Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import re
 from transformers import pipeline as hf_pipeline
 
-# Load models
+# Load SST sentiment classifier
 sst_classifier = hf_pipeline(
     "text-classification",
     model="SamanthaStorm/tether-sst",
@@ -10,6 +10,7 @@ sst_classifier = hf_pipeline(
     truncation=True
 )
 
+# Load emotion classifier
 emotion_pipeline = hf_pipeline(
     "text-classification",
     model="j-hartmann/emotion-english-distilroberta-base",
@@ -17,14 +18,30 @@ emotion_pipeline = hf_pipeline(
     truncation=True
 )
 
-# Functions
+# Load Tether's abuse pattern classifier
+abuse_classifier = hf_pipeline(
+    "text-classification",
+    model="SamanthaStorm/tether-multilabel-v2",
+    top_k=None,
+    truncation=True
+)
+
+# Extract abuse pattern labels
+def get_abuse_patterns(text, threshold=0.5):
+    results = abuse_classifier(text)
+    if isinstance(results, list) and isinstance(results[0], list):
+        results = results[0]
+    return [r['label'] for r in results if r['score'] >= threshold]
+
+# Emotion extraction
 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(emotions, sentiment, patterns, abuse_score):
+# Emotional tone tagging based on behavior + emotion + sentiment
+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)
@@ -77,17 +94,22 @@ def get_emotional_tone_tag(emotions, sentiment, patterns, abuse_score):
 
     return None
 
-# Main interface function
+# Analysis logic
 def analyze_message(text):
+    # Sentiment
     sst_result = sst_classifier(text)[0]
     sentiment_label = "supportive" if sst_result["label"] == "LABEL_0" else "undermining"
     sentiment_score = round(sst_result["score"] * 100, 2)
 
+    # Emotions
     emotions = get_emotion_profile(text)
     emotion_summary = "\n".join([f"{k.title()}: {v:.2f}" for k, v in emotions.items()])
 
+    # Abuse patterns (used internally)
+    patterns = get_abuse_patterns(text)
 
-    tone_tag = get_emotional_tone_tag(emotions, sentiment_label, patterns, abuse_score)
+    # Tone tag
+    tone_tag = get_emotional_tone_tag(emotions, sentiment_label, patterns)
     tone_output = tone_tag if tone_tag else "None detected"
 
     return (
@@ -96,7 +118,7 @@ def analyze_message(text):
         f"🔍 Tone Tag: {tone_output}"
     )
 
-# Gradio app
+# Gradio UI
 iface = gr.Interface(
     fn=analyze_message,
     inputs=gr.Textbox(lines=4, placeholder="Paste a message here..."),