Create app.py

app.py

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+   import gradio as gr
+   from transformers import pipeline
+   
+   # 加载模型
+   print("正在加载病理检测NER模型...")
+   ner = pipeline(
+       "token-classification", 
+       model="OpenMed/OpenMed-NER-PathologyDetect-BigMed-560M",
+       aggregation_strategy="max"
+   )
+   print("模型加载完成!")
+   
+   # 处理函数
+   def process_text(text):
+       if not text:
+           return "请输入医学文本"
+           
+       results = ner(text)
+       output = ""
+       
+       for result in results:
+           entity = result["entity_group"]
+           word = result["word"]
+           score = round(result["score"], 2)
+           output += f"检测到病理实体: {word} (类型: {entity}, 置信度: {score})\n"
+           
+       if not output:
+           output = "未检测到任何病理相关实体"
+           
+       return output
+   
+   # 创建界面
+   demo = gr.Interface(
+       fn=process_text,
+       inputs=gr.Textbox(placeholder="请输入医学文本...", lines=5),
+       outputs="text",
+       title="OpenMed 病理检测 NER 模型演示",
+       description="使用OpenMed-NER-PathologyDetect-BigMed-560M模型识别文本中的病理实体"
+   )
+   
+   # 启动服务
+   demo.launch()