73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
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from __future__ import annotations
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import logging
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import re
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logger = logging.getLogger(__name__)
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class MattermostGating:
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def __init__(self, account: dict):
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self.chatmode = account.get("chatmode", "oncall")
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self.onchar_prefixes = account.get("onchar_prefixes", [">", "!"])
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self.require_mention = account.get("require_mention", True)
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def should_respond(
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self,
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chat_type: str,
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message_text: str,
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was_mentioned: bool,
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) -> tuple[bool, str]:
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if chat_type == "direct":
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return True, "dm_always"
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if self.chatmode == "onmessage":
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return True, "onmessage"
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if self.chatmode == "onchar":
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return self._check_onchar(message_text)
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if self.chatmode == "oncall":
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if was_mentioned:
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return True, "mentioned"
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return self._check_onchar_fallback(message_text)
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return False, "unknown_chatmode"
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def _check_onchar(self, text: str) -> tuple[bool, str]:
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stripped = text.strip()
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for prefix in self.onchar_prefixes:
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if stripped.startswith(prefix):
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return True, "onchar_triggered"
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return False, "onchar_not_triggered"
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def _check_onchar_fallback(self, text: str) -> tuple[bool, str]:
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if not self.require_mention:
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return True, "mention_not_required"
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for prefix in self.onchar_prefixes:
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if text.strip().startswith(prefix):
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return True, "onchar_fallback"
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return False, "missing_mention"
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def was_mentioned(self, text: str, bot_user_id: str, bot_username: str | None = None) -> bool:
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mentions = self.extract_mentions(text)
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if bot_user_id in mentions:
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return True
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if bot_username:
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return bot_username in text or f"@{bot_username}" in text
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return False
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def extract_mentions(self, text: str) -> list[str]:
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user_ids = re.findall(r"@([a-z0-9]{26})", text, re.IGNORECASE)
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usernames = re.findall(r"@(\w+)", text)
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return user_ids + usernames
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def strip_onchar_prefix(self, text: str) -> str:
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stripped = text.strip()
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for prefix in self.onchar_prefixes:
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if stripped.startswith(prefix):
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return stripped[len(prefix):].strip()
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return text
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