Update app.py

app.py

@@ -66,15 +66,16 @@ def ACE(file):
     for seq in all:
         test_data = tokenizer(seq, max_length=max_len, padding="max_length",truncation=True, return_tensors='pt')
         out_probability = []
-        predict = model(test_data)
-        out_probability.extend(np.max(np.array(predict.cpu()),axis=1).tolist())
-        test_argmax = np.argmax(predict.cpu(), axis=1).tolist()
-        id2str = {0:"non-ACE", 1:"ACE"}
-        output = id2str[test_argmax[0]]
-        probability = out_probability[0]
-        seq_all.append(seq)
-        output_all.append(output)
-        probability_all.append(probability)
+        with torch.no_grad():
+            predict = model(test_data)
+            out_probability.extend(np.max(np.array(predict.cpu()),axis=1).tolist())
+            test_argmax = np.argmax(predict.cpu(), axis=1).tolist()
+            id2str = {0:"non-ACE", 1:"ACE"}
+            output = id2str[test_argmax[0]]
+            probability = out_probability[0]
+            seq_all.append(seq)
+            output_all.append(output)
+            probability_all.append(probability)
 
     summary = OrderedDict()
     summary['Seq'] = seq_all