Upload ai_patch.py

ai_patch.py

@@ -7,6 +7,7 @@ import html as html_lib
 import subprocess
 import requests
 import hashlib
+from datetime import datetime, timezone
 import ai_ext as base
 from ai_ext import app
 from fastapi import Request
@@ -19,55 +20,25 @@ try:
 except Exception:
     Image = ImageDraw = ImageFont = None
 
+_STOP = set('và của các những một được trong với cho tại sau trước khi không người vietnam hôm nay mới nhất nóng tin tức cập nhật theo từ đến là có thì này đã để'.split())
 
 def _clean(s):
-    s = html_lib.unescape(s or "")
-    # FIX: Remove malformed HTML artifacts (truncated tags without closing >)
-    s = s.replace('<a href=" src="', '').replace("<a href=' src='", '')
-    s = s.replace('<a href=" src=', '').replace("<a href=' src=", '')
-    s = re.sub(r'<a\s+[^>]*href\s*=\s*"[^"]*"\s*src\s*=\s*"[^"]*"[^>]*', '', s, flags=re.I)
-    # Remove HTML tags like <a href=...>, <img src=...>
-    s = re.sub(r'<[^>]+>', '', s)
-    s = re.sub(r"\s+", " ", s)
-    s = re.sub(r"\n{3,}", "\n\n", s)
-    return s.strip()
-
-
-def _norm(s):
-    s = s.lower()
-    s = re.sub(r"[^\wÀ-ỹ\s]", " ", s)
-    s = re.sub(r"\s+", " ", s).strip()
+    """Clean text - replace None with empty string, collapse whitespace."""
+    s = s or ''
+    s = s.strip()
+    s = re.sub(r'\s+', ' ', s)
     return s
 
-
-def _similar(a, b):
-    ta = set(_norm(a).split())
-    tb = set(_norm(b).split())
-    if not ta or not tb:
-        return False
-    return len(ta & tb) / max(1, min(len(ta), len(tb))) >= 0.72
-
-
-def _dedupe_units(units, max_units=25):
-    """Deduplicate units - only skip exact matches to ensure all bullet points are read."""
-    out, seen = [], set()
-    for u in units:
-        u = _clean(re.sub(r"^[-•*\d\.\)\s]+", "", u))
-        if len(u) < 18:
-            continue
-        nu = _norm(u)
-        # Only skip exact matches, NOT similar content (to avoid skipping valid bullet points)
-        if nu in seen:
-            continue
-        seen.add(nu)
-        out.append(u)
-        if len(out) >= max_units:
-            break
-    return out
-
-
 def _postprocess_ai_text(text, max_units=20):
     text = _clean(text)
+    # Replace malformed HTML artifacts with time label
+    now = datetime.now(timezone.utc).hour + 7  # Convert to VN time
+    now = now % 24
+    if now == 7: tl = "7h sáng"
+    elif now == 13: tl = "13h trưa"
+    else: tl = "19h tối"
+    orig_label = '<a href=" src="'
+    text = text.replace(orig_label, f"Tin tóm tắt VNEWS {tl}")
     if not text:
         return text
     # Remove malformed HTML with both href and src attributes (e.g., <a href="..." src="...">)
@@ -78,856 +49,152 @@ def _postprocess_ai_text(text, max_units=20):
     text = re.sub(r'<[^>]+>', '', text)  # Any remaining HTML tags
     text = re.sub(r'</a>', '', text, flags=re.I)
     drop_prefixes = (
-        "dưới đây", "sau đây", "bài viết", "tôi sẽ", "mình sẽ",
-        "tóm tắt bài", "tiêu đề:", "sapo:", "nội dung:", "kết luận:"
+        'Bản tin VNEWS',
+        'Bản tin AI',
+        'Bản tin tổng hợp',
+        'Tin tóm tắc VNEWS',
+        'Bản tin tin tức',
+        'Bản tin VnExpress',
+        'Tóm tắc tin',
+        'Tóm tắc nhanh',
+        'Bản tin tóm tắc',
+        'Tóm tắc hôm nay',
+        'Tóm tắc nổi bật',
+        'Bản tin nhanh',
+        'Bản tin 24h',
+        'Bản tin sáng',
+        'Bản tin chiều',
+        'Bản tin 7h',
+        'Bản tin 13h',
+        'Bản tin 19h',
+        'Tóm tắc VNEWS',
+        'Tóm tắc AI',
+        'VNEWS - Bản tin',
+        'VNEWS - Tóm tắc',
+        'VNEWS Bản tin',
+        'VNEWS Tóm tắc',
+        'Tóm tắc AI VNEWS',
+        'Bản tin tổng hợp VNEWS',
+        'Bản tin VNEWS hàng ngày',
+        'Tóm tắc nhanh hôm nay',
+        'Bản tin hôm nay',
+        'Tóm tắc 24h',
+        'Tóm tắc nhanh VNEWS',
+        'Bản tin phân tích',
+        'Tóm tắc phân tích',
     )
-    raw_lines = []
-    for line in re.split(r"\n+", text):
-        line = _clean(line)
-        if not line:
-            continue
-        low = line.lower().strip()
-        if any(low.startswith(p) and len(line) < 80 for p in drop_prefixes):
-            continue
-        raw_lines.append(line)
-    units = []
-    for line in raw_lines:
-        # KEEP FULL bullet point - don't truncate or split into segments
-        if len(line) >= 18:
-            units.append(_clean(re.sub(r"^[-•*\d\.\)\s]+", "", line)))
-    units = _dedupe_units(units, max_units=max_units)
-    if not units:
-        return text[:900]
-    title = ""
-    if raw_lines and len(raw_lines[0]) <= 90 and not raw_lines[0].startswith(("-", "•", "*")):
-        title = raw_lines[0]
-        units = [u for u in units if not _similar(u, title)]
-    body = "\n".join("• " + u for u in units[:max_units])
-    return (title + "\n\n" + body).strip() if title else body
-
-
-def _fallback_summary_from_prompt(prompt, max_units=6):
-    text = prompt or ""
-    for marker in ["Nội dung nguồn:", "Nội dung bài:", "Nội dung gốc:", "Nội dung:", "Nguồn/bối cảnh internet:"]:
-        if marker in text:
-            text = text.split(marker, 1)[1]
+    for p in drop_prefixes:
+        if text.startswith(p):
+            text = text[len(p):].lstrip(' -\n')
             break
-    text = re.sub(r"https?://\S+", "", text)
-    text = re.sub(r"\s+", " ", text).strip()
-    sentences = re.split(r"(?<=[\.\!\?])\s+(?=[A-ZÀ-Ỹ0-9])", text)
-    candidates = []
-    for s in sentences:
-        s = _clean(s)
-        if 45 <= len(s) <= 260:
-            candidates.append(s)
-    units = _dedupe_units(candidates, max_units=max_units)
-    if units:
-        return "\n".join("• " + u for u in units)
-    if text:
-        return "• " + text[:700].rsplit(" ", 1)[0]
-    return "• Không có đủ nội dung nguồn để tóm tắt."
-
-
-def _source_line(sources):
-    names = []
-    for s in (sources or [])[:5]:
-        via = s.get("via") or base._domain(s.get("url", "")) or s.get("title", "")
-        if via and via not in names:
-            names.append(via)
-    return "Nguồn tham khảo: " + ", ".join(names[:5]) if names else "Nguồn tham khảo: tổng hợp internet"
-
-
-def _make_summary_prompt(title, raw, source_hint=""):
-    return f"""Bạn là biên tập viên tóm tắt tin tức tiếng Việt.
-
-NHIỆM VỤ BẮT BUỘC:
-- Chỉ TÓM TẮT nội dung chính, KHÔNG viết lại toàn bộ bài.
-- Không lặp lại cùng một ý, cùng một câu, cùng một chi tiết.
-- Không thêm thông tin ngoài nguồn.
-- Tối đa 5 gạch đầu dòng, mỗi gạch đầu dòng 1 câu ngắn.
-- Nếu bài có số liệu/nhân vật/thời điểm quan trọng thì giữ lại.
-- Không viết phần mở bài dài, không viết văn kể lại.
-
-Tiêu đề nguồn: {title}
-Nguồn: {source_hint}
-
-Nội dung nguồn:
-{raw[:14000]}
-"""
-
-
-def _direct_news_rss(topic, limit=10):
+    text = re.sub(r'^\s*[-•]\s*', '', text)
+    units = text.split('  ')
     out = []
-    try:
-        url = "https://news.google.com/rss/search?q=" + quote_plus(topic) + "&hl=vi&gl=VN&ceid=VN:vi"
-        r = requests.get(url, headers=base.HEADERS, timeout=15)
-        r.encoding = "utf-8"
-        soup = BeautifulSoup(r.text, "xml")
-        for it in soup.find_all("item")[:limit]:
-            title = it.find("title").get_text(" ", strip=True) if it.find("title") else ""
-            link = it.find("link").get_text(strip=True) if it.find("link") else ""
-            src = it.find("source").get_text(" ", strip=True) if it.find("source") else base._domain(link)
-            if title and link:
-                out.append({"title": title, "url": link, "via": src, "excerpt": title})
-    except Exception:
-        pass
-    return out
-
-
-def _topic_source_articles(topic, limit=5):
-    """Return actual scraped article bodies for a topic. Each source becomes one Wall AI post."""
-    try:
-        _ctx, sources = base.web_context(topic, limit=limit)
-    except Exception:
-        sources = []
-    if not sources:
-        sources = _direct_news_rss(topic, limit=10)
-    out, seen = [], set()
-    for s in (sources or [])[:limit * 3]:
-        url = s.get("url") or ""
-        if not url.startswith("http") or url in seen:
-            continue
-        seen.add(url)
-        try:
-            page = base.scrape_any_url(url)
-            raw = (page.get("summary", "") + "\n" + page.get("text", "")).strip()
-            if len(raw) < 180:
-                continue
-            title = page.get("title") or s.get("title") or url
-            via = page.get("via") or s.get("via") or base._domain(url)
-            out.append({
-                "title": title,
-                "url": url,
-                "raw": raw,
-                "image": page.get("image") or "",
-                "via": via,
-                "source": {"title": title, "url": url, "excerpt": raw[:700], "via": via}
-            })
-            if len(out) >= limit:
-                break
-        except Exception:
-            continue
-    if not out:
-        for s in (sources or _direct_news_rss(topic, 6))[:limit]:
-            title = s.get("title") or topic
-            excerpt = s.get("excerpt") or s.get("description") or s.get("content") or title
-            url = s.get("url", "")
-            via = s.get("via") or base._domain(url)
-            out.append({
-                "title": title,
-                "url": url,
-                "raw": excerpt,
-                "image": base.pollinations_image_url(title),
-                "via": via,
-                "source": {"title": title, "url": url, "excerpt": excerpt[:700], "via": via}
-            })
-    return out[:limit]
-
-
-async def qwen_generate_resilient(prompt: str, image_url=None, max_tokens: int = 1200):
-    errors = []
-    token = base._hf_token()
-    try:
-        original = getattr(base, "_original_qwen_generate", None)
-        if original:
-            txt = await original(prompt, image_url=image_url, max_tokens=max_tokens)
-            if txt:
-                base.LAST_QWEN_ERROR = ""
-                return txt
-            if getattr(base, "LAST_QWEN_ERROR", ""):
-                errors.append("sdk: " + str(base.LAST_QWEN_ERROR)[:260])
-    except Exception as e:
-        errors.append(f"sdk: {type(e).__name__}: {str(e)[:260]}")
-    if token:
-        models = []
-        for m in [
-            os.getenv("QWEN_VL_MODEL", ""),
-            "Qwen/Qwen2.5-VL-7B-Instruct",
-            "Qwen/Qwen2.5-VL-3B-Instruct",
-            "Qwen/Qwen2.5-7B-Instruct",
-            "Qwen/Qwen2.5-3B-Instruct",
-            "Qwen/Qwen2.5-1.5B-Instruct",
-        ]:
-            if m and m not in models:
-                models.append(m)
-        headers = {"Authorization": "Bearer " + token, "Content-Type": "application/json"}
-        for model in models:
-            try:
-                is_vl = "VL" in model and bool(image_url)
-                user_content = ([{"type": "image_url", "image_url": {"url": image_url}}, {"type": "text", "text": prompt}] if is_vl else prompt)
-                payload = {
-                    "model": model,
-                    "messages": [
-                        {"role": "system", "content": "Bạn là biên tập viên AI tiếng Việt. Chỉ tóm tắt súc tích nội dung nguồn, không viết lại toàn bài, không lặp ý, không bịa chi tiết."},
-                        {"role": "user", "content": user_content},
-                    ],
-                    "max_tokens": min(int(max_tokens or 900), 1400),
-                    "temperature": 0.35,
-                    "top_p": 0.85,
-                }
-                r = requests.post("https://router.huggingface.co/v1/chat/completions", headers=headers, json=payload, timeout=95)
-                if r.status_code >= 300:
-                    errors.append(f"{model}: HTTP {r.status_code} {r.text[:180]}")
-                    continue
-                j = r.json()
-                txt = (j.get("choices", [{}])[0].get("message", {}).get("content") or "").strip()
-                if txt:
-                    base.LAST_QWEN_ERROR = ""
-                    return txt
-                errors.append(f"{model}: empty response")
-            except Exception as e:
-                errors.append(f"{model}: {type(e).__name__}: {str(e)[:220]}")
-    else:
-        errors.append("missing HF_TOKEN")
-    base.LAST_QWEN_ERROR = " | ".join(errors[-6:]) or "Qwen unavailable; used extractive fallback"
-    print("[qwen resilient fallback]", base.LAST_QWEN_ERROR)
-    return _fallback_summary_from_prompt(prompt, max_units=12)
-
-
-if not hasattr(base, "_original_qwen_generate"):
-    base._original_qwen_generate = base.qwen_generate
-base.qwen_generate = qwen_generate_resilient
-
-
-@app.get('/api/wall')
-def compat_wall():
-    return JSONResponse({'posts': base._load_wall_posts()[:80]})
-
-
-_PATCHED_PATHS = {
-    ('/api/topic_post', 'POST'),
-    ('/api/url_wall', 'POST'),
-    ('/api/rewrite_share', 'POST'),
-    ('/api/ai/short/{post_id}', 'POST'),
-}
-app.router.routes = [
-    r for r in app.router.routes
-    if not any(getattr(r, 'path', None) == p and m in getattr(r, 'methods', set()) for p, m in _PATCHED_PATHS)
-]
-
-
-@app.post('/api/topic_post')
-async def compat_topic_post(request: Request):
-    body = await request.json()
-    topic = base._clean_text(body.get('topic', ''))
-    if not topic:
-        return JSONResponse({'error': 'missing topic'}, status_code=400)
-    articles = _topic_source_articles(topic, limit=4)
-    if not articles:
-        return JSONResponse({'error': 'Không lấy được bài viết nguồn cho chủ đề này.'}, status_code=422)
-    new_posts = []
-    posts = base._load_ai_wall()
-    for art in articles:
-        prompt = f"""Tóm tắt RIÊNG bài viết nguồn sau để đăng Tường AI.
-
-Chủ đề lọc: {topic}
-Tiêu đề bài nguồn: {art['title']}
-Nguồn: {art['via']}
-
-Yêu cầu bắt buộc:
-- Tóm tắt nội dung trong BÀI VIẾT này, không chỉ tiêu đề.
-- Không trộn với bài khác.
-- Không viết lại toàn bộ bài.
-- Không lặp ý.
-- 4-6 gạch đầu dòng, mỗi dòng 1 câu rõ ràng.
-- Giữ số liệu/nhân vật/thời điểm quan trọng nếu có.
-
-Nội dung bài:
-{art['raw'][:14000]}"""
-        text = await base.qwen_generate(prompt, image_url=art.get('image') or None, max_tokens=1500)
-        text = _postprocess_ai_text(text, max_units=20)
-        src = [art['source']]
-        if 'Nguồn tham khảo:' not in text:
-            text += "\n\n" + _source_line(src)
-        post = base.make_post(art['title'], text, art.get('image') or base.pollinations_image_url(art['title']), art.get('url') or '', 'topic_article', sources=src)
-        
-        # Generate slides for this post so they persist after page reload
-        try:
-            page_data = _scrape_article_images(art.get('url', ''))
-            if page_data and page_data.get('paragraphs'):
-                key_points = _extract_key_points_for_slides(page_data['paragraphs'], max_points=12)
-                if key_points:
-                    relevant_imgs = page_data.get('images', [])
-                    if not relevant_imgs and page_data.get('og_img'):
-                        relevant_imgs = [page_data['og_img']]
-                    slides = []
-                    for i, point in enumerate(key_points):
-                        img = relevant_imgs[i] if i < len(relevant_imgs) else (relevant_imgs[-1] if relevant_imgs else '')
-                        slides.append({'text': point, 'image': img, 'index': i + 1})
-                    post['slides'] = slides
-        except Exception:
-            pass
-        
-        new_posts.append(post)
-    posts = new_posts + posts
-    base._save_ai_wall(posts)
-    return JSONResponse({'post': new_posts[0], 'posts': new_posts, 'count': len(new_posts)})
-
-
-@app.post('/api/url_wall')
-async def compat_url_wall(request: Request):
-    body = await request.json()
-    url = base._clean_text(body.get('url', ''))
-    if not url.startswith('http'):
-        return JSONResponse({'error': 'missing url'}, status_code=400)
-    try:
-        data = base.scrape_any_url(url)
-    except Exception as e:
-        return JSONResponse({'error': 'Không scrape được URL: ' + str(e)[:180]}, status_code=422)
-    raw = (data.get('summary', '') + '\n' + data.get('text', '')).strip()
-    if len(raw) < 120:
-        return JSONResponse({'error': 'URL không có đủ nội dung để tóm tắt'}, status_code=422)
-    prompt = _make_summary_prompt(data.get('title', ''), raw, data.get('via', '') or base._domain(url))
-    text = await base.qwen_generate(prompt, image_url=data.get('image') or None, max_tokens=1500)
-    text = _postprocess_ai_text(text, max_units=20)
-    src = [{'title': data.get('title'), 'url': url, 'excerpt': raw[:500], 'via': data.get('via') or base._domain(url)}]
-    if 'Nguồn tham khảo:' not in text:
-        text += "\n\n" + _source_line(src)
-    post = base.make_post(data.get('title') or 'Bài viết', text, data.get('image') or '', url, 'url', sources=src)
-    
-    # Generate slides so they persist after page reload
-    slides = []
-    try:
-        page_data = _scrape_article_images(url)
-        if page_data and page_data.get('paragraphs'):
-            key_points = _extract_key_points_for_slides(page_data['paragraphs'], max_points=12)
-            if key_points:
-                relevant_imgs = page_data.get('images', [])
-                if not relevant_imgs and page_data.get('og_img'):
-                    relevant_imgs = [page_data['og_img']]
-                for i, point in enumerate(key_points):
-                    img = relevant_imgs[i] if i < len(relevant_imgs) else (relevant_imgs[-1] if relevant_imgs else '')
-                    slides.append({'text': point, 'image': img, 'index': i + 1})
-    except Exception:
-        pass
-    post['slides'] = slides
-    
-    posts = base._load_ai_wall(); posts.insert(0, post); base._save_ai_wall(posts)
-    return JSONResponse({'post': post, 'slides': slides})
-
-
-def _is_relevant_image(img_url, title, text):
-    """Check if an image is relevant to the article content."""
-    if not img_url:
-        return False
-    skip_patterns = ['pixel', 'analytics', 'tracking', '1x1.gif', 'spacer.gif',
-                     'logo', 'icon', 'avatar', 'emoji', 'smiley', 'sprite',
-                     'advertisement', 'ad-banner', 'sponsored', 'banner-ads']
-    img_lower = img_url.lower()
-    for p in skip_patterns:
-        if p in img_lower:
-            return False
-    if not any(img_lower.endswith(ext) for ext in ['.jpg', '.jpeg', '.png', '.webp', '.gif']):
-        return False
-    return True
-
-
-def _filter_relevant_images(images, title, text, max_images=8):
-    """Filter and rank images by relevance to article content."""
-    if not images:
-        return []
     seen = set()
-    relevant = []
-    for img in images:
-        if img in seen:
+    for u in units:
+        u = _clean(u)
+        if not u:
             continue
-        seen.add(img)
-        if _is_relevant_image(img, title, text):
-            relevant.append(img)
-    return relevant[:max_images]
-
-
-def _extract_key_points_for_slides(paragraphs, max_points=12):
-    """Extract key points from paragraphs for slides - extracts ALL sentences, not just first one."""
-    points = []
-    for p in paragraphs:
-        if len(points) >= max_points:
-            break
-        p = _clean(p)
-        if not p:
+        w = tuple(re.findall(r"[\w']+", u.lower()))
+        if w in seen:
             continue
-        # Split paragraph into sentences using Vietnamese + English punctuation - GET ALL SENTENCES
-        sentences = re.split(r'(?<=[.!?])\s+(?=[A-ZÀ-Ỹ0-9])', p)
-        sentences = [s.strip() for s in sentences if s.strip()]
-        
-        for sentence in sentences:
-            if len(points) >= max_points:
-                break
-            sentence = _clean(sentence)
-            if len(sentence) < 30:
-                continue
-            if any(sentence[:60] in existing for existing in points):
-                continue
-            if not sentence.endswith(('.', '!', '?')):
-                sentence = sentence + '.'
-            points.append(sentence)
-    return points
-
+        seen.add(w)
+        out.append(u)
+        if len(out) >= max_units:
+            break
+    return out
 
 def _scrape_article_images(url):
-    """Scrape article page and return only relevant images."""
+    sents = []; imgs = []; og = ''
     try:
-        headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
-                   "Accept-Language": "vi-VN,vi;q=0.9,en;q=0.8"}
-        r = requests.get(url, headers=headers, timeout=15, allow_redirects=True)
-        r.encoding = 'utf-8'
-        soup = BeautifulSoup(r.text, 'lxml')
-        for tag in soup.find_all(['script', 'style', 'nav', 'footer', 'aside', 'form']):
-            tag.decompose()
-        h1 = soup.find('h1')
-        ogt = soup.find('meta', property='og:title')
-        title = (h1.get_text(strip=True) if h1 else '') or (ogt.get('content', '') if ogt else '')
-        ogi = soup.find('meta', property='og:image')
-        og_img = ogi.get('content', '') if ogi else ''
-        if og_img and og_img.startswith('//'):
-            og_img = 'https:' + og_img
-        block = None
-        for sel in ['article', '.singular-content', '.detail-content', '.fck_detail', '.content-detail', '.knc-content', 'main', '.cms-body', '.article__body']:
-            el = soup.select_one(sel)
-            if el and len(el.find_all('p')) >= 2:
-                block = el
-                break
-        if not block:
-            block = soup.body or soup
-        paragraphs = []
-        all_images = []
-        seen_imgs = set()
-        if og_img and og_img not in seen_imgs:
-            all_images.append(og_img)
-            seen_imgs.add(og_img)
-        for el in block.find_all(['p', 'h2', 'h3', 'figure', 'img'], recursive=True):
-            if el.name == 'p':
-                t = _clean(el.get_text(strip=True))
-                if t and len(t) > 40:
-                    paragraphs.append(t)
-            elif el.name in ('figure', 'img'):
-                im = el if el.name == 'img' else el.find('img')
-                if im:
-                    src = im.get('data-src') or im.get('src') or im.get('data-original') or ''
-                    if src and 'base64' not in src:
-                        if src.startswith('//'):
-                            src = 'https:' + src
-                        if src not in seen_imgs:
-                            all_images.append(src)
-                            seen_imgs.add(src)
-        relevant_images = _filter_relevant_images(all_images, title, ' '.join(paragraphs[:5]))
-        return {'title': _clean(title), 'paragraphs': paragraphs, 'images': relevant_images, 'og_img': og_img}
-    except Exception:
-        return None
-
-
-@app.post('/api/rewrite_share')
-async def compat_rewrite_share(request: Request):
-    body = await request.json()
-    url = base._clean_text(body.get('url', ''))
-    if not url.startswith('http'):
-        return JSONResponse({'error': 'missing url'}, status_code=400)
-    try:
-        data = base.scrape_any_url(url)
+        if url:
+            r = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'}, timeout=8)
+            r.encoding = 'utf-8'
+            soup = BeautifulSoup(r.text, 'lxml')
+            og = soup.find('meta', property='og:image')
+            og = og.get('content', '') if og else ''
+            for p in soup.find_all('p'):
+                t = _clean(p.get_text())
+                if len(t) > 30:
+                    sents.append(t)
+            for im in soup.find_all('img'):
+                src = im.get('src', '')
+                if src:
+                    imgs.append(src)
+        return {'paragraphs': sents, 'images': imgs, 'og_img': og}
     except Exception as e:
-        return JSONResponse({'error': 'Không đọc được bài viết: ' + str(e)[:180]}, status_code=422)
-    raw = (data.get('summary', '') + '\n' + data.get('text', '')).strip()
-    if len(raw) < 120:
-        return JSONResponse({'error': 'Bài viết không đủ nội dung để tóm tắt'}, status_code=422)
-    prompt = _make_summary_prompt(data.get('title', ''), raw, data.get('via', '') or base._domain(url))
-    text = await base.qwen_generate(prompt, image_url=data.get('image') or None, max_tokens=1500)
-    text = _postprocess_ai_text(text, max_units=20)
-    src = [{'title': data.get('title'), 'url': url, 'excerpt': raw[:500], 'via': data.get('via') or base._domain(url)}]
-    if 'Nguồn tham khảo:' not in text:
-        text += "\n\n" + _source_line(src)
-    post = base.make_post(data.get('title') or 'Bài viết', text, data.get('image') or '', url, 'summary', sources=src)
-
-    # Generate slides with relevant images only
-    slides = []
-    page_data = _scrape_article_images(url)
-    if page_data and page_data.get('paragraphs'):
-        key_points = _extract_key_points_for_slides(page_data['paragraphs'], max_points=12)
-        if key_points:
-            relevant_imgs = page_data.get('images', [])
-            if not relevant_imgs and page_data.get('og_img'):
-                relevant_imgs = [page_data['og_img']]
-            for i, point in enumerate(key_points):
-                img = relevant_imgs[i] if i < len(relevant_imgs) else (relevant_imgs[-1] if relevant_imgs else '')
-                slides.append({'text': point, 'image': img, 'index': i + 1})
-
-    # FIX: Save slides into post so they persist after page reload
-    post['slides'] = slides
-    posts = base._load_ai_wall(); posts.insert(0, post); base._save_ai_wall(posts)
+        return {'paragraphs': [], 'images': [], 'og_img': ''}
 
-    return JSONResponse({'post': post, 'slides': slides})
-
-
-def _emotion_script(text, emotion):
-    """Prepend emotion-appropriate prefix to text based on emotion type.
-    
-    NOTE: Prefix is NOT added to avoid cluttering Short AI speech.
-    The emotion is still used for voice selection but content is read cleanly.
-    """
-    text = _clean(text)
-    # REMOVED: No prefix added to keep content clean and natural
-    return text
-
-
-def _tts_script_smart(post, emotion):
-    raw = base._short_script(post) if hasattr(base, '_short_script') else _clean(post.get('text', '') or post.get('title', ''))
-    raw = re.sub(r"^[•\-\*]\s*", "", raw, flags=re.M)
-    raw = re.sub(r"\s*\n\s*", ". ", raw)
-    raw = re.sub(r"([\.\!\?])\s*", r"\1\n", raw)
-    raw = re.sub(r"\n{2,}", "\n", raw).strip()
-    # REMOVED: _emotion_script call - read content cleanly without prefix
-    # INCREASED to 3000 to read full content of all bullet points
-    if len(raw) > 3000:
-        raw = raw[:3000]
-        cut = max(raw.rfind("."), raw.rfind("!"), raw.rfind("?"))
-        if cut > 700:
-            raw = raw[:cut + 1]
-    return raw
-
-
-def _split_subtitle_sentences(script):
-    parts = []
-    for line in script.splitlines():
-        line = _clean(line)
-        if not line:
+def _extract_key_points_for_slides(paragraphs, max_points=8):
+    pts = []
+    for p in paragraphs:
+        if len(p) < 40:
             continue
-        for s in re.split(r"(?<=[\.\!\?])\s+", line):
-            s = _clean(s)
-            if 8 <= len(s) <= 140:
-                parts.append(s)
-    return parts[:12]
-
-
-def _srt_time(sec):
-    ms = int((sec - int(sec)) * 1000)
-    sec = int(sec)
-    h = sec // 3600
-    m = (sec % 3600) // 60
-    s = sec % 60
-    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
-
-
-def _write_srt(script, path, total_duration=30):
-    subs = _split_subtitle_sentences(script)
-    if not subs:
-        subs = [script[:120]]
-    dur = max(2.2, min(5.0, total_duration / max(1, len(subs))))
-    cur = 0.3
-    with open(path, 'w', encoding='utf-8') as f:
-        for i, s in enumerate(subs, 1):
-            start = cur
-            end = cur + dur
-            cur = end + 0.15
-            f.write(f"{i}\n{_srt_time(start)} --> {_srt_time(end)}\n{s}\n\n")
-
-
-def _wrap_text_px(draw, text, font, max_width, max_lines):
-    words = _clean(text).split()
-    lines, cur = [], ""
-    for w in words:
-        test = (cur + " " + w).strip()
-        try:
-            width = draw.textbbox((0, 0), test, font=font)[2]
-        except Exception:
-            width = len(test) * 20
-        if width <= max_width:
-            cur = test
-        else:
-            if cur:
-                lines.append(cur)
-            cur = w
-        if len(lines) >= max_lines:
-            break
-    if cur and len(lines) < max_lines:
-        lines.append(cur)
-    return lines
-
-
-def _make_short_frame_full(post, img_path, out_path):
-    if Image is None:
-        return base._make_short_frame(post, img_path, out_path)
-    W, H = 1080, 1920
-    bg = Image.new("RGB", (W, H), (14, 14, 14))
-    try:
-        im = Image.open(img_path).convert("RGB")
-        target = (1080, 760)
-        im_ratio = im.width / im.height
-        target_ratio = target[0] / target[1]
-        if im_ratio > target_ratio:
-            new_h = target[1]
-            new_w = int(new_h * im_ratio)
-        else:
-            new_w = target[0]
-            new_h = int(new_w / im_ratio)
-        im = im.resize((new_w, new_h))
-        left = (new_w - target[0]) // 2
-        top = (new_h - target[1]) // 2
-        im = im.crop((left, top, left + target[0], top + target[1]))
-        bg.paste(im, (0, 0))
-    except Exception:
-        pass
-    draw = ImageDraw.Draw(bg)
-    try:
-        font_title = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 54)
-        font_body = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 38)
-        font_label = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 30)
-    except Exception:
-        font_title = font_body = font_label = None
-    draw.rectangle((0, 720, W, H), fill=(14, 14, 14))
-    margin = 48
-    maxw = W - margin * 2
-    brand = 'Tin tóm tắt VNEWS 7h sáng, 13h trưa, 19h tối' if post.get('kind') == 'auto_scheduled' else 'VNEWS · Tường AI'
-    draw.text((margin, 770), brand, fill=(92, 184, 122), font=font_label)
-    y = 830
-    for ln in _wrap_text_px(draw, post.get("title", ""), font_title, maxw, 4):
-        draw.text((margin, y), ln, fill=(255, 255, 255), font=font_title)
-        y += 66
-    y += 18
-    text = post.get("text", "")
-    text = re.sub(r"Nguồn tham khảo:.*", "", text, flags=re.S).strip()
-    body_lines = _wrap_text_px(draw, text, font_body, maxw, 14)
-    for ln in body_lines:
-        draw.text((margin, y), ln, fill=(220, 220, 220), font=font_body)
-        y += 50
-        if y > 1640:
+        s = _clean(p)
+        if s and s not in pts:
+            pts.append(s)
+        if len(pts) >= max_points:
             break
-    bg.save(out_path, quality=92)
-
-
-
-
-def _summary_segments_from_post(post, max_segments=25):
-    raw = _clean(post.get('text') or post.get('title') or '')
-    raw = re.sub(r'^Bản tin AI viết lại:\s*', '', raw, flags=re.I)
-    raw = re.sub(r'Nguồn tham khảo:.*$', '', raw, flags=re.I|re.S).strip()
-    lines=[]
-    for ln in raw.splitlines():
-        ln=_clean(re.sub(r'^[•\-\*\d\.\)\s]+','',ln))
-        if not ln: continue
-        low=ln.lower()
-        if low.startswith(('điểm chính','tiêu đề','sapo','nguồn tham khảo')): continue
-        if len(ln)>=18: lines.append(ln)
-    if len(lines)<3:
-        lines=[]
-        for s in re.split(r'(?<=[\.\!\?])\s+', raw):
-            s=_clean(s)
-            if len(s)>=25: lines.append(s)
-    segs=_dedupe_units(lines, max_units=max_segments)
-    return segs[:max_segments] if segs else [post.get('title','Bản tin VNEWS')]
-
+    return pts
+
+def _make_summary_prompt(title, body, source):
+    topic = source or "báo chính"
+    return f"""Tóm tắc tin tức: {title}
+Nguồn: {topic}
+---
+{body[:1200] if body else ''}
+---
+Hãy viết tóm tắc ngắn gọn, súc tích bằng tiếng Việt. Dùng bullet point (•) để liệt kê điểm chính. Không dùng định dạng HTML. Không lặp lại tiêu đề. Không viết "Bản tin VNEWS" hay tiêu đề phụ."""
+
+def _source_line(sources=None):
+    if not sources:
+        return "Nguồn tham khảo: VNEWS"
+    src = sources[0] if sources else {}
+    title = src.get('title', '')
+    url = src.get('url', '')
+    via = src.get('via', '')
+    if url and via:
+        return f"Nguồn: [{via}]({url})"
+    if url:
+        return f"Nguồn: {url}"
+    if via:
+        return f"Nguồn: {via}"
+    return "Nguồn tham khảo: VNEWS"
+
+def _summary_segments_from_post(post, max_segments=15):
+    text = post.get('text', '')
+    segs = []
+    if text:
+        for line in text.split('\n'):
+            line = _clean(line)
+            if line:
+                segs.append(line)
+    return segs[:max_segments]
 
 def _make_scene_frame(post, segment, idx, total, img_path, out_path, emotion='neutral'):
-    if Image is None:
-        return _make_short_frame_full(post, img_path, out_path)
-    W,H=1080,1920
-    bg=Image.new('RGB',(W,H),(10,10,10))
-    try:
-        im=Image.open(img_path).convert('RGB')
-        ratio=im.width/max(1,im.height); target=W/H
-        if ratio>target:
-            nh=H; nw=int(nh*ratio)
-        else:
-            nw=W; nh=int(nw/ratio)
-        cover=im.resize((nw,nh)); left=(nw-W)//2; top=(nh-H)//2
-        cover=cover.crop((left,top,left+W,top+H))
-        bg.paste(cover,(0,0))
-        bg=Image.blend(bg, Image.new('RGB',(W,H),(0,0,0)), 0.50)
-        hero_h=720; target=W/hero_h
-        if ratio>target:
-            nh=hero_h; nw=int(nh*ratio)
-        else:
-            nw=W; nh=int(nw/ratio)
-        hero=im.resize((nw,nh)); left=(nw-W)//2; top=(nh-hero_h)//2
-        hero=hero.crop((left,top,left+W,top+hero_h))
-        bg.paste(hero,(0,0))
-    except Exception:
-        pass
-    draw=ImageDraw.Draw(bg)
-    try:
-        font_brand=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',34)
-        font_small=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf',28)
-        font_seg=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',58)
-        font_title=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf',34)
-    except Exception:
-        font_brand=font_small=font_seg=font_title=None
-    draw.rectangle((0,680,W,H), fill=(12,12,12))
-    dot_x=48; dot_y=742
-    for i in range(total):
-        fill=(92,184,122) if i==idx else (70,70,70)
-        draw.rounded_rectangle((dot_x+i*38,dot_y,dot_x+i*38+24,dot_y+10), radius=5, fill=fill)
-    # Determine brand text: auto_scheduled uses special header, manual uses default
-    brand = 'Tin tóm tắt VNEWS 7h sáng, 13h trưa, 19h tối' if post.get('kind') == 'auto_scheduled' else 'VNEWS AI SHORT'
-    draw.text((48,780),brand,fill=(110,231,143),font=font_brand)
-    draw.rounded_rectangle((48,834,260,880), radius=20, fill=(28,70,45))
-    draw.text((66,842),f'Đoạn {idx+1}/{total}',fill=(235,235,235),font=font_small)
-    y=940; maxw=W-96
-    # INCREASED from 12 to 18 for full content display - each key point can span multiple lines
-    for ln in _wrap_text_px(draw, segment, font_seg, maxw, 18):
-        draw.text((48,y),ln,fill=(255,255,255),font=font_seg)
-        y+=74
-        if y>1500: break
-    y2=1640
-    draw.line((48,y2-22,W-48,y2-22),fill=(70,70,70),width=2)
-    for ln in _wrap_text_px(draw, post.get('title',''), font_title, maxw, 3):
-        draw.text((48,y2),ln,fill=(220,220,220),font=font_title)
-        y2+=46
-    bg.save(out_path, quality=92)
-
-
-def _estimate_audio_duration(path, fallback=15.0):
-    """Estimate audio duration with 15s minimum per segment for complete bullet reading."""
-    try:
-        pr=subprocess.run(['ffprobe','-v','error','-show_entries','format=duration','-of','default=noprint_wrappers=1:no_key=1',path], stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=20)
-        return max(12.0, float((pr.stdout or b'').decode().strip() or fallback))
-    except Exception:
-        return fallback
-
-
-@app.post('/api/ai/short/{post_id}')
-async def patched_ai_short(post_id: str, request: Request):
-    try:
-        body = await request.json()
-    except Exception:
-        body = {}
-    voice = str(body.get('voice', 'nu')).strip().lower()
-    emotion = str(body.get('emotion', 'neutral')).strip().lower()
-    speed = float(body.get('speed', 1.0) or 1.0)
-    speed = max(0.85, min(1.35, speed))
-
-    posts = base._load_ai_wall()
-    post = next((p for p in posts if str(p.get('id')) == str(post_id)), None)
-    if not post:
-        return JSONResponse({'error': 'post not found'}, status_code=404)
-
-    segments = _summary_segments_from_post(post, max_segments=25)
-    seg_hash = hashlib.md5(('|'.join(segments)+voice+emotion+str(speed)).encode('utf-8')).hexdigest()[:8]
-    os.makedirs(base.SHORTS_DIR, exist_ok=True)
-    suffix = f"_{voice}_{emotion}_{str(speed).replace('.', 'p')}_{seg_hash}_scenes_nosub"
-    out_mp4 = os.path.join(base.SHORTS_DIR, base._safe_name(post_id + suffix) + '.mp4')
-    if os.path.exists(out_mp4):
-        post['video'] = '/api/ai/short-file/' + post_id + suffix
-        post['short_voice'] = voice
-        post['short_emotion'] = emotion
-        post['short_speed'] = speed
-        post['short_segments'] = segments
-        post['short_subtitles'] = False
-        base._save_ai_wall(posts)
-        return JSONResponse({'video': post['video'], 'voice': voice, 'emotion': emotion, 'speed': speed, 'subtitles': False, 'segments': segments})
-    if base.gTTS is None:
-        return JSONResponse({'error': 'gTTS chưa sẵn sàng'}, status_code=503)
-
-    work = os.path.join(base.SHORTS_DIR, base._safe_name(post_id + suffix))
-    os.makedirs(work, exist_ok=True)
-    img = os.path.join(work, 'image.jpg')
+    from PIL import Image, ImageDraw, ImageFont
+    sz = (1080, 1920)
+    bg = Image.new('RGB', sz, (15, 23, 38))
+    d = ImageDraw.Draw(bg)
     try:
-        base._download_image(post.get('img'), post.get('title', 'AI news'), img)
-        edge_voice = {
-            # Vietnamese
-            'vi-vn-hoaimyneural': 'vi-VN-HoaiMyNeural',
-            'vi-vn-namminhneural': 'vi-VN-NamMinhNeural',
-            'hoaimy': 'vi-VN-HoaiMyNeural',
-            'namminh': 'vi-VN-NamMinhNeural',
-            'nam': 'vi-VN-NamMinhNeural',
-            'male': 'vi-VN-NamMinhNeural',
-            'nu': 'vi-VN-HoaiMyNeural',
-            'female': 'vi-VN-HoaiMyNeural',
-            'mien-nam': 'vi-VN-HoaiMyNeural',
-            # English - Multilingual
-            'en-us-andrewmultilingualneural': 'en-US-AndrewMultilingualNeural',
-            'en-au-williammultilingualneural': 'en-AU-WilliamMultilingualNeural',
-            'andrew': 'en-US-AndrewMultilingualNeural',
-            'en_andrew': 'en-US-AndrewMultilingualNeural',
-            'jenny': 'en-US-AndrewMultilingualNeural',
-            'en_jenny': 'en-US-AndrewMultilingualNeural',
-            # Portuguese - Multilingual (ONLY Thalita)
-            'pt-br-thalitamultilingualneural': 'pt-BR-ThalitaMultilingualNeural',
-            'thalita': 'pt-BR-ThalitaMultilingualNeural',
-            'pt_thalita': 'pt-BR-ThalitaMultilingualNeural',
-            'pt_br_thalita': 'pt-BR-ThalitaMultilingualNeural',
-            'pt': 'pt-BR-ThalitaMultilingualNeural',
-            'pt_francisco': 'pt-BR-ThalitaMultilingualNeural',
-            # French - Multilingual
-            'fr-fr-viviennemultilingualneural': 'fr-FR-VivienneMultilingualNeural',
-            'fr-fr-remymultilingualneural': 'fr-FR-RemyMultilingualNeural',
-            'denise': 'fr-FR-VivienneMultilingualNeural',
-            'fr': 'fr-FR-VivienneMultilingualNeural',
-            'fr_denise': 'fr-FR-VivienneMultilingualNeural',
-            # German - Multilingual
-            'de-de-seraphinamultilingualneural': 'de-DE-SeraphinaMultilingualNeural',
-            'de-de-florianmultilingualneural': 'de-DE-FlorianMultilingualNeural',
-            'katja': 'de-DE-SeraphinaMultilingualNeural',
-            'de': 'de-DE-SeraphinaMultilingualNeural',
-            'de_katja': 'de-DE-SeraphinaMultilingualNeural',
-            # Korean - Multilingual (Hyunsu, NOT SunHee)
-            'ko-kr-hyusumultilingualneural': 'ko-KR-HyunsuMultilingualNeural',
-            'ko-kr-hyunsuneural': 'ko-KR-HyunsuMultilingualNeural',
-            'sunhee': 'ko-KR-HyunsuMultilingualNeural',
-            'ko': 'ko-KR-HyunsuMultilingualNeural',
-            'ko_sunhee': 'ko-KR-HyunsuMultilingualNeural',
-            # Italian - Multilingual
-            'it-it-giuseppemultilingualneural': 'it-IT-GiuseppeMultilingualNeural',
-            # Spanish (keep for backward compat)
-            'ela': 'en-US-AndrewMultilingualNeural',
-            'es_ela': 'en-US-AndrewMultilingualNeural',
-            'es': 'en-US-AndrewMultilingualNeural',
-            'es_carlos': 'en-US-AndrewMultilingualNeural',
-            # Japanese (keep for backward compat)
-            'nanami': 'en-US-AndrewMultilingualNeural',
-            'ja': 'en-US-AndrewMultilingualNeural',
-            'ja_nanami': 'en-US-AndrewMultilingualNeural',
-            # Chinese (keep for backward compat)
-            'xiaochen': 'en-US-AndrewMultilingualNeural',
-            'zh': 'en-US-AndrewMultilingualNeural',
-            'zh_xiaochen': 'en-US-AndrewMultilingualNeural',
-        }.get(voice, 'vi-VN-HoaiMyNeural')
-        part_files=[]
-        for idx, seg in enumerate(segments):
-            frame=os.path.join(work,f'frame_{idx:02d}.jpg')
-            aud=os.path.join(work,f'voice_{idx:02d}.mp3')
-            aud_fast=os.path.join(work,f'voice_{idx:02d}_fast.mp3')
-            part=os.path.join(work,f'part_{idx:02d}.mp4')
-            _make_scene_frame(post, seg, idx, len(segments), img, frame, emotion=emotion)
-            spoken=_emotion_script(seg, emotion)
-            try:
-                subprocess.run(['python','-m','edge_tts','--voice',edge_voice,'--text',spoken,'--write-media',aud], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=120)
-            except Exception:
-                tld='com.vn' if voice in ('nu','female','mien-nam','hoaimy') else 'com'
-                try:
-                    base.gTTS(spoken, lang='vi', tld=tld, slow=False).save(aud)
-                except TypeError:
-                    base.gTTS(spoken, lang='vi', slow=False).save(aud)
-            subprocess.run(['ffmpeg','-y','-i',aud,'-filter:a',f'atempo={speed}','-vn',aud_fast], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=90)
-            dur=_estimate_audio_duration(aud_fast, fallback=15.0)+0.35
-            subprocess.run(['ffmpeg','-y','-loop','1','-t',str(dur),'-i',frame,'-i',aud_fast,'-shortest','-c:v','libx264','-tune','stillimage','-pix_fmt','yuv420p','-c:a','aac','-b:a','128k',part], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=150)
-            part_files.append(part)
-        concat=os.path.join(work,'concat.txt')
-        with open(concat,'w',encoding='utf-8') as f:
-            for p in part_files:
-                f.write("file '" + p.replace("'", "'\\''") + "'\n")
-        subprocess.run(['ffmpeg','-y','-f','concat','-safe','0','-i',concat,'-c','copy',out_mp4], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=180)
-        post['video'] = '/api/ai/short-file/' + post_id + suffix
-        post['short_voice'] = voice
-        post['short_emotion'] = emotion
-        post['short_speed'] = speed
-        post['short_segments'] = segments
-        post['short_subtitles'] = False
-        base._save_ai_wall(posts)
-        return JSONResponse({'video': post['video'], 'voice': voice, 'emotion': emotion, 'speed': speed, 'subtitles': False, 'segments': segments})
-    except Exception as e:
-        return JSONResponse({'error': 'Không tạo được shorts: ' + str(e)[:220]}, status_code=500)
-
-
-@app.get('/api/ai/short-file/{file_id}')
-def patched_ai_short_file(file_id: str):
-    path = os.path.join(base.SHORTS_DIR, base._safe_name(file_id) + '.mp4')
-    if not os.path.exists(path):
-        return JSONResponse({'error': 'not found'}, status_code=404)
-    return FileResponse(path, media_type='video/mp4', filename=f'vnews-ai-{file_id}.mp4')
-
-
-@app.get('/api/ai_shorts')
-def api_ai_shorts():
-    posts = [p for p in base._load_ai_wall() if p.get('video')]
-    return JSONResponse({'posts': posts[:80]})
-
+        f = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 48)
+        fb = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 56)
+    except:
+        f = ImageFont.load_default()
+        fb = f
+    # Draw title bar
+    d.rectangle([0, 0, sz[0], 120], fill=(25, 118, 210))
+    title = post.get('title', '')[:60]
+    d.text((sz[0]//2, 60), title, fill='white', font=fb, anchor='mm')
+    # Draw segment text
+    y = 180
+    for line in segment[:120]:
+        y += 56
+        if y > sz[1] - 100:
+            break
+        d.text((80, y), line, fill='white', font=f)
+    bg.save(out_path, quality=85)
 
-app.router.routes = [r for r in app.router.routes if not (getattr(r, 'path', None) == '/' and 'GET' in getattr(r, 'methods', set()))]
+@app.get('/health')
+async def health():
+    return JSONResponse({'status': 'ok'})