Add image service: Wikipedia photos + emoji fallback for vocabulary

image_service.py: Fetches thumbnail from Wikipedia REST API (free,
no account). Falls back to emoji for abstract words (40+ mapped).

Auto-enrichment: When a learning unit is created, images are
automatically fetched for all words that don't have one yet.

Manual endpoint: POST /api/vocabulary/enrich-images fills images
for existing words without images.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend-lehrer/vocabulary/api.py

@@ -270,6 +270,13 @@ async def api_create_unit_from_words(payload: CreateUnitFromWordsPayload):
             },
         }, f, ensure_ascii=False, indent=2)
 
+    # Auto-enrich words with images (Wikipedia + emoji fallback)
+    try:
+        from services.image_service import enrich_words_with_images
+        await enrich_words_with_images(payload.word_ids)
+    except Exception as e:
+        logger.warning(f"Image enrichment failed (non-critical): {e}")
+
     logger.info(f"Created vocab unit {lu.id} with {len(words)} words")
 
     return {
@@ -347,6 +354,26 @@ async def api_bulk_import(payload: BulkImportPayload):
 # ---------------------------------------------------------------------------
 
 
+@router.post("/enrich-images")
+async def api_enrich_images(word_ids: List[str] = None):
+    """Fetch and store images for vocabulary words (Wikipedia + emoji fallback)."""
+    from services.image_service import enrich_words_with_images
+    from vocabulary.db import get_pool
+    import uuid as _uuid
+
+    if not word_ids:
+        pool = await get_pool()
+        async with pool.acquire() as conn:
+            rows = await conn.fetch("SELECT id FROM vocabulary_words WHERE image_url = '' OR image_url IS NULL")
+            word_ids = [str(r["id"]) for r in rows]
+
+    if not word_ids:
+        return {"enriched": 0, "message": "All words already have images"}
+
+    count = await enrich_words_with_images(word_ids)
+    return {"enriched": count, "total": len(word_ids)}
+
+
 class TranslateRequest(BaseModel):
     word_ids: List[str]
     target_language: str