Update app.py

app.py

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+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('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)
+
+
+ner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)
+examples=["江苏警方通报特斯拉冲进店铺","李沐，深度学习专家。\
+李沐于2004年进入上海交通大学计算机科学与工程系进行本科学习；2009年至2010年担任香港科技大学研究助理；2011年至2012年担任百度高级研究员；2012年至2017年在美国卡内基梅隆大学攻读博士学位。2019年编著的《动手学深度学习》出版。\
+李沐专注于分布式系统和机器学习算法的研究。"]
+
+def ner(text):
+    output1 = ner_pipeline(text)
+   
+    output = [output1[0]]
+    if output[0]['entity'][1] == '-':
+        output[0]['entity'] = output[0]['entity'][2:len(output[0]['entity'])]
+    # j = 0 
+    for i in range(1,len(output1)):
+        if output1[i]['entity'][1] == '-':
+            output1[i]['entity'] = output1[i]['entity'][2:len(output1[i]['entity'])]
+        dict1 = output1[i]
+        u = len(output) - 1 
+        dict0 = output[u]
+        if (dict0['end'] == dict1['start']) and (dict0['entity'] == dict1['entity']):
+            dict = {
+                'entity':dict0['entity'],
+                'score':min(dict0['score'],dict1['score']),
+                'index':dict1['index'],
+                'word':dict0['word']+dict1['word'],
+                'start':dict0['start'],
+                'end':dict1['end'],
+            }
+  
+            output[len(output) - 1] = dict        
+        else:     
+            dict = dict1
+            output.append(dict)   
+        # print('output_before',output)
+        # print('output_after',output)
+    return {"text": text, "entities": output}    
+
+demo = gr.Interface(ner,
+             gr.Textbox(placeholder="Enter sentence here..."), 
+             gr.HighlightedText(),
+             examples=examples)
+
+demo.launch()
+
+
+
+
+