feat(agent): idle-unload GPU models to free VRAM when the queue is idle

The SigLIP embedder + YOLO proposers load lazily then stay resident for the
container's whole lifetime — a 24/7 agent with an empty queue squats on ~5GB of
VRAM doing nothing (operator-observed: 4900MiB held at GPU-util 8% / P8). Sleep
mode only sheds downloaders + poll cadence; even a UI Stop left the models loaded.

Add a monitor thread that unloads the torch-owned models after
cfg.idle_unload_seconds (env IDLE_UNLOAD_SECONDS, default 300; 0 disables) with
the GPU genuinely idle (active==0, buffer drained, no job completed in the
window), then torch.cuda.empty_cache() to hand the blocks back to the driver.
They reload lazily on the next job via the existing _ensure_embedder /
_proposers_for. Covers both sleep-mode idle and a full Stop. Surfaced in
/status (models_loaded) and the agent UI pipe line; the VRAM meter drops too.

Residual: imgutils CCIP/person ONNX sessions + the CUDA context stay resident
(no clean unload API) — idle VRAM drops substantially, not to zero.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TbrA36zNczjVhrM6cWThQa

agent/fc_agent/embedder.py

@@ -75,3 +75,18 @@ class CropEmbedder:
         pooled = out.pooler_output if hasattr(out, "pooler_output") else out
         arr = pooled.float().cpu().numpy().astype(np.float32)
         return [row.reshape(-1).tolist() for row in arr]
+
+    def unload(self) -> bool:
+        """Drop the loaded model so its VRAM can be reclaimed — the idle monitor
+        calls this after a spell with no work so an idle agent doesn't squat on
+        the card; the next embed() reloads it lazily (a few seconds). Held under
+        BOTH the load and inference locks so it can never race a concurrent load
+        or an in-flight forward pass. Returns True if a model was actually
+        released (the caller then runs one empty_cache() to hand the freed blocks
+        back to the driver)."""
+        with self._load_lock, self._infer_lock:
+            if self._model is None:
+                return False
+            self._model = None
+            self._processor = None
+            return True