chore: retire the tag-eval harness — it proved the heads system, job done (operator-approved)

The head-vs-centroid eval (#1130) existed to prove the 'frozen embedding +
trained head' spine; the operator accepted the tagging system and dropped the
harness. Removed per rule 22: TagEvalCard + store, /api/tag_eval blueprint,
tag_eval_run ml task, recover-stalled-tag-eval-runs sweep + beat entry,
TagEvalRun model + table (migration 0073), and its tests.

The eval's data loaders + metric helpers were NOT eval-specific — the nightly
heads trainer runs on them — so they moved verbatim to
services/ml/training_data.py (heads.py import updated; behavior unchanged).

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM

alembic/versions/0073_drop_tag_eval_run.py

@@ -0,0 +1,46 @@
+"""drop tag_eval_run — the head-vs-centroid eval harness is retired
+
+The eval (#1130) existed to prove the heads tagging spine on the operator's own
+data. It did; the operator accepted the system and retired the harness
+(2026-07-02) — card, API, task, model and this table all go. The eval's data
+loaders + metric helpers live on in services/ml/training_data.py, where the
+production heads trainer uses them nightly.
+
+Revision ID: 0073
+Revises: 0072
+Create Date: 2026-07-02
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+revision: str = "0073"
+down_revision: Union[str, None] = "0072"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.drop_index("ix_tag_eval_run_status", table_name="tag_eval_run")
+    op.drop_table("tag_eval_run")
+
+
+def downgrade() -> None:
+    # Recreates the shape from 0056 (data is not restorable).
+    op.create_table(
+        "tag_eval_run",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("params", postgresql.JSONB(), nullable=False),
+        sa.Column("status", sa.String(length=16), nullable=False,
+                  server_default="running"),
+        sa.Column("started_at", sa.DateTime(timezone=True), nullable=False,
+                  server_default=sa.func.now()),
+        sa.Column("finished_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column("report", postgresql.JSONB(), nullable=True),
+        sa.Column("error", sa.Text(), nullable=True),
+        sa.Column("last_progress_at", sa.DateTime(timezone=True),
+                  nullable=True),
+    )
+    op.create_index("ix_tag_eval_run_status", "tag_eval_run", ["status"])

backend/app/api/__init__.py

@@ -38,7 +38,6 @@ def all_blueprints() -> list[Blueprint]:
     from .suggestions import suggestions_bp
     from .system_activity import system_activity_bp
     from .system_backup import system_backup_bp
-    from .tag_eval import tag_eval_bp
     from .tags import tags_bp
     from .thumbnails import thumbnails_bp
     return [
@@ -58,7 +57,6 @@ def all_blueprints() -> list[Blueprint]:
         import_admin_bp,
         suggestions_bp,
         aliases_bp,
-        tag_eval_bp,
         heads_bp,
         gpu_bp,
         ccip_bp,

backend/app/api/tag_eval.py

@@ -1,70 +0,0 @@
-"""Tag-eval API (#1130): trigger + revisit the head-vs-centroid eval.
-
-The run + full report live in the tag_eval_run row, so the admin card rehydrates
-from GET (history / detail) on mount — the report survives navigation rather than
-living in transient frontend state.
-"""
-
-from quart import Blueprint, jsonify, request
-from sqlalchemy import select
-
-from ..extensions import get_session
-from ..models import TagEvalRun
-from ..services.ml.tag_eval import EvalAlreadyRunning, start_tag_eval_run
-
-tag_eval_bp = Blueprint("tag_eval", __name__, url_prefix="/api/tag-eval")
-
-
-def _serialize(run: TagEvalRun, *, include_report: bool) -> dict:
-    out = {
-        "id": run.id,
-        "params": run.params,
-        "status": run.status,
-        "started_at": run.started_at.isoformat() if run.started_at else None,
-        "finished_at": run.finished_at.isoformat() if run.finished_at else None,
-        "error": run.error,
-    }
-    if include_report:
-        out["report"] = run.report
-    return out
-
-
-@tag_eval_bp.route("", methods=["POST"])
-async def create():
-    body = await request.get_json(silent=True) or {}
-    params = body.get("params") or body or {}
-    async with get_session() as session:
-        try:
-            run_id = await session.run_sync(
-                lambda s: start_tag_eval_run(s, params)
-            )
-        except EvalAlreadyRunning as running:
-            return jsonify({
-                "error": "eval_already_running",
-                "running_id": int(running.args[0]),
-            }), 409
-        await session.commit()
-    return jsonify({"run_id": run_id, "status": "running"}), 202
-
-
-@tag_eval_bp.route("", methods=["GET"])
-async def history():
-    try:
-        limit = min(int(request.args.get("limit", "20")), 100)
-    except ValueError:
-        return jsonify({"error": "invalid_limit"}), 400
-    async with get_session() as session:
-        rows = (await session.execute(
-            select(TagEvalRun).order_by(TagEvalRun.id.desc()).limit(limit)
-        )).scalars().all()
-        # List is light — no full report (the detail endpoint carries it).
-        return jsonify({"runs": [_serialize(r, include_report=False) for r in rows]})
-
-
-@tag_eval_bp.route("/<int:run_id>", methods=["GET"])
-async def detail(run_id: int):
-    async with get_session() as session:
-        run = await session.get(TagEvalRun, run_id)
-        if run is None:
-            return jsonify({"error": "not_found"}), 404
-        return jsonify(_serialize(run, include_report=True))

backend/app/celery_app.py

@@ -183,10 +183,6 @@ def make_celery() -> Celery:
                 "task": "backend.app.tasks.maintenance.recover_stalled_library_audit_runs",
                 "schedule": 300.0,
             },
-            "recover-stalled-tag-eval-runs": {
-                "task": "backend.app.tasks.maintenance.recover_stalled_tag_eval_runs",
-                "schedule": 300.0,
-            },
             "recover-stalled-head-training-runs": {
                 "task": "backend.app.tasks.maintenance.recover_stalled_head_training_runs",
                 "schedule": 300.0,

backend/app/models/__init__.py

@@ -33,7 +33,6 @@ from .subscribestar_failed_media import SubscribeStarFailedMedia
 from .subscribestar_seen_media import SubscribeStarSeenMedia
 from .tag import Tag, TagKind, image_tag
 from .tag_alias import TagAlias
-from .tag_eval_run import TagEvalRun
 from .tag_head import TagHead
 from .tag_positive_confirmation import TagPositiveConfirmation
 from .tag_suggestion_rejection import TagSuggestionRejection
@@ -75,7 +74,6 @@ __all__ = [
     "HeadMetricsSnapshot",
     "HeadTrainingRun",
     "TagAlias",
-    "TagEvalRun",
     "TagHead",
     "TagPositiveConfirmation",
     "TagSuggestionRejection",

backend/app/models/head_training_run.py

@@ -1,7 +1,7 @@
 """HeadTrainingRun — persisted lifecycle of a head-training batch (#114).
 
-Mirrors TagEvalRun so the run SURVIVES navigation and the admin card can show
-live + historical status instead of holding it in transient frontend state.
+A persisted run row (not transient frontend state) so the run SURVIVES
+navigation and the admin card can show live + historical status.
 Training is idempotent (it upserts tag_head rows), so a SIGKILL'd run is harmless
 — a maintenance recovery sweep flips a stalled `running` row to `error`, and the
 next run re-trains. State machine: running → ready / error.
@@ -37,8 +37,8 @@ class HeadTrainingRun(Base):
     n_trained: Mapped[int | None] = mapped_column(Integer, nullable=True)
     n_skipped: Mapped[int | None] = mapped_column(Integer, nullable=True)
     error: Mapped[str | None] = mapped_column(Text, nullable=True)
-    # Last time the task made progress — the recovery sweep tells a live run from
-    # a SIGKILL'd one by this (mirrors TagEvalRun).
+    # Last time the task made progress — the recovery sweep tells a live run
+    # from a SIGKILL'd one by this.
     last_progress_at: Mapped[datetime | None] = mapped_column(
         DateTime(timezone=True), nullable=True
     )

backend/app/models/tag_eval_run.py

@@ -1,45 +0,0 @@
-"""TagEvalRun — persisted lifecycle of a head-vs-centroid tagging eval (#1130).
-
-Mirrors LibraryAuditRun so the result SURVIVES navigation: the run + its full
-report live in this row, and the admin card rehydrates from it on mount instead
-of holding the report in transient frontend state. State machine:
-running → ready / error. The async ml-queue task writes `report` (JSONB) when
-done; a maintenance recovery sweep flips a stalled `running` row to `error`.
-"""
-
-from datetime import datetime
-from typing import Any
-
-from sqlalchemy import DateTime, Integer, String, Text, func
-from sqlalchemy.dialects.postgresql import JSONB
-from sqlalchemy.orm import Mapped, mapped_column
-
-from .base import Base
-
-
-class TagEvalRun(Base):
-    __tablename__ = "tag_eval_run"
-
-    id: Mapped[int] = mapped_column(Integer, primary_key=True)
-    # The eval parameters: {concepts: [...], curve_points: [...], neg_ratio,
-    # cv_folds, ...} — echoed back so the report is self-describing.
-    params: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
-    status: Mapped[str] = mapped_column(
-        String(16), nullable=False, default="running", index=True,
-    )
-    # running | ready | error
-    started_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now(),
-    )
-    finished_at: Mapped[datetime | None] = mapped_column(
-        DateTime(timezone=True), nullable=True,
-    )
-    # The full result: per-concept metrics (head vs centroid), learning-curve
-    # points, and example image ids. Null until the task finishes.
-    report: Mapped[dict[str, Any] | None] = mapped_column(JSONB, nullable=True)
-    error: Mapped[str | None] = mapped_column(Text, nullable=True)
-    # Last time the task made progress — the recovery sweep tells a live run
-    # from a SIGKILL'd one by this (mirrors LibraryAuditRun).
-    last_progress_at: Mapped[datetime | None] = mapped_column(
-        DateTime(timezone=True), nullable=True,
-    )

backend/app/services/ml/heads.py

@@ -1,12 +1,13 @@
 """Production heads: train + score the per-concept classifiers (#114).
 
-The eval (#1130, tag_eval.py) proved the spine; this is its production form.
+The eval harness (#1130) proved the spine, then retired 2026-07-02 once the
+tagging system was accepted; this is the production form.
 - TRAIN (sync, ml worker — needs scikit-learn): for every general/character tag
   with enough labelled positives, fit a logistic-regression head on the FROZEN
   SigLIP embeddings (positives + negatives = rejections + sampled unlabeled),
   derive an honest suggest threshold + earned-auto-apply point from CROSS-
-  VALIDATED scores, and upsert a TagHead row. Reuses tag_eval's proven data
-  loaders + metric helpers so production heads match the eval's measured numbers.
+  VALIDATED scores, and upsert a TagHead row. Uses the eval-proven data loaders
+  + metric helpers (training_data.py) so heads match the measured numbers.
 - SCORE (async, API worker — numpy via pgvector, NO scikit-learn): score one
   image's embedding against all current heads → the suggestions the rail shows,
   REPLACING Camie predictions + per-tag centroids.
@@ -37,7 +38,7 @@ from ...models import (
     TagSuggestionRejection,
 )
 from ...models.tag import image_tag
-from .tag_eval import (
+from .training_data import (
     _auto_apply_point,
     _ids_with_tag,
     _l2norm,

backend/app/services/ml/tag_eval.py

@@ -1,430 +0,0 @@
-"""Head-vs-centroid tagging eval (#1130, milestone #114 slice 1).
-
-Proves the "frozen embedding + small trained head (with negatives)" spine on the
-operator's OWN data, reusing the SigLIP embeddings already stored on
-image_record. For each concept tag it compares:
-  - CENTROID baseline (the old approach): cosine to the mean of positive vectors.
-  - HEAD (the new approach): logistic regression trained on positives + negatives.
-and reports cross-validated precision/recall/AP for both, a LEARNING CURVE
-(accuracy as the number of tagged positives grows), and example image ids to
-eyeball.
-
-numpy + scikit-learn are imported LAZILY inside run_eval so the API worker (base
-image, no ML stack) can still import start_tag_eval_run to enqueue the ml-queue
-task — the heavy compute only runs on the ml worker.
-"""
-
-from __future__ import annotations
-
-import logging
-from datetime import UTC, datetime
-from typing import Any
-
-from sqlalchemy import func, select
-from sqlalchemy.orm import Session
-
-from ...models import (
-    ImageRecord,
-    Tag,
-    TagEvalRun,
-    TagKind,
-    TagPositiveConfirmation,
-    TagSuggestionRejection,
-)
-from ...models.tag import image_tag
-
-log = logging.getLogger(__name__)
-
-# The operator's real concept list (mix of whole-ish + small/local cues). The
-# admin trigger can override; this is the default eval set.
-DEFAULT_CONCEPTS = [
-    "glasses", "cat", "dog", "horse", "goblin",
-    "cum", "lactation", "fellatio", "xray", "stomach bulge",
-]
-DEFAULT_CURVE_POINTS = [10, 30, 100, 300]
-DEFAULT_NEG_RATIO = 3       # negatives per positive (rejections + sampled unlabeled)
-DEFAULT_CV_FOLDS = 5
-MIN_POSITIVES = 8           # below this, a concept can't be evaluated meaningfully
-_UNLABELED_POOL = 4000      # cap on sampled unlabeled rows pulled per concept
-_EXAMPLES_K = 12
-
-
-def start_tag_eval_run(session: Session, params: dict[str, Any]) -> int:
-    """Create a TagEvalRun (status='running') and dispatch the ml-queue task.
-    Returns the new run id. Light guard: one running eval at a time."""
-    existing = session.execute(
-        select(TagEvalRun.id).where(TagEvalRun.status == "running")
-    ).scalar_one_or_none()
-    if existing is not None:
-        raise EvalAlreadyRunning(existing)
-    norm = _normalize_params(params)
-    run = TagEvalRun(params=norm, status="running", last_progress_at=datetime.now(UTC))
-    session.add(run)
-    session.flush()
-    run_id = run.id
-    # Same enqueue-by-import pattern api/suggestions.py uses for ml tasks; the
-    # commit happens in the API handler so row + dispatch are visible together.
-    from ...tasks.ml import tag_eval_run as _task
-    _task.delay(run_id)
-    return run_id
-
-
-class EvalAlreadyRunning(Exception):
-    """Raised by start_tag_eval_run when an eval is already in flight."""
-
-
-def _normalize_params(params: dict[str, Any] | None) -> dict[str, Any]:
-    params = params or {}
-    concepts = [str(c).strip() for c in (params.get("concepts") or []) if str(c).strip()]
-    try:
-        neg_ratio = max(1, int(params.get("neg_ratio", DEFAULT_NEG_RATIO)))
-    except (TypeError, ValueError):
-        neg_ratio = DEFAULT_NEG_RATIO
-    try:
-        cv_folds = max(2, int(params.get("cv_folds", DEFAULT_CV_FOLDS)))
-    except (TypeError, ValueError):
-        cv_folds = DEFAULT_CV_FOLDS
-    try:
-        auto_top_n = min(max(int(params.get("auto_top_n", 0) or 0), 0), 200)
-    except (TypeError, ValueError):
-        auto_top_n = 0
-    try:
-        precision_target = min(max(float(params.get("precision_target", 0.97)), 0.5), 0.999)
-    except (TypeError, ValueError):
-        precision_target = 0.97
-    # No explicit concepts and auto-discovery off → fall back to the hand list.
-    if not concepts and not auto_top_n:
-        concepts = list(DEFAULT_CONCEPTS)
-    curve = params.get("curve_points") or DEFAULT_CURVE_POINTS
-    curve = sorted({int(n) for n in curve if int(n) > 0})
-    return {
-        "concepts": concepts,
-        "neg_ratio": neg_ratio,
-        "cv_folds": cv_folds,
-        "auto_top_n": auto_top_n,
-        "precision_target": round(precision_target, 4),
-        "curve_points": curve,
-    }
-
-
-def _top_general_concepts(session: Session, n: int, min_count: int) -> list[str]:
-    """The n most-tagged general (concept) tags with >= min_count images — a fast
-    server-side way to broaden the eval beyond the hand-picked list (counts all
-    sources; source-aware filtering is a separate concern)."""
-    rows = session.execute(
-        select(Tag.name)
-        .join(image_tag, image_tag.c.tag_id == Tag.id)
-        .where(Tag.kind == TagKind.general)
-        .group_by(Tag.id)
-        .having(func.count(image_tag.c.image_record_id) >= min_count)
-        .order_by(func.count(image_tag.c.image_record_id).desc())
-        .limit(n)
-    ).all()
-    return [r[0] for r in rows]
-
-
-def _resolve_tag_id(session: Session, name: str) -> int | None:
-    """Case-insensitive tag-name match; if several share a name, take the one
-    applied to the most images (the one the operator actually uses)."""
-    rows = session.execute(
-        select(Tag.id, func.count(image_tag.c.image_record_id))
-        .outerjoin(image_tag, image_tag.c.tag_id == Tag.id)
-        .where(func.lower(Tag.name) == name.lower())
-        .group_by(Tag.id)
-        .order_by(func.count(image_tag.c.image_record_id).desc())
-    ).all()
-    return rows[0][0] if rows else None
-
-
-def _ids_with_tag(session: Session, tag_id: int) -> list[int]:
-    return [
-        r[0] for r in session.execute(
-            select(image_tag.c.image_record_id).where(image_tag.c.tag_id == tag_id)
-        ).all()
-    ]
-
-
-def _rejected_ids(session: Session, tag_id: int) -> list[int]:
-    return [
-        r[0] for r in session.execute(
-            select(TagSuggestionRejection.image_record_id)
-            .where(TagSuggestionRejection.tag_id == tag_id)
-        ).all()
-    ]
-
-
-def _confirmed_ids(session: Session, tag_id: int) -> set[int]:
-    """Positives the operator explicitly affirmed ('keep') — excluded from the
-    doubts list so confirmed-correct images don't resurface every run."""
-    return {
-        r[0] for r in session.execute(
-            select(TagPositiveConfirmation.image_record_id)
-            .where(TagPositiveConfirmation.tag_id == tag_id)
-        ).all()
-    }
-
-
-def _sample_unlabeled(session: Session, exclude: set[int], limit: int) -> list[int]:
-    """Random image ids (with an embedding) NOT carrying the tag. Concepts are
-    sparse, so an untagged image is almost always a true negative."""
-    stmt = (
-        select(ImageRecord.id)
-        .where(ImageRecord.siglip_embedding.is_not(None))
-        .order_by(func.random())
-        .limit(limit)
-    )
-    if exclude:
-        stmt = stmt.where(ImageRecord.id.not_in(exclude))
-    return [r[0] for r in session.execute(stmt).all()]
-
-
-def _load_embeddings(session: Session, ids: list[int]) -> dict[int, Any]:
-    import numpy as np
-
-    out: dict[int, Any] = {}
-    if not ids:
-        return out
-    # Chunk the IN list to stay well under psycopg's parameter ceiling.
-    for i in range(0, len(ids), 2000):
-        chunk = ids[i:i + 2000]
-        for rid, emb in session.execute(
-            select(ImageRecord.id, ImageRecord.siglip_embedding)
-            .where(ImageRecord.id.in_(chunk))
-            .where(ImageRecord.siglip_embedding.is_not(None))
-        ).all():
-            out[rid] = np.asarray(emb, dtype=np.float32)
-    return out
-
-
-def run_eval(session: Session, params: dict[str, Any]) -> dict[str, Any]:
-    """Compute the full report. Per-concept failures are captured, not fatal."""
-    import numpy as np
-
-    cfg = _normalize_params(params)
-    # Auto-discovery: union the explicit concepts with the top-N most-tagged
-    # general tags (server-side, fast) so the eval can broaden itself.
-    concepts = list(cfg["concepts"])
-    if cfg["auto_top_n"]:
-        seen = {c.lower() for c in concepts}
-        for name in _top_general_concepts(session, cfg["auto_top_n"], MIN_POSITIVES):
-            if name.lower() not in seen:
-                concepts.append(name)
-                seen.add(name.lower())
-    cfg["concepts"] = concepts
-    concepts_out = []
-    for name in cfg["concepts"]:
-        try:
-            concepts_out.append(_eval_concept(session, name, cfg, np))
-        except Exception as exc:  # one bad concept shouldn't kill the run
-            log.exception("tag-eval concept %r failed", name)
-            concepts_out.append({"name": name, "skipped": f"error: {exc}"})
-    return {
-        "generated_at": datetime.now(UTC).isoformat(),
-        "params": cfg,
-        "concepts": concepts_out,
-    }
-
-
-def _eval_concept(session: Session, name: str, cfg: dict, np) -> dict[str, Any]:
-    tag_id = _resolve_tag_id(session, name)
-    if tag_id is None:
-        return {"name": name, "skipped": "no such tag"}
-    pos_ids = _ids_with_tag(session, tag_id)
-    if len(pos_ids) < MIN_POSITIVES:
-        return {"name": name, "tag_id": tag_id, "n_pos": len(pos_ids),
-                "skipped": f"too few positives (<{MIN_POSITIVES})"}
-
-    neg_ratio = cfg["neg_ratio"]
-    pos_set = set(pos_ids)
-    rejected = [i for i in _rejected_ids(session, tag_id) if i not in pos_set]
-    want_neg = max(len(pos_ids) * neg_ratio, _EXAMPLES_K * 4)
-    sampled = _sample_unlabeled(session, pos_set | set(rejected),
-                                min(_UNLABELED_POOL, want_neg))
-    neg_ids = rejected + [i for i in sampled if i not in pos_set]
-
-    emb = _load_embeddings(session, pos_ids + neg_ids)
-    pos = [(i, emb[i]) for i in pos_ids if i in emb]
-    neg = [(i, emb[i]) for i in neg_ids if i in emb]
-    if len(pos) < MIN_POSITIVES or len(neg) < MIN_POSITIVES:
-        return {"name": name, "tag_id": tag_id, "n_pos": len(pos),
-                "n_neg": len(neg), "skipped": "too few embedded examples"}
-
-    ids = np.array([i for i, _ in pos] + [i for i, _ in neg])
-    X = np.vstack([v for _, v in pos] + [v for _, v in neg]).astype(np.float32)
-    y = np.array([1] * len(pos) + [0] * len(neg))
-    Xn = _l2norm(X, np)
-
-    head = _eval_head(Xn, y, cfg["cv_folds"], cfg["precision_target"], np)
-    centroid = _eval_centroid(Xn, y, cfg["cv_folds"], np)
-    curve = _learning_curve(Xn, y, cfg["curve_points"], neg_ratio, np)
-    confirmed = _confirmed_ids(session, tag_id)
-    examples = _examples(session, Xn, y, ids, np, set(rejected), confirmed)
-
-    return {
-        "name": name, "tag_id": tag_id,
-        "n_pos": len(pos), "n_neg": len(neg),
-        "n_rejected": len(rejected),
-        "head": head, "centroid": centroid,
-        "curve": curve, "examples": examples,
-    }
-
-
-def _l2norm(X, np):
-    n = np.linalg.norm(X, axis=1, keepdims=True)
-    n[n == 0] = 1.0
-    return X / n
-
-
-def _metrics_from_scores(y, scores, np) -> dict[str, float]:
-    from sklearn.metrics import average_precision_score, precision_recall_curve
-
-    ap = float(average_precision_score(y, scores))
-    prec, rec, thr = precision_recall_curve(y, scores)
-    f1 = (2 * prec * rec) / np.clip(prec + rec, 1e-9, None)
-    best = int(np.argmax(f1))
-    # thr has len = len(prec)-1; map best index safely.
-    t = float(thr[min(best, len(thr) - 1)]) if len(thr) else 0.5
-    return {
-        "ap": round(ap, 4),
-        "precision": round(float(prec[best]), 4),
-        "recall": round(float(rec[best]), 4),
-        "f1": round(float(f1[best]), 4),
-        "threshold": round(t, 4),
-    }
-
-
-def _safe_folds(y, folds, np) -> int:
-    minority = int(min(np.bincount(y)))
-    return max(2, min(folds, minority))
-
-
-def _eval_head(Xn, y, folds, target, np) -> dict[str, float]:
-    from sklearn.linear_model import LogisticRegression
-    from sklearn.model_selection import StratifiedKFold, cross_val_predict
-
-    clf = LogisticRegression(max_iter=1000, class_weight="balanced")
-    cv = StratifiedKFold(n_splits=_safe_folds(y, folds, np), shuffle=True,
-                         random_state=0)
-    probs = cross_val_predict(clf, Xn, y, cv=cv, method="predict_proba")[:, 1]
-    m = _metrics_from_scores(y, probs, np)
-    m["auto_apply"] = _auto_apply_point(y, probs, target, np)
-    return m
-
-
-def _auto_apply_point(y, scores, target, np) -> dict | None:
-    """The auto-apply operating point: the threshold that yields the MOST recall
-    while holding precision >= target. This answers 'could this concept fire
-    without a human, and how much would it catch?' Returns None if no threshold
-    reaches the precision target (concept not auto-apply-ready)."""
-    from sklearn.metrics import precision_recall_curve
-
-    prec, rec, thr = precision_recall_curve(y, scores)
-    best = None  # (threshold, precision, recall) maximizing recall s.t. prec>=target
-    for i in range(len(thr)):  # thr[i] corresponds to prec[i], rec[i]
-        if prec[i] >= target and (best is None or rec[i] > best[2]):
-            best = (float(thr[i]), float(prec[i]), float(rec[i]))
-    if best is None:
-        return None
-    return {
-        "target": round(float(target), 4),
-        "threshold": round(best[0], 4),
-        "precision": round(best[1], 4),
-        "recall": round(best[2], 4),
-    }
-
-
-def _eval_centroid(Xn, y, folds, np) -> dict[str, float]:
-    """Cross-validated cosine-to-positive-mean — the OLD method's quality."""
-    from sklearn.model_selection import StratifiedKFold
-
-    cv = StratifiedKFold(n_splits=_safe_folds(y, folds, np), shuffle=True,
-                         random_state=0)
-    scores = np.zeros(len(y), dtype=np.float32)
-    for train, test in cv.split(Xn, y):
-        c = Xn[train][y[train] == 1].mean(axis=0)
-        cn = c / (np.linalg.norm(c) or 1.0)
-        scores[test] = Xn[test] @ cn
-    return _metrics_from_scores(y, scores, np)
-
-
-def _learning_curve(Xn, y, points, neg_ratio, np) -> list[dict[str, float]]:
-    """Hold out a fixed test split; train the head on a growing number of
-    positives and watch AP/F1 climb — answers 'does tagging more sharpen it?'"""
-    from sklearn.linear_model import LogisticRegression
-    from sklearn.model_selection import train_test_split
-
-    rng = np.random.default_rng(0)
-    idx = np.arange(len(y))
-    try:
-        tr, te = train_test_split(idx, test_size=0.3, stratify=y, random_state=0)
-    except ValueError:
-        return []
-    tr_pos = tr[y[tr] == 1]
-    tr_neg = tr[y[tr] == 0]
-    out = []
-    for n in points:
-        if n > len(tr_pos):
-            break
-        sp = rng.choice(tr_pos, size=n, replace=False)
-        nn = min(len(tr_neg), n * neg_ratio)
-        sn = rng.choice(tr_neg, size=nn, replace=False)
-        sub = np.concatenate([sp, sn])
-        clf = LogisticRegression(max_iter=1000, class_weight="balanced")
-        clf.fit(Xn[sub], y[sub])
-        prob = clf.predict_proba(Xn[te])[:, 1]
-        m = _metrics_from_scores(y[te], prob, np)
-        out.append({"n_pos": int(n), "ap": m["ap"], "f1": m["f1"]})
-    return out
-
-
-def _examples(session, Xn, y, ids, np, rejected_set, confirmed_set) -> dict[str, list[dict]]:
-    """Train on all data, then surface: top-scoring negatives the operator has
-    NOT already rejected (= fresh suggestions) and lowest-scoring POSITIVES the
-    operator has NOT already confirmed (= unreviewed doubts). Excluding rejected
-    ids stops an adjudicated near-miss from resurfacing in 'would suggest';
-    excluding confirmed ids stops a 'kept' correct positive from resurfacing in
-    'head doubts' every run. Resolves thumbnail urls for a self-contained report."""
-    from sklearn.linear_model import LogisticRegression
-
-    clf = LogisticRegression(max_iter=1000, class_weight="balanced")
-    clf.fit(Xn, y)
-    s = clf.predict_proba(Xn)[:, 1]
-    neg_idx = np.where(y == 0)[0]
-    pos_idx = np.where(y == 1)[0]
-    top_neg = []
-    for i in neg_idx[np.argsort(s[neg_idx])[::-1]]:  # high score → low
-        rid = int(ids[i])
-        if rid in rejected_set:
-            continue  # already told the head 'no' — don't re-suggest it
-        top_neg.append(rid)
-        if len(top_neg) >= _EXAMPLES_K:
-            break
-    low_pos = []
-    for i in pos_idx[np.argsort(s[pos_idx])]:  # low score → high
-        rid = int(ids[i])
-        if rid in confirmed_set:
-            continue  # already kept/confirmed — don't re-doubt it
-        low_pos.append(rid)
-        if len(low_pos) >= _EXAMPLES_K:
-            break
-    thumbs = _resolve_thumbs(session, top_neg + low_pos)
-    return {
-        "head_would_suggest": [thumbs[i] for i in top_neg if i in thumbs],
-        "head_doubts_positive": [thumbs[i] for i in low_pos if i in thumbs],
-    }
-
-
-def _resolve_thumbs(session, ids: list[int]) -> dict[int, dict]:
-    from ..gallery_service import thumbnail_url
-
-    out: dict[int, dict] = {}
-    if not ids:
-        return out
-    for rid, tp, sha, mime in session.execute(
-        select(
-            ImageRecord.id, ImageRecord.thumbnail_path,
-            ImageRecord.sha256, ImageRecord.mime,
-        ).where(ImageRecord.id.in_(ids))
-    ).all():
-        out[rid] = {"id": rid, "thumbnail_url": thumbnail_url(tp, sha, mime)}
-    return out

backend/app/services/ml/training_data.py

@@ -0,0 +1,121 @@
+"""Shared data-selection + validated-metric helpers for the heads trainer.
+
+Born in the head-vs-centroid eval harness (#1130, tag_eval.py) that proved the
+"frozen embedding + small trained head (with negatives)" spine; the harness was
+retired 2026-07-02 (operator: the tagging system is proven, the eval isn't
+needed) and these survivors moved here — they ARE the heads' production data
+pipeline (heads.py trains and scores with them nightly).
+
+numpy/scikit-learn are imported lazily inside the functions that need them so
+the API worker (base image, no ML stack) can import this module.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from sqlalchemy import func, select
+from sqlalchemy.orm import Session
+
+from ...models import ImageRecord, TagSuggestionRejection
+from ...models.tag import image_tag
+
+
+def _ids_with_tag(session: Session, tag_id: int) -> list[int]:
+    return [
+        r[0] for r in session.execute(
+            select(image_tag.c.image_record_id).where(image_tag.c.tag_id == tag_id)
+        ).all()
+    ]
+
+
+def _rejected_ids(session: Session, tag_id: int) -> list[int]:
+    return [
+        r[0] for r in session.execute(
+            select(TagSuggestionRejection.image_record_id)
+            .where(TagSuggestionRejection.tag_id == tag_id)
+        ).all()
+    ]
+
+
+def _sample_unlabeled(session: Session, exclude: set[int], limit: int) -> list[int]:
+    """Random image ids (with an embedding) NOT carrying the tag. Concepts are
+    sparse, so an untagged image is almost always a true negative."""
+    stmt = (
+        select(ImageRecord.id)
+        .where(ImageRecord.siglip_embedding.is_not(None))
+        .order_by(func.random())
+        .limit(limit)
+    )
+    if exclude:
+        stmt = stmt.where(ImageRecord.id.not_in(exclude))
+    return [r[0] for r in session.execute(stmt).all()]
+
+
+def _load_embeddings(session: Session, ids: list[int]) -> dict[int, Any]:
+    import numpy as np
+
+    out: dict[int, Any] = {}
+    if not ids:
+        return out
+    # Chunk the IN list to stay well under psycopg's parameter ceiling.
+    for i in range(0, len(ids), 2000):
+        chunk = ids[i:i + 2000]
+        for rid, emb in session.execute(
+            select(ImageRecord.id, ImageRecord.siglip_embedding)
+            .where(ImageRecord.id.in_(chunk))
+            .where(ImageRecord.siglip_embedding.is_not(None))
+        ).all():
+            out[rid] = np.asarray(emb, dtype=np.float32)
+    return out
+
+
+def _l2norm(X, np):
+    n = np.linalg.norm(X, axis=1, keepdims=True)
+    n[n == 0] = 1.0
+    return X / n
+
+
+def _metrics_from_scores(y, scores, np) -> dict[str, float]:
+    from sklearn.metrics import average_precision_score, precision_recall_curve
+
+    ap = float(average_precision_score(y, scores))
+    prec, rec, thr = precision_recall_curve(y, scores)
+    f1 = (2 * prec * rec) / np.clip(prec + rec, 1e-9, None)
+    best = int(np.argmax(f1))
+    # thr has len = len(prec)-1; map best index safely.
+    t = float(thr[min(best, len(thr) - 1)]) if len(thr) else 0.5
+    return {
+        "ap": round(ap, 4),
+        "precision": round(float(prec[best]), 4),
+        "recall": round(float(rec[best]), 4),
+        "f1": round(float(f1[best]), 4),
+        "threshold": round(t, 4),
+    }
+
+
+def _safe_folds(y, folds, np) -> int:
+    minority = int(min(np.bincount(y)))
+    return max(2, min(folds, minority))
+
+
+def _auto_apply_point(y, scores, target, np) -> dict | None:
+    """The auto-apply operating point: the threshold that yields the MOST recall
+    while holding precision >= target. This answers 'could this concept fire
+    without a human, and how much would it catch?' Returns None if no threshold
+    reaches the precision target (concept not auto-apply-ready)."""
+    from sklearn.metrics import precision_recall_curve
+
+    prec, rec, thr = precision_recall_curve(y, scores)
+    best = None  # (threshold, precision, recall) maximizing recall s.t. prec>=target
+    for i in range(len(thr)):  # thr[i] corresponds to prec[i], rec[i]
+        if prec[i] >= target and (best is None or rec[i] > best[2]):
+            best = (float(thr[i]), float(prec[i]), float(rec[i]))
+    if best is None:
+        return None
+    return {
+        "target": round(float(target), 4),
+        "threshold": round(best[0], 4),
+        "precision": round(best[1], 4),
+        "recall": round(best[2], 4),
+    }

backend/app/tasks/maintenance.py

@@ -21,7 +21,6 @@ from ..models import (
     ImportTask,
     LibraryAuditRun,
     Source,
-    TagEvalRun,
     TaskRun,
 )
 from ..utils.phash import compute_phash
@@ -96,9 +95,6 @@ BACKUP_DB_STALL_THRESHOLD_MINUTES = 40
 # Library audit: scan_library_for_rule has time_limit=7500s (2h5m).
 # 2h15m gives a 10-min buffer.
 LIBRARY_AUDIT_STALL_THRESHOLD_MINUTES = 135
-# tag-eval (#1130) has a 30-min soft limit; flag a run with no progress past 40.
-TAG_EVAL_STALL_THRESHOLD_MINUTES = 40
-TAG_EVAL_KEEP_RUNS = 20
 # head training (#114) has a 60-min soft limit; flag no-progress past 75.
 HEAD_TRAINING_STALL_THRESHOLD_MINUTES = 75
 HEAD_TRAINING_KEEP_RUNS = 20
@@ -743,46 +739,6 @@ def recover_stalled_library_audit_runs() -> int:
         return recovered
 
 
-@celery.task(name="backend.app.tasks.maintenance.recover_stalled_tag_eval_runs")
-def recover_stalled_tag_eval_runs() -> int:
-    """Flip TagEvalRun rows stuck in 'running' past the stall threshold to
-    'error', and prune old runs to the last TAG_EVAL_KEEP_RUNS (retention,
-    rule 89). Runs every 5 min on the maintenance lane; no-op when idle."""
-    SessionLocal = _sync_session_factory()
-    now = datetime.now(UTC)
-    cutoff = now - timedelta(minutes=TAG_EVAL_STALL_THRESHOLD_MINUTES)
-    with SessionLocal() as session:
-        result = session.execute(
-            update(TagEvalRun)
-            .where(TagEvalRun.status == "running")
-            .where(
-                func.coalesce(TagEvalRun.last_progress_at, TagEvalRun.started_at)
-                < cutoff
-            )
-            .values(
-                status="error", finished_at=now,
-                error=(
-                    f"stranded by recovery sweep (no progress for "
-                    f"{TAG_EVAL_STALL_THRESHOLD_MINUTES} min)"
-                ),
-            )
-        )
-        # Retention: keep only the most recent N runs.
-        keep = session.execute(
-            select(TagEvalRun.id).order_by(TagEvalRun.id.desc())
-            .limit(TAG_EVAL_KEEP_RUNS)
-        ).scalars().all()
-        if keep:
-            session.execute(
-                delete(TagEvalRun).where(TagEvalRun.id.not_in(keep))
-            )
-        session.commit()
-        recovered = result.rowcount or 0
-        if recovered:
-            log.info("recover_stalled_tag_eval_runs: recovered %d rows", recovered)
-        return recovered
-
-
 @celery.task(name="backend.app.tasks.maintenance.recover_stalled_head_training_runs")
 def recover_stalled_head_training_runs() -> int:
     """Flip HeadTrainingRun rows stuck in 'running' past the stall threshold to

backend/app/tasks/ml.py

@@ -250,51 +250,6 @@ def backfill(self) -> int:
     return enqueued
 
 
-@celery.task(
-    name="backend.app.tasks.ml.tag_eval_run",
-    bind=True,
-    # The head-vs-centroid eval (#1130) loads embeddings + fits sklearn heads
-    # for several concepts — minutes, not seconds. Runs on the ml queue because
-    # only that worker has numpy/scikit-learn.
-    soft_time_limit=1800, time_limit=2100,
-)
-def tag_eval_run(self, run_id: int) -> str:
-    """Compute the eval report into the persisted TagEvalRun row so it survives
-    navigation (the admin card rehydrates from the row, not transient state)."""
-    from datetime import UTC, datetime
-
-    from ..models import TagEvalRun
-    from ..services.ml.tag_eval import run_eval
-
-    SessionLocal = _sync_session_factory()
-    with SessionLocal() as session:
-        run = session.get(TagEvalRun, run_id)
-        if run is None:
-            return "missing"
-        run.last_progress_at = datetime.now(UTC)
-        session.commit()
-        try:
-            report = run_eval(session, run.params)
-        except SoftTimeLimitExceeded:
-            run.status = "error"
-            run.error = "timed out"
-            run.finished_at = datetime.now(UTC)
-            session.commit()
-            raise
-        except Exception as exc:
-            log.exception("tag_eval_run %d failed", run_id)
-            run.status = "error"
-            run.error = str(exc)
-            run.finished_at = datetime.now(UTC)
-            session.commit()
-            return "error"
-        run.report = report
-        run.status = "ready"
-        run.finished_at = datetime.now(UTC)
-        session.commit()
-        return "ready"
-
-
 @celery.task(
     name="backend.app.tasks.ml.train_heads",
     bind=True,

frontend/src/components/modal/SuggestionItem.vue

@@ -132,8 +132,8 @@ const hasMenu = computed(() =>
   color: rgb(var(--v-theme-on-surface-variant, var(--v-theme-on-surface)));
   font-family: 'JetBrains Mono', monospace;
 }
-/* Green ✓ / red ✗ verdict pair — same circular language as the eval card
-   (TagEvalCard .fc-act) so accept/reject read identically across surfaces. */
+/* Green ✓ / red ✗ verdict pair — circular buttons so accept/reject read
+   identically across surfaces. */
 .fc-suggestion__acts {
   flex: 0 0 auto; display: flex; gap: 4px;
 }

frontend/src/components/settings/MaintenancePanel.vue

@@ -28,7 +28,6 @@
         <HeadsCard />
         <GpuAgentCard />
         <AliasTable />
-        <TagEvalCard />
       </div>
     </section>
 
@@ -55,7 +54,6 @@ import MLThresholdSliders from './MLThresholdSliders.vue'
 import HeadsCard from './HeadsCard.vue'
 import GpuAgentCard from './GpuAgentCard.vue'
 import AliasTable from './AliasTable.vue'
-import TagEvalCard from './TagEvalCard.vue'
 import BackupCard from './BackupCard.vue'
 import { useSystemActivityStore } from '../../stores/systemActivity.js'
 

frontend/src/components/settings/TagEvalCard.vue

@@ -1,303 +0,0 @@
-<template>
-  <MaintenanceTile
-    icon="mdi-flask-outline"
-    title="Tagging eval (heads vs centroid)"
-    blurb="Measure whether a trained head beats the old centroid on your own tags — and whether tagging more sharpens it."
-    :open="running"
-  >
-    <p class="fc-muted text-body-2 mb-3">
-      Reuses the SigLIP embeddings already stored on your images (no re-embed, no
-      GPU). For each concept it trains a logistic-regression <strong>head</strong>
-      on your positives + negatives and compares it to the old single
-      <strong>centroid</strong>, with cross-validated AP/F1 and a learning curve.
-      Runs as a background task; the result is saved and reloads here.
-    </p>
-
-    <v-textarea
-      v-model="conceptsText" label="Concepts (comma-separated)"
-      rows="2" auto-grow density="compact" hide-details class="mb-3"
-      :disabled="running"
-    />
-
-    <div class="d-flex mb-3" style="gap: 12px;">
-      <v-text-field
-        v-model.number="autoTopN" label="+ auto-add top-N concepts"
-        type="number" min="0" max="200" density="compact" hide-details
-        :disabled="running" style="max-width: 220px;"
-      />
-      <v-text-field
-        v-model.number="precisionTarget" label="Auto-apply precision target"
-        type="number" min="0.5" max="0.999" step="0.01" density="compact" hide-details
-        :disabled="running" style="max-width: 220px;"
-      />
-    </div>
-
-    <v-btn
-      v-if="!running"
-      color="accent" variant="flat" rounded="pill"
-      prepend-icon="mdi-play" :loading="busy" @click="onStart"
-    >Run eval</v-btn>
-
-    <div v-if="running" class="mt-3">
-      <v-progress-linear indeterminate color="accent" />
-      <div class="text-body-2 mt-2 fc-muted">Running… (started {{ startedAgo }})</div>
-    </div>
-
-    <v-alert
-      v-if="run && run.status === 'error'"
-      type="error" variant="tonal" density="compact" class="mt-3"
-    >Eval failed: {{ run.error }}</v-alert>
-
-    <div v-if="report" class="mt-4">
-      <div class="fc-muted text-caption mb-2">
-        Ran {{ formatTime(report.generated_at) }} ·
-        {{ report.concepts.length }} concept(s) ·
-        neg ratio {{ report.params.neg_ratio }}, {{ report.params.cv_folds }}-fold CV
-      </div>
-
-      <div v-for="c in report.concepts" :key="c.name" class="fc-cc">
-        <div class="fc-cc__head">
-          <span class="fc-cc__name">{{ c.name }}</span>
-          <span v-if="c.skipped" class="fc-muted text-caption">— skipped: {{ c.skipped }}</span>
-          <span v-else class="fc-muted text-caption">
-            {{ c.n_pos }} pos · {{ c.n_neg }} neg<span v-if="c.n_rejected"> ({{ c.n_rejected }} rejected)</span>
-          </span>
-        </div>
-
-        <template v-if="!c.skipped">
-          <table class="fc-metrics">
-            <thead>
-              <tr><th></th><th>AP</th><th>F1</th><th>Prec</th><th>Rec</th></tr>
-            </thead>
-            <tbody>
-              <tr>
-                <td class="fc-metrics__lbl">Head</td>
-                <td class="fc-num fc-win">{{ c.head.ap }}</td>
-                <td class="fc-num">{{ c.head.f1 }}</td>
-                <td class="fc-num">{{ c.head.precision }}</td>
-                <td class="fc-num">{{ c.head.recall }}</td>
-              </tr>
-              <tr>
-                <td class="fc-metrics__lbl fc-muted">Centroid</td>
-                <td class="fc-num fc-muted">{{ c.centroid.ap }}</td>
-                <td class="fc-num fc-muted">{{ c.centroid.f1 }}</td>
-                <td class="fc-num fc-muted">{{ c.centroid.precision }}</td>
-                <td class="fc-num fc-muted">{{ c.centroid.recall }}</td>
-              </tr>
-            </tbody>
-          </table>
-          <div class="text-caption mb-2" :class="apDelta(c) >= 0 ? 'fc-up' : 'fc-down'">
-            Δ AP {{ apDelta(c) >= 0 ? '+' : '' }}{{ apDelta(c).toFixed(3) }}
-            (head − centroid)
-          </div>
-
-          <div class="text-caption mb-2">
-            <span class="fc-muted">Auto-apply:</span>
-            <template v-if="c.head.auto_apply">
-              <span class="fc-up">ready</span> — at P≥{{ c.head.auto_apply.target }}
-              catches recall <strong>{{ c.head.auto_apply.recall }}</strong>
-              (thr {{ c.head.auto_apply.threshold }})
-            </template>
-            <span v-else class="fc-down">not reachable at P≥{{ report.params.precision_target }}</span>
-          </div>
-
-          <div v-if="c.curve && c.curve.length" class="fc-curve">
-            <span class="fc-muted text-caption">Learning curve (AP @ N positives):</span>
-            <span v-for="p in c.curve" :key="p.n_pos" class="fc-curve__pt">
-              {{ p.n_pos }}→<strong>{{ p.ap }}</strong>
-            </span>
-          </div>
-
-          <div v-if="c.examples" class="fc-ex">
-            <div
-              v-for="grp in [
-                { dir: 'suggest', items: c.examples.head_would_suggest,
-                  label: `Head would suggest — ✓ tag it, ✗ not ${c.name}` },
-                { dir: 'doubts', items: c.examples.head_doubts_positive,
-                  label: `Head doubts your tag — ✓ keep, ✗ remove (not ${c.name})` },
-              ]" :key="grp.dir" class="fc-ex__row"
-            >
-              <div class="fc-muted text-caption mb-1">{{ grp.label }}</div>
-              <div class="fc-ex__thumbs">
-                <div
-                  v-for="it in grp.items" :key="`${grp.dir}${it.id}`"
-                  class="fc-ex__item"
-                  :class="actedLabel(c, grp.dir, it) ? 'fc-ex__item--acted' : ''"
-                >
-                  <button
-                    type="button" class="fc-ex__thumb"
-                    :title="`#${it.id} — click to enlarge`" @click="modal.open(it.id)"
-                  >
-                    <img :src="it.thumbnail_url" loading="lazy" />
-                  </button>
-                  <div v-if="actedLabel(c, grp.dir, it)" class="fc-ex__badge">
-                    {{ actedLabel(c, grp.dir, it) }}
-                  </div>
-                  <div v-else class="fc-ex__acts">
-                    <button
-                      class="fc-act fc-act--yes" type="button"
-                      :title="`Yes — it is ${c.name}`" @click="act(c, it, grp.dir, 'yes')"
-                    ><v-icon size="15">mdi-check</v-icon></button>
-                    <button
-                      class="fc-act fc-act--no" type="button"
-                      :title="`No — not ${c.name}`" @click="act(c, it, grp.dir, 'no')"
-                    ><v-icon size="15">mdi-close</v-icon></button>
-                  </div>
-                </div>
-              </div>
-            </div>
-          </div>
-        </template>
-      </div>
-    </div>
-  </MaintenanceTile>
-</template>
-
-<script setup>
-import { toast } from '../../utils/toast.js'
-import { computed, onMounted, onUnmounted, ref } from 'vue'
-
-import MaintenanceTile from '../common/MaintenanceTile.vue'
-import { useTagEvalStore } from '../../stores/tagEval.js'
-import { useModalStore } from '../../stores/modal.js'
-
-const DEFAULT_CONCEPTS =
-  'glasses, cat, dog, horse, goblin, cum, lactation, fellatio, xray, stomach bulge'
-
-const store = useTagEvalStore()
-const modal = useModalStore()
-const run = ref(null)
-const conceptsText = ref(DEFAULT_CONCEPTS)
-const autoTopN = ref(0)
-const precisionTarget = ref(0.97)
-const busy = ref(false)
-let pollTimer = null
-
-const running = computed(() => run.value?.status === 'running')
-const report = computed(() => (run.value?.status === 'ready' ? run.value.report : null))
-const startedAgo = computed(() =>
-  run.value?.started_at ? formatTime(run.value.started_at) : '')
-
-// Rehydrate the persisted run on mount so the report survives navigation — the
-// task runs backend-side regardless; we just reconnect to its row.
-onMounted(async () => {
-  try {
-    const latest = await store.latest()
-    if (latest) {
-      run.value = await store.getRun(latest.id)
-      if (run.value.status === 'running') startPoll(latest.id)
-    }
-  } catch { /* non-fatal — card still works for a fresh run */ }
-})
-onUnmounted(stopPoll)
-
-function startPoll(id) {
-  stopPoll()
-  pollTimer = setInterval(async () => {
-    try {
-      run.value = await store.getRun(id)
-      if (run.value.status !== 'running') stopPoll()
-    } catch (e) {
-      stopPoll()
-      toast({ text: `Eval poll failed: ${e.message}`, type: 'error' })
-    }
-  }, 5000)
-}
-function stopPoll() {
-  if (pollTimer) { clearInterval(pollTimer); pollTimer = null }
-}
-
-async function onStart() {
-  busy.value = true
-  try {
-    const concepts = conceptsText.value.split(',').map(s => s.trim()).filter(Boolean)
-    const res = await store.start({
-      concepts,
-      auto_top_n: Number(autoTopN.value) || 0,
-      precision_target: Number(precisionTarget.value) || 0.97,
-    })
-    run.value = await store.getRun(res.run_id)
-    startPoll(res.run_id)
-  } catch (e) {
-    const msg = e.body?.running_id
-      ? 'An eval is already running.'
-      : e.message
-    toast({ text: `Could not start eval: ${msg}`, type: 'error' })
-  } finally {
-    busy.value = false
-  }
-}
-
-function apDelta(c) { return (c.head?.ap ?? 0) - (c.centroid?.ap ?? 0) }
-function formatTime(iso) {
-  if (!iso) return ''
-  try { return new Date(iso).toLocaleString() } catch { return iso }
-}
-
-// Acting on an example writes the SAME tables the head trains on, so a re-run
-// reflects the correction. Keyed per (concept, list, image); the report ids are
-// frozen at run time, so we just grey out what's been handled in this view.
-const acted = ref({})
-const actedKey = (c, dir, it) => `${c.tag_id}:${dir}:${it.id}`
-const actedLabel = (c, dir, it) => acted.value[actedKey(c, dir, it)] || ''
-
-async function act(c, it, dir, verdict) {
-  const key = actedKey(c, dir, it)
-  let call, label
-  if (dir === 'suggest' && verdict === 'yes') { call = store.applyTag(it.id, c.tag_id); label = 'tagged' }
-  else if (dir === 'suggest' && verdict === 'no') { call = store.rejectTag(it.id, c.tag_id); label = 'rejected' }
-  else if (dir === 'doubts' && verdict === 'no') { call = store.removeTag(it.id, c.tag_id); label = 'removed' }
-  else { call = store.confirmTag(it.id, c.tag_id); label = 'kept' }   // doubt + yes = keep (confirm)
-  try {
-    await call
-    acted.value[key] = label
-  } catch (e) {
-    toast({ text: `Action failed: ${e.message}`, type: 'error' })
-  }
-}
-</script>
-
-<style scoped>
-.fc-muted { color: rgb(var(--v-theme-on-surface-variant)); }
-.fc-cc {
-  padding: 12px 0;
-  border-top: 1px solid rgb(var(--v-theme-surface-light));
-}
-.fc-cc__head { display: flex; align-items: baseline; gap: 8px; margin-bottom: 6px; }
-.fc-cc__name { font-weight: 600; }
-.fc-metrics { width: 100%; max-width: 360px; border-collapse: collapse; font-size: 13px; }
-.fc-metrics th { text-align: right; font-weight: 600; color: rgb(var(--v-theme-on-surface-variant)); padding: 0 8px; }
-.fc-metrics__lbl { text-align: left; }
-.fc-num { text-align: right; font-variant-numeric: tabular-nums; padding: 1px 8px; }
-.fc-win { color: rgb(var(--v-theme-accent)); font-weight: 600; }
-.fc-up { color: rgb(var(--v-theme-success)); }
-.fc-down { color: rgb(var(--v-theme-error)); }
-.fc-curve { margin-bottom: 8px; }
-.fc-curve__pt { margin-left: 10px; font-size: 13px; font-variant-numeric: tabular-nums; }
-.fc-ex__row { margin-top: 8px; }
-.fc-ex__thumbs { display: flex; flex-wrap: wrap; gap: 6px; }
-.fc-ex__item { position: relative; width: 120px; height: 120px; }
-.fc-ex__item--acted { opacity: 0.45; }
-.fc-ex__thumb {
-  display: block; width: 100%; height: 100%; border-radius: 6px;
-  overflow: hidden; background: rgb(var(--v-theme-surface-light));
-  outline: 1px solid transparent; transition: outline-color 0.12s;
-  border: none; padding: 0; cursor: pointer;
-}
-.fc-ex__thumb:hover { outline-color: rgb(var(--v-theme-accent)); }
-.fc-ex__thumb img { width: 100%; height: 100%; object-fit: cover; display: block; }
-.fc-ex__acts { position: absolute; top: 4px; right: 4px; display: flex; gap: 4px; }
-.fc-act {
-  width: 26px; height: 26px; border-radius: 50%; border: none; cursor: pointer;
-  display: flex; align-items: center; justify-content: center; color: #fff;
-  opacity: 0.9; box-shadow: 0 1px 3px rgba(0, 0, 0, 0.4); transition: transform 0.1s;
-}
-.fc-act:hover { opacity: 1; transform: scale(1.1); }
-.fc-act--yes { background: rgb(var(--v-theme-success)); }
-.fc-act--no { background: rgb(var(--v-theme-error)); }
-.fc-ex__badge {
-  position: absolute; bottom: 4px; left: 4px; right: 4px; text-align: center;
-  font-size: 10px; text-transform: uppercase; letter-spacing: 0.05em;
-  background: rgba(0, 0, 0, 0.65); color: #fff; border-radius: 3px; padding: 1px 0;
-}
-</style>

frontend/src/stores/tagEval.js

@@ -1,57 +0,0 @@
-import { defineStore } from 'pinia'
-
-import { useApi } from '../composables/useApi.js'
-
-// Tag-eval (#1130): trigger + revisit the head-vs-centroid learning-curve eval.
-// The run + full report live server-side (tag_eval_run), so the card rehydrates
-// from getRun() on mount — the report survives navigation.
-export const useTagEvalStore = defineStore('tagEval', () => {
-  const api = useApi()
-
-  async function start(params) {
-    return await api.post('/api/tag-eval', { body: { params } })
-  }
-
-  async function getRun(id) {
-    return await api.get(`/api/tag-eval/${id}`)   // includes the full report
-  }
-
-  // The most recent run (light row, no report) — the card calls getRun() with
-  // its id to pull the persisted report on mount.
-  async function latest() {
-    const body = await api.get('/api/tag-eval', { params: { limit: 1 } })
-    return (body.runs && body.runs[0]) || null
-  }
-
-  // --- Acting on the head's example lists (closes the learn-from-tags loop).
-  // These write the SAME tables the head trains on: image_tag (positives) and
-  // tag_suggestion_rejection (negatives, via the dismiss endpoint).
-
-  // "Yes, it is this" — apply the tag (new positive).
-  async function applyTag(imageId, tagId) {
-    return await api.post(`/api/images/${imageId}/tags`,
-      { body: { tag_id: tagId, source: 'manual' } })
-  }
-
-  // "No, it's not" on an UNtagged suggestion — record a rejection (hard negative).
-  async function rejectTag(imageId, tagId) {
-    return await api.post(`/api/images/${imageId}/suggestions/dismiss`,
-      { body: { tag_id: tagId } })
-  }
-
-  // "Not it" on one of YOUR positives the head doubts — remove the tag AND
-  // record the rejection (kills the bad positive, leaves a hard negative).
-  async function removeTag(imageId, tagId) {
-    await api.delete(`/api/images/${imageId}/tags/${tagId}`)
-    return await api.post(`/api/images/${imageId}/suggestions/dismiss`,
-      { body: { tag_id: tagId } })
-  }
-
-  // "Keep" — affirm a doubted positive is correct. Records a confirmation so it
-  // stops resurfacing in the doubts list (it stays a positive either way).
-  async function confirmTag(imageId, tagId) {
-    return await api.post(`/api/images/${imageId}/tags/${tagId}/confirm`)
-  }
-
-  return { start, getRun, latest, applyTag, rejectTag, removeTag, confirmTag }
-})

tests/test_api_tag_eval.py

@@ -1,77 +0,0 @@
-import pytest
-
-from backend.app.models import TagEvalRun
-from backend.app.services.ml.tag_eval import (
-    DEFAULT_CONCEPTS,
-    _normalize_params,
-)
-
-pytestmark = pytest.mark.integration
-
-
-def test_normalize_params_defaults_and_overrides():
-    d = _normalize_params(None)
-    assert d["concepts"] == DEFAULT_CONCEPTS
-    assert d["neg_ratio"] >= 1 and d["cv_folds"] >= 2
-    over = _normalize_params(
-        {"concepts": ["glasses", " ", "cat"], "neg_ratio": "4",
-         "cv_folds": "1", "curve_points": [30, 10, 10]}
-    )
-    assert over["concepts"] == ["glasses", "cat"]   # blanks dropped
-    assert over["neg_ratio"] == 4
-    assert over["cv_folds"] == 2                      # clamped to >=2
-    assert over["curve_points"] == [10, 30]           # deduped + sorted
-
-
-@pytest.mark.asyncio
-async def test_history_and_detail_rehydrate(client, db):
-    # A finished run with a report — the persisted row IS the survives-navigation
-    # source: history is light (no report), detail carries it.
-    run = TagEvalRun(
-        params={"concepts": ["glasses"]},
-        status="ready",
-        report={"concepts": [{"name": "glasses", "head": {"ap": 0.9}}]},
-    )
-    db.add(run)
-    await db.flush()
-    await db.commit()
-    rid = run.id
-
-    h = await client.get("/api/tag-eval?limit=10")
-    assert h.status_code == 200
-    hbody = await h.get_json()
-    row = next(r for r in hbody["runs"] if r["id"] == rid)
-    assert row["status"] == "ready"
-    assert "report" not in row                        # list stays light
-
-    d = await client.get(f"/api/tag-eval/{rid}")
-    assert d.status_code == 200
-    dbody = await d.get_json()
-    assert dbody["report"]["concepts"][0]["name"] == "glasses"
-
-
-@pytest.mark.asyncio
-async def test_create_enqueues_running(client, db, monkeypatch):
-    monkeypatch.setattr(
-        "backend.app.tasks.ml.tag_eval_run.delay", lambda *a, **k: None
-    )
-    resp = await client.post("/api/tag-eval", json={"params": {"concepts": ["cat"]}})
-    assert resp.status_code == 202
-    body = await resp.get_json()
-    assert body["status"] == "running"
-    got = await db.get(TagEvalRun, body["run_id"])
-    assert got is not None and got.status == "running"
-
-
-@pytest.mark.asyncio
-async def test_create_conflicts_when_one_running(client, db, monkeypatch):
-    monkeypatch.setattr(
-        "backend.app.tasks.ml.tag_eval_run.delay", lambda *a, **k: None
-    )
-    db.add(TagEvalRun(params={}, status="running"))
-    await db.flush()
-    await db.commit()
-    resp = await client.post("/api/tag-eval", json={"params": {}})
-    assert resp.status_code == 409
-    body = await resp.get_json()
-    assert body["error"] == "eval_already_running"