update demo.

app.py

@@ -15,7 +15,7 @@ model = BertForMaskedLM.from_pretrained("shibing624/macbert4csc-base-chinese")
 
 def ai_text(text):
     with torch.no_grad():
-        outputs = model(**tokenizer(text, padding=True, return_tensors='pt'))
+        outputs = model(**tokenizer([text], padding=True, return_tensors='pt'))
 
     def get_errors(corrected_text, origin_text):
         sub_details = []
@@ -35,7 +35,7 @@ def ai_text(text):
         sub_details = sorted(sub_details, key=operator.itemgetter(2))
         return corrected_text, sub_details
 
-    _text = tokenizer.decode(torch.argmax(outputs.logits, dim=-1), skip_special_tokens=True).replace(' ', '')
+    _text = tokenizer.decode(torch.argmax(outputs.logits[0], dim=-1), skip_special_tokens=True).replace(' ', '')
     corrected_text = _text[:len(text)]
     corrected_text, details = get_errors(corrected_text, text)
     print(text, ' => ', corrected_text, details)