Update app.py

app.py

@@ -13,14 +13,15 @@ tokenizer=BertTokenizer.from_pretrained("./")
 
 def predict(text=None) -> dict:  
     model.eval()
-    inputs = tokenizer(text, return_tensors="pt")
+    inputs = tokenizer(str(text), return_tensors="pt")
     input_ids = inputs["input_ids"].to(device)
     attention_mask = inputs["attention_mask"].to(device)
     model.to(device)
     token_logits = model(input_ids, attention_mask=attention_mask).logits
+    print(mask_token_index)
     mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
     mask_token_logits = token_logits[0, mask_token_index, :]
-    print(mask_token_logits.shape)
+    print(mask_token_logits)
     top_5_tokens = torch.topk(mask_token_logits, NUM_CLASSES, dim=1).indices[0].tolist()
     score = torch.nn.functional.softmax(mask_token_logits)[0]
     top_5_score = torch.topk(score, NUM_CLASSES).values.tolist()