app.py

import gradio as gr
from transformers import pipeline

#pp = pipeline("question-answering",model="deepset/roberta-base-squad2")
pp = pipeline("question-answering",model="luhua/chinese_pretrain_mrc_roberta_wwm_ext_large")

def qa_fn(ask,ctxt):
  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"],["乔治住在哪里呀？","我是小猪佩奇，我是乔治的哥哥，我的家在北京颐和园"]];

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")],
       outputs=[gr.HighlightedText(label='答案位置'),gr.Textbox(label="答案"),gr.Number(label="Score")],
       examples=samples);

demo.launch()