Update app.py

app.py

@@ -1,20 +1,13 @@
 import gradio as gr
 import torch
 import numpy as np
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
 from transformers import RobertaForSequenceClassification, RobertaTokenizer
 from motif_tagging import detect_motifs
 import re
 
-# --- Sentiment Model ---
-sentiment_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
-sentiment_model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-emotion")
-
-EMOTION_TO_SENTIMENT = {
-    "joy": "supportive", "love": "supportive", "surprise": "supportive", "neutral": "supportive",
-    "sadness": "undermining", "anger": "undermining", "fear": "undermining",
-    "disgust": "undermining", "shame": "undermining", "guilt": "undermining"
-}
+# --- SST Sentiment Model ---
+sst_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 
 # --- Abuse Model ---
 model_name = "SamanthaStorm/autotrain-jlpi4-mllvp"
@@ -125,27 +118,19 @@ def generate_risk_snippet(abuse_score, top_label):
 def analyze_single_message(text, thresholds, motif_flags):
     motif_hits, matched_phrases = detect_motifs(text)
 
-    # Sentiment Analysis
-    input_ids = sentiment_tokenizer(f"emotion: {text}", return_tensors="pt").input_ids
-    with torch.no_grad():
-        outputs = sentiment_model.generate(input_ids)
-    emotion = sentiment_tokenizer.decode(outputs[0], skip_special_tokens=True).strip().lower()
-    sentiment = EMOTION_TO_SENTIMENT.get(emotion, "undermining")
-    sentiment_score = 0.5 if sentiment == "undermining" else 0.0
+    # SST Sentiment
+    result = sst_pipeline(text)[0]
+    sentiment = "supportive" if result['label'] == "POSITIVE" else "undermining"
+    sentiment_score = result['score'] if sentiment == "undermining" else 0.0
 
-    # Raise thresholds slightly if the sentiment is supportive
     adjusted_thresholds = {
         k: v + 0.05 if sentiment == "supportive" else v
         for k, v in thresholds.items()
     }
 
-    # Contradiction Check
     contradiction_flag = detect_contradiction(text)
-
-    # Motifs
     motifs = [phrase for _, phrase in matched_phrases]
 
-    # Model Prediction
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
         outputs = model(**inputs)
@@ -169,7 +154,7 @@ def analyze_single_message(text, thresholds, motif_flags):
         threshold_labels,
         top_patterns,
         darvo_score,
-        {"label": sentiment, "emotion": emotion}
+        {"label": sentiment, "raw_label": result['label'], "score": result['score']}
     )
 
 def analyze_composite(msg1, msg2, msg3, *answers_and_none):
@@ -211,4 +196,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()