| """Emotion detection using a local transformer model with lexicon fallback.""" |
|
|
| import os |
| from typing import Dict |
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| EMOTION_KEYWORDS = { |
| "anxious": { |
| "worried", |
| "nervous", |
| "scared", |
| "stress", |
| "stressed", |
| "panic", |
| "afraid", |
| "deadline", |
| }, |
| "angry": {"angry", "mad", "furious", "annoyed", "irritated", "rage", "unfair"}, |
| "sad": {"sad", "hurt", "grief", "down", "depressed", "empty"}, |
| "lonely": {"lonely", "alone", "isolated", "unseen"}, |
| "ashamed": {"ashamed", "embarrassed", "guilty", "worthless"}, |
| "curious": {"curious", "wonder", "why", "how", "learn", "explore", "question"}, |
| "joyful": {"happy", "joy", "grateful", "glad"}, |
| "hopeful": {"hope", "hopeful", "possible", "better"}, |
| "confused": {"confused", "lost", "stuck", "unclear", "messy"}, |
| "overwhelmed": {"overwhelmed", "too", "much", "can't", "cannot"}, |
| "tired": {"tired", "exhausted", "sleepy", "drained"}, |
| "vulnerable": {"honest", "scared", "hard", "sensitive", "open"}, |
| "excited": {"excited", "amazing", "awesome", "wow", "unbelievable", "huge"}, |
| "focused": {"build", "fix", "ship", "implement", "test", "run", "phase", "ready"}, |
| } |
|
|
| EMOTION_DIMENSIONS = { |
| "anxious": (-0.6, 0.8, "grounding,clarity"), |
| "angry": (-0.5, 0.85, "validation,boundaries"), |
| "sad": (-0.75, 0.35, "presence,validation"), |
| "lonely": (-0.7, 0.35, "connection,validation"), |
| "ashamed": (-0.8, 0.6, "low_judgment,reframing"), |
| "curious": (0.35, 0.55, "exploration,questions"), |
| "joyful": (0.8, 0.65, "celebration,continuity"), |
| "hopeful": (0.65, 0.55, "encouragement,planning"), |
| "confused": (-0.2, 0.55, "clarity,structure"), |
| "overwhelmed": (-0.65, 0.9, "grounding,prioritization"), |
| "focused": (0.45, 0.75, "problem_solving,challenge"), |
| "excited": (0.85, 0.8, "momentum,validation"), |
| "tired": (-0.35, 0.2, "rest,simplification"), |
| "vulnerable": (-0.25, 0.5, "care,trust"), |
| "neutral": (0.0, 0.2, "natural"), |
| } |
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| _TRANSFORMER_CLASSIFIER = None |
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| _MODEL_TO_LABEL = { |
| "fear": "anxious", |
| "sadness": "sad", |
| "anger": "angry", |
| "joy": "joyful", |
| "surprise": "excited", |
| "disgust": "ashamed", |
| "neutral": "neutral", |
| } |
|
|
| _MODEL_VALENCE = { |
| "fear": -0.6, |
| "sadness": -0.7, |
| "anger": -0.5, |
| "joy": 0.8, |
| "surprise": 0.4, |
| "disgust": -0.6, |
| "neutral": 0.0, |
| } |
|
|
| _MODEL_AROUSAL = { |
| "fear": 0.85, |
| "sadness": 0.3, |
| "anger": 0.9, |
| "joy": 0.7, |
| "surprise": 0.8, |
| "disgust": 0.6, |
| "neutral": 0.2, |
| } |
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|
| def _load_classifier(): |
| global _TRANSFORMER_CLASSIFIER |
| if _TRANSFORMER_CLASSIFIER is not None: |
| return _TRANSFORMER_CLASSIFIER |
| try: |
| from transformers import pipeline |
|
|
| |
| os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1" |
| _TRANSFORMER_CLASSIFIER = pipeline( |
| "text-classification", |
| model="j-hartmann/emotion-english-distilroberta-base", |
| top_k=None, |
| device="cpu", |
| ) |
| return _TRANSFORMER_CLASSIFIER |
| except Exception as exc: |
| print( |
| f"[emotion] Transformer model unavailable ({exc}), using lexicon fallback." |
| ) |
| _TRANSFORMER_CLASSIFIER = False |
| return _TRANSFORMER_CLASSIFIER |
|
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|
|
| def _detect_transformer(text: str) -> Dict: |
| clf = _load_classifier() |
| if clf is False: |
| return {} |
| |
| text_truncated = text[-1500:] if len(text) > 1500 else text |
| raw = clf(text_truncated)[0] |
| |
| raw = sorted(raw, key=lambda x: x["score"], reverse=True) |
| top = raw[0] |
| secondary = raw[1] if len(raw) > 1 else {"label": "neutral", "score": 0.0} |
|
|
| label = _MODEL_TO_LABEL.get(str(top["label"]), "neutral") |
| sec_label = _MODEL_TO_LABEL.get(str(secondary["label"]), "neutral") |
|
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| |
| valence = 0.0 |
| arousal = 0.0 |
| total = 0.0 |
| for item in raw[:3]: |
| w = item["score"] |
| valence += _MODEL_VALENCE.get(item["label"], 0.0) * w |
| arousal += _MODEL_AROUSAL.get(item["label"], 0.0) * w |
| total += w |
| if total > 0: |
| valence /= total |
| arousal /= total |
|
|
| intensity = min(1.0, 0.25 + top["score"] * 0.75 + text.count("!") * 0.03) |
| confidence = min(0.95, 0.35 + top["score"] * 0.6) |
|
|
| return { |
| "label": label, |
| "secondary": sec_label, |
| "confidence": confidence, |
| "valence": valence, |
| "arousal": arousal, |
| "intensity": intensity, |
| "needs": EMOTION_DIMENSIONS.get(label, EMOTION_DIMENSIONS["neutral"])[2], |
| "detector": "local_transformer_v1", |
| "raw_model": top["label"], |
| } |
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| def _detect_lexicon(text: str) -> Dict: |
| words = {word.strip(".,!?;:\"'()[]{}").lower() for word in text.split()} |
| scores = { |
| label: len(words & keywords) for label, keywords in EMOTION_KEYWORDS.items() |
| } |
| label, score = max(scores.items(), key=lambda item: item[1]) |
| if score == 0: |
| label = "neutral" |
| sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True) |
| secondary = ( |
| sorted_scores[1][0] |
| if len(sorted_scores) > 1 and sorted_scores[1][1] |
| else "neutral" |
| ) |
| intensity = min(1.0, 0.25 + score * 0.18 + text.count("!") * 0.05) |
| confidence = 0.2 if score == 0 else min(0.95, 0.35 + score * 0.12) |
| valence, arousal, needs = EMOTION_DIMENSIONS[label] |
| return { |
| "label": label, |
| "secondary": secondary, |
| "confidence": confidence, |
| "valence": valence, |
| "arousal": arousal, |
| "intensity": intensity, |
| "needs": needs, |
| "detector": "offline_lexicon_v1", |
| "raw_model": None, |
| } |
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| def detect_emotion(text: str) -> Dict: |
| result = _detect_transformer(text) |
| if result is not None: |
| return result |
| return _detect_lexicon(text) |
|
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|
|
| def emotion_prompt_hint(emotion): |
| label = emotion.get("label", "neutral") |
| hints = { |
| "anxious": "Use steadier pacing, reduce ambiguity, and separate the next small action from the larger fear.", |
| "angry": "Validate the signal without amplifying heat; look for boundaries, facts, and leverage.", |
| "sad": "Lead with warmth and presence before analysis. Do not rush to fix what first needs witnessing.", |
| "curious": "Lean into exploration, pattern finding, and generative questions.", |
| "excited": "Channel momentum into concrete next steps and keep assumptions checked.", |
| "confused": "Compress the situation into simple handles, then rebuild clarity.", |
| "focused": "Be direct, operational, and concise; prioritize execution.", |
| } |
| return hints.get( |
| label, "Respond naturally; infer the needed mode from the message and context." |
| ) |
|
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|
|
| if __name__ == "__main__": |
| samples = [ |
| "I am so worried about the deadline", |
| "I feel happy and grateful today", |
| "That makes me furious", |
| "I feel empty and alone", |
| "I am curious about how this works", |
| "This is too much, I cannot handle it", |
| "The sky is blue", |
| ] |
| for s in samples: |
| r = detect_emotion(s) |
| print( |
| f"{s[:40]:40s} -> {r['label']:10s} (conf={r['confidence']:.2f}, val={r['valence']:.2f}, ar={r['arousal']:.2f}, det={r['detector']})" |
| ) |
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