Fix HF_HOME caching in Dockerfile and update UI

feat: dialect-to-MSA conversion — backend API + frontend + Docker model cache

fix: add quran.py + quran_master.db to Dockerfile COPY

feat: NLP-3 PuncAra-v1 Integration — Local punctuation model - Created src/nlp/punctuation/ package (rules + service) - Extracted from PuncAra.py: preprocessing, postprocessing, chunking logic - PunctuationChecker: lazy-loaded EncoderDecoderModel from bayan10/PuncAra-v1 - Wired into /api/analyze as Step 3 (Spelling -> Grammar -> Punctuation) - Updated /api/punctuation standalone endpoint - Updated /api/health to report punctuation status - Dockerfile pre-downloads PuncAra-v1 weights during build - Increased gunicorn timeout to 300s for full pipeline

feat: NLP-2 Grammar Integration — dependency-aware pipeline - Add grammar_rules.py (ArabicGrammarGuard from Grammer_Rules.py with camel-tools) - Add grammar_service.py (lazy-loaded Gradio Client + rules pipeline) - Wire /api/grammar endpoint to real grammar service - Wire /api/analyze Step 2 to grammar (receives AraSpell output) - Update /api/health to report grammar status - Add gradio_client to requirements.txt - Add camel_data download to Dockerfile - Frontend unchanged (already supports grammar-error yellow highlighting)

feat: Professional editor UI redesign - 3-column layout (docs | editor | sidebar) - Documents panel always visible on desktop - Import/export moved to left sidebar - Removed CTA button from nav - Replaced all emojis with SVG icons - Clean toolbar with separator - Mobile: docs stay as overlay sidebar

feat: NLP-2 Grammar model integration (Bayan_Arabic_Grammar T5) - New: src/nlp/grammar/grammar_service.py (lazy T5 loading) - Dockerfile: pre-download grammar model (2.14GB safetensors) - Wire /api/analyze Step 2 to grammar service - Update /api/grammar standalone endpoint - Add _grammar_available() health check

feat: Enable ContextualCorrector (BERT MLM) for spelling - Pre-download AutoModelForMaskedLM in Dockerfile

feat: Add AraSpell spelling correction endpoint (POST /api/spelling) with lazy model loading - Extract AraSpell pipeline into src/nlp/spelling/ (araspell_rules.py + araspell_service.py) - Pre-download AraBERT encoder-decoder + checkpoint in Dockerfile - Lazy load on first request, contextual=False to save RAM - Update health check to report spelling status

fix: Pre-download summarization model during Docker build (runtime has no DNS)

fix: Load summarization model locally with float16 (HF free tier has no outbound DNS)

Switch backend from Render to HuggingFace Spaces (free, no card required)