagger_store_floor": s.tagger_store_floor,
             "video_frame_interval_seconds": s.video_frame_interval_seconds,
             "video_max_frames": s.video_max_frames,
-            "video_min_tag_frames": s.video_min_tag_frames,
-            "tagger_model_version": s.tagger_model_version,
             "embedder_model_version": s.embedder_model_version,
             "head_min_positives": s.head_min_positives,
             "head_auto_apply_precision": s.head_auto_apply_precision,
@@ -88,31 +79,12 @@ async def patch_settings():
 
 
 def _validate(p: dict) -> str | None:
-    """Returns an error string if the proposed settings are invalid, else None.
-
-    Invariant (plan-task #764): the per-category suggestion thresholds can't
-    drop below tagger_store_floor — nothing below the floor is stored, so a
-    lower threshold would silently surface nothing in that gap. The UI clamps
-    the sliders to the floor; this is the server-side backstop.
-    """
-    floor = p["tagger_store_floor"]
-    if not (0.0 <= floor <= 1.0):
-        return "tagger_store_floor must be between 0 and 1"
-    for cat in ("character", "general"):
-        if p[f"suggestion_threshold_{cat}"] < floor:
-            return (
-                f"suggestion_threshold_{cat} cannot be below tagger_store_floor "
-                f"({floor}) — predictions below the floor are not stored"
-            )
-    # Video tagging (#747).
+    """Returns an error string if the proposed settings are invalid, else None."""
+    # Video embedding (#747).
     if p["video_frame_interval_seconds"] <= 0:
         return "video_frame_interval_seconds must be > 0"
     if p["video_max_frames"] < 1:
         return "video_max_frames must be >= 1"
-    if p["video_min_tag_frames"] < 1:
-        return "video_min_tag_frames must be >= 1"
-    if p["video_min_tag_frames"] > p["video_max_frames"]:
-        return "video_min_tag_frames cannot exceed video_max_frames"
     # Head training (#114).
     if int(p["head_min_positives"]) < 1:
         return "head_min_positives must be >= 1"

backend/app/models/image_record.py

@@ -9,7 +9,6 @@ from datetime import datetime
 
 from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
-    JSON,
     BigInteger,
     DateTime,
     Enum,
@@ -77,19 +76,13 @@ class ImageRecord(Base):
         ForeignKey("artist.id", ondelete="SET NULL"), nullable=True, index=True
     )
 
-    # ML fields (populated by FC-2's ml-worker). Per-tag predictions live in the
-    # normalized image_prediction table (#768) — the tagger_predictions JSON
-    # column was dropped in migration 0046. tagger_model_version stays as the
-    # "has this been tagged / is it current?" signal the backfill sweep reads.
-    tagger_model_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
-    # 1152 = SigLIP-so400m embedding dim. Swapping models in FC-2 may require
-    # a column-width migration.
+    # ML fields (populated by the ml-worker / GPU agent). 1152 = SigLIP-so400m
+    # embedding dim; siglip_model_version stamps which model produced it (so an
+    # operator model swap, #1190, can re-embed the stale rows). A different-dim
+    # model would need a column-width migration.
     siglip_embedding: Mapped[list[float] | None] = mapped_column(Vector(1152), nullable=True)
     siglip_model_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
 
-    # Centroid score cache (populated post-tagging)
-    centroid_scores: Mapped[dict | None] = mapped_column(JSON, nullable=True)
-
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )

backend/app/models/ml_settings.py

@@ -23,39 +23,16 @@ class MLSettings(Base):
     __table_args__ = (CheckConstraint("id = 1", name="singleton"),)
 
     id: Mapped[int] = mapped_column(Integer, primary_key=True)
-    suggestion_threshold_character: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.70
-    )
-    # Default raised 0.50 → 0.70 on 2026-06-02 — operator-flagged 0.50
-    # surfaced too many low-confidence picks; 0.70 keeps the rail
-    # signal-rich while still surfacing more than the original 0.95
-    # which hid almost everything. Operator-tunable via Settings → ML.
-    suggestion_threshold_general: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.70
-    )
-    # Ingest floor: tagger predictions below this confidence are not stored
-    # (tagger.Tagger.infer). Default 0.70 — the suggestion path already filters
-    # there, so the sub-0.70 tail is redundant weight (it had bloated
-    # image_record's TOAST to ~100 GB; plan-task #764). Operator-tunable via
-    # Settings → ML; must stay ≤ the suggestion thresholds.
-    tagger_store_floor: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.70
-    )
-    # Video tagging (#747). Sample one frame every N seconds (fixed CADENCE, not a
-    # fixed count) so a tag's frame-presence reflects real screen time regardless
-    # of video length; cap the total so a long video can't explode into hundreds
-    # of inferences (the cadence stretches past the cap). A tag is kept only if it
-    # appears in >= video_min_tag_frames sampled frames (≈ that many × interval
-    # seconds on screen) — duration-independent noise rejection. Operator-tunable.
+    # Video embedding (#747). Sample one frame every N seconds (fixed CADENCE, not
+    # a fixed count) so coverage reflects real screen time regardless of length;
+    # cap the total so a long video can't explode into hundreds of embeds. The
+    # per-frame SigLIP embeddings are mean-pooled. Operator-tunable.
     video_frame_interval_seconds: Mapped[float] = mapped_column(
         Float, nullable=False, default=4.0
     )
     video_max_frames: Mapped[int] = mapped_column(
         Integer, nullable=False, default=64
     )
-    video_min_tag_frames: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=3
-    )
     # Tagging-v2 head training (#114). The head is the suggestion source that
     # LEARNS from the operator's tags (replacing Camie + centroid). A concept
     # needs >= head_min_positives labelled images before a head is trained;
@@ -94,9 +71,6 @@ class MLSettings(Base):
     ccip_auto_apply_threshold: Mapped[float] = mapped_column(
         Float, nullable=False, default=0.92
     )
-    tagger_model_version: Mapped[str] = mapped_column(
-        String(128), nullable=False, default="camie-tagger-v2"
-    )
     embedder_model_version: Mapped[str] = mapped_column(
         String(128), nullable=False, default="siglip-so400m-patch14-384"
     )

backend/app/scripts/download_models.py

@@ -7,7 +7,6 @@ import sys
 from pathlib import Path
 
 MODEL_ROOT = Path(os.environ.get("ML_MODEL_DIR", "/models"))
-CAMIE_REPO = os.environ.get("CAMIE_HF_REPO", "Camais03/camie-tagger-v2")
 SIGLIP_REPO = os.environ.get(
     "SIGLIP_HF_REPO", "google/siglip-so400m-patch14-384"
 )
@@ -24,34 +23,6 @@ def _snapshot(repo_id: str, dest: Path, allow_patterns: list[str] | None) -> Non
     )
 
 
-def ensure_camie() -> None:
-    """Fetch Camie v2 weights + metadata.
-
-    v2 layout (HuggingFace Camais03/camie-tagger-v2): the ONNX file is
-    named camie-tagger-v2.onnx (not model.onnx) and tags ship inside
-    camie-tagger-v2-metadata.json (not selected_tags.csv). Both at root.
-    The repo also contains app/, game/, training/, images/ subdirs full
-    of setup/demo files we don't need — allow_patterns scopes the fetch
-    to just the inference essentials (~790 MB instead of ~2 GB).
-    """
-    dest = MODEL_ROOT / "camie"
-    model_file = dest / "camie-tagger-v2.onnx"
-    meta_file = dest / "camie-tagger-v2-metadata.json"
-    if model_file.is_file() and meta_file.is_file():
-        print(f"[download_models] Camie present at {dest}")
-        return
-    print(f"[download_models] Fetching {CAMIE_REPO} -> {dest}")
-    _snapshot(
-        CAMIE_REPO, dest,
-        [
-            "camie-tagger-v2.onnx",
-            "camie-tagger-v2-metadata.json",
-            "config.json",
-            "config.yaml",
-        ],
-    )
-
-
 def ensure_siglip() -> None:
     dest = MODEL_ROOT / "siglip"
     if (dest / "config.json").is_file() and any(dest.glob("*.safetensors")):
@@ -62,7 +33,6 @@ def ensure_siglip() -> None:
 
 
 def main() -> int:
-    ensure_camie()
     ensure_siglip()
     print("[download_models] Done.")
     return 0

backend/app/services/cleanup_service.py

@@ -395,9 +395,8 @@ def delete_images(
 def delete_tag(session: Session, *, tag_id: int) -> dict:
     """Simple DELETE FROM tag WHERE id=?.
 
-    Postgres cascades the rest (image_tag, tag_alias, tag_allowlist,
-    tag_reference_embedding, tag_suggestion_rejection, series_page).
-    Returns counts BEFORE delete so the caller can surface them.
+    Postgres cascades the rest (image_tag, tag_alias, tag_suggestion_rejection,
+    series_page). Returns counts BEFORE delete so the caller can surface them.
     Raises LookupError if tag_id not found.
     """
     tag = session.get(Tag, tag_id)
@@ -742,8 +741,7 @@ def purge_legacy_tags(session: Session, *, dry_run: bool = False) -> dict:
     artist-kind tags PLUS general tags whose name matches a legacy
     prefix (source:*).
 
-    CASCADE on image_tag / tag_alias / tag_allowlist /
-    tag_reference_embedding / tag_suggestion_rejection / series_page
+    CASCADE on image_tag / tag_alias / tag_suggestion_rejection / series_page
     clears the related rows on the parent DELETE.
 
     Returns:
@@ -785,23 +783,21 @@ def purge_legacy_tags(session: Session, *, dry_run: bool = False) -> dict:
     return result
 
 
-# The Camie-suggestable CONTENT vocabulary. "Reset content tagging" wipes
-# these so the operator can re-tag from scratch via auto-suggest. fandom +
-# series (and series_page ordering) are deliberately NOT here — they're kept.
+# The CONTENT vocabulary. "Reset content tagging" wipes these so the operator
+# can re-tag from scratch. fandom + series (and series_page ordering) are
+# deliberately NOT here — they're kept.
 RESETTABLE_TAG_KINDS = ("general", "character")
 
 
 def reset_content_tagging(session: Session, *, dry_run: bool = False) -> dict:
     """Count (dry_run) or DELETE every general + character tag so the operator
-    can re-tag from scratch via the Camie auto-suggest.
+    can re-tag from scratch (heads/CCIP repopulate suggestions).
 
-    PRESERVED: fandom + series tags and their series_page ordering, plus every
-    image's image_prediction rows (untouched) so suggestions
-    repopulate immediately. CASCADE on image_tag / tag_alias / tag_allowlist /
-    tag_reference_embedding / tag_suggestion_rejection clears each deleted
-    tag's applications + metadata. Tag.fandom_id is SET NULL, so deleting
-    character tags never touches the fandom rows. Irreversible except via DB
-    backup restore.
+    PRESERVED: fandom + series tags and their series_page ordering. CASCADE on
+    image_tag / tag_alias / tag_suggestion_rejection clears each deleted tag's
+    applications + metadata. Tag.fandom_id is SET NULL, so deleting character
+    tags never touches the fandom rows. Irreversible except via DB backup
+    restore.
 
     Returns:
       {"by_kind": {"general": N, "character": M},

backend/app/services/importer.py

@@ -1475,10 +1475,8 @@ class Importer:
         existing.duration_seconds = duration  # #871: keep the kept copy's duration
         existing.thumbnail_path = None
         existing.integrity_status = "unknown"
-        existing.tagger_model_version = None
         existing.siglip_embedding = None
         existing.siglip_model_version = None
-        existing.centroid_scores = None
         # created_at intentionally preserved; updated_at auto-bumps.
         self.session.flush()
         self.session.commit()