feat(heads): nightly auto-retrain + inline Retrain button in Explore

Two cadences for keeping heads in sync with your tagging:
- PASSIVE: a nightly `scheduled_train_heads` beat (skips if a run is already
  in flight; creates+commits the run row before dispatching train_heads so the
  ml worker always finds it). Folds the day's accepts/rejects + newly-eligible
  concepts into the heads without anyone clicking.
- ACTIVE: a "Retrain heads" button in the Explore trail bar — bank the +/-
  feedback you just gave while walking content, without a trip to Settings.

Shared logic in a new useHeadTraining composable (trigger + poll + start/finish
toasts), used by the Explore button; reflects an already-running run (incl. the
nightly one) on mount.

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

backend/app/celery_app.py

@@ -109,6 +109,10 @@ def make_celery() -> Celery:
                 "task": "backend.app.tasks.ml.apply_allowlist_tags",
                 "schedule": 86400.0,
             },
+            "train-heads-nightly": {
+                "task": "backend.app.tasks.ml.scheduled_train_heads",
+                "schedule": 86400.0,  # passive cadence; manual retrain stays available
+            },
             "integrity-verify-weekly": {
                 "task": "backend.app.tasks.maintenance.verify_integrity",
                 "schedule": 604800.0,  # weekly

backend/app/tasks/ml.py

@@ -629,3 +629,33 @@ def train_heads(self, run_id: int) -> str:
         run.finished_at = datetime.now(UTC)
         session.commit()
         return "ready"
+
+
+@celery.task(name="backend.app.tasks.ml.scheduled_train_heads")
+def scheduled_train_heads() -> str:
+    """Nightly passive retrain (#114): fold the day's accepts/rejects + any
+    newly-eligible concepts into the heads without the operator clicking. Skips
+    if a run is already in flight (one at a time). Creates + COMMITS the run row
+    before dispatching so the ml-queue worker can always find it."""
+    from datetime import UTC, datetime
+
+    from sqlalchemy import select as sa_select
+
+    from ..models import HeadTrainingRun
+
+    SessionLocal = _sync_session_factory()
+    with SessionLocal() as session:
+        running = session.execute(
+            sa_select(HeadTrainingRun.id).where(HeadTrainingRun.status == "running")
+        ).scalar_one_or_none()
+        if running is not None:
+            return "already running"
+        run = HeadTrainingRun(
+            params={"source": "scheduled"}, status="running",
+            last_progress_at=datetime.now(UTC),
+        )
+        session.add(run)
+        session.commit()
+        run_id = run.id
+    train_heads.delay(run_id)
+    return "dispatched"