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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=="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"],["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"]]; | |
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(["deepset","chinese"],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() |