ruffed
This commit is contained in:
parent
c1e481ded8
commit
a7b1925e8d
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@ -1,12 +1,13 @@
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from bot.api import telegram_api
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from utils.mention import mention, userdata_extract
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from utils.store import redis
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from utils.mention import userdata_extract
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from state.redis import redis
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import logging
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logger = logging.getLogger(__name__)
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logging.basicConfig(level=logging.INFO)
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def get_newcomer_message(msg):
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lang = msg["from"].get("language_code", "ru")
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r = "хочет присоединиться к нам здесь" if lang == "ru" else " wants to join us here"
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@ -27,27 +28,36 @@ async def show_announce(msg):
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userphotos_response = await telegram_api("getUserphotos", user_id=from_id)
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file_id = ""
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if isinstance(userphotos_response, dict) and userphotos_response["ok"] and userphotos_response["result"]["total_count"] > 0:
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if (
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isinstance(userphotos_response, dict)
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and userphotos_response["ok"]
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and userphotos_response["result"]["total_count"] > 0
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):
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logger.info("showing button with photo")
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file_id = userphotos_response["result"]["photos"][0][0]["file_id"]
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r = await telegram_api("sendPhoto",
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r = await telegram_api(
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"sendPhoto",
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chat_id=chat_id,
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file_id=file_id,
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caption=newcomer_message,
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reply_to=mid
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reply_to=mid,
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)
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announce_msg_id = r.get("message_id")
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await redis.set(f"announce:{chat_id}:{from_id}", announce_message_id)
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await redis.set(f"announce:{chat_id}:{from_id}", announce_msg_id)
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async def edit_announce(msg):
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logger.info("editing announce")
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chat_id = str(msg["chat"]["id"])
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from_id = str(msg["from"]["id"])
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mid = msg.get("message_id", "")
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caption = get_newcomer_message(msg) + msg.get("text").replace("/message ", "")
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announce_message_id = await redis.get(f"announce:{chat_id}:{from_id}")
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if announce_message_id:
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r = await telegram_api("editMessageCaption", chat_id=chat_id, message_id=int(announce_message_id), caption=caption)
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r = await telegram_api(
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"editMessageCaption",
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chat_id=chat_id,
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message_id=int(announce_message_id),
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caption=caption,
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)
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await redis.set(f"announce:{chat_id}:{from_id}", r.get("message_id"))
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15
bot/api.py
15
bot/api.py
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@ -5,7 +5,7 @@ from bot.config import BOT_TOKEN
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import logging
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# Create a logger instance
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logger = logging.getLogger('bot.api')
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logger = logging.getLogger("bot.api")
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logging.basicConfig(level=logging.DEBUG)
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api_base = f"https://api.telegram.org/bot{BOT_TOKEN}/"
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@ -14,17 +14,20 @@ api_base = f"https://api.telegram.org/bot{BOT_TOKEN}/"
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async def telegram_api(endpoint: str, json_data=None, **kwargs):
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try:
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url = api_base + f"{endpoint}?{urlencode(kwargs)}"
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is_polling = endpoint == 'getUpdates'
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headers = {'Content-Type': 'application/json'}
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is_polling = endpoint == "getUpdates"
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headers = {"Content-Type": "application/json"}
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async with aiohttp.ClientSession() as session:
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url = api_base + f"{endpoint}?{urlencode(kwargs)}"
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if not is_polling:
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logger.info(f' >>> {url} {json_data if json_data else ""}')
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async with session.get(url, data=json.dumps(json_data), headers=headers) as response:
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async with session.get(
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url, data=json.dumps(json_data), headers=headers
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) as response:
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data = await response.json()
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if not is_polling:
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logger.info(f' <<< {data}')
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logger.info(f" <<< {data}")
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return data
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except Exception as ex:
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except Exception:
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import traceback
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traceback.print_exc()
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@ -9,10 +9,30 @@ logger = logging.getLogger(__name__)
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logging.basicConfig(level=logging.INFO)
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positive_reactions = ["👍", "❤", "🔥", "🥰", "👏", "🎉", "🙏", "👌", "🕊", "😍", "❤🔥", "🍓", "🍾", "💋", "😇", "🤝", "🤗", "💘", "😘"]
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positive_reactions = [
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"👍",
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"❤",
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"🔥",
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"🥰",
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"👏",
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"🎉",
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"🙏",
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"👌",
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"🕊",
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"😍",
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"❤🔥",
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"🍓",
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"🍾",
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"💋",
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"😇",
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"🤝",
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"🤗",
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"💘",
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"😘",
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]
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announced_message = {
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"ru": "Запрос на вступление опубликован в чате, как только вас узнают и отреагируют - она будет принята",
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"en": "The join request is posted in the chat, once you are recognized and someone reacted to - it will be accepted"
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"en": "The join request is posted in the chat, once you are recognized and someone reacted to - it will be accepted",
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}
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@ -22,12 +42,14 @@ async def handle_join_request(join_request):
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lang = user.get("language_code", "ru")
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# показываем для FEEDBACK_CHAT
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await telegram_api("sendMessage", chat_id=FEEDBACK_CHAT_ID, text="новая заявка от " + mention(user))
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await telegram_api(
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"sendMessage", chat_id=FEEDBACK_CHAT_ID, text="новая заявка от " + mention(user)
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)
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# показываем анонс с заявкой
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await show_announce(join_request)
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# сообщаем пользователю, что опубликовали анонс его заявки
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await telegram_api("sendMessage", chat_id=user['id'], text=announced_message[lang])
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await telegram_api("sendMessage", chat_id=user["id"], text=announced_message[lang])
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async def handle_reaction_on_request(update):
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@ -39,5 +61,7 @@ async def handle_reaction_on_request(update):
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new_reaction = reaction.get("new_reaction")
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if new_reaction.get("emoji") in positive_reactions:
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# за пользователя поручились
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r = await telegram_api("approveChatJoinRequest", chat_id=chat_id, user_id=from_id)
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r = await telegram_api(
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"approveChatJoinRequest", chat_id=chat_id, user_id=from_id
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)
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logger.debug(r)
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@ -2,14 +2,14 @@ from bot.config import FEEDBACK_CHAT_ID
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from bot.announce import edit_announce
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from bot.api import telegram_api
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import logging
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from utils.store import get_all_pattern, get_average_pattern
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from state.scan import get_all_pattern, get_average_pattern
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logger = logging.getLogger(__name__)
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logging.basicConfig(level=logging.INFO)
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start_message = {
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'en': "Welcome home! You can type any message here to be passed to chat",
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'ru': "Доброе утро! Можешь напечатать здесь любое сообщение для передачи в чат"
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"en": "Welcome home! You can type any message here to be passed to chat",
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"ru": "Доброе утро! Можешь напечатать здесь любое сообщение для передачи в чат",
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}
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@ -21,25 +21,35 @@ async def handle_private(msg, state):
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if lang != "ru" and lang != "en":
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lang = "en"
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if text and text.startswith("/"):
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if text == '/start':
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if text == "/start":
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await telegram_api("sendMessage", chat_id=uid, text=start_message[lang])
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state['welcome'] = True
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elif state.get('welcome'):
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state["welcome"] = True
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elif state.get("welcome"):
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await edit_announce(msg)
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state['welcome'] = False
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state["welcome"] = False
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return
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elif text.startswith('/toxic'):
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elif text.startswith("/toxic"):
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cid = msg.get("chat", {}).get("id")
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toxic_pattern = f"toxic:{uid}:{cid}:*"
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toxic_score = await get_average_toxic(toxic_pattern)
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toxic_score = await get_average_pattern(toxic_pattern)
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text = f"Средняя токсичность сообщений: {toxic_score}%"
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mid = msg.get("message_id")
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await telegram_api("sendMessage", chat_id=uid, reply_to_message_id=mid, text=text)
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await telegram_api(
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"sendMessage", chat_id=uid, reply_to_message_id=mid, text=text
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)
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return
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elif text == '/removed':
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elif text == "/removed":
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removed_pattern = f"removed:{uid}:*"
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removed_messages = await get_all_pattern(removed_pattern)
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if removed_messages:
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await telegram_api("sendMessage", chat_id=uid, text="\n\n".join(removed_messages))
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await telegram_api(
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"sendMessage", chat_id=uid, text="\n\n".join(removed_messages)
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)
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return
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await telegram_api("forwardMessage", from_chat_id=sender.get("id"), message_id=msg.get("message_id"), chat_id=FEEDBACK_CHAT_ID)
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await telegram_api(
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"forwardMessage",
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from_chat_id=sender.get("id"),
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message_id=msg.get("message_id"),
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chat_id=FEEDBACK_CHAT_ID,
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)
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@ -1,15 +1,17 @@
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import logging
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import math
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from utils.store import redis, get_average_pattern
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from state.redis import redis
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from state.scan import get_average_pattern
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from bot.api import telegram_api
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from bot.config import FEEDBACK_CHAT_ID
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from nlp.toxicity_detector import detector
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from handlers.handle_private import handle_private
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from utils.normalize import normalize
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from nlp.toxicity_detector import detector
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from nlp.normalize import normalize
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logger = logging.getLogger('handlers.messages_routing')
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logger = logging.getLogger("handlers.messages_routing")
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logging.basicConfig(level=logging.DEBUG)
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async def messages_routing(msg, state):
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cid = msg["chat"]["id"]
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uid = msg["from"]["id"]
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@ -28,11 +30,16 @@ async def messages_routing(msg, state):
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if reply_msg:
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reply_chat_id = reply_msg.get("chat", {}).get("id")
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if reply_chat_id != FEEDBACK_CHAT_ID:
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await telegram_api("sendMessage", chat_id=reply_chat_id, text=text, reply_to_message_id=reply_msg.get("message_id"))
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await telegram_api(
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"sendMessage",
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chat_id=reply_chat_id,
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text=text,
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reply_to_message_id=reply_msg.get("message_id"),
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)
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elif bool(text):
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mid = msg.get("message_id")
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if text == '/toxic@welcomecenter_bot':
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if text == "/toxic@welcomecenter_bot":
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# latest in chat
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latest_toxic_message_id = await redis.get(f"toxic:{cid}")
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@ -52,46 +59,44 @@ async def messages_routing(msg, state):
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one_score = await redis.get(f"toxic:{cid}:{uid}:{reply_to_msg_id}")
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if one_score:
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logger.debug(one_score)
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emoji = '😳' if toxic_score > 90 else '😟' if toxic_score > 80 else '😏' if toxic_score > 60 else '🙂' if toxic_score > 20 else '😇'
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emoji = (
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"😳"
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if toxic_score > 90
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else "😟"
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if toxic_score > 80
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else "😏"
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if toxic_score > 60
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else "🙂"
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if toxic_score > 20
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else "😇"
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)
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text = f"{int(one_score)}% токсичности\nСредняя токсичность сообщений: {toxic_score}% {emoji}"
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await telegram_api(
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"sendMessage",
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chat_id=cid,
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reply_to_message_id=reply_to_msg_id,
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text=text
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text=text,
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)
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await telegram_api(
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"deleteMessage",
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chat_id=cid,
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message_id=mid
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)
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elif text == '/removed@welcomecenter_bot':
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await telegram_api(
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"deleteMessage",
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chat_id=cid,
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message_id=mid
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)
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await telegram_api("deleteMessage", chat_id=cid, message_id=mid)
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elif text == "/removed@welcomecenter_bot":
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await telegram_api("deleteMessage", chat_id=cid, message_id=mid)
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else:
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toxic_score = detector(normalize(text))
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toxic_perc = math.floor(toxic_score*100)
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toxic_perc = math.floor(toxic_score * 100)
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await redis.set(f"toxic:{cid}", mid)
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await redis.set(f"toxic:{cid}:{uid}:{mid}", toxic_perc, ex=60*60*24*3)
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logger.info(f'\ntext: {text}\ntoxic: {toxic_perc}%')
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await redis.set(f"toxic:{cid}:{uid}:{mid}", toxic_perc, ex=60 * 60 * 24 * 3)
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logger.info(f"\ntext: {text}\ntoxic: {toxic_perc}%")
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if toxic_score > 0.81:
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if toxic_score > 0.90:
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await redis.set(f"removed:{uid}:{cid}:{mid}", text)
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await telegram_api(
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"deleteMessage",
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chat_id=cid,
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message_id=mid
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)
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await telegram_api("deleteMessage", chat_id=cid, message_id=mid)
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else:
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await telegram_api(
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"setMessageReaction",
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chat_id=cid,
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is_big=True,
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message_id=mid,
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reaction=f'[{{"type":"emoji", "emoji":"🙉"}}]'
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reaction='[{"type":"emoji", "emoji":"🙉"}]',
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)
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else:
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16
main.py
16
main.py
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@ -14,7 +14,11 @@ async def start():
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logger.info("\n\npolling started\n\n")
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offset = 0 # init offset
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while True:
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response = await telegram_api("getUpdates", offset=offset, allowed_updates=['message', 'message_reaction', 'chat_join_request'])
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response = await telegram_api(
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"getUpdates",
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offset=offset,
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allowed_updates=["message", "message_reaction", "chat_join_request"],
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)
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# logger.debug(response)
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if isinstance(response, dict):
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result = response.get("result", [])
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@ -35,15 +39,21 @@ async def start():
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except Exception as e:
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logger.error(e)
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import traceback
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text = traceback.format_exc()
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formatted_text = f"```log\n{text}```"
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await telegram_api("sendMessage", chat_id=FEEDBACK_CHAT_ID, text=formatted_text, parse_mode='MarkdownV2')
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await telegram_api(
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"sendMessage",
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chat_id=FEEDBACK_CHAT_ID,
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text=formatted_text,
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parse_mode="MarkdownV2",
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)
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offset = update["update_id"] + 1
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await asyncio.sleep(1.0)
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else:
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logger.error(' \n\n\n!!! getUpdates polling error\n\n\n')
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logger.error(" \n\n\n!!! getUpdates polling error\n\n\n")
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await asyncio.sleep(30.0)
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@ -1,4 +1,3 @@
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import logging
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import torch
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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@ -6,16 +5,18 @@ from transformers import T5Tokenizer, T5ForConditionalGeneration
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tokenizer = T5Tokenizer.from_pretrained("google/byt5-small")
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model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
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def is_russian_wording(text):
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"""
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Check if the text contains any Russian characters by checking
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each character against the Unicode range for Cyrillic.
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"""
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for char in text:
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if '\u0400' <= char <= '\u04FF': # Unicode range for Cyrillic characters
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if "\u0400" <= char <= "\u04ff": # Unicode range for Cyrillic characters
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return True
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return False
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def segment_text(text):
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"""
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Use a neural network model to segment text into words.
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@ -32,49 +33,53 @@ def segment_text(text):
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return segmented_text
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def normalize(text):
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"""
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Normalize English text to resemble Russian characters.
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"""
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# Segment the text first
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segmented_text = segment_text(text.replace(' ', ' ').replace(' ', ' ').replace(' ', ' '))
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segmented_text = segment_text(
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text.replace(" ", " ").replace(" ", " ").replace(" ", " ")
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)
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# Normalize after segmentation
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segmented_text = segmented_text.lower()
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if is_russian_wording(segmented_text):
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# Normalize the text by replacing characters
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normalized_text = (segmented_text
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.replace('e', 'е')
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.replace('o', 'о')
|
||||
.replace('x', 'х')
|
||||
.replace('a', 'а')
|
||||
.replace('r', 'г')
|
||||
.replace('m', 'м')
|
||||
.replace('u', 'и')
|
||||
.replace('n', 'п')
|
||||
.replace('p', 'р')
|
||||
.replace('t', 'т')
|
||||
.replace('y', 'у')
|
||||
.replace('h', 'н')
|
||||
.replace('i', 'й')
|
||||
.replace('c', 'с')
|
||||
.replace('k', 'к')
|
||||
.replace('b', 'в')
|
||||
.replace('3', 'з')
|
||||
.replace('4', 'ч')
|
||||
.replace('0', 'о')
|
||||
.replace('d', 'д')
|
||||
.replace('z', 'з'))
|
||||
normalized_text = (
|
||||
segmented_text.replace("e", "е")
|
||||
.replace("o", "о")
|
||||
.replace("x", "х")
|
||||
.replace("a", "а")
|
||||
.replace("r", "г")
|
||||
.replace("m", "м")
|
||||
.replace("u", "и")
|
||||
.replace("n", "п")
|
||||
.replace("p", "р")
|
||||
.replace("t", "т")
|
||||
.replace("y", "у")
|
||||
.replace("h", "н")
|
||||
.replace("i", "й")
|
||||
.replace("c", "с")
|
||||
.replace("k", "к")
|
||||
.replace("b", "в")
|
||||
.replace("3", "з")
|
||||
.replace("4", "ч")
|
||||
.replace("0", "о")
|
||||
.replace("d", "д")
|
||||
.replace("z", "з")
|
||||
)
|
||||
|
||||
return normalized_text
|
||||
|
||||
return segmented_text
|
||||
|
||||
|
||||
# Example usage
|
||||
if __name__ == "__main__":
|
||||
input_text = "Hello, this is a test input."
|
||||
|
||||
normalized_output = normalize(input_text)
|
||||
print(normalized_output)
|
||||
|
||||
|
|
@ -3,12 +3,17 @@ import torch
|
|||
import torch.nn.functional as F
|
||||
|
||||
# Load tokenizer and model weights
|
||||
tokenizer = BertTokenizer.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier')
|
||||
model = BertForSequenceClassification.from_pretrained('SkolkovoInstitute/russian_toxicity_classifier')
|
||||
tokenizer = BertTokenizer.from_pretrained(
|
||||
"SkolkovoInstitute/russian_toxicity_classifier"
|
||||
)
|
||||
model = BertForSequenceClassification.from_pretrained(
|
||||
"SkolkovoInstitute/russian_toxicity_classifier"
|
||||
)
|
||||
|
||||
|
||||
def detector(text):
|
||||
# Prepare the input
|
||||
batch = tokenizer.encode(text, return_tensors='pt')
|
||||
batch = tokenizer.encode(text, return_tensors="pt")
|
||||
|
||||
# Inference
|
||||
with torch.no_grad():
|
||||
|
|
@ -22,8 +27,10 @@ def detector(text):
|
|||
|
||||
return probabilities[0][1].item()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
p = detector(sys.argv[1])
|
||||
toxicity_percentage = p * 100 # Assuming index 1 is for toxic class
|
||||
|
|
|
|||
|
|
@ -1,11 +1,10 @@
|
|||
from bot.config import REDIS_URL
|
||||
import asyncio
|
||||
import redis.asyncio as aredis
|
||||
import logging
|
||||
|
||||
# Create a logger instance
|
||||
logger = logging.getLogger('store')
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
logger = logging.getLogger("state.redis")
|
||||
logging.basicConfig(level=logging.WARNING)
|
||||
|
||||
|
||||
class RedisService:
|
||||
|
|
@ -68,9 +67,9 @@ class RedisService:
|
|||
# Execute the command with the provided arguments
|
||||
await self.execute("set", *args)
|
||||
|
||||
async def scan_iter(self, pattern='*'):
|
||||
async def scan_iter(self, pattern="*"):
|
||||
"""Asynchronously iterate over keys matching the given pattern."""
|
||||
cursor = '0'
|
||||
cursor = "0"
|
||||
while cursor != 0:
|
||||
cursor, keys = await self._client.scan(cursor=cursor, match=pattern)
|
||||
for key in keys:
|
||||
|
|
@ -83,32 +82,3 @@ class RedisService:
|
|||
redis = RedisService()
|
||||
|
||||
__all__ = ["redis"]
|
||||
|
||||
|
||||
|
||||
async def get_all_pattern(uid):
|
||||
pattern = f"removed:{uid}:*"
|
||||
|
||||
# Create a dictionary to hold the keys and values
|
||||
texts = []
|
||||
|
||||
# Use scan_iter to find all keys matching the pattern
|
||||
async for key in redis.scan_iter(pattern):
|
||||
# Fetch the value for each key
|
||||
value = await redis.get(key)
|
||||
if value:
|
||||
texts.append(value.decode('utf-8'))
|
||||
|
||||
return texts
|
||||
|
||||
|
||||
async def get_average_pattern(pattern):
|
||||
scores = []
|
||||
scoring_msg_id = 0
|
||||
async for key in redis.scan_iter(pattern):
|
||||
scr = await redis.get(key)
|
||||
if isinstance(scr, int):
|
||||
scores.append(scr)
|
||||
logger.debug(f'found {len(scores)} messages')
|
||||
toxic_score = math.floor(sum(scores)/len(scores)) if scores else 0
|
||||
return toxic_score
|
||||
34
state/scan.py
Normal file
34
state/scan.py
Normal file
|
|
@ -0,0 +1,34 @@
|
|||
from state.redis import redis
|
||||
import logging
|
||||
import math
|
||||
|
||||
# Create a logger instance
|
||||
logger = logging.getLogger("state.scan")
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
|
||||
|
||||
async def get_all_pattern(uid):
|
||||
pattern = f"removed:{uid}:*"
|
||||
|
||||
# Create a dictionary to hold the keys and values
|
||||
texts = []
|
||||
|
||||
# Use scan_iter to find all keys matching the pattern
|
||||
async for key in redis.scan_iter(pattern):
|
||||
# Fetch the value for each key
|
||||
value = await redis.get(key)
|
||||
if value:
|
||||
texts.append(value.decode("utf-8"))
|
||||
|
||||
return texts
|
||||
|
||||
|
||||
async def get_average_pattern(pattern):
|
||||
scores = []
|
||||
async for key in redis.scan_iter(pattern):
|
||||
scr = await redis.get(key)
|
||||
if isinstance(scr, int):
|
||||
scores.append(scr)
|
||||
logger.debug(f"found {len(scores)} messages")
|
||||
toxic_score = math.floor(sum(scores) / len(scores)) if scores else 0
|
||||
return toxic_score
|
||||
|
|
@ -1,8 +1,9 @@
|
|||
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
|
||||
# Define SVG code generation function with member_id parameter
|
||||
def generate_chart(members, member_id=None):
|
||||
if not member_id:
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ def mention(user):
|
|||
|
||||
|
||||
def userdata_extract(user):
|
||||
ln = " " + user.get('last_name', "") if user.get('last_name', "") else ""
|
||||
ln = " " + user.get("last_name", "") if user.get("last_name", "") else ""
|
||||
identity = f"{user['first_name']}{ln}"
|
||||
uid = user["id"]
|
||||
username = user.get("username", "")
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user