feat(heads): auto-apply observability + on by default (#114 auto-apply B)

Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration
0059 + model default flipped. The support (>=30) + measured-precision gates keep
it safe and every auto-tag is reversible.

Observability so the operator can tune from real data:
- MISFIRE = an auto-applied (source='head_auto') tag the operator later removes.
  UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it).
  Both captured at correction time in TagService.add_to_image/remove_from_image
  (source is lost on delete) into durable per-tag counters (head_metric), keyed
  by tag so they survive head retrain/prune.
- Daily snapshot_head_metrics writes a per-concept time-series point
  (head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires
  + head quality; 180-day retention; daily beat.
- GET /api/heads/metrics: per-concept current counts + realized misfire rate +
  head quality, plus the snapshot time-series — the report to tune the precision
  target + support floor.

Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual
removal isn't a misfire, headless manual add isn't an under-fire), snapshot
time-series, metrics API.

What's the autofire threshold? There's no single number — each graduated head
derives its OWN probability cutoff from its PR curve: the operating point that
holds precision >= head_auto_apply_precision (0.97) at max recall. The global
knobs are that target + the >=30 support floor.

NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends.

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

backend/app/api/heads.py

@@ -12,7 +12,15 @@ from quart import Blueprint, jsonify, request
 from sqlalchemy import desc, func, select
 
 from ..extensions import get_session
-from ..models import HeadAutoApplyRun, HeadTrainingRun, Tag, TagHead
+from ..models import (
+    HeadAutoApplyRun,
+    HeadMetric,
+    HeadMetricsSnapshot,
+    HeadTrainingRun,
+    Tag,
+    TagHead,
+)
+from ..models.tag import image_tag
 from ..services.ml.heads import (
     HeadAutoApplyAlreadyRunning,
     HeadAutoApplyDisabled,
@@ -181,3 +189,97 @@ async def auto_apply_status():
         "running_id": running,
         "runs": [_serialize_apply_run(r) for r in runs],
     })
+
+
+@heads_bp.route("/metrics", methods=["GET"])
+async def metrics():
+    """Auto-apply observability: per-concept current counts (volume, misfires,
+    under-fires, realized misfire rate, head quality) + the daily time-series so
+    the operator can tune the precision target + support floor from real data."""
+    async with get_session() as session:
+        head_rows = (
+            await session.execute(
+                select(
+                    TagHead.tag_id, Tag.name, TagHead.ap, TagHead.precision_cv,
+                    TagHead.recall, TagHead.auto_apply_threshold, TagHead.n_pos,
+                ).join(Tag, Tag.id == TagHead.tag_id)
+            )
+        ).all()
+        heads = {r.tag_id: r for r in head_rows}
+        metric_rows = (
+            await session.execute(
+                select(
+                    HeadMetric.tag_id, HeadMetric.n_misfires, HeadMetric.n_underfires
+                )
+            )
+        ).all()
+        mets = {r.tag_id: r for r in metric_rows}
+        applied = dict(
+            (
+                await session.execute(
+                    select(image_tag.c.tag_id, func.count())
+                    .where(image_tag.c.source == "head_auto")
+                    .group_by(image_tag.c.tag_id)
+                )
+            ).all()
+        )
+        names = {r.tag_id: r.name for r in head_rows}
+        # Names for metric-only tags (head pruned but corrections recorded).
+        missing = [t for t in mets if t not in names]
+        if missing:
+            for tid, nm in (
+                await session.execute(
+                    select(Tag.id, Tag.name).where(Tag.id.in_(missing))
+                )
+            ).all():
+                names[tid] = nm
+
+        concepts = []
+        for tid in set(heads) | set(mets):
+            h = heads.get(tid)
+            m = mets.get(tid)
+            n_applied = applied.get(tid, 0)
+            n_mis = m.n_misfires if m else 0
+            denom = n_applied + n_mis
+            concepts.append({
+                "tag_id": tid,
+                "name": names.get(tid, str(tid)),
+                "n_auto_applied": n_applied,
+                "n_misfires": n_mis,
+                "n_underfires": m.n_underfires if m else 0,
+                # Of everything this head ever auto-applied, the fraction you
+                # removed — the misfire rate (null until something fired).
+                "misfire_rate": round(n_mis / denom, 4) if denom else None,
+                "ap": h.ap if h else None,
+                "precision_cv": h.precision_cv if h else None,
+                "recall": h.recall if h else None,
+                "auto_apply": bool(h and h.auto_apply_threshold is not None),
+                "n_pos": h.n_pos if h else None,
+            })
+        concepts.sort(key=lambda c: (c["n_misfires"], c["n_auto_applied"]), reverse=True)
+
+        snaps = (
+            await session.execute(
+                select(HeadMetricsSnapshot)
+                .order_by(HeadMetricsSnapshot.snapshot_at.desc())
+                .limit(1000)
+            )
+        ).scalars().all()
+    return jsonify({
+        "concepts": concepts,
+        "snapshots": [
+            {
+                "tag_id": s.tag_id,
+                "name": s.name,
+                "snapshot_at": s.snapshot_at.isoformat() if s.snapshot_at else None,
+                "n_auto_applied": s.n_auto_applied,
+                "n_misfires": s.n_misfires,
+                "n_underfires": s.n_underfires,
+                "ap": s.ap,
+                "precision_cv": s.precision_cv,
+                "recall": s.recall,
+                "n_pos": s.n_pos,
+            }
+            for s in snaps
+        ],
+    })