Update app.py

app.py

@@ -20,7 +20,7 @@ def is_false_alarm(code_text):
     code_text = re.sub('(\\\\n)+', '\\n', code_text)
 
     # 1. CFA-CodeBERTa-small.pt -> CodeBERTa-small-v1 finetunig model
-    path = os.getcwd() + '\models\CFA-CodeBERTa-small.pt'
+    path = os.getcwd() + '/models/CFA-CodeBERTa-small.pt'
     tokenizer = AutoTokenizer.from_pretrained("huggingface/CodeBERTa-small-v1")
     input_ids = tokenizer.encode(
         code_text, max_length=512, truncation=True, padding='max_length')
@@ -32,7 +32,7 @@ def is_false_alarm(code_text):
     # model(input_ids)[0].argmax().detach().cpu().numpy().item()
 
     # 2. CFA-codebert-c.pt -> codebert-c finetuning model
-    path = os.getcwd() + '\models\CFA-codebert-c.pt'
+    path = os.getcwd() + '/models/CFA-codebert-c.pt'
     tokenizer = AutoTokenizer.from_pretrained(path)
     input_ids = tokenizer(code_text, padding=True, max_length=512,
                           truncation=True, return_token_type_ids=True)['input_ids']
@@ -43,7 +43,7 @@ def is_false_alarm(code_text):
     pred_2 = model(input_ids)[0].detach().cpu().numpy()[0]
 
     # 3. CFA-codebert-c-v2.pt -> undersampling + codebert-c finetuning model
-    path = os.getcwd() + '\models\CFA-codebert-c-v2.pt'
+    path = os.getcwd() + '/models/CFA-codebert-c-v2.pt'
     tokenizer = RobertaTokenizer.from_pretrained(path)
     input_ids = tokenizer(code_text, padding=True, max_length=512,
                           truncation=True, return_token_type_ids=True)['input_ids']
@@ -54,7 +54,7 @@ def is_false_alarm(code_text):
     pred_3 = model(input_ids)[0].detach().cpu().numpy()
 
     # 4. codeT5 finetuning model
-    path = os.getcwd() + '\models\CFA-codeT5'
+    path = os.getcwd() + '/models/CFA-codeT5'
     model_params = {
         # model_type: t5-base/t5-large
         "MODEL": path,