Update app.py

app.py

@@ -428,18 +428,17 @@ def compute_abuse_score(matched_scores, sentiment):
     if any(score > 0.8 for _, score, _ in matched_scores):
         base_score *= 1.05  # Reduced
 
-    # Sentiment modifier (more nuanced)
-    if sentiment == "supportive":
-        manipulative_patterns = {'guilt tripping', 'gaslighting', 'blame shifting', 'love bombing'}
-        if any(label in manipulative_patterns for label, score, _ in matched_scores if score > 0.6): # Higher threshold
-            base_score *= 0.95  # Smaller reduction for strongly manipulative "support"
-        elif any(label in manipulative_patterns for label, score, _ in matched_scores if score > 0.4): # Moderate threshold
-            base_score *= 0.9  # Moderate reduction for manipulative "support"
-        else:
-            base_score *= 0.8  # Larger reduction for genuine support
-
-    elif sentiment == "undermining":
-        base_score *= 1.15
+    def get_sentiment_predictions(texts, sentiment_model, sentiment_tokenizer, batch_size=16):
+        predictions = []
+        for i in tqdm(range(0, len(texts), batch_size), desc="Predicting sentiment"):
+            batch = texts[i:i+batch_size]
+            inputs = sentiment_tokenizer(batch, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
+            with torch.no_grad():  # Correct indentation
+                outputs = sentiment_model(**inputs) # Indent within 'with' block
+                logits = outputs.logits # Indent within 'with' block
+                predicted_classes = logits.argmax(dim=-1).tolist() # Indent within 'with' block
+                predictions.extend(predicted_classes) # Indent within 'with' block
+        return predictions
 
     # Reduce minimum score and threshold for activation
     if any(score > 0.9 for _, score, _ in matched_scores):  # Higher threshold