feat(ccip): read-only observability API for the crop/CCIP work (#114)

So the work can be checked through an API as the agent fills in vectors (same
pattern as /api/heads/metrics):
- GET /api/ccip/overview: regions by kind, images with figure CCIP vectors, the
  per-character reference counts (which characters have enough examples to match
  on), and the embedding versions present.
- GET /api/ccip/images/<id>: that image's stored regions (bbox, frame_time,
  has_ccip/has_siglip, versions) + the CCIP character matches it would get — for
  spot-checking detector + matcher output.

Read-only, no GPU. (Queue depth is already at /api/gpu/status.)

Tests: overview coverage counts + per-character refs; per-image regions + matches.

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

backend/app/api/__init__.py

@@ -24,6 +24,7 @@ def all_blueprints() -> list[Blueprint]:
     from .credentials import credentials_bp
     from .downloads import downloads_bp
     from .extension import extension_bp
+    from .ccip import ccip_bp
     from .gallery import gallery_bp
     from .gpu import gpu_bp
     from .heads import heads_bp
@@ -62,6 +63,7 @@ def all_blueprints() -> list[Blueprint]:
         tag_eval_bp,
         heads_bp,
         gpu_bp,
+        ccip_bp,
         ml_admin_bp,
         thumbnails_bp,
         sources_bp,

backend/app/api/ccip.py

@@ -0,0 +1,106 @@
+"""CCIP / region observability API (#114) — read-only, analysis-shaped.
+
+So the work can be checked through an API as the agent fills in vectors: overall
+coverage (regions by kind, how many images have figure CCIP vectors, which
+characters have enough reference examples to match on) + a per-image drill-down
+(its regions + the CCIP character matches it would get). Mirrors the heads
+metrics endpoint; no GPU, just reads what's stored.
+"""
+
+from quart import Blueprint, jsonify
+from sqlalchemy import distinct, func, select
+
+from ..extensions import get_session
+from ..models import ImageRegion, Tag, TagKind
+from ..models.tag import image_tag
+from ..services.ml.ccip import match_image
+
+ccip_bp = Blueprint("ccip", __name__, url_prefix="/api/ccip")
+
+_FIGURE_KINDS = ("face", "figure")
+
+
+@ccip_bp.route("/overview", methods=["GET"])
+async def overview():
+    async with get_session() as session:
+        by_kind = dict(
+            (
+                await session.execute(
+                    select(ImageRegion.kind, func.count()).group_by(ImageRegion.kind)
+                )
+            ).all()
+        )
+        images_with_figure_ccip = (
+            await session.execute(
+                select(func.count(distinct(ImageRegion.image_record_id)))
+                .where(ImageRegion.kind.in_(_FIGURE_KINDS))
+                .where(ImageRegion.ccip_embedding.is_not(None))
+            )
+        ).scalar_one()
+        # Per-character reference counts (no vectors loaded) — which characters
+        # have enough examples to match on.
+        ref_rows = (
+            await session.execute(
+                select(image_tag.c.tag_id, Tag.name, func.count())
+                .select_from(ImageRegion)
+                .join(
+                    image_tag,
+                    image_tag.c.image_record_id == ImageRegion.image_record_id,
+                )
+                .join(Tag, Tag.id == image_tag.c.tag_id)
+                .where(Tag.kind == TagKind.character)
+                .where(ImageRegion.kind.in_(_FIGURE_KINDS))
+                .where(ImageRegion.ccip_embedding.is_not(None))
+                .group_by(image_tag.c.tag_id, Tag.name)
+                .order_by(func.count().desc())
+            )
+        ).all()
+        versions = [
+            v for (v,) in (
+                await session.execute(
+                    select(distinct(ImageRegion.embedding_version))
+                )
+            ).all() if v
+        ]
+    return jsonify({
+        "regions_by_kind": by_kind,
+        "images_with_figure_ccip": images_with_figure_ccip,
+        "characters_with_references": len(ref_rows),
+        "character_references": [
+            {"tag_id": t, "name": n, "n_refs": c} for (t, n, c) in ref_rows
+        ],
+        "embedding_versions": versions,
+    })
+
+
+@ccip_bp.route("/images/<int:image_id>", methods=["GET"])
+async def image_detail(image_id: int):
+    """An image's stored regions + the CCIP character matches it would get —
+    for spot-checking the agent's output + the matcher."""
+    async with get_session() as session:
+        regions = (
+            await session.execute(
+                select(ImageRegion)
+                .where(ImageRegion.image_record_id == image_id)
+                .order_by(ImageRegion.id)
+            )
+        ).scalars().all()
+        matches = await match_image(session, image_id)
+    return jsonify({
+        "image_id": image_id,
+        "regions": [
+            {
+                "id": r.id,
+                "kind": r.kind,
+                "bbox": [r.rx, r.ry, r.rw, r.rh],
+                "frame_time": r.frame_time,
+                "score": r.score,
+                "detector_version": r.detector_version,
+                "embedding_version": r.embedding_version,
+                "has_ccip": r.ccip_embedding is not None,
+                "has_siglip": r.siglip_embedding is not None,
+            }
+            for r in regions
+        ],
+        "ccip_matches": matches,
+    })