Update app.py

app.py

@@ -47,7 +47,6 @@ def CTXGen(X1, X2, τ, g_num, length_range):
 
     model.eval()
     with torch.no_grad():
-        new_seq = None
         IDs = []
         generated_seqs = []
         generated_seqs_FINAL = []
@@ -64,6 +63,7 @@ def CTXGen(X1, X2, τ, g_num, length_range):
                         '<α9α10>','<α6α3β4>', '<NaTTXS>', '<Na17>','<high>','<low>','[UNK]','[SEP]','[PAD]','[CLS]','[MASK]']             
         start_time = time.time()
         while count < gen_num:
+            new_seq = None
             if is_stopped:
                 return pd.DataFrame(), "output.csv"
 
@@ -109,7 +109,7 @@ def CTXGen(X1, X2, τ, g_num, length_range):
             input_ids = vocab_mlm.__getitem__(generated_seq)
             logits = model(torch.tensor([input_ids]).to(device), idx_msa)
             cls_mask_logits = logits[0, 1, :]
-            cls_probability, cls_mask_probs = torch.topk((torch.softmax(cls_mask_logits, dim=-1)), k=10)
+            cls_probability, cls_mask_probs = torch.topk((torch.softmax(cls_mask_logits, dim=-1)), k=85)
 
             generated_seq[2] = "[MASK]"
             input_ids = vocab_mlm.__getitem__(generated_seq)