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| : import gradio as gr | |
| import re | |
| from transformers import pipeline as hf_pipeline | |
| # Load SST model (temporary baseline) | |
| sst_classifier = hf_pipeline( | |
| "text-classification", | |
| model="distilbert-base-uncased-finetuned-sst-2-english", | |
| top_k=None, | |
| truncation=True | |
| ) | |
| # Load emotion classifier | |
| emotion_pipeline = hf_pipeline( | |
| "text-classification", | |
| model="j-hartmann/emotion-english-distilroberta-base", | |
| top_k=None, | |
| truncation=True | |
| ) | |
| # Lexicon enhancement preprocessing | |
| negations = {"not", "never", "no", "none", "nobody", "nothing", "neither", "nowhere", "hardly", "scarcely", "barely"} | |
| amplifiers = {"very", "really", "extremely", "so", "totally", "completely", "absolutely", "utterly", "super"} | |
| softeners = {"slightly", "somewhat", "a bit", "a little", "mildly", "fairly", "kind of"} | |
| def preprocess_sentiment_text(text): | |
| words = text.lower().split() | |
| modified = [] | |
| negate = False | |
| for word in words: | |
| stripped = re.sub(r'\W+', '', word) | |
| if stripped in negations: | |
| negate = True | |
| modified.append("<NEG>") | |
| continue | |
| if stripped in amplifiers: | |
| modified.append(f"<AMP>{word}") | |
| continue | |
| if stripped in softeners: | |
| modified.append(f"<SOFT>{word}") | |
| continue | |
| if negate: | |
| modified.append(f"<NEG>{word}") | |
| negate = False | |
| else: | |
| modified.append(word) | |
| return " ".join(modified) | |
| # Emotion mapping | |
| def get_emotion_profile(text): | |
| emotions = emotion_pipeline(text) | |
| if isinstance(emotions, list) and isinstance(emotions[0], list): | |
| emotions = emotions[0] | |
| return {e['label'].lower(): round(e['score'], 3) for e in emotions} | |
| # Tone tagging logic | |
| def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score=0): | |
| sadness = emotions.get("sadness", 0) | |
| joy = emotions.get("joy", 0) | |
| neutral = emotions.get("neutral", 0) | |
| disgust = emotions.get("disgust", 0) | |
| anger = emotions.get("anger", 0) | |
| fear = emotions.get("fear", 0) | |
| if sadness > 0.4 and any(p in patterns for p in ["blame shifting", "guilt tripping", "recovery phase"]) and (sentiment == "undermining" or abuse_score > 40): | |
| return "performative regret" | |
| if (joy > 0.3 or sadness > 0.4) and any(p in patterns for p in ["control", "gaslighting"]) and sentiment == "undermining": | |
| return "coercive warmth" | |
| if (neutral + disgust) > 0.5 and any(p in patterns for p in ["dismissiveness", "projection", "obscure language"]) and sentiment == "undermining": | |
| return "cold invalidation" | |
| if (sadness + fear) > 0.5 and sentiment == "supportive" and all(p in ["recovery phase"] for p in patterns): | |
| return "genuine vulnerability" | |
| if (anger + disgust) > 0.5 and any(p in patterns for p in ["control", "threat", "insults", "dismissiveness"]) and sentiment == "undermining": | |
| return "emotional threat" | |
| if sadness > 0.6 and any(p in patterns for p in ["guilt tripping", "projection"]) and sentiment == "undermining": | |
| return "weaponized sadness" | |
| if neutral > 0.5 and any(p in patterns for p in ["dismissiveness", "obscure language"]) and sentiment == "undermining": | |
| return "toxic resignation" | |
| return None | |
| # Main function | |
| def analyze_message(text): | |
| preprocessed = preprocess_sentiment_text(text) | |
| sst_output = sst_classifier(preprocessed) | |
| sentiment = sst_output[0] | |
| sentiment_label = "supportive" if sentiment["label"] == "POSITIVE" else "undermining" | |
| sentiment_score = round(sentiment["score"] * 100, 2) | |
| emotions = get_emotion_profile(text) | |
| emotion_summary = "\n".join([f"{k.title()}: {v:.2f}" for k, v in emotions.items()]) | |
| # Temporarily pass empty abuse pattern list until Tether model is added | |
| tone_tag = get_emotional_tone_tag(emotions, sentiment_label, patterns=[]) | |
| tone_output = tone_tag if tone_tag else "None detected" | |
| return ( | |
| f"🧠 Sentiment: {sentiment_label.title()} ({sentiment_score}%)\n\n" | |
| f"🎭 Emotional Profile:\n{emotion_summary}\n\n" | |
| f"🔍 Tone Tag: {tone_output}" | |
| ) | |
| # Interface | |
| iface = gr.Interface( | |
| fn=analyze_message, | |
| inputs=gr.Textbox(lines=4, placeholder="Paste a message here..."), | |
| outputs="text", | |
| title="Tether SST + Emotional Tone Tagger", | |
| description="Applies lexicon-enhanced preprocessing, classifies sentiment, profiles emotion, and infers tone tags based on behavior logic." | |
| ) | |
| iface.launch() |