Update app.py

app.py

@@ -24,7 +24,7 @@ def get_relevance_score_and_excerpt(query, paragraph, threshold_weight):
     base_relevance_score = torch.sigmoid(torch.tensor(logit)).item()
 
     # Calculate dynamic threshold using sigmoid-based formula
-    sigmoid_factor = 1 / (1 + torch.exp(-5 * (base_relevance_score - 0.5))).item()
+    sigmoid_factor = 1 / (1 + torch.exp(torch.tensor(-5 * (base_relevance_score - 0.5)))).item()
     dynamic_threshold = max(0.02, threshold_weight * sigmoid_factor)
 
     # Extract attention scores (last layer)