90 lines
3.4 KiB
Python
90 lines
3.4 KiB
Python
import logging
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import math
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from store import redis
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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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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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text = msg.get("text")
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reply_msg = msg.get("reply_to_message")
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if cid == uid:
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# сообщения в личке с ботом
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logger.info("private chat message")
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await handle_private(msg, state)
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elif str(cid) == FEEDBACK_CHAT_ID:
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# сообщения из группы обратной связи
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logger.info("feedback chat message")
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logger.debug(msg)
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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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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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text = ''
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toxic_score = 0
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if not reply_msg:
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logger.debug(f'scoring average for {uid}')
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scoring_msg_id = mid
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pattern = f"toxic:{cid}:{uid}:*"
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scores = []
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async for key in redis.scan_iter(pattern):
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scr = int(await redis.get(key))
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scores.append(scr)
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toxic_score = math.floor(sum(scores)/len(scores)) if scores else 0
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text = f"Средняя токсичность сообщений: {toxic_score}%"
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else:
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latest_toxic_message_id = await redis.get(f"toxic:{cid}")
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scoring_msg_id = reply_msg.get("message_id") or latest_toxic_message_id
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toxic_score = await redis.get(f"toxic:{cid}:{uid}:{scoring_msg_id}")
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if toxic_score:
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text = f"{int(toxic_score)}% токсичности"
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if text:
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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=scoring_msg_id,
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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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else:
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toxic_score = detector(text)
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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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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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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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)
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else:
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pass
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