Add per-request AI logging, DB batch queue, WS entity updates, and UI polish

- log_thread.py: thread-safe ContextVar bridge so executor threads can log
  individual LLM calls and archive searches back to the event loop
- ai_log.py: init_thread_logging(), notify_entity_update(); WS now pushes
  entity_update messages when book data changes after any plugin or batch run
- batch.py: replace batch_pending.json with batch_queue SQLite table;
  run_batch_consumer() reads queue dynamically so new books can be added
  while batch is running; add_to_queue() deduplicates
- migrate.py: fix _migrate_v1 (clear-on-startup bug); add _migrate_v2 for
  batch_queue table
- _client.py / archive.py / identification.py: wrap each LLM API call and
  archive search with log_thread start/finish entries
- api.py: POST /api/batch returns {already_running, added}; notify_entity_update
  after identify pipeline
- models.default.yaml: strengthen ai_identify confidence-scoring instructions;
  warn against placeholder data
- detail-render.js: book log entries show clickable ID + spine thumbnail;
  book spine/title images open full-screen popup
- events.js: batch-start handles already_running+added; open-img-popup action
- init.js: entity_update WS handler; image popup close listeners
- overlays.css / index.html: full-screen image popup overlay
- eslint.config.js: add new globals; fix no-redeclare/no-unused-vars for
  multi-file global architecture; all lint errors resolved

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

static/js/api.js

@@ -7,16 +7,22 @@
  * Depends on: nothing
  */
 
+/* exported req */
+
 // ── API ──────────────────────────────────────────────────────────────────────
 async function req(method, url, body = null, isForm = false) {
-  const opts = {method};
+  const opts = { method };
   if (body) {
-    if (isForm) { opts.body = body; }
-    else { opts.headers = {'Content-Type':'application/json'}; opts.body = JSON.stringify(body); }
+    if (isForm) {
+      opts.body = body;
+    } else {
+      opts.headers = { 'Content-Type': 'application/json' };
+      opts.body = JSON.stringify(body);
+    }
   }
   const r = await fetch(url, opts);
   if (!r.ok) {
-    const e = await r.json().catch(() => ({detail:'Request failed'}));
+    const e = await r.json().catch(() => ({ detail: 'Request failed' }));
     throw new Error(e.detail || 'Request failed');
   }
   return r.json();