import base64
import aiohttp
import logging
from aiogram import Dispatcher, Bot
from aiogram.types import Message
from utils.antispam import saving, save_message
from aiogram.filters import Command
from models.state import BotState

logger = logging.getLogger(__name__)

def register_handlers(dp: Dispatcher, state: BotState, bot: Bot):
    chat_history = {}
    MAX_HISTORY = 20  # храним последние 20 сообщений (user+assistant)

    @dp.message(Command("gpt"))
    @saving
    async def ask_gpt(message: Message):
        chat_id = message.chat.id
        if chat_id not in chat_history:
            chat_history[chat_id] = []

        content_blocks = []
        user_prompt = None

        # Текст после команды или caption
        if message.text:
            parts = message.text.split(maxsplit=1)
            if len(parts) > 1:
                user_prompt = parts[1]
        if message.caption:
            user_prompt = message.caption

        if user_prompt:
            content_blocks.append({"type": "text", "text": user_prompt})

        # Фото → base64 → image_url
        if message.photo:
            photo = message.photo[-1]
            file = await bot.get_file(photo.file_id)
            file_bytes = await bot.download_file(file.file_path)
            image_b64 = base64.b64encode(file_bytes.read()).decode("utf-8")
            content_blocks.append(
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"},
                }
            )

        if not content_blocks:
            await message.reply("❗ Укажи текст или прикрепи фото")
            return

        url = "http://192.168.31.197:1234/v1/chat/completions"

        # Добавляем новое сообщение в историю
        chat_history[chat_id].append({"role": "user", "content": content_blocks})

        # Ограничиваем историю (оставляем последние MAX_HISTORY сообщений)
        if len(chat_history[chat_id]) > MAX_HISTORY:
            chat_history[chat_id] = chat_history[chat_id][-MAX_HISTORY:]

        payload = {
            "model": "qwen/qwen3-vl-4b",
            "messages": chat_history[   chat_id],
            "temperature": 0.7,
            "max_tokens": 4096,
            "stream": False,
        }

        try:
            async with aiohttp.ClientSession() as session:
                async with session.post(url, json=payload) as resp:
                    if resp.status != 200:
                        error_text = await resp.text()
                        await message.reply(
                            f"❌ Ошибка LM Studio: {resp.status} {error_text}"
                        )
                        return

                    data = await resp.json()
                    reply_text = data["choices"][0]["message"]["content"]

                    # Сохраняем ответ ассистента в историю
                    chat_history[chat_id].append(
                        {
                            "role": "assistant",
                            "content": [{"type": "text", "text": reply_text}],
                        }
                    )

                    # Ограничиваем снова (чтобы не разрасталось)
                    if len(chat_history[chat_id]) > MAX_HISTORY:
                        chat_history[chat_id] = chat_history[chat_id][-MAX_HISTORY:]

                    msg = await message.reply(f"🤖 Ответ:\n{reply_text}")
                    save_message(msg.chat.id, msg.message_id)

        except Exception as e:
            logger.error(f"Ошибка при запросе к LM Studio: {e}")
            await message.reply(f"❌ Ошибка при запросе к LM Studio: {e}")

    @dp.message(Command("clear"))
    async def clear(message: Message):
        chat_history.pop(message.chat.id, None)