Update app.py

app.py

@@ -7,27 +7,6 @@ device = torch.device('cpu')
 
 NUM_CLASSES=6
 
-#model=BertForMaskedLM.from_pretrained("./")
-#tokenizer=BertTokenizer.from_pretrained("./")
-
-
-def predict(text=None) -> dict:  
-    print(text)
-    model.eval()
-    inputs = tokenizer(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
-    mask_token_index = torch.where(inputs_ex["input_ids"] == tokenizer.mask_token_id)[1]
-    mask_token_logits = token_logits[0, mask_token_index, :]
-    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()
-    print(top_5_tokens)
-    return {tokenizer.decode([tok]): float(score) for tok, score in zip(top_5_tokens, top_5_score)}
-    
-    
 def test():
     return {"token": 0.57}