Update app.py

app.py

@@ -9,11 +9,15 @@ import io
 from PIL import Image
 from datetime import datetime
 from transformers import pipeline as hf_pipeline  # prevent name collision with gradio pipeline
-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}
+def get_emotional_tone_tag(emotion_profile, sentiment, patterns, abuse_score, text):
+    sadness = emotion_profile.get("sadness", 0)
+    joy = emotion_profile.get("joy", 0)
+    neutral = emotion_profile.get("neutral", 0)
+    disgust = emotion_profile.get("disgust", 0)
+    anger = emotion_profile.get("anger", 0)
+    fear = emotion_profile.get("fear", 0)
+
+    # [then all the logic for performative regret → hostile moralizing]
 # Emotion model (no retraining needed)
 emotion_pipeline = hf_pipeline(
     "text-classification",
@@ -131,13 +135,15 @@ ESCALATION_QUESTIONS = [
 ]
 
 
-def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score, text):
-    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)
+def get_emotional_tone_tag(emotion_profile, sentiment, patterns, abuse_score, text):
+    sadness = emotion_profile.get("sadness", 0)
+    joy = emotion_profile.get("joy", 0)
+    neutral = emotion_profile.get("neutral", 0)
+    disgust = emotion_profile.get("disgust", 0)
+    anger = emotion_profile.get("anger", 0)
+    fear = emotion_profile.get("fear", 0)
+
+    # [then all the logic for performative regret → hostile moralizing]
     # 1. Performative Regret
     if (
         sadness > 0.4 and
@@ -453,7 +459,7 @@ def analyze_single_message(text, thresholds):
         label for label, score in zip(LABELS, scores)
         if score > adjusted_thresholds[label]
     ]
-    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, 0, text)
+    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score, text)
 
 
     top_patterns = sorted(