Update app.py

app.py

@@ -1,13 +1,13 @@
 from transformers import AutoModelForTokenClassification,AutoTokenizer,pipeline
 import gradio as gr
 import torch 
-model = AutoModelForTokenClassification.from_pretrained('uer/roberta-base-finetuned-cluener2020-chinese')#,local_files_only=True)#cache_dir="C:\2023\Huggingface_4_12\Gradio Tutorial",force_download=True)
+model = AutoModelForTokenClassification.from_pretrained('uer/roberta-base-finetuned-cluener2020-chinese',local_files_only=True)#cache_dir="C:\2023\Huggingface_4_12\Gradio Tutorial",force_download=True)
 # model = AutoModelForTokenClassification.from_pretrained('C:\\2023\Huggingface_4_12\Gradio Tutorial\cache3\Huggingface_4_12\Gradio Tutorial\models--uer--roberta-base-finetuned-cluener2020-chinese\blobs\3d20fdef0b0f04d283e1693ef4c030b133fa7c3c')
-tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-cluener2020-chinese')#,local_files_only=True)
+tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-finetuned-cluener2020-chinese',local_files_only=True)
 
 
 ner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)
-examples=["江苏警方通报特斯拉冲进店铺","安卓手机变天！没想到“照相机”和“折叠机”跑分也能破140万"]
+examples=["江苏警方通报特斯拉冲进店铺"]
 
 def ner(text):
     output1 = ner_pipeline(text)