import gradio as gr from transformers import pipeline pp_en = pipeline("question-answering",model="deepset/roberta-base-squad2") pp_ch = pipeline("question-answering",model="luhua/chinese_pretrain_mrc_roberta_wwm_ext_large") def qa_fn(ask,ctxt,model): pp = (pp_en if model=="deepset" else pp_ch); ret = pp(context=ctxt, question=ask); ret['entity']='Answer'; return {"text":ctxt,"entities":[ret]}, ret['answer'], ret['score'] #注意HighlightedText的用法。有两种不同用法:https://gradio.app/named_entity_recognition/ # 一种是list of dict ,一种是list of tuple. 详细用法参考https://gradio.app/named_entity_recognition/吧 samples= [ ["乔治的哥哥叫什么名字?","我是小猪佩奇,我是乔治的哥哥,我家住在北京","deepset"], ["图书馆主页的网址是多少啊?","读者进入图书馆主页(http://lib.tjut.edu.cn)后,点击文献传递菜单,即可查看文献传递的具体流程步骤","mrc"], ]; introStr = "用于演示使用人工智能自动寻找问题答案,这将是一种更加高效便捷的新型信息检索方式。"; titleStr = "智能问答演示程序"; demo = gr.Interface(qa_fn, inputs=[gr.Textbox(label="Question",placeholder='请输入问题'), gr.Textbox(label="Contexkht",lines=10,placeholder="请输入一段文本"), gr.Radio(["deepset","mrc"],label="Select Model", value="deepset"), ], outputs=[gr.HighlightedText(label='答案位置'),gr.Textbox(label="答案"),gr.Number(label="Score")], examples=samples, description=introStr, title=titleStr); demo.launch()