fix(ai,reader): robust sentence segmentation without abbreviation cutoffs & context-aware literary translation

- Implement robustSentenceSplit across reader.js and backend (miner.py, cefr.py) to prevent false cutoffs on honorifics (Mr., Mrs., Dr.), single-letter initials (John F. Kennedy), decimals, acronyms (U.S., U.K.), and abbreviations (No. 4, etc.)
- Upgrade translation prompt to professional literary Russian, eliminating literal word-for-word translation and preserving titles/honorifics
- Inject surrounding paragraph context into translation API to avoid out-of-context translations
- Inject offline dictionary hints into LLM prompt for accurate contextual word meaning
- Prefer qwen2.5:3b for higher translation fluency and fidelity
- Purge truncated cache entries from sentence_translations and restore Card 30 (briefcase) with authentic full sentence
- Bump Service Worker cache to notes-v6.0
- Add unit tests for robust sentence splitting, prompt formatting, and translation API

tests/test_api.py

@@ -1867,6 +1867,94 @@ def test_cefr_context_resolution_and_repair():
         assert "negotiate" in repaired["context_sentence"].lower()
 
 
+def test_robust_sentence_split_and_ai_translation(monkeypatch):
+    from app.miner import robust_sentence_split
+    from app.ai import build_translation_prompt
+
+    # 1. Test robust sentence segmentation
+    # Case A: Initials in names (John F. Kennedy)
+    s_jfk = "Perhaps the most famous is the John F. Kennedy International Airport in New York, but there is also the John F. Kennedy Space Center."
+    splits_jfk = robust_sentence_split(s_jfk)
+    assert len(splits_jfk) == 1
+    assert "John F. Kennedy International Airport" in splits_jfk[0]
+
+    # Case B: Honorifics with dots (Mr., Mrs., Dr., etc.)
+    s_dursley = "When Mr. and Mrs. Dursley woke up on the dull, gray Tuesday our story starts, there was nothing about the cloudy sky outside to suggest that strange things would happen."
+    splits_dursley = robust_sentence_split(s_dursley)
+    assert len(splits_dursley) == 1
+    assert "Mr. and Mrs. Dursley" in splits_dursley[0]
+
+    # Case C: Abbreviations (No. 4, U.S. Navy)
+    s_no4 = "They lived at No. 4 Privet Drive, which was a very quiet street. Next sentence starts here."
+    splits_no4 = robust_sentence_split(s_no4)
+    assert len(splits_no4) == 2
+    assert splits_no4[0] == "They lived at No. 4 Privet Drive, which was a very quiet street."
+    assert splits_no4[1] == "Next sentence starts here."
+
+    s_us = "The men thought that the U.S. Navy would send a plane to look for them. They waited patiently."
+    splits_us = robust_sentence_split(s_us)
+    assert len(splits_us) == 2
+    assert "U.S. Navy" in splits_us[0]
+
+    # Case D: Complex paragraph with Mr., Mrs., dialogue
+    s_hp = (
+        "At half past eight, Mr. Dursley picked up his briefcase, pecked Mrs. Dursley on the cheek, "
+        "and tried to kiss Dudley good-bye but missed, because Dudley was now having a tantrum and "
+        "throwing his cereal at the walls. “Little tyke,” chortled Mr. Dursley as he left the house."
+    )
+    splits_hp = robust_sentence_split(s_hp)
+    assert len(splits_hp) == 2
+    assert "pecked Mrs. Dursley on the cheek" in splits_hp[0]
+    assert splits_hp[0].startswith("At half past eight, Mr. Dursley")
+    assert "Little tyke" in splits_hp[1]
+
+    # 2. Test build_translation_prompt
+    prompt = build_translation_prompt(
+        sentence="At half past eight, Mr. Dursley picked up his briefcase.",
+        target_word="briefcase",
+        context="Full context paragraph here.",
+        dict_hint="портфель, кейс, дипломат",
+    )
+    assert "Контекст абзаца:" in prompt
+    assert "Full context paragraph here." in prompt
+    assert "briefcase" in prompt
+    assert "портфель, кейс, дипломат" in prompt
+    assert "Mr. -> мистер" in prompt
+    assert "НЕ дословным, без топорного калькирования" in prompt
+
+    # 3. Test API endpoint /api/translate-sentence
+    # Authenticate client
+    login_resp = client.post("/api/login", json={"username": "testadmin", "password": "testpass12345"})
+    assert login_resp.status_code == 200
+
+    # Mock translate_sentence_ai
+    import app.main as main_mod
+
+    async def mock_translate_sentence_ai(sentence, target_word="", context=""):
+        return {
+            "original_sentence": sentence,
+            "sentence_ru": "В половине девятого мистер Дурсли взял свой портфель.",
+            "target_word": target_word,
+            "word_meaning": "портфель",
+            "source": "mock_ollama",
+            "cached": False,
+        }
+
+    monkeypatch.setattr(main_mod, "translate_sentence_ai", mock_translate_sentence_ai)
+
+    tr_resp = client.post("/api/translate-sentence", json={
+        "sentence": "At half past eight, Mr. Dursley picked up his briefcase.",
+        "target_word": "briefcase",
+        "context": "Context paragraph",
+    })
+    assert tr_resp.status_code == 200
+    tr_data = tr_resp.json()
+    assert tr_data["sentence_ru"] == "В половине девятого мистер Дурсли взял свой портфель."
+    assert tr_data["word_meaning"] == "портфель"
+    assert tr_data["target_word"] == "briefcase"
+
+
+