FASE 7d: Lazy Loading + Minificación + Auto-serve minified

Cambios implementados:

1. Lazy loading de imágenes:
   - catalog.js: loading="lazy" decoding="async" en part cards y detail panel
   - inventory.js: lazy loading en imagen de detalle de item

2. Minificación de assets:
   - scripts/minify-assets.sh: minifica JS (terser) y CSS para POS y Dashboard
   - 25 archivos .min.js + 5 .min.css generados en pos/static/
   - 14 archivos .min.js + 8 .min.css generados en dashboard/

3. Nginx auto-serve minified:
   - try_files $1.min.js antes de servir .js original
   - try_files $1.min.css antes de servir .css original
   - Transparente para los templates HTML (cero cambios en HTML)

4. Cache warming script:
   - scripts/warm_vehicle_cache.py: pobla Redis con vehicle info por batches
   - Mitiga DISTINCT ON + 4 JOINs sobre 2B filas
   - Corre en background, procesa ~1.5M parts

Tests: 73/73 pasando

scripts/warm_vehicle_cache.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+"""Warm Redis cache for vehicle info (part_vehicle_preview alternative).
+
+Runs in batches over all parts in the catalog, populating
+nexus:vehicle:{part_id} keys in Redis. This eliminates the
+DISTINCT ON + 4 JOINs query on vehicle_parts (2B rows) for
+cached parts.
+
+Usage:
+    export MASTER_DB_URL="postgresql://..."
+    export REDIS_URL="redis://localhost:6379/0"
+    python3 warm_vehicle_cache.py
+"""
+
+import os, sys, json, time
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'pos'))
+
+import psycopg2
+import redis
+
+def _fix_dsn(dsn):
+    if dsn and 'host=' not in dsn and '@/' in dsn:
+        dsn = dsn.replace('@/', '@localhost/')
+    return dsn
+
+
+MASTER_DB_URL = _fix_dsn(os.environ.get('MASTER_DB_URL', 'postgresql://postgres@localhost/nexus_autoparts'))
+REDIS_URL = os.environ.get('REDIS_URL', 'redis://localhost:6379/0')
+BATCH_SIZE = 5000
+TTL_SECONDS = 3600
+
+
+def main():
+    print("Connecting to master DB and Redis...")
+    conn = psycopg2.connect(MASTER_DB_URL)
+    cur = conn.cursor()
+    r = redis.from_url(REDIS_URL, decode_responses=True)
+    r.ping()
+
+    # Get all part_ids
+    cur.execute("SELECT id_part FROM parts WHERE oem_part_number IS NOT NULL ORDER BY id_part")
+    all_ids = [r[0] for r in cur.fetchall()]
+    total = len(all_ids)
+    print(f"Total parts to warm: {total}")
+
+    processed = 0
+    cached = 0
+    start = time.time()
+
+    for i in range(0, total, BATCH_SIZE):
+        batch = all_ids[i:i + BATCH_SIZE]
+        cur.execute("""
+            SELECT DISTINCT ON (vp.part_id)
+                   vp.part_id, b.name_brand, m.name_model, y.year_car
+            FROM vehicle_parts vp
+            JOIN model_year_engine mye ON mye.id_mye = vp.model_year_engine_id
+            JOIN models m ON m.id_model = mye.model_id
+            JOIN brands b ON b.id_brand = m.brand_id
+            JOIN years y ON y.id_year = mye.year_id
+            WHERE vp.part_id = ANY(%s)
+            ORDER BY vp.part_id, y.year_car DESC
+        """, (batch,))
+
+        pipe = r.pipeline()
+        batch_cached = 0
+        for row in cur.fetchall():
+            info = f"{row[1]} {row[2]} {row[3]}"
+            pipe.setex(f'nexus:vehicle:{row[0]}', TTL_SECONDS, info)
+            batch_cached += 1
+        pipe.execute()
+
+        processed += len(batch)
+        cached += batch_cached
+        elapsed = time.time() - start
+        rate = processed / elapsed if elapsed > 0 else 0
+        print(f"  [{processed}/{total}] cached={batch_cached} ({rate:.0f}/s)")
+
+    cur.close()
+    conn.close()
+    print(f"\nDone. Cached {cached} vehicle entries in {elapsed:.0f}s")
+
+
+if __name__ == '__main__':
+    main()