import json, os, logging, base64, time, hashlib, re, http.client, ssl, concurrent.futures
from urllib.parse import urlparse
from searx import network
try:
    from searx.network import get_network
except ImportError:
    get_network = None
from flask import Response, request, abort, jsonify
from searx.plugins import Plugin, PluginInfo
from searx.result_types import Answer
from searx import settings
from flask_babel import gettext
from markupsafe import Markup

logger = logging.getLogger(__name__)

TOKEN_EXPIRY_SEC = 3600
STREAM_CHUNK_SIZE = 512
STREAM_TIMEOUT_SEC = 60

def _get_streaming_connection(url: str, verify_ssl: bool = True):
    parsed = urlparse(url)
    host = parsed.hostname
    port = parsed.port or (443 if parsed.scheme == 'https' else 80)
    path = parsed.path + ('?' + parsed.query if parsed.query else '')

    if verify_ssl and get_network is not None:
        try:
            net = get_network()
            verify_ssl = getattr(net, 'verify', True)
        except Exception:
            pass

    if parsed.scheme == 'https':
        if not verify_ssl:
            ctx = ssl._create_unverified_context()
        else:
            try:
                import certifi
                ctx = ssl.create_default_context(cafile=certifi.where())
            except ImportError:
                ctx = ssl.create_default_context()
        conn = http.client.HTTPSConnection(host, port, timeout=STREAM_TIMEOUT_SEC, context=ctx)
    else:
        conn = http.client.HTTPConnection(host, port, timeout=STREAM_TIMEOUT_SEC)

    return conn, path



PLUGIN_NAME = "AI Answers"
DEFAULT_TABS = "general,science,it,news"

# UI assets
_ASSETS = os.path.join(os.path.dirname(__file__), 'assets')
INTERACTIVE_CSS = open(os.path.join(_ASSETS, 'ui.css')).read()
INTERACTIVE_HTML = open(os.path.join(_ASSETS, 'ui.html')).read()
_js_raw = open(os.path.join(_ASSETS, 'ui.js')).read()
FRONTEND_JS_TEMPLATE = _js_raw.split('// === CITATION_HELPER_JS ===')[0].replace('// === FRONTEND_JS_TEMPLATE ===', '').strip()
CITATION_HELPER_JS = _js_raw.split('// === CITATION_HELPER_JS ===')[1].split('// === INTERACTIVE_JS ===')[0].strip()
INTERACTIVE_JS = _js_raw.split('// === INTERACTIVE_JS ===')[1].strip()

import typing
if typing.TYPE_CHECKING:
    from searx.search import SearchWithPlugins
    from searx.extended_types import SXNG_Request
    from . import PluginCfg

class SXNGPlugin(Plugin):
    id = "ai_answers"

    def __init__(self, plg_cfg: "PluginCfg"):
        super().__init__(plg_cfg)
        self.info = PluginInfo(
            id=self.id,
            name=gettext(f"{PLUGIN_NAME} Plugin"),
            description=gettext("Live AI search answers using LLM providers."),
            preference_section="general",
        )
        self._load_config()



    def _load_config(self):
        self.interactive = os.getenv('LLM_INTERACTIVE', 'true').lower().strip() in ('true', '1', 'yes', 'on')
        self.question_mark_required = os.getenv('LLM_QUESTION_MARK_REQUIRED', 'false').lower().strip() in ('true', '1', 'yes', 'on')

        raw_url = os.getenv('LLM_URL', 'http://ollama:11434/v1/chat/completions').strip()
        if not raw_url.startswith(('http://', 'https://')):
            raw_url = f"http://{raw_url}"
        self.endpoint_url = raw_url

        self.api_key = 'ollama'
        self.model = os.getenv('LLM_MODEL', 'qwen3.5:9b').strip()

        try:
            self.max_tokens = max(1, int(os.getenv('LLM_MAX_TOKENS', 200)))
        except ValueError:
            logger.warning(f"{PLUGIN_NAME}: Invalid LLM_MAX_TOKENS value. Enforcing default (200).")
            self.max_tokens = 200
        try:
            self.temperature = float(os.getenv('LLM_TEMPERATURE', 0.2))
        except ValueError:
            logger.warning(f"{PLUGIN_NAME}: Invalid LLM_TEMPERATURE value. Enforcing default (0.2).")
            self.temperature = 0.2
        try:
            self.context_deep_count = max(0, int(os.getenv('LLM_CONTEXT_DEEP_COUNT', 5)))
        except ValueError:
            logger.warning(f"{PLUGIN_NAME}: Invalid LLM_CONTEXT_DEEP_COUNT value. Enforcing default (5).")
            self.context_deep_count = 5
        try:
            self.context_shallow_count = max(0, int(os.getenv('LLM_CONTEXT_SHALLOW_COUNT', 15)))
        except ValueError:
            logger.warning(f"{PLUGIN_NAME}: Invalid LLM_CONTEXT_SHALLOW_COUNT value. Enforcing default (15).")
            self.context_shallow_count = 15

        self.allowed_tabs = set(t.strip() for t in os.getenv('LLM_TABS', DEFAULT_TABS).split(','))

        server_secret = settings.get('server', {}).get('secret_key', '')
        self.secret = hashlib.sha256(f"ai_answers_{server_secret}".encode()).hexdigest()

        self.system_prompt = os.getenv('LLM_SYSTEM_PROMPT', '').strip()

    def _parse_aux_results(self, raw_results, raw_infoboxes, raw_answers):
        results = []
        limit = self.context_deep_count + self.context_shallow_count
        for r in raw_results[:limit]:
            # MainResult (attribute access) and LegacyResult (dict access)
            if hasattr(r, 'title'):
                results.append({
                    'title': getattr(r, 'title', ''),
                    'content': getattr(r, 'content', ''),
                    'url': getattr(r, 'url', ''),
                    'publishedDate': getattr(r, 'publishedDate', '')
                })
            else:
                # Legacy dictionary-style access
                results.append({
                    'title': r.get('title', ''),
                    'content': r.get('content', ''),
                    'url': r.get('url', ''),
                    'publishedDate': r.get('publishedDate', '')
                })

        # SearXNG already merges infoboxes by ID, use first
        infoboxes = []
        for ib in raw_infoboxes[:1]:
            infoboxes.append({
                'name': ib.get('infobox', '') or ib.get('title', ''),
                'content': str(ib.get('content') or '')[:2000],
                'attributes': ib.get('attributes', [])
            })
            
        answers = []
        for a in list(raw_answers)[:2]:
            ans_text = ""
            if hasattr(a, 'answer') and isinstance(getattr(a, 'answer', None), str):
                ans_text = a.answer
            elif isinstance(a, dict) and a.get('answer'):
                ans_text = str(a['answer'])
            if ans_text and 'id="sxng-stream-box"' not in ans_text and not ans_text.strip().startswith('<'):
                answers.append(ans_text)
                   
        return results, infoboxes, answers

    def init(self, app):
        @app.route('/ai-auxiliary-search', methods=['POST'])
        def ai_auxiliary_search():
            if not self.api_key:
                abort(403)
            
            data = request.json or {}
            token = data.get('tk', '')
            
            # Token access control
            try:
                ts, sig = token.rsplit('.', 1)
                expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
                if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
                    abort(403)
            except (ValueError, KeyError, AttributeError):
                abort(403)

            aux_ip = request.headers.get('X-Real-IP') or request.headers.get('X-Forwarded-For')
            if aux_ip:
                logger.debug(f"{PLUGIN_NAME}: /ai-auxiliary-search from proxied IP {aux_ip}")

            query = data.get('query', '').strip()
            lang = data.get('lang', 'all')
            categories = data.get('categories', 'general')
            offset = data.get('offset', 0)
            if not query:
                return jsonify({'results': []})
            
            try:
                from searx.search import SearchWithPlugins
                from searx.search.models import SearchQuery
                from searx.query import RawTextQuery
                from searx.webadapter import get_engineref_from_category_list
                
                preferences = getattr(request, 'preferences', None)
                disabled_engines = preferences.engines.get_disabled() if preferences else []
                rtq = RawTextQuery(query, disabled_engines)
                if isinstance(categories, str):
                    category_list = [c.strip() for c in categories.split(',') if c.strip()]
                else:
                    category_list = categories or ['general']
                
                enginerefs = get_engineref_from_category_list(category_list, disabled_engines)
                sq = SearchQuery(
                    query=rtq.getQuery(),
                    engineref_list=enginerefs,
                    lang=lang,
                    pageno=1,
                )
                search_obj = SearchWithPlugins(sq, request, user_plugins=[])
                result_container = search_obj.search()
                
                raw_results = result_container.get_ordered_results()
                raw_infoboxes = getattr(result_container, 'infoboxes', [])
                raw_answers = getattr(result_container, 'answers', [])
                
                results, infoboxes, answers = self._parse_aux_results(raw_results, raw_infoboxes, raw_answers)
                
                context_str, new_urls = self._assemble_context(results, infoboxes, answers, offset)

                return jsonify({
                    'context': context_str,
                    'new_urls': new_urls,
                    'results': results, 
                    'infoboxes': infoboxes,
                    'answers': answers,
                    'query': query
                })

            except Exception as e:
                logger.error(f"{PLUGIN_NAME}: Aux search failed: {e}")
                return jsonify({'results': [], 'error': 'Search failed'}), 500

        @app.route('/ai-models', methods=['GET'])
        def ai_models():
            token = request.args.get('tk', '')

            models_ip = request.headers.get('X-Real-IP') or request.headers.get('X-Forwarded-For')
            if models_ip:
                logger.debug(f"{PLUGIN_NAME}: /ai-models from proxied IP {models_ip}")

            try:
                ts, sig = token.rsplit('.', 1)
                expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
                if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
                    abort(403)
            except (ValueError, KeyError, AttributeError):
                abort(403)

            auth_headers = {"Authorization": f"Bearer {self.api_key}"}
            p = urlparse(self.endpoint_url)
            base = f"{p.scheme}://{p.netloc}"

            def fetch_get(start_url):
                url = start_url
                for _ in range(5):
                    conn, path = _get_streaming_connection(url)
                    conn.request("GET", path, headers=auth_headers)
                    res = conn.getresponse()
                    if res.status in (301, 302, 307, 308):
                        location = res.getheader('Location', '')
                        res.read(); conn.close()
                        if not location:
                            return None
                        url = location if location.startswith('http') else f"{urlparse(url).scheme}://{urlparse(url).netloc}{location}"
                        continue
                    return res
                return None

            for models_url, parse_fn in [
                (f"{base}/v1/models", lambda d: [m['id'] for m in d.get('data', [])]),
                (f"{base}/api/tags",  lambda d: [m['name'] for m in d.get('models', [])]),
            ]:
                try:
                    res = fetch_get(models_url)
                    if res and res.status == 200:
                        models = parse_fn(json.loads(res.read().decode('utf-8', errors='replace')))
                        if models:
                            return jsonify({'models': models})
                    elif res:
                        res.read()
                except Exception as e:
                    logger.debug(f"{PLUGIN_NAME}: /ai-models attempt {models_url} failed: {e}")

            return jsonify({'models': [self.model] if self.model else []})

        @app.route('/ai-stream', methods=['POST'])
        def handle_ai_stream():
            data = request.json or {}
            
            token = data.get('tk', '')
            q = data.get('q', '')
            lang = data.get('lang', 'all')
            
            try:
                ts, sig = token.rsplit('.', 1)
                expected = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
                if sig != expected or (time.time() - float(ts)) > TOKEN_EXPIRY_SEC:
                    abort(403)
            except (ValueError, KeyError, AttributeError):
                abort(403)

            context_text = data.get('context', '')
            prev_answer = (data.get('prev_answer') or '')[-4000:]
            req_model = (data.get('model') or '').strip()
            effective_model = req_model or self.model

            client_ip = request.headers.get('X-Real-IP') or request.headers.get('X-Forwarded-For')
            if client_ip:
                logger.debug(f"{PLUGIN_NAME}: /ai-stream from proxied IP {client_ip}")

            if not self.api_key:
                return Response("Missing API key or query", status=400)
            
            today = time.strftime("%Y-%m-%d")
            lang_instruction = f" Respond in {lang}." if lang not in ('all', 'auto') else ""

            base_sys = self.system_prompt if self.system_prompt else "You are a direct, citation-accurate search synthesis engine."
            SYSTEM = (f"{base_sys} Today is {today}.{lang_instruction} "
                      "Output only your final answer. Do not output your thinking process, "
                      "reasoning steps, or internal monologue. Begin your response with the "
                      "direct answer immediately. "
                      "Be concise. Give a 2-4 sentence overview that directly answers the query. "
                      "The user can ask follow-up questions for more detail. "
                      "Do not enumerate or list everything from the sources.")
            max_source_idx = 0
            if context_text:
                indices = re.findall(r'\[(\d+)\]', context_text)
                if indices:
                    max_source_idx = max(map(int, indices))

            CORE_RULES = [
                "Answer the question directly using the provided context.",
                "MUST CITE SOURCES by tailing a sentence with [n] or [n,n] etc. If citing general knowledge, use [*].",
                "Never explain your process. The user expects a direct response.",
                "Response format must be plain text with no markdown. "
                "Be brief: 2-4 sentences maximum. Lead with the direct answer. "
                "Cite the most relevant source(s) only. Stop after the overview.",
                "If sources and general knowledge are insufficient, respond with 'Insufficient information to answer.'"
            ]

            if q == "Continue":
                task = "CONTINUE: Pick up exactly where previous answer stopped. No repetition. Seamless flow."
            elif prev_answer:
                task = "FOLLOW-UP: Address the new question using prior context. Prioritize the new query."
            else:
                task = "ANSWER FIRST: Lead with the direct answer. No preamble, no context-setting."

            grounding = "GROUNDING: KNOWLEDGE GRAPH > DEEP > SHALLOW." if context_text else "GROUNDING: No sources available. Use general knowledge and cite as [*] which means based on general knowledge."
            history_rule = "HISTORY: Refer to prior exchange for context. Ideally, do not repeat any claims." if prev_answer else None

            instructions = [task] + CORE_RULES + [grounding]
            if history_rule:
                instructions.append(history_rule)

            numbered_instructions = "\n".join(f"{i+1}. {r}" for i, r in enumerate(instructions))
            prompt = f"""<system>{SYSTEM}</system>

<GROUNDING_SOURCES>
{context_text or 'None.'}
</GROUNDING_SOURCES>

<HISTORY>
{prev_answer or 'None.'}
</HISTORY>

<USER_QUERY>{q}</USER_QUERY>

<CORE_DIRECTIVES>
{numbered_instructions}
</CORE_DIRECTIVES>"""

            def call_ollama():
                conn = None
                try:
                    payload_dict = {
                        "model": effective_model,
                        "messages": [
                            {"role": "system",    "content": SYSTEM},
                            {"role": "user",      "content": prompt},
                            {"role": "assistant", "content": ""},
                        ],
                        "stream": False,
                        "max_tokens": self.max_tokens,
                        "temperature": self.temperature,
                    }
                    payload = json.dumps(payload_dict)
                    headers = {
                        "Content-Type": "application/json",
                        "Authorization": f"Bearer {self.api_key}",
                    }
                    url = self.endpoint_url
                    res = None  # type: ignore[assignment]
                    for _ in range(3):
                        conn, path = _get_streaming_connection(url)
                        conn.request("POST", path, body=payload.encode('utf-8'), headers=headers)
                        res = conn.getresponse()
                        if res.status in (301, 302, 307, 308):
                            location = res.getheader('Location', '')
                            res.read()
                            conn.close()
                            conn = None
                            if not location:
                                return '', f"Redirect {res.status} with no Location header"
                            url = location if location.startswith('http') else f"{urlparse(url).scheme}://{urlparse(url).netloc}{location}"
                            logger.info(f"{PLUGIN_NAME}: Following redirect to {url}")
                            continue
                        break
                    if res.status != 200:
                        body = res.read(1024).decode('utf-8', errors='replace')
                        logger.error(f"{PLUGIN_NAME}: Ollama {res.status}: {body}")
                        return '', f"Ollama error {res.status}"
                    obj = json.loads(res.read().decode('utf-8', errors='replace'))
                    if 'error' in obj:
                        err = obj['error']
                        msg = err.get('message', str(err)) if isinstance(err, dict) else str(err)
                        return '', msg
                    choices = obj.get('choices', [])
                    if not choices:
                        return '', "No choices in Ollama response."
                    message = choices[0].get('message', {})
                    content = message.get('content') or ''
                    reasoning = message.get('reasoning') or message.get('reasoning_content') or ''
                    content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
                    if not content and reasoning:
                        logger.warning(f"{PLUGIN_NAME}: content empty, extracting from reasoning field")
                        lines = reasoning.splitlines()
                        header_re = re.compile(r'^\s*\*?\*?[A-Z][^:]{0,40}:\*?\*?\s*$')
                        last_header_idx = -1
                        for i, line in enumerate(lines):
                            if header_re.match(line):
                                last_header_idx = i
                        if last_header_idx >= 0 and last_header_idx < len(lines) - 1:
                            content = '\n'.join(lines[last_header_idx + 1:]).strip()
                        if not content:
                            paragraphs = [p.strip() for p in reasoning.split('\n\n') if p.strip()]
                            content = '\n\n'.join(paragraphs[-2:]) if len(paragraphs) >= 2 else paragraphs[-1] if paragraphs else ''
                    if reasoning and content:
                        full = (f"<think>\n{reasoning}\n</think>\n\n" if reasoning else "") + content
                    else:
                        full = content
                    full = re.sub(r'<think>.*?</think>', '', full, flags=re.DOTALL).strip()
                    return full, None
                except Exception as e:
                    logger.error(f"{PLUGIN_NAME}: Ollama call error: {e}", exc_info=True)
                    return '', f"Connection Error: {e}"
                finally:
                    if conn:
                        conn.close()

            text, error = call_ollama()
            return jsonify({"text": text, "error": error})
        return True

    def _fetch_page_text(self, url: str, timeout: int = 5) -> str:
        SKIP_DOMAINS = ('youtube.com', 'twitter.com', 'x.com', 'instagram.com', 'facebook.com', 'reddit.com')
        try:
            if url.endswith('.pdf'):
                return ''
            if any(d in url for d in SKIP_DOMAINS):
                return ''

            current_url = url
            for _ in range(3):  # initial request + up to 2 redirects
                parsed = urlparse(current_url)
                host = parsed.hostname or ''
                if not host:
                    return ''
                port = parsed.port or (443 if parsed.scheme == 'https' else 80)
                path = (parsed.path or '/') + ('?' + parsed.query if parsed.query else '')

                if parsed.scheme == 'https':
                    try:
                        import certifi
                        ctx = ssl.create_default_context(cafile=certifi.where())
                    except ImportError:
                        ctx = ssl.create_default_context()
                    conn = http.client.HTTPSConnection(host, port, timeout=timeout, context=ctx)
                else:
                    conn = http.client.HTTPConnection(host, port, timeout=timeout)

                try:
                    conn.request('GET', path, headers={'User-Agent': 'Mozilla/5.0 (compatible; SearXNG-AI/1.0)'})
                    res = conn.getresponse()

                    if res.status in (301, 302, 303, 307, 308):
                        location = res.getheader('Location', '')
                        res.read()
                        if not location:
                            return ''
                        current_url = location if location.startswith('http') else f"{parsed.scheme}://{parsed.netloc}{location}"
                        continue

                    if res.status != 200:
                        return ''

                    html = res.read(512 * 1024).decode('utf-8', errors='replace')
                finally:
                    conn.close()

                html = re.sub(r'<script[^>]*>.*?</script>', '', html, flags=re.DOTALL | re.IGNORECASE)
                html = re.sub(r'<style[^>]*>.*?</style>', '', html, flags=re.DOTALL | re.IGNORECASE)
                html = re.sub(r'<nav[^>]*>.*?</nav>', '', html, flags=re.DOTALL | re.IGNORECASE)
                html = re.sub(r'<header[^>]*>.*?</header>', '', html, flags=re.DOTALL | re.IGNORECASE)
                html = re.sub(r'<footer[^>]*>.*?</footer>', '', html, flags=re.DOTALL | re.IGNORECASE)
                text = re.sub(r'<[^>]+>', '', html)
                text = (text.replace('&amp;', '&').replace('&lt;', '<').replace('&gt;', '>')
                            .replace('&quot;', '"').replace('&#39;', "'").replace('&nbsp;', ' '))
                text = re.sub(r'\s+', ' ', text).strip()

                logger.debug(f"{PLUGIN_NAME}: fetched {len(text)} chars from {url}")
                return text[:2000]

            return ''
        except Exception:
            return ''

    def _enrich_results(self, clean_results: list, query: str) -> list:
        enrich_count = min(3, self.context_deep_count)
        for r in clean_results:
            r['fetched_content'] = ''

        futures_map: dict = {}
        with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
            for r in clean_results[:enrich_count]:
                futures_map[executor.submit(self._fetch_page_text, r.get('url', ''))] = r

            for future, r in futures_map.items():
                try:
                    text = future.result(timeout=6)
                    if text and len(text) > 100:
                        words = query.lower().split()
                        text_lower = text.lower()
                        best_pos = len(text) // 2
                        best_count = -1

                        keyword_positions = []
                        for word in words:
                            start = 0
                            while True:
                                idx = text_lower.find(word, start)
                                if idx == -1:
                                    break
                                keyword_positions.append(idx)
                                start = idx + 1

                        for pos in (keyword_positions or [best_pos]):
                            window_start = max(0, pos - 400)
                            window_end = min(len(text), pos + 400)
                            count = sum(w in text_lower[window_start:window_end] for w in words)
                            if count > best_count:
                                best_count = count
                                best_pos = pos

                        start = max(0, best_pos - 400)
                        r['fetched_content'] = text[start:start + 800]
                except Exception:
                    pass

        return clean_results

    def _assemble_context(self, clean_results, infoboxes, answers, offset=0) -> tuple[str, list]:
        """Builds context string from normalized search data. Returns (context_str, urls)."""
        context_parts = []
        result_urls = []
        
        knowledge_graph_lines = []
        for ib in infoboxes:
            ib_name = ib.get('name', '') or ib.get('infobox', '') or ib.get('title', '')
            ib_content = str(ib.get('content', '')).replace('\n', ' ').strip()
            
            if ib_name:
                parts = [f"INFOBOX [{ib_name}]:"]
                if ib_content:
                    parts.append(ib_content)
                for attr in ib.get('attributes', []):
                    attr_label = attr.get('label', '')
                    attr_value = attr.get('value', '')
                    if attr_label and attr_value:
                        parts.append(f"  {attr_label}: {attr_value}")
                
                knowledge_graph_lines.append(" ".join(parts) if len(parts) == 2 else "\n".join(parts))

        for ans_text in answers:
            if ans_text and not str(ans_text).startswith('<'):
                knowledge_graph_lines.append(f"ANSWER: {str(ans_text)[:300]}")
        
        if knowledge_graph_lines:
            context_parts.append("KNOWLEDGE GRAPH:\n" + "\n".join(knowledge_graph_lines))
        
        deep_lines = []
        for i, r in enumerate(clean_results[:self.context_deep_count]):
            url = r.get('url', '')
            result_urls.append(url)
            domain = urlparse(url).netloc.replace('www.', '')
            date_str = f" ({r.get('publishedDate')})" if r.get('publishedDate') else ""
            title = r.get('title', '').replace('\n', ' ').strip()
            idx = i + 1 + offset
            fetched_content = r.get('fetched_content', '')
            if fetched_content:
                deep_lines.append(f"[{idx}] {domain}{date_str}: {title}: {fetched_content}")
            else:
                logger.debug(f"{PLUGIN_NAME}: falling back to snippet for [{idx}] {domain}")
                content = str(r.get('content', '')).replace('\n', ' ').strip()[:800]
                deep_lines.append(f"[{idx}] {domain}{date_str}: {title}: {content}")
        
        if deep_lines:
            context_parts.append("DEEP SOURCES:\n" + "\n".join(deep_lines))
            
        if self.context_shallow_count > 0:
            shallow_lines = []
            start_idx = self.context_deep_count
            end_idx = self.context_deep_count + self.context_shallow_count
            for i, r in enumerate(clean_results[start_idx:end_idx]):
                url = r.get('url', '')
                result_urls.append(url)
                domain = urlparse(url).netloc.replace('www.', '')
                title = r.get('title', '').replace('\n', ' ').strip()[:60]
                idx = i + 1 + start_idx + offset
                shallow_lines.append(f"[{idx}] {domain}: {title}")
            
            if shallow_lines:
                context_parts.append("SHALLOW SOURCES (headlines):\n" + "\n".join(shallow_lines))
        
        return "\n\n".join(context_parts), result_urls

    def post_search(self, request: "SXNG_Request", search: "SearchWithPlugins") -> list:
        try:
            if request and hasattr(request, 'headers') and request.headers.get('X-AI-Auxiliary'):
                return []

            if request and request.form.get('format', 'html') != 'html':
                return []

            if self.question_mark_required and '?' not in search.search_query.query:
                return []

            current_tabs = set(search.search_query.categories)
            if not current_tabs: current_tabs = {'general'}

            if not self.active or not self.api_key or search.search_query.pageno > 1 or not self.allowed_tabs.intersection(current_tabs):
                return []

            raw_results = search.result_container.get_ordered_results()
            raw_infoboxes = getattr(search.result_container, 'infoboxes', [])
            raw_answers = getattr(search.result_container, 'answers', [])
            
            q_clean = search.search_query.query.strip()
            clean_results, infoboxes, answers = self._parse_aux_results(raw_results, raw_infoboxes, raw_answers)
            clean_results = self._enrich_results(clean_results, q_clean)
            context_str, _ = self._assemble_context(clean_results, infoboxes, answers)

            ts = str(int(time.time()))
            lang = search.search_query.lang
            sig = hashlib.sha256(f"{ts}{self.secret}".encode()).hexdigest()
            tk = f"{ts}.{sig}"
            
            # XSS blocking
            safe_json = lambda x: json.dumps(x).replace('<', '\\u003c').replace('>', '\\u003e').replace('&', '\\u0026')
            
            b64_context = base64.b64encode(context_str.encode('utf-8')).decode('utf-8')
            total_context_count = self.context_deep_count + self.context_shallow_count
            
            raw_urls = [r.get('url', '') for r in clean_results[:total_context_count]]
            
            js_q = safe_json(q_clean)
            js_lang = safe_json(lang)
            js_urls = safe_json(raw_urls)
            js_b64_context = safe_json(b64_context)
            js_tk = safe_json(tk)
            js_script_root = safe_json((request.script_root if request else '').rstrip('/'))
            js_model_init = safe_json(self.model)

            is_interactive = self.interactive

            interactive_css = INTERACTIVE_CSS if is_interactive else ''
            interactive_html = INTERACTIVE_HTML if is_interactive else ''
            interactive_js_init = INTERACTIVE_JS if is_interactive else ''

            if is_interactive:
                interactive_js_complete = "footer.style.display = 'flex';"
            else:
                interactive_js_complete = ''
            stream_fn_sig = 'async function startStream(overrideQ = null, prevAnswer = null, auxContext = null)'
            stream_q = 'overrideQ || q_init' if is_interactive else 'q_init'
            stream_body = f'''prev_answer: prevAnswer''' if is_interactive else ''
            
            js_code = FRONTEND_JS_TEMPLATE \
                .replace("__IS_INTERACTIVE__", 'true' if is_interactive else 'false') \
                .replace("__TK__", js_tk) \
                .replace("__SCRIPT_ROOT__", js_script_root) \
                .replace("__MODEL_INIT__", js_model_init) \
                .replace("__CITATION_HELPER_JS__", CITATION_HELPER_JS) \
                .replace("__INTERACTIVE_JS_INIT__", interactive_js_init) \
                .replace("__STREAM_FN_SIG__", stream_fn_sig) \
                .replace("__STREAM_Q__", stream_q) \
                .replace("__STREAM_BODY__", ', ' + stream_body if stream_body else '') \
                .replace("__INTERACTIVE_JS_COMPLETE__", interactive_js_complete) \
                .replace("__JS_LANG__", js_lang) \
                .replace("__JS_URLS__", js_urls) \
                .replace("__B64_CONTEXT__", js_b64_context) \
                .replace("__JS_Q__", js_q)

            html_payload = f'''
                <article id="sxng-stream-box" class="answer" style="display:none; margin-top: 0; margin-bottom: 0;">
                    <style>
                        @keyframes sxng-fade-pulse {{
                            0%, 100% {{ opacity: 0.1; }}
                            50% {{ opacity: 1; }}
                        }}
                        @keyframes sxng-fade-in {{
                            from {{ opacity: 0; }}
                            to {{ opacity: 1; }}
                        }}
                        #sxng-stream-data {{
                            position: relative;
                            margin-top: .5rem !important;
                            min-height: 1.5em;
                        }}
                        .sxng-cursor {{
                            display: inline-block;
                            width: 0.6em;
                            height: 1.2em;
                            background: var(--color-result-link-visited, var(--color-result-link, #b48ead));
                            vertical-align: text-bottom;
                            animation: sxng-fade-pulse 1s ease-in-out infinite;
                            margin-right: 0.2rem;
                            border-radius: 2px;
                        }}
                        .sxng-chunk {{
                            opacity: 1;
                        }}
                        @media (min-width: 769px) {{
                            .sxng-chunk {{
                                animation: sxng-fade-in 0.3s ease-out;
                            }}
                        }}
                        .sxng-ai-header {{
                            display: flex;
                            justify-content: space-between;
                            align-items: center;
                            margin-top: -4px;
                        }}
                        .sxng-ai-label {{
                            font-size: 1.05em;
                            font-weight: 700;
                            letter-spacing: 0.04em;
                            text-transform: none;
                            color: var(--color-base-font, #333);
                        }}
                        {interactive_css}
                    </style>
                    <div class="sxng-ai-header">
                        <span class="sxng-ai-label">
                            <span style="color:#4a9eff;font-size:1.1em;">✦</span> AI Overview
                        </span>
                        <select id="sxng-model-select" class="sxng-model-select" title="Select model"></select>
                    </div>
                    <p id="sxng-stream-data" style="white-space: pre-wrap; color: var(--color-result-description); font-size: 0.95rem; margin:0;"><span class="sxng-cursor"></span></p>
                    {interactive_html}
                    <script>
                    {js_code}
                    </script>
                </article>
            '''
            return [Answer(answer=Markup(html_payload))]
        except Exception as e:
            logger.error(f"{PLUGIN_NAME}: {e}")
        return []