Update app.py

app.py

@@ -309,7 +309,7 @@ def generate_dream_response(
                     if num_samples > 0:
                         transfer_indices_relative = torch.tensor([], dtype=torch.long, device=device) # Init empty
                         if alg_temp_val is None or alg_temp_val <= 0: # Top-k
-                            sort_metric = confidence if alg != 'entropy' else -confidence
+                            sort_metric = confidence
                             k_topk = min(num_samples, sort_metric.numel())
                             if k_topk > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_topk)
                         else: # Sample based on temp
@@ -330,12 +330,12 @@ def generate_dream_response(
                                     try: transfer_indices_relative = torch.multinomial(conf_probs, num_samples=num_samples, replacement=False)
                                     except RuntimeError as e:
                                         print(f"Warning step {i}: Multinomial sampling failed ('{e}'). Falling back to top-k.")
-                                        sort_metric = confidence if alg != 'entropy' else -confidence
+                                        sort_metric = confidence
                                         k_multinomial_fallback = min(num_samples, sort_metric.numel())
                                         if k_multinomial_fallback > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_multinomial_fallback)
                                 else: # Fallback if probs invalid for multinomial
                                     # print(f"Warning step {i}: Invalid probabilities for multinomial sampling (sum={final_prob_sum_check:.4f}). Falling back to top-k.")
-                                    sort_metric = confidence if alg != 'entropy' else -confidence
+                                    sort_metric = confidence
                                     k_multinomial_fallback = min(num_samples, sort_metric.numel())
                                     if k_multinomial_fallback > 0: _, transfer_indices_relative = torch.topk(sort_metric, k=k_multinomial_fallback)