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

agent/fc_agent/embedder.py

@@ -0,0 +1,69 @@
+"""Crop EMBEDDER for the concept bag — model-agnostic (CLIP/SigLIP-family).
+
+The server trains its per-concept heads in the embedding space of whatever model
+its `embedder_model_version` names; a crop must be embedded with the SAME model
+or its vector lands in a different coordinate system and every head misfires. So
+the model identity (HF name + version) is ANNOUNCED BY THE SERVER in the lease —
+nothing here is hardcoded to SigLIP. Whatever name the server sends is loaded via
+transformers `get_image_features` (the CLIP/SigLIP-family image-tower call); a
+non-CLIP backbone (e.g. a DINO encoder) would need its own pooling adapter.
+
+torch on CUDA, fp16 by default to keep VRAM low on a shared desktop GPU — the
+tiny fp16-vs-fp32 difference is negligible for the linear heads (cosine ~0.999).
+A single inference lock serializes the forward pass: the pipeline is I/O-bound,
+so the GPU isn't the bottleneck, and one model shared across worker threads is
+safest behind a lock.
+"""
+import threading
+
+import numpy as np
+from PIL import Image
+
+
+class CropEmbedder:
+    def __init__(self, model_name: str, dtype: str = "float16"):
+        self._name = model_name
+        self._dtype_name = dtype
+        self._model = None
+        self._processor = None
+        self._torch = None
+        self._device = None
+        self._dt = None
+        self._load_lock = threading.Lock()
+        self._infer_lock = threading.Lock()
+
+    @property
+    def model_name(self) -> str:
+        return self._name
+
+    def load(self) -> None:
+        if self._model is not None:
+            return
+        with self._load_lock:
+            if self._model is not None:
+                return
+            import torch
+            from transformers import AutoImageProcessor, AutoModel
+
+            self._torch = torch
+            self._device = "cuda" if torch.cuda.is_available() else "cpu"
+            dt = getattr(torch, self._dtype_name, torch.float16)
+            if self._device == "cpu":
+                dt = torch.float32   # fp16 matmul is unsupported/slow on CPU
+            self._dt = dt
+            self._processor = AutoImageProcessor.from_pretrained(self._name)
+            model = AutoModel.from_pretrained(self._name, torch_dtype=dt)
+            model.eval().to(self._device)
+            self._model = model
+
+    def embed(self, image: Image.Image) -> list[float]:
+        """A crop → its embedding as a plain float list, ready to POST."""
+        self.load()
+        torch = self._torch
+        enc = self._processor(images=image, return_tensors="pt")
+        pixel_values = enc["pixel_values"].to(self._device, self._dt)
+        with self._infer_lock, torch.no_grad():
+            out = self._model.get_image_features(pixel_values=pixel_values)
+        pooled = out.pooler_output if hasattr(out, "pooler_output") else out
+        vec = pooled[0].float().cpu().numpy().astype(np.float32).reshape(-1)
+        return vec.tolist()