feat(tagging): SigLIP concept crops + max-over-bag scoring (#114)

Lift recall on small/local concepts (glasses, cum, stomach-bulge, xray,
lactation) that the whole-image SigLIP vector washes out: the GPU agent now
embeds figure crops with SigLIP too, stored as kind='concept' regions, and the
suggestion rail scores each image as a BAG (whole-image + every concept crop),
taking each head's MAX over the bag. The whole-image vector is always in the
bag, so this can never score lower than before.

Model-agnostic by construction: the server ANNOUNCES the embedding model
(HF name + version) in the lease, so the agent loads whatever the heads were
trained in and stays in lock-step — a model swap is a server setting + a
re-embed migration, never an agent change.

- agent: model-agnostic CropEmbedder (torch/transformers get_image_features,
  fp16 on CUDA, inference-locked); worker branches on job.task — 'ccip' emits
  figure(CCIP)+concept(SigLIP) in one pass, 'siglip' emits concept-only so the
  back-catalogue backfill never churns figure/CCIP regions; torch cu124 +
  transformers in the image.
- server: lease announces embed_model_name/embed_version; score_image is
  max-over-bag (version-filtered region embeddings); enqueue_gpu_backfill
  'siglip' gates on a missing concept region (drains the back-catalogue,
  retries failures, no double-enqueue); daily siglip-backfill beat; UI button;
  /api/ccip/overview reports images_with_concept_siglip.
- v1 scope: suggestion rail only — auto-apply stays whole-image (conservative;
  heads' thresholds were calibrated on whole-image). Bulk-apply bag = follow-up.

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

backend/app/services/ml/heads.py

@@ -29,6 +29,7 @@ from ...models import (
     HeadAutoApplyRun,
     HeadTrainingRun,
     ImageRecord,
+    ImageRegion,
     MLSettings,
     Tag,
     TagHead,
@@ -296,7 +297,14 @@ async def score_image(
     category, score}], ranked. A concept surfaces when its score clears the
     head's own suggest_threshold — or, when threshold_override is given (the
     typed-dropdown "show everything" mode), that flat floor instead (0 → every
-    head). Empty if the image has no embedding or no heads exist yet."""
+    head). Empty if the image has no embedding or no heads exist yet.
+
+    MAX-OVER-BAG: the image is scored as a BAG of embeddings — the whole-image
+    vector PLUS every concept-region crop the agent embedded (same model
+    version) — and each head takes its MAX score across the bag. A small/local
+    concept (glasses, a stomach bulge) that the whole-image vector washes out
+    can still surface from the crop where it dominates. The whole-image vector is
+    always in the bag, so this can never score lower than whole-image alone."""
     import numpy as np
 
     img = await session.get(ImageRecord, image_id)
@@ -306,11 +314,26 @@ async def score_image(
     heads = await _current_heads(session, settings.embedder_model_version)
     if heads["W"] is None:
         return []
-    x = np.asarray(img.siglip_embedding, dtype=np.float32)
-    n = float(np.linalg.norm(x)) or 1.0
-    xn = x / n
-    z = heads["W"] @ xn + heads["b"]
-    probs = 1.0 / (1.0 + np.exp(-z))
+
+    bag = [np.asarray(img.siglip_embedding, dtype=np.float32)]
+    region_vecs = (
+        await session.execute(
+            select(ImageRegion.siglip_embedding)
+            .where(ImageRegion.image_record_id == image_id)
+            .where(ImageRegion.siglip_embedding.is_not(None))
+            .where(ImageRegion.embedding_version == settings.embedder_model_version)
+        )
+    ).all()
+    for (vec,) in region_vecs:
+        if vec is not None:
+            bag.append(np.asarray(vec, dtype=np.float32))
+
+    X = np.vstack(bag)                                  # (B, D)
+    norms = np.linalg.norm(X, axis=1, keepdims=True)
+    norms[norms == 0] = 1.0
+    Xn = X / norms
+    Z = Xn @ heads["W"].T + heads["b"]                  # (B, H)
+    probs = (1.0 / (1.0 + np.exp(-Z))).max(axis=0)      # (H,) best over the bag
     out = []
     for i, p in enumerate(probs):
         cut = threshold_override if threshold_override is not None else heads["thr"][i]