Duplicates, delete & storage, users page with J-ID avatars

Backend:
- Perceptual hashes (aHash/dHash/pHash/wHash via imagehash, no imgdd)
  stored on items, computed on upload/download and by the new
  compute_visual_hashes command
- Duplicates API: exact duplicates (multi-location items), visual matches
  for one item, union-find similarity groups with pagination
- Delete API with ownership/staff checks, per-item and per-copy deletion,
  watched-folder path validation; storage overview and temp cleanup;
  file list accepts j_ids batches
- Staged uploads are flagged visual_match with their library matches
  (threshold via VISUAL_MATCH_THRESHOLD)
- Staff users API: list with upload counts, set role and avatar by J-ID;
  User.avatar FK with signed avatar URLs
- Download threads close their DB connection and stale tasks are reaped,
  keeping behaviour Gunicorn-friendly

Frontend:
- /duplicates: exact duplicate groups with per-copy delete, visual
  similarity controls, search similar to a J-ID, paginated groups with
  selection, bulk delete and dismiss
- /delete: storage cards, delete by J-ID with preview grid, temp cleanup
- /users: staff directory with role selects and avatar J-ID inputs
- Nav + command palette entries; top-bar avatar; upload cards and the
  metadata modal show library visual matches

backend/apps/library/services.py

@@ -1,5 +1,6 @@
 import hashlib
 import json
+import logging
 import mimetypes
 import os
 import re
@@ -7,12 +8,19 @@ import shutil
 import subprocess
 from pathlib import Path
 
+import imagehash
 from django.conf import settings
 from django.http import FileResponse, Http404, HttpResponse
 from django.utils.text import get_valid_filename
+from PIL import Image
 
 from .models import MediaItem, MediaLocation
 
+logger = logging.getLogger(__name__)
+
+HASH_FIELDS = ("ahash", "dhash", "phash", "whash")
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".gif", ".apng", ".webp"}
+
 ALLOWED_EXTENSIONS = {
     ".jpg",
     ".jpeg",
@@ -82,6 +90,41 @@ def rename_location_to_j_id(item, location):
     return location
 
 
+def compute_visual_hashes(path):
+    """Perceptual hashes for an image file (empty dict for other files)."""
+    path = Path(path)
+    if path.suffix.lower() not in IMAGE_EXTENSIONS:
+        return {}
+    try:
+        with Image.open(path) as image:
+            converted = image.convert("RGB")
+            return {
+                "ahash": str(imagehash.average_hash(converted, hash_size=8)),
+                "dhash": str(imagehash.dhash(converted, hash_size=8)),
+                "phash": str(imagehash.phash(converted, hash_size=8)),
+                "whash": str(imagehash.whash(converted, hash_size=8)),
+            }
+    except Exception:  # noqa: BLE001 - hashing must never break indexing
+        logger.exception("Could not compute visual hashes for %s", path)
+        return {}
+
+
+def ensure_visual_hashes(item):
+    """Fill in missing perceptual hashes for a media item."""
+    if all(getattr(item, field) for field in HASH_FIELDS):
+        return item
+    location = item.locations.first()
+    if location is None:
+        return item
+    hashes = compute_visual_hashes(location.path)
+    if not hashes:
+        return item
+    for field, value in hashes.items():
+        setattr(item, field, value)
+    item.save(update_fields=[*hashes.keys(), "updated_at"])
+    return item
+
+
 def parse_tags(raw):
     """Normalize a comma-separated string or JSON list into a list of tags."""
     if raw is None: