Update app.py

app.py

@@ -9,15 +9,12 @@ 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_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]
+
+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}
 # Emotion model (no retraining needed)
 emotion_pipeline = hf_pipeline(
     "text-classification",
@@ -133,17 +130,15 @@ ESCALATION_QUESTIONS = [
     ("Violence has increased in frequency or severity", 3),
     ("Partner monitors your calls/GPS/social media", 2)
 ]
+def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score):
+    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)
+    disgust = emotions.get("disgust", 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
@@ -262,14 +257,6 @@ def get_emotional_tone_tag(emotion_profile, sentiment, patterns, abuse_score, te
         sentiment == "undermining"
     ):
         return "emotional instability"
-
-    # 15. Hostile Moralizing
-    if (
-        any(p in patterns for p in ["dismissiveness", "blame shifting"]) and
-        sentiment == "undermining" and
-        any(word in text.lower() for word in ["victim", "sympathy", "expect", "started"])
-    ):
-        return "hostile moralizing"
         
     return None
 # 🔄 New DARVO score model (regression-based)
@@ -459,7 +446,7 @@ 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, abuse_score, text)
+    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, 0)
 
 
     top_patterns = sorted(
@@ -495,7 +482,7 @@ def analyze_single_message(text, thresholds):
     )
 
 # Tag must happen after abuse score is finalized
-    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score, text)
+    tone_tag = get_emotional_tone_tag(emotion_profile, sentiment, threshold_labels, abuse_score)
 
 # ---- Profanity + Anger Override Logic ----
     profane_words = {"fuck", "fucking", "bitch", "shit", "cunt", "ho", "asshole", "dick", "whore", "slut"}
@@ -639,6 +626,7 @@ def analyze_composite(msg1, msg2, msg3, *answers_and_none):
         escalation_text += "📋 This score combines your safety checklist answers *and* detected high-risk behavior.\n"
         escalation_text += f"• Pattern Risk: {pattern_escalation_risk}\n"
         escalation_text += f"• Checklist Risk: {checklist_escalation_risk}\n"
+        escalation_text += f"• Escalation Bump: +{escalation_bump} (from DARVO, tone, intensity, etc.)"
 
     # Composite Abuse Score
     composite_abuse_scores = []