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

app/static/js/modules/api.js

@@ -114,13 +114,14 @@ export const api = {
             const resp = await fetch(`/api/detect-phrasal-verb?sentence=${encodeURIComponent(sentence)}&word=${encodeURIComponent(word)}`);
             return resp.ok ? await resp.json() : { detected: false, phrasal_verb: null };
         },
-        async translateSentence(sentence, targetWord = '') {
+        async translateSentence(sentence, targetWord = '', context = '') {
             const resp = await fetch('/api/translate-sentence', {
                 method: 'POST',
                 headers: { 'Content-Type': 'application/json' },
                 body: JSON.stringify({
                     sentence: sentence,
-                    target_word: targetWord
+                    target_word: targetWord,
+                    context: context
                 })
             });
             if (resp.ok) {