File size: 1,318 Bytes
a704a0c |
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 |
from transformers import pipeline
# Emotion to tone instruction mapping
emotion_tone_map = {
"Sadness": "Be comforting, empathetic, and gentle.",
"Anger": "Stay calm, respectful, and de-escalate.",
"Love": "Be warm, appreciative, and encouraging.",
"Surprise": "Be affirming and help clarify what's surprising.",
"Fear": "Be reassuring and emphasize safety/facts.",
"Happiness": "Be enthusiastic and congratulatory.",
"Neutral": "Be informative and straightforward.",
"Disgust": "Be clinical, non-judgmental, and clarify facts.",
"Shame": "Be kind, avoid blame, and uplift the user.",
"Guilt": "Be compassionate and reduce self-blame.",
"Confusion": "Be extra clear and explain step-by-step.",
"Desire": "Be supportive and help guide constructively.",
"Sarcasm": "Stay serious, clarify misunderstandings politely.",
}
emotion_classifier = pipeline("text-classification", model="boltuix/bert-emotion")
def get_emotion_and_tone(text):
emotions = emotion_classifier(text)
detected_emotion = emotions[0]["label"].capitalize() if emotions else "Neutral"
emotion = detected_emotion if detected_emotion in emotion_tone_map else "Neutral"
tone_instruction = emotion_tone_map.get(emotion, "Be informative and polite.")
return emotion, tone_instruction |