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from transformers import pipeline
# Load the model (use a fine-tuned model for abuse detection)
classifier = pipeline("text-classification", model="unitary/toxic-bert")
def analyze_text(text):
results = classifier(text)
# Convert to readable format
final_result = {
"bullying": any(res["label"] == "toxic" and res["score"] > 0.5 for res in results),
"threat": any(res["label"] == "threat" and res["score"] > 0.5 for res in results),
"scolding": any(res["label"] == "insult" and res["score"] > 0.5 for res in results),
"abuse": any(res["label"] in ["toxic", "severe_toxic"] and res["score"] > 0.6 for res in results),
"detailed_scores": results
}
# Make detected categories bold
for key in ["bullying", "threat", "scolding", "abuse"]:
if final_result[key]:
final_result[key] = f"**{key.upper()} DETECTED**"
return final_result
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