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| 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=="english" 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","english"], | |
| ["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","chinese"], | |
| ]; | |
| 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="Context",lines=10,placeholder="请输入一段文本"), | |
| gr.Radio(["english","chinese"],label="Select a Model", value="english"), | |
| ], | |
| 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() | |