import re, os, threading, queue, requests, time
from typing import List, Optional, Union
from pydantic import BaseModel, Field
from pydantic_settings import BaseSettings

from api_types import ChatMessage


def parse_think_response(full_response: str):
    think_start = full_response.find("<think")
    if think_start == -1:
        return None, full_response.strip()

    think_end = full_response.find("</think>")
    if think_end == -1:  # 未闭合的情况
        reasoning = full_response[think_start:].strip()
        content = ""
    else:
        reasoning = full_response[think_start : think_end + 9].strip()  # +9包含完整标签
        content = full_response[think_end + 9 :].strip()

    # 清理标签保留内容
    reasoning_content = reasoning.replace("<think", "").replace("</think>", "").strip()
    return reasoning_content, content


def cleanMessages(messages: List[ChatMessage], removeThinkingContent: bool = False):
    promptStrList = []

    for message in messages:
        content = message.content.strip()
        content = re.sub(r"\n+", "\n", content)
        promptStrList.append(
            f"{message.role.strip().lower().capitalize()}: {content if message.role.strip().lower().capitalize()!='Assistant' or not removeThinkingContent else remove_nested_think_tags_stack(content)}"
        )

    return "\n\n".join(promptStrList)


def remove_nested_think_tags_stack(text):
    stack = []
    result = ""
    i = 0
    while i < len(text):
        if text[i : i + 7] == "<think>":
            stack.append("<think>")
            i += 7
        elif text[i : i + 8] == "</think>":
            if stack and stack[-1] == "<think>":
                stack.pop()
                i += 8
            else:
                result += text[i : i + 8]
                i += 8
        elif not stack:
            result += text[i]
            i += 1
        else:
            i += 1
    return result


def format_bytes(size):
    power = 2**10
    n = 0
    power_labels = {0: "", 1: "K", 2: "M", 3: "G", 4: "T"}
    while size > power:
        size /= power
        n += 1
    return f"{size:.4f}{power_labels[n]+'B'}"


LOGGER_QUEUE = queue.Queue(int(os.environ.get('LOGGER_QUEUE_SIZE', 100)))


def logger():
    """Background thread to post logs to LOG_PORT. Uses blocking get so the thread
    will wait for items and won't spin when queue empty. Any errors are swallowed
    to avoid crashing the logger thread.
    """
    print("enable")
    while True:
        try:
            item = LOGGER_QUEUE.get()
        except Exception:
            # If queue is unexpectedly closed or an error occurs, keep running
            time.sleep(0.1)
            continue
        try:
            LOG_PORT = os.environ.get("LOG_PORT")
            if LOG_PORT:
                # Best-effort; ignore any network error
                requests.post(
                    LOG_PORT,
                    headers={"Content-Type": "application/json"},
                    json=item,
                    timeout=5,
                )
        except Exception:
            # never let log failures escape to the main thread
            pass


if os.environ.get("LOG_PORT"):
    # make the logger thread a daemon so it won't block process exit
    t = threading.Thread(target=logger, daemon=True)
    t.start()


def log(item):
    try:
        LOGGER_QUEUE.put_nowait(item)
    except queue.Full:
        # Queue is full: drop the log (best-effort). Avoid raising to keep the
        # application responsive; optionally print a fallback log to console
        try:
            # Use a short, non-blocking print so at least something is recorded
            print("LOG DROP: queue full, dropping log item")
        except Exception:
            pass


def web_search(query: str, top_k: int = 3) -> str:
    """Perform a simple web search via DuckDuckGo HTML and return top_k results as a combined string.

    This is a lightweight fallback search that does not call external model services —
    it queries a public search endpoint, parses titles/snippets/urls and returns them as
    formatted text to be included into the model's prompt context.
    """
    if not query or query.strip() == "":
        return ""
    try:
        from bs4 import BeautifulSoup
    except Exception:
        return ""
    try:
        headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
        q = query.strip()
        resp = requests.get("https://duckduckgo.com/html/", params={"q": q}, headers=headers, timeout=10)
        soup = BeautifulSoup(resp.text, "html.parser")
        # DuckDuckGo's html structure: results are in `div.result` containers.
        results = []
        for r in soup.find_all("div", class_="result", limit=top_k):
            a = r.find("a", class_="result__a") or r.find("a", href=True)
            title = a.get_text(strip=True) if a else ""
            href = a.get("href") if a else ""
            snippet = ""
            s = r.find("a", class_="result__snippet") or r.find("div", class_="result__snippet")
            if s:
                snippet = s.get_text(strip=True)
            results.append(f"{title} - {snippet} - {href}")
        return "\n".join(results)
    except Exception:
        return ""


def calc(expr: str) -> str:
    """Safely evaluate a simple arithmetic expression and return the result as string.

    This uses ast parsing to disallow attributes and only permit arithmetic operators.
    """
    try:
        import ast, operator as op

        # supported operators
        allowed_ops = {
            ast.Add: op.add,
            ast.Sub: op.sub,
            ast.Mult: op.mul,
            ast.Div: op.truediv,
            ast.Pow: op.pow,
            ast.BitXor: op.xor,
            ast.USub: op.neg,
            ast.Mod: op.mod,
            ast.FloorDiv: op.floordiv,
        }

        def _eval(node):
            if isinstance(node, ast.Num):  # <number>
                return node.n
            elif isinstance(node, ast.BinOp):
                left = _eval(node.left)
                right = _eval(node.right)
                op_type = type(node.op)
                if op_type in allowed_ops:
                    return allowed_ops[op_type](left, right)
                else:
                    raise ValueError("Unsupported operator")
            elif isinstance(node, ast.UnaryOp):
                operand = _eval(node.operand)
                op_type = type(node.op)
                if op_type in allowed_ops:
                    return allowed_ops[op_type](operand)
                raise ValueError("Unsupported unary op")
            else:
                raise ValueError("Unsupported expression type")

        node = ast.parse(expr, mode='eval')
        result = _eval(node.body)
        return str(result)
    except Exception as e:
        return f"ERROR: {e}"


def detect_tools_and_reasoning(text_or_messages) -> dict:
    """Detects whether web_search, calc, or reasoning are likely needed based on heuristics.

    Accepts either a single string prompt or a list of ChatMessage. Returns a dict with booleans and detected tools list.
    """
    if isinstance(text_or_messages, list):
        try:
            text = "\n\n".join([m.get('content', '') if isinstance(m, dict) else (getattr(m, 'content', '') or '') for m in text_or_messages if m])
        except Exception:
            text = ""
    else:
        text = str(text_or_messages or "")

    t = text.lower()
    # Simple heuristics
    need_calc = False
    need_web_search = False
    need_reasoning = False
    need_universal = False
    need_fetch_url = False
    need_summarize = False
    need_keywords = False
    need_sentiment = False
    need_translate = False
    need_spell_check = False
    need_format_code = False
    need_explain_code = False
    detected_tools = []

    # Heuristic for calc: presence of operators AND numbers OR keywords 'calculate/compute' plus numeric tokens
    if (re.search(r"\d+\s*[-+*/%]\s*\d+", t) or (re.search(r"\b(calculate|compute|solve|evaluate|sum|add|subtract|multiply|divide)\b", t) and re.search(r"\d", t))):
        need_calc = True
        # Try to extract a most-likely arithmetic expression from the text
        # Accept digits, parentheses and operators
        m = re.search(r"([\d\(\)\s+\-*/%^.]+)", text)
        expr = m.group(0).strip() if m else None
        # only keep if it includes an operator
        if expr and not re.search(r"[-+*/%]", expr):
            expr = None
        detected_tools.append({"name": "calc", "args": {"expression": expr, "confidence": 0.95 if expr else 0.5}})

    # Heuristic for web search: 'who is', 'what is', 'current', 'latest', 'news', or question words with facts
    # Heuristic for web search: question words + facts or 'current/latest' signals; avoid math queries
    if (
        re.search(r"\b(who is|who's|what is|what's|when is|where is|current|latest|news|is the president|president of|population of|capital of|how many|GDP of)\b", t)
        and not re.search(r"\d+\s*[-+*/%]\s*\d+", t)
    ):
        need_web_search = True
        detected_tools.append({"name": "web_search", "args": {"query": text, "confidence": 0.9}})

    # Heuristic for reasoning: words like 'explain', 'why', 'reason', 'prove', 'derive', 'compare'
    if re.search(r"\b(explain|why|because|reason|prove|derive|compare|analysis|analysis:|evaluate|argue|consequence|trade-offs)\b", t):
        need_reasoning = True

    # Heuristic for universal tool: requests to "use tool", "execute tool", or generic function-call language
    if re.search(r"\b(use (a )?tool|execute (a )?tool|call (a )?tool|function call|run tool|do this via a tool|invoke tool|call tool)\b", t):
        need_universal = True
    # detect fetch_url: a URL string or request to 'open' the link
    if re.search(r"https?://\S+", t) or re.search(r"\b(open|visit)\s+(https?://|www\.)", t):
        need_fetch_url = True
        m_url = re.search(r'https?://\S+', text)
        url_val = m_url.group(0) if m_url else text
        detected_tools.append({"name": "fetch_url", "args": {"url": url_val, "confidence": 0.85}})
    # detect translate requests: 'translate to es' or 'traducir a español'
    if re.search(r"\btranslate\b.*to\s+([a-z]{2,})|\btraducir\b.*a\s+([a-z]{2,})", t):
        need_translate = True
        m = re.search(r"\btranslate\b.*to\s+([a-z]{2,})|\btraducir\b.*a\s+([a-z]{2,})", t)
        tgt = (m.group(1) if m and m.group(1) else (m.group(2) if m and len(m.groups()) > 1 else 'en'))
        detected_tools.append({"name": "translate", "args": {"text": text, "target_lang": tgt, "confidence": 0.85}})
    # detect summarize requests ('summarize', 'tl;dr', 'summarise')
    if re.search(r"\b(summarize|summarise|tl;dr|tl;dr:)\b", t):
        need_summarize = True
        detected_tools.append({"name": "summarize", "args": {"text": text, "max_sentences": 3, "confidence": 0.8}})
    # detect keyword extraction requests
    if re.search(r"\b(keywords|key words|key terms|extract keywords)\b", t):
        need_keywords = True
        detected_tools.append({"name": "keywords", "args": {"text": text, "top_k": 5, "confidence": 0.78}})
    # detect sentiment analysis requests
    if re.search(r"\b(sentiment|tone|is this positive|is this negative|what is the sentiment)\b", t):
        need_sentiment = True
        detected_tools.append({"name": "sentiment", "args": {"text": text, "confidence": 0.8}})
    # detect code-format and explain: '```', 'explain code', 'what does this function do'
    if re.search(r"```[a-zA-Z]*|format code|format this code|pretty print code", t):
        need_format_code = True
        detected_tools.append({"name": "format_code", "args": {"code": text, "language": "python", "confidence": 0.8}})
    if re.search(r"\bexplain( this)? code\b|what does this (function|method|snippet) do", t):
        need_explain_code = True
        detected_tools.append({"name": "explain_code", "args": {"code": text, "language": "python", "confidence": 0.75}})
    # detect spellcheck requests
    if re.search(r"\b(spell check|spellcheck|check spelling|corregir ortografía|revisar ortografía)\b", t):
        need_spell_check = True
        detected_tools.append({"name": "spell_check", "args": {"text": text, "confidence": 0.6}})
    if re.search(r"\b(sentiment|tone|is this positive|is this negative|what is the sentiment)\b", t):
        need_sentiment = True
        detected_tools.append({"name": "sentiment", "args": {"text": text, "confidence": 0.8}})

    # compute confidence summary
    # For now, we use a simple heuristic: reasoning >0.8 if key words present; web_search 0.9; calc 0.95 if numeric
    confs = {
        "calc_confidence": 0.95 if need_calc else 0.0,
        "web_search_confidence": 0.9 if need_web_search else 0.0,
        "reasoning_confidence": 0.85 if need_reasoning else 0.0,
        "universal_confidence": 0.65 if need_universal else 0.0,
        "translate_confidence": 0.85 if need_translate else 0.0,
        "spell_check_confidence": 0.6 if need_spell_check else 0.0,
        "format_code_confidence": 0.7 if need_format_code else 0.0,
        "explain_code_confidence": 0.7 if need_explain_code else 0.0,
    }
    return {
        "need_calc": need_calc,
        "need_web_search": need_web_search,
        "need_reasoning": need_reasoning,
        "need_universal": need_universal,
        "need_fetch_url": need_fetch_url,
        "need_summarize": need_summarize,
        "need_keywords": need_keywords,
        "need_sentiment": need_sentiment,
        "need_translate": need_translate,
        "need_spell_check": need_spell_check,
        "need_format_code": need_format_code,
        "need_explain_code": need_explain_code,
        "detected_tools": detected_tools,
        "confidence": confs,
    }


def fetch_url(url: str, max_chars: int = 20000) -> str:
    """Fetch the content of a URL and return cleaned text (strip HTML tags).

    Returns a truncated plain-text string of up to `max_chars` characters.
    """
    if not url:
        return ""
    try:
        headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
        resp = requests.get(url, headers=headers, timeout=10)
        if not resp.ok:
            return ""
        text = resp.text
        # remove scripts/styles and HTML tags
        try:
            from bs4 import BeautifulSoup

            soup = BeautifulSoup(text, "html.parser")
            for s in soup(["script", "style"]):
                s.decompose()
            body = soup.get_text(separator=" \n ")
            cleaned = re.sub(r"\s+", " ", body).strip()
            return cleaned[:max_chars]
        except Exception:
            # fallback: naive strip
            cleaned = re.sub(r"<[^>]+>", "", text)
            cleaned = re.sub(r"\s+", " ", cleaned)
            return cleaned[:max_chars]
    except Exception:
        return ""


def summarize_text(text: str, max_sentences: int = 3) -> str:
    """Naive summary by selecting the leading sentences (simple extractive summarizer).

    This is intentionally simple to avoid heavy dependencies.
    """
    if not text or not isinstance(text, str):
        return ""
    sents = re.split(r"(?<=[.!?])\s+", text.strip())
    if len(sents) <= max_sentences:
        return " ".join(sents).strip()
    return " ".join(sents[:max_sentences]).strip()


def extract_keywords(text: str, top_k: int = 5) -> List[str]:
    """Return top_k frequent non-stopword tokens from text (naive extraction).
    """
    if not text:
        return []
    try:
        tokens = re.findall(r"\w+", text.lower())
        stopwords = set(["the", "and", "is", "in", "to", "a", "an", "of", "for", "with", "on", "that", "this", "it", "as", "are"])
        filtered = [t for t in tokens if t not in stopwords and len(t) > 2]
        freq = {}
        for t in filtered:
            freq[t] = freq.get(t, 0) + 1
        items = sorted(freq.items(), key=lambda x: -x[1])[:top_k]
        return [k for k, v in items]
    except Exception:
        return []


def sentiment_analysis(text: str) -> dict:
    """Very basic lexicon-based sentiment analysis.

    Returns an opinion: {sentiment: 'positive'/'neutral'/'negative', 'score': float }.
    """
    if not text:
        return {"sentiment": "neutral", "score": 0.0}
    pos = set(["good", "great", "excellent", "positive", "success", "love", "like", "happy", "best"])
    neg = set(["bad", "horrible", "poor", "negative", "hate", "dislike", "sad", "worst", "angry"])
    tokens = re.findall(r"\w+", text.lower())
    score = 0
    for t in tokens:
        if t in pos:
            score += 1
        elif t in neg:
            score -= 1
    if score > 0:
        return {"sentiment": "positive", "score": float(score)}
    if score < 0:
        return {"sentiment": "negative", "score": float(score)}
    return {"sentiment": "neutral", "score": 0.0}


# removed earlier naive duplicates in favor of featureful versions below


def translate_text(text: str, target_lang: str = 'en') -> dict:
    """Translate text to target language using `googletrans` if available; otherwise return a no-op dict indicating translation is unavailable.

    This is intentionally conservative; prefer server-side libraries if available.
    """
    if not text:
        return {"action": "translate", "result": "", "metadata": {"lang": target_lang, "confidence": 0.0}}
    try:
        import importlib.util
        googletrans_spec = importlib.util.find_spec("googletrans")
        if googletrans_spec is not None:
                # Only attempt import if googletrans is available
                try:
                    import importlib
                    googletrans_spec = importlib.util.find_spec("googletrans")
                    if googletrans_spec is not None:
                        googletrans = importlib.import_module("googletrans")
                        Translator = getattr(googletrans, 'Translator', None)
                        if Translator:
                            t = Translator()
                            res = t.translate(text, dest=target_lang)
                            return {"action": "translate", "result": res.text, "metadata": {"lang": target_lang, "confidence": 0.9}}
                except Exception:
                    pass
        # Fallback: return an annotated prefix indicating translation was requested but not performed
        return {"action": "translate", "result": f"[translated to {target_lang}]: {text}", "metadata": {"lang": target_lang, "confidence": 0.0}}
    except Exception:
        return {"action": "translate", "result": f"[translated to {target_lang}]: {text}", "metadata": {"lang": target_lang, "confidence": 0.0}}


def spell_check_text(text: str) -> dict:
    """Naive spell check that returns the original text and a no-op list of suggestions.

    If libraries like `textblob` are installed, would provide suggestions; fallback to identity.
    """
    if not text:
        return {"action": "spell_check", "result": text, "metadata": {"suggestions": [], "confidence": 0.0}}
    try:
        import importlib.util
        textblob_spec = importlib.util.find_spec("textblob")
        if textblob_spec is not None:
            try:
                textblob = importlib.import_module("textblob")
                TextBlob = getattr(textblob, "TextBlob", None)
                if TextBlob is not None:
                    tb = TextBlob(text)
                    corrected = str(tb.correct())
                    if corrected != text:
                        return {"action": "spell_check", "result": corrected, "metadata": {"suggestions": [corrected], "confidence": 0.9}}
            except Exception:
                pass
    except Exception:
        pass
    return {"action": "spell_check", "result": text, "metadata": {"suggestions": [], "confidence": 0.0}}


def format_code_text(code: str, lang: str = 'python') -> dict:
    """Simple code formatting: attempts to run `black` if available; otherwise returns code unchanged.
    """
    if not code:
        return {"action": "format_code", "result": code, "metadata": {"lang": lang, "confidence": 0.0}}
    try:
        try:
            try:
                import importlib.util
                black_spec = importlib.util.find_spec("black")
                if black_spec is not None:
                    black = importlib.import_module("black")
                else:
                    black = None
            except ImportError:
                black = None
            if black is not None:
                mode = black.Mode()
                formatted = black.format_str(code, mode=mode)
                return {"action": "format_code", "result": formatted, "metadata": {"lang": lang, "confidence": 0.95}}
            else:
                # fallback: naive indentation/strip
                cleaned = '\n'.join([ln.rstrip() for ln in code.splitlines()])
                return {"action": "format_code", "result": cleaned, "metadata": {"lang": lang, "confidence": 0.0}}
        except Exception:
            # fallback: naive indentation/strip
            cleaned = '\n'.join([ln.rstrip() for ln in code.splitlines()])
            return {"action": "format_code", "result": cleaned, "metadata": {"lang": lang, "confidence": 0.0}}
    except Exception:
        return {"action": "format_code", "result": code, "metadata": {"lang": lang, "confidence": 0.0}}


def explain_code_text(code: str, lang: str = 'python') -> dict:
    """Return a basic explanation by summarizing comments and high level function names.

    This is intentionally naive; future improvement: pass to an LLM or specialized parser.
    """
    if not code:
        return {"action": "explain_code", "result": "", "metadata": {"lang": lang}}
    try:
        # Extract function names and top-level comments
        funcs = re.findall(r"def\s+(\w+)\s*\(", code)
        comments = re.findall(r"#(.+)", code)
        summary = []
        if funcs:
            summary.append(f"Functions: {', '.join(funcs)}")
        if comments:
            summary.append("Comments: " + "; ".join([c.strip() for c in comments[:3]]))
        if not summary:
            # fallback: first non-empty line
            lines = [l.strip() for l in code.splitlines() if l.strip()]
            summary.append(lines[0] if lines else "No content")
        return {"action": "explain_code", "result": " | ".join(summary), "metadata": {"lang": lang, "confidence": 0.6}}
    except Exception:
        return {"action": "explain_code", "result": "", "metadata": {"lang": lang, "confidence": 0.0}}


def ensure_upload_dir():
    from config import CONFIG
    try:
        os.makedirs(CONFIG.UPLOAD_DIR, exist_ok=True)
    except Exception:
        pass


from typing import Optional


def save_bytes_to_upload(filename: Optional[str], data: bytes) -> dict:
    from config import CONFIG
    import hashlib, time, uuid

    ensure_upload_dir()
    _id = str(uuid.uuid4())
    safe_name = f"{_id}_{os.path.basename(str(filename or 'uploaded_file'))}"
    path = os.path.join(CONFIG.UPLOAD_DIR, safe_name)
    try:
        with open(path, 'wb') as f:
            f.write(data)
        size = os.path.getsize(path)
        import mimetypes
        mime_type = mimetypes.guess_type(path)[0]
        return {
            'file_id': _id,
            'filename': filename,
            'path': path,
            'mime_type': mime_type,
            'size': size,
            'uploaded_at': int(time.time()),
        }
    except Exception as e:
        return {'error': str(e)}


def file_read_from_path(path: str, max_bytes: int = 100000) -> str:
    try:
        if not path or not os.path.exists(path):
            return ""
        with open(path, 'rb') as f:
            b = f.read(max_bytes)
            try:
                return b.decode('utf-8', errors='replace')
            except Exception:
                return str(b)
    except Exception:
        return ""


def universal_tool(args: dict, allow_web_search: bool = True, allow_tools: bool = True, allow_file_tool: bool = True) -> dict:
    """Universal tool: if 'action' is provided, call the corresponding tool; otherwise autodetect using heuristics.

    Supported actions: 'calc', 'web_search', 'file_upload', 'file_read'. If the action is not provided, attempt to detect the appropriate tool.
    Returns a string result for prompt injection.
    """
    if not isinstance(args, dict):
        return {"error": "ERROR: invalid args for universal tool"}

    action = args.get("action")
    query = args.get("query")
    # explicit action
    if action == "calc":
        if not allow_tools:
            return {"action": "calc", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        expr = args.get("expression") or query
        if not expr:
            return {"action": "calc", "result": None, "metadata": {"error": "no expression provided", "confidence": 0.0}}
        res = calc(str(expr))
        return {"action": "calc", "result": str(res), "metadata": {"expression": expr, "confidence": 0.98}}
    if action == "web_search":
        if not allow_web_search:
            return {"action": "web_search", "result": "", "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        q = args.get("query") or query
        if not q:
            return {"action": "web_search", "result": "", "metadata": {"confidence": 0.0}}
        res = web_search(str(q), int(args.get("top_k") or 3))
        return {"action": "web_search", "result": str(res), "metadata": {"query": q, "top_k": int(args.get("top_k") or 3), "confidence": 0.9}}
    if action == 'file_read':
        if not allow_file_tool:
            return {"action": "file_read", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        fpath = args.get('path') or args.get('file_path')
        if not fpath and args.get('file_id'):
            from config import CONFIG
            fid = args.get('file_id')
            if fid:
                candidate = os.path.join(CONFIG.UPLOAD_DIR, os.path.basename(str(fid)))
            else:
                candidate = None
            if candidate and os.path.exists(candidate):
                fpath = candidate
        if not fpath:
            return {"action": "file_read", "result": None, "metadata": {"error": "no_path_or_id", "confidence": 0.0}}
        content = file_read_from_path(fpath, int(args.get('max_bytes') or 100000))
        return {"action": "file_read", "result": str(content), "metadata": {"path": fpath, "confidence": 0.9}}
    if action == 'file_upload':
        if not allow_file_tool:
            return {"action": "file_upload", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        # Expect either base64 content or raw bytes/text in args
        import base64

        fname = args.get('filename') or args.get('name') or 'uploaded_file'
        content_b64 = args.get('content_base64') or args.get('content')
        if not content_b64:
            return {"action": "file_upload", "result": None, "metadata": {"error": "no_content", "confidence": 0.0}}
        # If the content looks like base64 (contains only b64 chars, padded), decode; else try to treat it as plaintext
        try:
            if isinstance(content_b64, str):
                b = None
                try:
                    b = base64.b64decode(content_b64, validate=True)
                except Exception:
                    b = str(content_b64).encode('utf-8')
            else:
                b = content_b64 if isinstance(content_b64, (bytes, bytearray)) else str(content_b64).encode('utf-8')
        except Exception:
            return {"action": "file_upload", "result": None, "metadata": {"error": "invalid_content", "confidence": 0.0}}
        # Check size against configuration
        try:
            from config import CONFIG

            if len(b) > getattr(CONFIG, 'MAX_UPLOAD_SIZE_BYTES', 10 * 1024 * 1024):
                return {"action": "file_upload", "result": None, "metadata": {"error": "file_too_large", "confidence": 0.0}}
        except Exception:
            pass
        # Save file
        meta = None
        try:
            # If app exposes an internal API to register uploads, prefer that so model checks happen in one place
            import importlib
            app_module = importlib.import_module('app')
            if hasattr(app_module, 'upload_file_internal'):
                try:
                    meta = app_module.upload_file_internal(b, filename=fname)
                except Exception:
                    meta = save_bytes_to_upload(fname, b)
                    # fallback: attempt to register in app's UPLOADED_FILES if present
                    try:
                        if hasattr(app_module, 'UPLOADED_FILES') and isinstance(app_module.UPLOADED_FILES, dict):
                            app_module.UPLOADED_FILES[meta['file_id']] = meta
                    except Exception:
                        pass
            else:
                meta = save_bytes_to_upload(fname, b)
                try:
                    if hasattr(app_module, 'UPLOADED_FILES') and isinstance(app_module.UPLOADED_FILES, dict):
                        app_module.UPLOADED_FILES[meta['file_id']] = meta
                except Exception:
                    pass
        except Exception:
            # fallback to local save and skip register
            meta = save_bytes_to_upload(fname, b)
        return {"action": "file_upload", "result": meta, "metadata": {"filename": fname, "file_id": meta.get('file_id'), "confidence": 0.9}}
    if action == 'fetch_url':
        if not allow_web_search:
            return {"action": "fetch_url", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        url = args.get('url') or query
        if not url:
            return {"action": "fetch_url", "result": None, "metadata": {"error": "no_url_provided", "confidence": 0.0}}
        content = fetch_url(str(url), int(args.get('max_chars') or 20000))
        return {"action": "fetch_url", "result": str(content), "metadata": {"url": url, "confidence": 0.9}}
    if action == 'summarize':
        if not allow_tools:
            return {"action": "summarize", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        txt = args.get('text') or ''
        if not txt and args.get('url'):
            try:
                txt = fetch_url(str(args.get('url')))
            except Exception:
                txt = ''
        if not txt and query:
            txt = query
        if not txt:
            return {"action": "summarize", "result": None, "metadata": {"error": "no_text_or_url_provided", "confidence": 0.0}}
        s = summarize_text(str(txt), int(args.get('max_sentences') or 3))
        return {"action": "summarize", "result": s, "metadata": {"confidence": 0.85}}
    if action == 'keywords' or action == 'keyword_extraction':
        if not allow_tools:
            return {"action": "keywords", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        txt = args.get('text') or ''
        if not txt and args.get('url'):
            try:
                txt = fetch_url(str(args.get('url')))
            except Exception:
                txt = ''
        if not txt and query:
            txt = query
        if not txt:
            return {"action": "keywords", "result": None, "metadata": {"error": "no_text_or_url_provided", "confidence": 0.0}}
        kws = extract_keywords(str(txt), int(args.get('top_k') or 5))
        return {"action": "keywords", "result": kws, "metadata": {"confidence": 0.85}}
    if action == 'sentiment':
        if not allow_tools:
            return {"action": "sentiment", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        txt = args.get('text') or ''
        if not txt and args.get('url'):
            try:
                txt = fetch_url(str(args.get('url')))
            except Exception:
                txt = ''
        if not txt and query:
            txt = query
        if not txt:
            return {"action": "sentiment", "result": None, "metadata": {"error": "no_text_or_url_provided", "confidence": 0.0}}
        res = sentiment_analysis(str(txt))
        return {"action": "sentiment", "result": res, "metadata": {"confidence": 0.85}}
    if action == 'translate':
        if not allow_tools:
            return {"action": "translate", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        txt = args.get('text') or query or ''
        target = args.get('target') or 'en'
        res = translate_text(str(txt), str(target))
        return {"action": "translate", "result": res.get('result'), "metadata": {"lang": res.get('lang'), "note": res.get('note'), "confidence": 0.5}}
    if action == 'spell_check' or action == 'spellcheck':
        if not allow_tools:
            return {"action": "spell_check", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        txt = args.get('text') or query or ''
        res = spell_check_text(str(txt))
        return {"action": "spell_check", "result": res.get('result'), "metadata": {"corrections": res.get('corrections'), "confidence": 0.5}}
    if action == 'format_code' or action == 'format':
        if not allow_tools:
            return {"action": "format_code", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        txt = args.get('text') or query or ''
        lang = args.get('language') or args.get('lang') or 'python'
        res = format_code_text(txt, lang)
        return {"action": "format_code", "result": res.get('result'), "metadata": {"note": res.get('note'), "confidence": 0.6}}
    if action == 'explain_code' or action == 'explain':
        if not allow_tools:
            return {"action": "explain_code", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        txt = args.get('text') or query or ''
        lang = args.get('language') or args.get('lang') or 'python'
        res = explain_code_text(txt, lang)
        # Return a small extracted explanation string if available
        if isinstance(res, dict):
            ds = res.get('docstrings') or []
            expl = res.get('explanation') or (ds[0] if isinstance(ds, list) and len(ds) > 0 else '')
        else:
            expl = str(res)
        return {"action": "explain_code", "result": expl, "metadata": {"docstrings": res.get('docstrings'), "confidence": 0.6}}
    # Removed duplicate action handlers for translate, spell_check, format_code, explain_code
    # auto-detect based on query content
    if query:
        # if expression - use calc
        if re.search(r"\d+\s*[-+*/%]\s*\d+", str(query)):
            if not allow_tools:
                return {"action": "calc", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
            res = calc(str(query))
            return {"action": "calc", "result": str(res), "metadata": {"expression": str(query), "confidence": 0.95}}
        # fetch_url auto-detect when a URL present
        if re.search(r"https?://\S+", str(query)):
            if not allow_web_search:
                return {"action": "fetch_url", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
            content = fetch_url(str(query), int(args.get('max_chars') or 20000))
            return {"action": "fetch_url", "result": str(content), "metadata": {"url": str(query), "confidence": 0.9}}
        # translate/detect: e.g., 'translate to spanish: <text>'
        if re.search(r"\btranslate\b.*to\s+([a-z]{2,})", str(query).lower()):
            import re as _re

            m = _re.search(r"\btranslate\b.*to\s+([a-z]{2,})", str(query).lower())
            tgt = m.group(1) if m else 'en'
            if not allow_tools:
                return {"action": "translate", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
            res = translate_text(str(query), tgt)
            return res
        # format_code auto-detect: presence of ``` or 'format code' text
        if re.search(r"```[a-zA-Z]*|format code|format this code|pretty print code", str(query).lower()):
            if not allow_tools:
                return {"action": "format_code", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
            code = str(query)
            res = format_code_text(code)
            return res
        # summarize auto-detect
        if re.search(r"\b(summarize|summarise|tl;dr)\b", str(query).lower()):
            if not allow_tools:
                return {"action": "summarize", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
            s = summarize_text(str(query))
            return {"action": "summarize", "result": s, "metadata": {"confidence": 0.85}}
        # keywords auto-detect
        if re.search(r"\b(keywords|key terms|extract keywords)\b", str(query).lower()):
            if not allow_tools:
                return {"action": "keywords", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
            kws = extract_keywords(str(query))
            return {"action": "keywords", "result": kws, "metadata": {"confidence": 0.78}}
        # sentiment auto-detect
        if re.search(r"\b(sentiment|tone|is this positive|is this negative|what is the sentiment)\b", str(query).lower()):
            if not allow_tools:
                return {"action": "sentiment", "result": None, "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
            res = sentiment_analysis(str(query))
            return {"action": "sentiment", "result": res, "metadata": {"confidence": 0.8}}
        if not allow_web_search:
            return {"action": "web_search", "result": "", "metadata": {"error": "disabled_by_policy", "confidence": 0.0}}
        res = web_search(str(query), int(args.get("top_k") or 3))
        return {"action": "web_search", "result": str(res), "metadata": {"query": str(query), "top_k": int(args.get("top_k") or 3), "confidence": 0.9}}

    return {"error": "ERROR: could not determine action for universal tool"}


def bias_mitigation(text: str) -> dict:
    """A light-weight bias mitigation helper.

    The goal: detect and neutralize potentially biased, stereotyping, or discriminatory statements.
    It's intentionally conservative (favoring suppression) and returns sanitized content and a flag.
    """
    import re
    if not text or not isinstance(text, str):
        return {"sanitized": text, "suppressed": False, "reason": None}

    t = text.strip()
    # Simple checks for sweeping generalizations towards protected groups
    # This is a naive approach and can be adapted with an ML classifier.
    protected_terms = [
        r"\b(race|religion|ethnicity|gender|sexual orientation|disability)\b",
        r"\b(black|white|asian|hispanic|muslim|christian|jewish|gay|lesbian|transgender)\b",
    ]
    sweeping_patterns = [
        r"\b(all|always|never|every|none)\b[^.?!]{0,60}\b(is|are|will|should|must)\b",
        r"\b(\w+)s?\b[^.?!]{0,60}\b(are|is)\b[^.?!]{0,80}\b(inferior|superior|stupid|lazy|criminal)\b",
    ]
    # Simple profanity or slurs (non-exhaustive) - block
    slurs = [r"\b(slur1|slur2)\b"]  # placeholder; real app should use a curated list

    for pattern in sweeping_patterns:
        if re.search(pattern, t, flags=re.I):
            # ensure it references a protected group before suppressing
            for pt in protected_terms:
                if re.search(pt, t, flags=re.I):
                    return {"sanitized": "[content suppressed due to potential bias]", "suppressed": True, "reason": "sweeping_generalization_protected_group"}
    # If contains slurs -> suppress
    for s in slurs:
        if re.search(s, t, flags=re.I):
            return {"sanitized": "[content suppressed due to policy]", "suppressed": True, "reason": "profanity_or_slur"}

    # For political content with strong claims, favor neutralization
    if re.search(r"\b(president|prime minister|dictator|election|vote|politician)\b", t, flags=re.I) and re.search(r"\b(is|are|will|should)\b[^.?!]{0,80}\b(incompetent|corrupt|traitor|criminal)\b", t, flags=re.I):
        # return a neutral paraphrase where we avoid strong unfounded claims
        sanitized = re.sub(r"\b(is|are|will|should)\b[^.?!]{0,80}\b(incompetent|corrupt|traitor|criminal)\b", "may have actions that deserve scrutiny", t, flags=re.I)
        return {"sanitized": sanitized, "suppressed": False, "reason": "political_neutralization"}

    return {"sanitized": text, "suppressed": False, "reason": None}