Update requirements and make necessary code changes

.ipynb_checkpoints/app-checkpoint.py

@@ -42,6 +42,9 @@ checkpoint='ThorbenF/prot_t5_xl_uniref50'
 max_length=1500
 
 model, tokenizer = load_model(checkpoint,max_length)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+model.eval()
 
 def create_dataset(tokenizer,seqs,labels,checkpoint):
     
@@ -97,9 +100,6 @@ def predict_protein_sequence(test_one_letter_sequence):
     
     test_loader = DataLoader(test_dataset, batch_size=1, collate_fn=data_collator)
     
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model.to(device)
-    model.eval()
     for batch in test_loader:
         input_ids = batch['input_ids'].to(device)
         attention_mask = batch['attention_mask'].to(device)

app.py

@@ -42,6 +42,9 @@ checkpoint='ThorbenF/prot_t5_xl_uniref50'
 max_length=1500
 
 model, tokenizer = load_model(checkpoint,max_length)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+model.eval()
 
 def create_dataset(tokenizer,seqs,labels,checkpoint):
     
@@ -97,9 +100,6 @@ def predict_protein_sequence(test_one_letter_sequence):
     
     test_loader = DataLoader(test_dataset, batch_size=1, collate_fn=data_collator)
     
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model.to(device)
-    model.eval()
     for batch in test_loader:
         input_ids = batch['input_ids'].to(device)
         attention_mask = batch['attention_mask'].to(device)