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d40eb53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | import re
LABEL_COLS = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
LABEL_META = {
'toxic' : {'fr': 'Toxique', 'color': '#D85A30'},
'severe_toxic' : {'fr': 'Très toxique', 'color': '#993C1D'},
'obscene' : {'fr': 'Obscène', 'color': '#EF9F27'},
'threat' : {'fr': 'Menace', 'color': '#A32D2D'},
'insult' : {'fr': 'Insulte', 'color': '#534AB7'},
'identity_hate' : {'fr': 'Haine identitaire','color': '#0F6E56'},
}
def clean_text(text: str) -> str:
if not isinstance(text, str):
return ''
text = re.sub(r'[\r\n\t]+', ' ', text)
text = re.sub(r'\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b', '[USER]', text)
text = re.sub(r'http[s]?://\S+', '[URL]', text)
text = re.sub(r'==+\s*.+?\s*==+', '', text)
text = re.sub(r'\{\{.*?\}\}', '', text)
text = re.sub(r'([!?.])\1{2,}', r'\1\1', text)
text = re.sub(r' {2,}', ' ', text)
return text.strip()
def toxicity_level(probs: dict) -> tuple[str, str]:
"""Retourne (niveau, couleur) selon les probabilités détectées."""
max_prob = max(probs.values())
if probs.get('threat', 0) > 0.5 or probs.get('severe_toxic', 0) > 0.5:
return 'Très élevé', '#A32D2D'
elif max_prob > 0.7:
return 'Élevé', '#D85A30'
elif max_prob > 0.4:
return 'Modéré', '#EF9F27'
elif max_prob > 0.2:
return 'Faible', '#854F0B'
else:
return 'Aucun', '#0F6E56' |