feat: a live path that uses the enrolled store, and a demo server a phone can reach

The live session scored crops with the matcher regardless of the configured identity
backend, so a live scan used the old mechanism while the still path used the enrolled
gallery - measured, the matcher accepts look-alikes and cannot say "unknown", which is
exactly what a real shelf needs it to do. score_crop now routes to the gallery, and
the decision uses a similarity scale (accept at 0.60 with a 0.04 margin) rather than
inlier-count thresholds that mean nothing on a cosine.

The synthetic walk is gone: demo.py, demo_replay.py, the /v1/demo endpoints and the
walk benchmark are deleted, and the app's frame source is the camera on a device (the
single-image replay stays for the simulator, which has no camera).

stock-demo merges both demo shelves (27 products, 62 views, trained head) so one
server recognises either image, and run-demo-server.sh starts it bound to 0.0.0.0 and
prints the exact URL to put in Config.xcconfig.

Verified over the LAN with 1280x960 frames: 40 frames in 5.3s, 9 products recognised
on demo image 1.

Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>

recognization/code/live.py

@@ -643,7 +643,9 @@ class LiveSession:
             mean_scores = {
                 sku_id: total / max(1, track.score_n) for sku_id, total in track.score_sums.items()
             }
-            decision = decide_identity(mean_scores, method=method)
+            decision = decide_identity(
+                mean_scores, method=method, scale=getattr(self.engine, "identity_scale", "inlier")
+            )
             track.candidates = [
                 {"sku_id": row.get("sku_id"), "score": row.get("score")}
                 for row in (decision.get("ranked_candidates") or [])[:3]