Update app.py

app.py

@@ -220,9 +220,6 @@ inputs = tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_l
 with torch.no_grad():
     logits = loaded_model(**inputs).logits
 
-# train
-saved_path = train_function_no_sweeps(base_model_path,train_dataset, test_dataset)
-
 # Get predictions
 tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])  # Convert input ids back to tokens
 predictions = torch.argmax(logits, dim=2)
@@ -237,7 +234,10 @@ id2label = {
 for token, prediction in zip(tokens, predictions[0].numpy()):
     if token not in ['<pad>', '<cls>', '<eos>']:
         print((token, id2label[prediction]))
-        
+
+# train
+saved_path = train_function_no_sweeps(base_model_path,train_dataset, test_dataset)
+
 # debug result
 dubug_result = saved_path  #predictions  #class_weights