fix(explore): diversify "more like this" so it stops getting stuck (#1188)

Pure nearest-cosine piled near-identical images into the neighbour grid — a
reposted banner filled all 24 slots, and once you wandered into a B&W /
comic-panel cluster every neighbour was more of the same with no way back to
colour without the Random button (operator-reported, with screenshot).

similar() now over-fetches a wide candidate pool (5x the requested limit, cap
200), then diversifies down to `limit`:
- pHash near-duplicate collapse: drop candidates within 6 Hamming bits of the
  anchor or an already-kept candidate, so a repost (and the anchor's own clones)
  appears at most once.
- MMR re-rank: greedily pick for closeness-to-anchor minus similarity-to-already
  -picked (lambda 0.55), so the result SPANS clusters instead of returning 40
  variations of one image. Falls back to nearest-order on any failure / small
  pool, so existing nearest-first behaviour is unchanged when there's nothing to
  diversify.

Frontend forwardTarget drops the now-redundant skip-nearest-third hack (the list
is already diversified server-side) — plain random-over-unvisited gives the
variance now.

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

backend/app/services/gallery_service.py

@@ -289,6 +289,75 @@ def _gallery_images(rows, artists: dict[int, dict]) -> list[GalleryImage]:
     ]
 
 
+def _diversify_similar(src, rows, limit, *, dup_threshold=6, lam=0.55):
+    """Trim a nearest-cosine candidate pool down to `limit` diverse picks.
+
+    1. pHash collapse: drop any candidate whose perceptual hash is within
+       `dup_threshold` Hamming bits of the anchor or an already-kept candidate —
+       so a reposted banner (and the anchor's own clones) appears at most once.
+    2. MMR (Maximal Marginal Relevance): greedily pick the candidate maximising
+       `lam * sim_to_anchor - (1 - lam) * max_sim_to_already_picked`. This keeps
+       the most relevant up top but pushes the selection to SPAN clusters
+       instead of returning 40 variations of one image.
+
+    Falls back to nearest-order (`rows[:limit]`) on any failure or a small pool.
+    """
+    if len(rows) <= 1:
+        return rows[:limit]
+    try:
+        import imagehash
+        import numpy as np
+    except Exception:
+        return rows[:limit]
+
+    # --- 1. pHash near-duplicate collapse (videos/NULL phash pass through) ---
+    kept = []
+    seen = []
+    if src.phash:
+        try:
+            seen.append(imagehash.hex_to_hash(src.phash))
+        except Exception:
+            pass
+    for row in rows:
+        ph = row[0].phash
+        if ph:
+            try:
+                h = imagehash.hex_to_hash(ph)
+                if any((h - k) <= dup_threshold for k in seen):
+                    continue
+                seen.append(h)
+            except Exception:
+                pass
+        kept.append(row)
+    if len(kept) <= limit:
+        return kept
+
+    # --- 2. MMR re-rank on the L2-normalised SigLIP embeddings ---
+    try:
+        a = np.asarray(src.siglip_embedding, dtype=np.float32)
+        a = a / (np.linalg.norm(a) or 1.0)
+        V = np.vstack([
+            np.asarray(row[0].siglip_embedding, dtype=np.float32) for row in kept
+        ])
+        V = V / np.clip(np.linalg.norm(V, axis=1, keepdims=True), 1e-8, None)
+    except Exception:
+        return kept[:limit]
+
+    rel = V @ a                                  # (N,) cosine to the anchor
+    n = len(kept)
+    picked_mask = np.zeros(n, dtype=bool)
+    max_sim = np.zeros(n, dtype=np.float32)      # max sim to anything picked yet
+    order = []
+    for _ in range(min(limit, n)):
+        scores = lam * rel - (1.0 - lam) * max_sim
+        scores[picked_mask] = -np.inf
+        i = int(np.argmax(scores))
+        order.append(i)
+        picked_mask[i] = True
+        max_sim = np.maximum(max_sim, V @ V[i])
+    return [kept[i] for i in order]
+
+
 async def _artists_for(session, image_ids: list[int]) -> dict[int, dict]:
     """Map image_id -> {"name","slug"} via the canonical
     image_record.artist_id (FC-2d-vii-c). Bounded by page size."""
@@ -565,14 +634,20 @@ class GalleryService:
         untagged: bool = False, no_artist: bool = False,
         date_from: datetime | None = None, date_to: datetime | None = None,
     ) -> list[GalleryImage] | None:
-        """Visual "more like this": images ranked by cosine distance to
-        `image_id`'s SigLIP embedding (pgvector, HNSW-indexed — alembic 0036).
-        No ML inference here; the embedding was computed at import.
+        """Visual "more like this": images near `image_id`'s SigLIP embedding
+        (pgvector, HNSW-indexed — alembic 0036), then DIVERSIFIED so the result
+        doesn't collapse into one cluster. No ML inference here.
 
-        Returns None if the source image doesn't exist (→ 404), [] if it has
-        no embedding (a video / not-yet-embedded). Composes with the Phase-1/2
-        scope filters (AND) but REPLACES the date sort — always nearest-first,
-        bounded to `limit` (no cursor; distance-ranking has no date cursor).
+        Pure nearest-cosine piles up near-identical images — a reposted banner
+        fills the whole grid, and once you wander into a B&W / comic-panel
+        cluster every neighbour is more of the same with no way back to colour
+        (operator-reported 2026-06-30). So we pull a WIDER candidate pool, then:
+          1. collapse near-duplicate pHashes (and drop clones of the anchor),
+          2. MMR re-rank — pick for closeness-to-anchor but penalise similarity
+             to what's already picked, so the result SPANS clusters.
+
+        Returns None if the source doesn't exist (→ 404), [] if it has no
+        embedding. Composes with the scope filters (AND); REPLACES the date sort.
         """
         if limit < 1 or limit > 200:
             raise ValueError("limit must be between 1 and 200")
@@ -582,6 +657,9 @@ class GalleryService:
         if src.siglip_embedding is None:
             return []
 
+        # Over-fetch so diversification has clusters to spread across — without a
+        # wide pool there's nothing but the near-dupes to choose from.
+        pool_n = min(200, max(limit * 5, 60))
         distance = ImageRecord.siglip_embedding.cosine_distance(src.siglip_embedding)
         eff = _effective_date_col()
         stmt = select(ImageRecord, Post.post_date, eff.label("eff"))
@@ -597,8 +675,9 @@ class GalleryService:
             platform=platform, untagged=untagged, no_artist=no_artist,
             date_from=date_from, date_to=date_to,
         )
-        stmt = stmt.order_by(distance.asc()).limit(limit)
+        stmt = stmt.order_by(distance.asc()).limit(pool_n)
         rows = (await self.session.execute(stmt)).all()
+        rows = _diversify_similar(src, rows, limit)
         artists = await _artists_for(self.session, [r[0].id for r in rows])
         return _gallery_images(rows, artists)