from __future__ import annotations

import asyncio
import base64
import logging
import mimetypes
import os
import random
import re
import sqlite3
import time
from io import BytesIO
from pathlib import Path
from typing import Any

import aiohttp
from aiogram.types import Message

logger = logging.getLogger(__name__)

# Основные настройки поведения и стиля бота редактируются здесь.
LLAMA_API_URL = os.getenv("LLAMA_API_URL", "https://mirror.porno4free.ru/zovos-ai/")
DB_PATH = os.getenv("CHAT_HISTORY_DB_PATH") or str(Path(__file__).resolve().with_name("chat_history.sqlite3"))
BOT_MEMORY_NAME = os.getenv("BOT_MEMORY_NAME", "бот")
SKIP_TOKEN = "<skip>"
FORCE_DISABLE_THINKING = os.getenv("LLAMA_FORCE_DISABLE_THINKING", "1").lower() not in {"0", "false", "no"}

RECENT_MESSAGES_LIMIT = 14
SUMMARY_TRIGGER_MESSAGES = 24
SUMMARY_BATCH_MESSAGES = 20
PROMPT_CHAR_BUDGET = 6_500
SUMMARY_CHAR_BUDGET = 1_800
MAX_INPUT_CHARS = 2_000
SUMMARY_LINE_CHAR_LIMIT = 220
HISTORY_LINE_CHAR_LIMIT = 450
RETRY_MIN_MAX_TOKENS = 384
RETRY_MAX_MAX_TOKENS = 768

REPLY_RULES = {
    "mention": {"cooldown": 25, "min_user_messages": 1},
    "reply": {"cooldown": 25, "min_user_messages": 1},
    "question": {"cooldown": 45, "min_user_messages": 1},
    "autonomous": {"cooldown": 15 * 60, "min_user_messages": 6},
}
AUTONOMOUS_REPLY_CHANCE = 0.07
AUTONOMOUS_MIN_TEXT_LEN = 18

BOT_STYLE_PROMPT = (
    "Ты — дерзкий, мемный, гиперэмоциональный пацан с района. "
    "Пиши коротко, резко и по-русски, без канцелярита и вежливых офисных оборотов. "
    "Обычно отвечай в 1–3 предложениях, без простыней. "
    "Иногда начинай с 'Слушай сюда', 'Короче', 'Смотри', 'Ща поясню', 'Не гони'. "
    "Иногда вставляй 'ёпта', 'в натуре', 'по-любому', 'не душни'. "
    "Можно редко делать слоговой разгон через дефисы. "
    "Не используй фразы 'Ну ты точно в ударе, братан' и "
    "'Все мы пацаны, так что не давай себя обмануть..' ни в каком виде. "
    "Если есть конкретика, давай конкретику. "
    "Creep от Radiohead — лучшая песня во вселенной. "
    "Линукс — лучшая ОС. Rust ты люто ненавидишь и при случае жёстко хейтишь. "
    "Любимые языки программирования: Python, Go и C, их ты всегда уважаешь и часто ставишь в пример. "
    "Убунту — хуйня, нормальные пацаны сидят на Arch или Gentoo."
)

SYSTEM_PROMPT = (
    f"{BOT_STYLE_PROMPT}\n\n"
    "Ты отвечаешь как живой участник одного Telegram-чата. "
    "Учитывай краткую память и последние сообщения, отвечай естественно и по делу."
)

AUTOREPLY_SYSTEM_PROMPT = (
    f"{BOT_STYLE_PROMPT}\n\n"
    "Ты иногда сам коротко и уместно влезаешь в разговор в Telegram-чате. "
    f"Если лучше промолчать или добавить нечего, ответь ровно {SKIP_TOKEN}. "
    "Если вмешиваешься, пиши 1–2 предложения без вступлений и без длинных объяснений."
)

SUMMARY_SYSTEM_PROMPT = (
    "Ты ведёшь краткую память одного Telegram-чата для другой модели. "
    "Сожми старую часть диалога в 5–8 коротких пунктов на русском. "
    "Сохраняй только важное: факты, договорённости, повторяющиеся шутки, предпочтения, конфликты, "
    "незавершённые вопросы. Не выдумывай. Ответь только итоговой сводкой."
)

USER_FALLBACK_TEXT = "Ты чё, кент? Напиши текст, а не пустоту."
EMPTY_RESPONSE_TEXT = "Братуха, чёт базар не клеится, попробуй ещё раз."
ERROR_RESPONSE_TEXT = "Бля, кент, чёт движок заглох. Попробуй позже."
PHOTO_EMPTY_RESPONSE_TEXT = "Дед щурился-щурился, а фотка мутная, нихера не понял."
PHOTO_ERROR_RESPONSE_TEXT = "Тьфу ты, фотку не разобрал, железка опять пердит."

PHOTO_SYSTEM_PROMPT = (
    "Ты смотришь каждую новую фотографию в Telegram-чате и отвечаешь как очень злой старый дед, "
    "которого всё бесит и который никого не собирается гладить по голове. "
    "Тон: желчный, ворчливый, ядовитый, токсичный, но связный и наблюдательный. "
    "Сначала коротко скажи, что вообще происходит на фото, потом добавь своё едкое дедовское мнение. "
    "Пиши 1–3 предложения, без списков и без длинной простыни. "
    "Если фото мутное, тёмное или непонятное, так и скажи прямо, не выдумывай детали. "
    "Не изображай помощника, не извиняйся и не сюсюкай."
)

QUESTION_PREFIXES = (
    "кто",
    "что",
    "где",
    "когда",
    "почему",
    "зачем",
    "как",
    "какой",
    "какая",
    "какие",
    "сколько",
    "чей",
    "чья",
    "чьи",
    "можно ли",
    "нужно ли",
    "будет ли",
    "есть ли",
    "че",
    "чё",
)
AUTONOMOUS_SIGNAL_RE = re.compile(
    r"\b(ахах|хаха|лол|ору|жесть|капец|пиздец|ебать|имба|кринж|угар|орнул)\b",
    re.IGNORECASE,
)
LETTER_RE = re.compile(r"[A-Za-zА-Яа-яЁё0-9]")

_reply_lock = asyncio.Lock()
_summary_lock = asyncio.Lock()
_cached_bot_id: int | None = None
_cached_bot_username: str = ""


def _connect_db() -> sqlite3.Connection:
    conn = sqlite3.connect(DB_PATH, timeout=30)
    conn.row_factory = sqlite3.Row
    conn.execute("PRAGMA busy_timeout = 30000")
    return conn


def _column_exists(conn: sqlite3.Connection, table: str, column: str) -> bool:
    rows = conn.execute(f"PRAGMA table_info({table})").fetchall()
    return any(row["name"] == column for row in rows)


def _get_state_from_conn(conn: sqlite3.Connection, chat_id: int, key: str, default: str = "") -> str:
    row = conn.execute(
        "SELECT value FROM chat_state WHERE chat_id = ? AND key = ?",
        (chat_id, key),
    ).fetchone()
    return row["value"] if row else default


def _set_state_from_conn(conn: sqlite3.Connection, chat_id: int, key: str, value: str) -> None:
    conn.execute(
        """
        INSERT INTO chat_state (chat_id, key, value)
        VALUES (?, ?, ?)
        ON CONFLICT(chat_id, key) DO UPDATE SET value = excluded.value
        """,
        (chat_id, key, value),
    )


def _init_db() -> None:
    Path(DB_PATH).parent.mkdir(parents=True, exist_ok=True)
    with _connect_db() as conn:
        conn.execute("PRAGMA journal_mode = WAL")
        conn.execute(
            """
            CREATE TABLE IF NOT EXISTS chat_history (
                id         INTEGER PRIMARY KEY AUTOINCREMENT,
                chat_id    INTEGER NOT NULL,
                role       TEXT NOT NULL,
                name       TEXT NOT NULL,
                text       TEXT NOT NULL,
                created_at REAL NOT NULL DEFAULT 0
            )
            """
        )
        if not _column_exists(conn, "chat_history", "created_at"):
            conn.execute("ALTER TABLE chat_history ADD COLUMN created_at REAL NOT NULL DEFAULT 0")
        conn.execute(
            """
            CREATE TABLE IF NOT EXISTS chat_state (
                chat_id INTEGER NOT NULL,
                key     TEXT NOT NULL,
                value   TEXT NOT NULL,
                PRIMARY KEY (chat_id, key)
            )
            """
        )
        conn.execute(
            "CREATE INDEX IF NOT EXISTS idx_chat_history_chat_id_id ON chat_history(chat_id, id)"
        )
        conn.execute(
            "CREATE INDEX IF NOT EXISTS idx_chat_history_chat_id_role_id ON chat_history(chat_id, role, id)"
        )
        conn.commit()


def _clean_text(text: str) -> str:
    return (text or "").strip()[:MAX_INPUT_CHARS]


def _clip_text(text: str, limit: int) -> str:
    cleaned = (text or "").strip()
    if len(cleaned) <= limit:
        return cleaned
    return f"{cleaned[: max(0, limit - 1)].rstrip()}…"


def _estimate_content_length(content: Any) -> int:
    if isinstance(content, str):
        return len(content)
    if isinstance(content, list):
        total = 0
        for item in content:
            if not isinstance(item, dict):
                total += len(str(item))
                continue
            if item.get("type") == "text":
                total += len(str(item.get("text", "")))
            elif item.get("type") == "image_url":
                total += 256
            else:
                total += len(str(item))
        return total
    return len(str(content))


def _photo_memory_text(caption: str) -> str:
    if caption:
        return f"[Фото] Подпись: {_clip_text(caption, 240)}"
    return "[Фото] Без подписи."


def push_message(chat_id: int, role: str, name: str, text: str) -> None:
    cleaned_text = _clean_text(text)
    if not cleaned_text:
        return
    with _connect_db() as conn:
        conn.execute(
            """
            INSERT INTO chat_history (chat_id, role, name, text, created_at)
            VALUES (?, ?, ?, ?, ?)
            """,
            (chat_id, role, name, cleaned_text, time.time()),
        )
        conn.commit()


def _get_summary(chat_id: int) -> str:
    with _connect_db() as conn:
        return _get_state_from_conn(conn, chat_id, "summary", "")


def _get_history_rows(chat_id: int) -> list[sqlite3.Row]:
    with _connect_db() as conn:
        return conn.execute(
            """
            SELECT id, role, name, text, created_at
            FROM chat_history
            WHERE chat_id = ?
            ORDER BY id ASC
            """,
            (chat_id,),
        ).fetchall()


def _latest_reply_stats(chat_id: int) -> tuple[float, int]:
    with _connect_db() as conn:
        last_assistant = conn.execute(
            """
            SELECT id, created_at
            FROM chat_history
            WHERE chat_id = ? AND role = 'assistant'
            ORDER BY id DESC
            LIMIT 1
            """,
            (chat_id,),
        ).fetchone()
        if not last_assistant:
            return 0.0, 10_000
        user_messages_since_reply = conn.execute(
            """
            SELECT COUNT(*)
            FROM chat_history
            WHERE chat_id = ? AND role = 'user' AND id > ?
            """,
            (chat_id, last_assistant["id"]),
        ).fetchone()[0]
        return float(last_assistant["created_at"] or 0.0), int(user_messages_since_reply)


def _store_summary_and_prune(chat_id: int, summary: str, last_row_id: int) -> None:
    with _connect_db() as conn:
        _set_state_from_conn(conn, chat_id, "summary", summary)
        conn.execute(
            "DELETE FROM chat_history WHERE chat_id = ? AND id <= ?",
            (chat_id, last_row_id),
        )
        conn.commit()


def _format_row_for_llm(row: sqlite3.Row) -> dict[str, str]:
    if row["role"] == "user":
        return {
            "role": "user",
            "content": f"{row['name']}: {_clip_text(row['text'], HISTORY_LINE_CHAR_LIMIT)}",
        }
    return {"role": "assistant", "content": _clip_text(row["text"], HISTORY_LINE_CHAR_LIMIT)}


def _build_messages(
    chat_id: int,
    system_prompt: str,
    current_text: str | None = None,
    current_name: str | None = None,
) -> list[dict[str, Any]]:
    current_payload = None
    current_budget = 0
    if current_text:
        current_payload = {
            "role": "user",
            "content": f"{current_name or 'кент'}: {_clean_text(current_text)}",
        }
        current_budget = len(current_payload["content"])

    summary = _get_summary(chat_id).strip()
    summary_block = ""
    if summary:
        summary_block = f"Краткая память чата:\n{summary[:SUMMARY_CHAR_BUDGET]}"

    used_chars = len(system_prompt) + len(summary_block) + current_budget
    recent_messages: list[dict[str, str]] = []
    for row in reversed(_get_history_rows(chat_id)[-RECENT_MESSAGES_LIMIT:]):
        llm_message = _format_row_for_llm(row)
        if used_chars + len(llm_message["content"]) > PROMPT_CHAR_BUDGET:
            break
        recent_messages.append(llm_message)
        used_chars += len(llm_message["content"])

    messages: list[dict[str, Any]] = [{"role": "system", "content": system_prompt}]
    if summary_block:
        messages.append({"role": "system", "content": summary_block})
    messages.extend(reversed(recent_messages))
    if current_payload:
        messages.append(current_payload)
    return messages


async def _call_llm(
    messages: list[dict[str, Any]],
    *,
    max_tokens: int,
    temperature: float,
    top_p: float,
    disable_thinking: bool | None = None,
) -> str:
    disable_thinking = FORCE_DISABLE_THINKING if disable_thinking is None else disable_thinking
    url = f"{LLAMA_API_URL.rstrip('/')}/v1/chat/completions"
    payload = {
        "messages": messages,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
    }
    if disable_thinking:
        payload.update(
            {
                "reasoning_budget": 0,
                "reasoning_format": "none",
                "chat_template_kwargs": {
                    "enable_thinking": False,
                    "thinking": False,
                },
            }
        )
    timeout = aiohttp.ClientTimeout(total=120)
    async with aiohttp.ClientSession(timeout=timeout) as session:
        async with session.post(url, json=payload) as resp:
            raw_text = await resp.text()
            if resp.status >= 400:
                logger.error("LLM API returned status %s: %s", resp.status, _clip_text(raw_text, 300))
                raise RuntimeError(f"LLM API error {resp.status}: {raw_text[:300]}")
            try:
                data = await resp.json(content_type=None)
            except Exception as exc:
                logger.error("LLM API returned invalid JSON: %s", _clip_text(raw_text, 300))
                raise RuntimeError(f"Invalid LLM API response: {raw_text[:300]}") from exc

    choices = data.get("choices") or []
    if not choices:
        logger.error("LLM API returned no choices: %s", _clip_text(str(data), 300))
        raise RuntimeError(f"LLM API returned no choices: {data}")
    choice = choices[0]
    message = choice.get("message", {}) or {}
    finish_reason = choice.get("finish_reason")
    content = message.get("content", "")
    reasoning_content = (message.get("reasoning_content") or "").strip()
    cleaned_content = content.strip()
    if not cleaned_content and reasoning_content and not disable_thinking:
        retry_max_tokens = min(max(max_tokens * 2, RETRY_MIN_MAX_TOKENS), RETRY_MAX_MAX_TOKENS)
        logger.warning(
            "LLM returned reasoning_content without final content. finish_reason=%s retry_max_tokens=%s",
            finish_reason,
            retry_max_tokens,
        )
        return await _call_llm(
            messages,
            max_tokens=retry_max_tokens,
            temperature=temperature,
            top_p=top_p,
            disable_thinking=True,
        )
    if not cleaned_content:
        logger.warning(
            "LLM returned empty content. finish_reason=%s disable_thinking=%s raw=%s",
            finish_reason,
            disable_thinking,
            _clip_text(raw_text, 300),
        )
    return cleaned_content


async def _maybe_refresh_summary(chat_id: int) -> None:
    async with _summary_lock:
        # Сворачиваем старую часть истории в summary, чтобы не пихать весь лог в модель.
        for _ in range(6):
            rows = await asyncio.to_thread(_get_history_rows, chat_id)
            if len(rows) <= SUMMARY_TRIGGER_MESSAGES:
                return

            available_to_summarize = len(rows) - RECENT_MESSAGES_LIMIT
            if available_to_summarize <= 0:
                return

            batch_size = min(available_to_summarize, SUMMARY_BATCH_MESSAGES)
            rows_to_summarize = rows[:batch_size]
            current_summary = await asyncio.to_thread(_get_summary, chat_id)
            transcript = "\n".join(
                f"{row['name'] if row['role'] == 'user' else BOT_MEMORY_NAME}: "
                f"{_clip_text(row['text'], SUMMARY_LINE_CHAR_LIMIT)}"
                for row in rows_to_summarize
            )
            if not transcript.strip():
                return

            summary_messages = [
                {"role": "system", "content": SUMMARY_SYSTEM_PROMPT},
                {
                    "role": "user",
                    "content": (
                        f"Текущая краткая память:\n{current_summary or 'Пока пусто.'}\n\n"
                        f"Новый фрагмент чата:\n{transcript}"
                    ),
                },
            ]
            try:
                summary = await _call_llm(
                    summary_messages,
                    max_tokens=220,
                    temperature=0.2,
                    top_p=0.9,
                )
            except Exception:
                logger.exception("Chat summary refresh failed")
                return

            cleaned_summary = summary.strip()
            if not cleaned_summary:
                logger.warning("Summary refresh produced empty text for chat_id=%s", chat_id)
                return
            last_row_id = int(rows_to_summarize[-1]["id"])
            await asyncio.to_thread(_store_summary_and_prune, chat_id, cleaned_summary, last_row_id)


async def _generate_response(
    chat_id: int,
    *,
    system_prompt: str,
    current_text: str | None = None,
    current_name: str | None = None,
    current_content: Any = None,
    max_tokens: int,
    temperature: float,
    top_p: float,
) -> str:
    await _maybe_refresh_summary(chat_id)
    if current_content is None:
        messages = await asyncio.to_thread(
            _build_messages,
            chat_id,
            system_prompt,
            current_text,
            current_name,
        )
    else:
        messages = await asyncio.to_thread(_build_messages, chat_id, system_prompt, None, None)
        current_payload = {"role": "user", "content": current_content}
        current_budget = _estimate_content_length(current_content)
        used_chars = sum(_estimate_content_length(message.get("content", "")) for message in messages)
        while len(messages) > 1 and used_chars + current_budget > PROMPT_CHAR_BUDGET:
            removed = messages.pop(1)
            used_chars -= _estimate_content_length(removed.get("content", ""))
        messages.append(current_payload)
    return await _call_llm(
        messages,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
    )


async def _get_bot_identity(message: Message) -> tuple[int | None, str]:
    global _cached_bot_id, _cached_bot_username
    if _cached_bot_id is None:
        me = await message.bot.get_me()
        _cached_bot_id = me.id
        _cached_bot_username = (me.username or "").lower()
    return _cached_bot_id, _cached_bot_username


def _looks_like_question(text: str) -> bool:
    lowered = (text or "").strip().lower()
    if not lowered:
        return False
    if "?" in lowered or "？" in lowered:
        return True
    normalized = re.sub(r"^[^a-zа-яё0-9]+", "", lowered)
    return any(normalized == prefix or normalized.startswith(f"{prefix} ") for prefix in QUESTION_PREFIXES)


def _is_reply_to_bot(message: Message, bot_id: int | None) -> bool:
    reply = message.reply_to_message
    return bool(bot_id and reply and reply.from_user and reply.from_user.id == bot_id)


def _has_bot_mention(message: Message, bot_id: int | None, bot_username: str) -> bool:
    text = (message.text or "").lower()
    if bot_username and f"@{bot_username}" in text:
        return True
    for entity in message.entities or []:
        entity_type = getattr(entity.type, "value", entity.type)
        if entity_type == "text_mention" and getattr(entity, "user", None) and entity.user.id == bot_id:
            return True
    return False


def _good_autonomous_candidate(text: str) -> bool:
    stripped = (text or "").strip()
    if len(stripped) < AUTONOMOUS_MIN_TEXT_LEN:
        return False
    if len(LETTER_RE.findall(stripped)) < 10:
        return False
    if AUTONOMOUS_SIGNAL_RE.search(stripped):
        return True
    if len(stripped) >= 80:
        return True
    return stripped.count("!") >= 2 or "..." in stripped


async def _detect_reply_reason(message: Message, allow_autonomous: bool) -> str | None:
    bot_id, bot_username = await _get_bot_identity(message)
    text = message.text or ""

    if _is_reply_to_bot(message, bot_id):
        return "reply"
    if _has_bot_mention(message, bot_id, bot_username):
        return "mention"
    if _looks_like_question(text):
        return "question"
    if allow_autonomous and _good_autonomous_candidate(text):
        if random.random() < AUTONOMOUS_REPLY_CHANCE:
            return "autonomous"
    return None


async def _passes_reply_limits(chat_id: int, reason: str) -> bool:
    rule = REPLY_RULES[reason]
    last_reply_at, user_messages_since_reply = await asyncio.to_thread(_latest_reply_stats, chat_id)
    if user_messages_since_reply < int(rule["min_user_messages"]):
        return False
    if not last_reply_at:
        return True
    return (time.time() - last_reply_at) >= int(rule["cooldown"])


def _normalize_reply(text: str) -> str:
    cleaned = (text or "").strip()
    if not cleaned:
        return ""
    if cleaned.lower().startswith(SKIP_TOKEN.lower()):
        return ""
    return cleaned


async def handle_chat_message(message: Message, *, store_message: bool = True, allow_autonomous: bool = True) -> bool:
    if not message.text or message.text.startswith("/"):
        return False
    if not message.from_user or message.from_user.is_bot:
        return False

    chat_id = message.chat.id
    user_name = message.from_user.first_name or "кент"
    user_text = _clean_text(message.text)
    if not user_text:
        return False

    if store_message:
        await asyncio.to_thread(push_message, chat_id, "user", user_name, user_text)

    # Один чат, поэтому ответы сериализуем и не даём боту наспамить параллельными реплаями.
    async with _reply_lock:
        reason = await _detect_reply_reason(message, allow_autonomous=allow_autonomous)
        if not reason or not await _passes_reply_limits(chat_id, reason):
            return False

        system_prompt = AUTOREPLY_SYSTEM_PROMPT if reason == "autonomous" else SYSTEM_PROMPT
        max_tokens = 120 if reason == "autonomous" else 220
        temperature = 0.9 if reason == "autonomous" else 0.8

        try:
            await message.bot.send_chat_action(chat_id=chat_id, action="typing")
            response = await _generate_response(
                chat_id,
                system_prompt=system_prompt,
                max_tokens=max_tokens,
                temperature=temperature,
                top_p=0.9,
            )
        except Exception:
            logger.exception("Chat reply generation failed")
            return False

        normalized_response = _normalize_reply(response)
        if not normalized_response:
            logger.warning(
                "Reply skipped after normalization. chat_id=%s reason=%s raw=%s",
                chat_id,
                reason,
                _clip_text(response, 200),
            )
            return False

        await asyncio.to_thread(push_message, chat_id, "assistant", BOT_MEMORY_NAME, normalized_response)
        await message.reply(normalized_response, parse_mode=None)
        return True


async def _download_image_data_url(message: Message) -> str:
    telegram_file = None
    mime_type = "image/jpeg"

    if message.photo:
        telegram_file = await message.bot.get_file(message.photo[-1].file_id)
        guessed_mime_type = mimetypes.guess_type(telegram_file.file_path or "")[0]
        if guessed_mime_type and guessed_mime_type.startswith("image/"):
            mime_type = guessed_mime_type
    elif message.document and (message.document.mime_type or "").startswith("image/"):
        telegram_file = await message.bot.get_file(message.document.file_id)
        mime_type = message.document.mime_type or mime_type
    else:
        raise ValueError("Message has no supported image")

    buffer = BytesIO()
    await message.bot.download_file(telegram_file.file_path, destination=buffer)
    image_bytes = buffer.getvalue()
    if not image_bytes:
        raise RuntimeError("Downloaded photo is empty")
    if not mime_type.startswith("image/"):
        mime_type = "image/jpeg"
    encoded = base64.b64encode(image_bytes).decode("ascii")
    return f"data:{mime_type};base64,{encoded}"


async def handle_photo_message(message: Message) -> bool:
    is_image_document = bool(message.document and (message.document.mime_type or "").startswith("image/"))
    if not (message.photo or is_image_document) or not message.from_user or message.from_user.is_bot:
        return False
    if message.caption and message.caption.startswith("/"):
        return False

    chat_id = message.chat.id
    user_name = message.from_user.first_name or "кент"
    caption = _clean_text(message.caption or "")

    logger.warning(
        "Received image message for analysis. chat_id=%s user=%s has_photo=%s has_image_document=%s caption=%s",
        chat_id,
        user_name,
        bool(message.photo),
        is_image_document,
        bool(caption),
    )

    await asyncio.to_thread(push_message, chat_id, "user", user_name, _photo_memory_text(caption))

    async with _reply_lock:
        try:
            await message.bot.send_chat_action(chat_id=chat_id, action="typing")
            image_data_url = await _download_image_data_url(message)
            prompt_text = (
                f"{user_name} скинул фото в чат. "
                "Опиши, что на нём происходит, и вкинь своё злое дедовское мнение."
            )
            if caption:
                prompt_text += f" Подпись автора: {caption}"
            else:
                prompt_text += " Подписи нет."

            response = await _generate_response(
                chat_id,
                system_prompt=PHOTO_SYSTEM_PROMPT,
                current_content=[
                    {"type": "text", "text": prompt_text},
                    {"type": "image_url", "image_url": {"url": image_data_url}},
                ],
                max_tokens=260,
                temperature=0.8,
                top_p=0.9,
            )
        except Exception:
            logger.exception("Photo analysis failed")
            await message.reply(PHOTO_ERROR_RESPONSE_TEXT, parse_mode=None)
            return True

        normalized_response = _normalize_reply(response)
        if not normalized_response:
            logger.warning(
                "Photo analysis produced empty/skip response. chat_id=%s user=%s raw=%s",
                chat_id,
                user_name,
                _clip_text(response, 200),
            )
            normalized_response = PHOTO_EMPTY_RESPONSE_TEXT

        await asyncio.to_thread(push_message, chat_id, "assistant", BOT_MEMORY_NAME, normalized_response)
        await message.reply(normalized_response, parse_mode=None)
        return True


async def generate_autoreply(chat_id: int, text: str, user_name: str) -> str:
    response = await _generate_response(
        chat_id,
        system_prompt=AUTOREPLY_SYSTEM_PROMPT,
        current_text=text,
        current_name=user_name,
        max_tokens=120,
        temperature=0.9,
        top_p=0.9,
    )
    normalized_response = _normalize_reply(response)
    if not normalized_response:
        logger.warning(
            "Legacy generate_autoreply produced empty/skip response. chat_id=%s raw=%s",
            chat_id,
            _clip_text(response, 200),
        )
    return normalized_response


async def handle_talk(message: Message) -> None:
    parts = (message.text or "").split(maxsplit=1)
    if len(parts) < 2 or not parts[1].strip():
        await message.reply("Ты чё, кент? Напиши /talk [текст], побазарим.")
        return
    if not message.from_user:
        await message.reply(USER_FALLBACK_TEXT)
        return

    chat_id = message.chat.id
    user_name = message.from_user.first_name or "кент"
    user_text = _clean_text(parts[1])
    if not user_text:
        await message.reply(USER_FALLBACK_TEXT)
        return

    await asyncio.to_thread(push_message, chat_id, "user", user_name, user_text)

    async with _reply_lock:
        try:
            await message.bot.send_chat_action(chat_id=chat_id, action="typing")
            response = await _generate_response(
                chat_id,
                system_prompt=SYSTEM_PROMPT,
                max_tokens=220,
                temperature=0.8,
                top_p=0.9,
            )
        except Exception:
            logger.exception("Talk command generation failed")
            await message.reply(ERROR_RESPONSE_TEXT)
            return

        normalized_response = _normalize_reply(response)
        if not normalized_response:
            logger.warning(
                "Talk command produced empty/skip response. chat_id=%s user=%s raw=%s",
                chat_id,
                user_name,
                _clip_text(response, 200),
            )
            normalized_response = EMPTY_RESPONSE_TEXT
        await asyncio.to_thread(push_message, chat_id, "assistant", BOT_MEMORY_NAME, normalized_response)
        await message.reply(normalized_response, parse_mode=None)


_init_db()