forked from gooberinc/goober
95 lines
2.5 KiB
Python
95 lines
2.5 KiB
Python
import re
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import discord.ext
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import discord.ext.commands
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from modules.globalvars import *
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import spacy
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from spacy.tokens import Doc
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from spacytextblob.spacytextblob import SpacyTextBlob
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import discord
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import modules.keys as k
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import logging
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logger = logging.getLogger("goober")
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def check_resources():
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try:
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nlp = spacy.load("en_core_web_sm")
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except OSError:
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logging.critical(k.spacy_model_not_found())
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spacy.cli.download("en_core_web_sm") # type: ignore
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nlp = spacy.load("en_core_web_sm")
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if "spacytextblob" not in nlp.pipe_names:
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nlp.add_pipe("spacytextblob")
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logger.info(k.spacy_initialized())
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check_resources()
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nlp = spacy.load("en_core_web_sm")
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nlp.add_pipe("spacytextblob")
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Doc.set_extension("polarity", getter=lambda doc: doc._.blob.polarity)
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def is_positive(sentence):
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doc = nlp(sentence)
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sentiment_score = doc._.polarity # from spacytextblob
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debug_message = f"{k.sentence_positivity()} {sentiment_score}{RESET}"
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logger.debug(debug_message)
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return (
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sentiment_score > 0.6
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) # had to raise the bar because it kept saying "death to jews" was fine and it kept reacting to them
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async def send_message(
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ctx: discord.ext.commands.Context,
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message: str | None = None,
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embed: discord.Embed | None = None,
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file: discord.File | None = None,
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edit: bool = False,
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message_reference: discord.Message | None = None,
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) -> discord.Message | None:
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sent_message: discord.Message | None = None
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if edit and message_reference:
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try:
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await message_reference.edit(content=message, embed=embed)
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return message_reference
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except Exception as e:
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await ctx.send(f"{k.edit_fail()} {e}")
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return None
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if embed:
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sent_message = await ctx.send(embed=embed, content=message)
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elif file:
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sent_message = await ctx.send(file=file, content=message)
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else:
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sent_message = await ctx.send(content=message)
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return sent_message
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def append_mentions_to_18digit_integer(message):
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pattern = r"\b\d{18}\b"
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return re.sub(pattern, lambda match: "", message)
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def preprocess_message(message):
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message = append_mentions_to_18digit_integer(message)
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doc = nlp(message)
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tokens = [token.text for token in doc if token.is_alpha or token.is_digit]
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return " ".join(tokens)
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def improve_sentence_coherence(sentence):
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return re.sub(r"\bi\b", "I", sentence)
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def rephrase_for_coherence(sentence):
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words = sentence.split()
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coherent_sentence = " ".join(words)
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return coherent_sentence
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