Update app.py

app.py

@@ -20,14 +20,7 @@ def greet(co):
         if not code:
             break
         code_text.append(code)
-    '''
-    iter_num = int(
-        input('false alarm을 분류하기 위해서 입력할 코드의 갯수는 몇개인가요? (숫자만 입력하세요.) : '))
-    code_text = []
-    for _ in range(iter_num):
-        code = input('코드를 입력하세요 : ')
-        code_text.append(code)
-    '''
+    
     code_text = ' '.join(code_text)
     code_text = re.sub('\/\*[\S\s]*\*\/', '', code_text)
     code_text = re.sub('\/\/.*', '', code_text)
@@ -53,6 +46,7 @@ def greet(co):
     input_ids = torch.tensor([input_ids])
     model = AutoModelForSequenceClassification.from_pretrained(
         path, num_labels=2)
+    model.to('cpu')
     pred_2 = model(input_ids)[0].detach().cpu().numpy()[0]
 
     # 3. CFA-codebert-c-v2.pt -> undersampling + codebert-c finetuning model
@@ -63,6 +57,7 @@ def greet(co):
     input_ids = torch.tensor([input_ids])
     model = RobertaForSequenceClassification.from_pretrained(
         path, num_labels=2)
+    model.to('cpu')
     pred_3 = model(input_ids)[0].detach().cpu().numpy()
 
     # 4. codeT5 finetuning model
@@ -217,12 +212,12 @@ with gr.Blocks() as demo1:
         """
         )
     with gr.Row():
-        with gr.Column():
+        with gr.Columns():
             inputs_1 = gr.Textbox(placeholder="코드를 입력하시오.", label='Text')
             with gr.Row():
                 btn = gr.Button("제출하기")
         with gr.Column():
-            outputs_1 = gr.Label(num_top_classes=2, label = 'Result')
+            outputs_1 = gr.Number(label = 'Result')
     btn.click(fn = greet, inputs = inputs_1, outputs= outputs_1)
 
 if __name__ == "__main__":