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>

src/migrate.py

@@ -0,0 +1,72 @@
+"""Database migration functions.
+
+Each migration is idempotent and safe to run on a database that has already been migrated.
+Run via run_migration() called from app startup after init_db().
+"""
+
+import sqlite3
+
+from db import DB_PATH
+
+
+def run_migration() -> None:
+    """Apply all pending schema migrations in order.
+
+    Currently applies:
+      - v1: Add ai_blocks column to books; clear AI-derived data while preserving user data.
+      - v2: Add batch_queue table for persistent batch processing queue.
+
+    Migrations are idempotent — running them on an already-migrated database is a no-op.
+    """
+    c = sqlite3.connect(DB_PATH)
+    c.row_factory = sqlite3.Row
+    c.execute("PRAGMA foreign_keys = ON")
+    try:
+        _migrate_v1(c)
+        _migrate_v2(c)
+        c.commit()
+    except Exception:
+        c.rollback()
+        raise
+    finally:
+        c.close()
+
+
+def _migrate_v1(c: sqlite3.Connection) -> None:
+    """Add ai_blocks column and clear stale AI data from all books (first run only).
+
+    - Adds ai_blocks TEXT DEFAULT NULL column if it does not exist.
+    - On first run only (when the column is absent): clears raw_text, ai_*, title_confidence,
+      analyzed_at, candidates, ai_blocks from all books (these are regenerated by the new pipeline).
+    - For user_approved books: copies user fields back to ai_* so that
+      compute_status() still returns 'user_approved' after the ai_* clear.
+
+    This migration assumes the database already has the base books schema.
+    It is a no-op if ai_blocks already exists.
+    """
+    cols = {row["name"] for row in c.execute("PRAGMA table_info(books)")}
+    if "ai_blocks" not in cols:
+        c.execute("ALTER TABLE books ADD COLUMN ai_blocks TEXT DEFAULT NULL")
+
+        # Clear AI-derived fields only when first adding the column.
+        c.execute(
+            "UPDATE books SET "
+            "raw_text='', ai_title='', ai_author='', ai_year='', ai_isbn='', ai_publisher='', "
+            "title_confidence=0, analyzed_at=NULL, candidates=NULL, ai_blocks=NULL"
+        )
+
+        # For user_approved books, restore ai_* = user fields so status stays user_approved.
+        c.execute(
+            "UPDATE books SET "
+            "ai_title=title, ai_author=author, ai_year=year, ai_isbn=isbn, ai_publisher=publisher "
+            "WHERE identification_status='user_approved'"
+        )
+
+
+def _migrate_v2(c: sqlite3.Connection) -> None:
+    """Add batch_queue table for persistent batch processing queue.
+
+    Replaces data/batch_pending.json with a DB table so batch state survives
+    across restarts alongside all other persistent data.
+    """
+    c.execute("CREATE TABLE IF NOT EXISTS batch_queue (" "book_id  TEXT PRIMARY KEY," "added_at REAL NOT NULL" ")")