File size: 1,937 Bytes
3021e28 9083bb9 3021e28 9083bb9 3021e28 9083bb9 3021e28 9083bb9 3021e28 9083bb9 3021e28 9083bb9 3021e28 9083bb9 3021e28 9083bb9 3021e28 bcabc85 3021e28 9083bb9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
# from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline
# import gradio as gr
# model = AutoModelForQuestionAnswering.from_pretrained('sundea/Work-QA')
# tokenizer = AutoTokenizer.from_pretrained('sundea/Work-QA')
# QA = pipeline('question-answering', model=model, tokenizer=tokenizer)
# def get_out(text1,text2):
# QA_input={'question':text1,'context':text2}
# res=QA(QA_input)
# # res['answer']
# return res['answer']
# with gr.Blocks() as demo:
# with gr.Row():
# question = gr.Textbox(label='question')
# greet_btn = gr.Button('compute')
# context=gr.Textbox(label='context')
# res=gr.Textbox(label='result')
# greet_btn.click(fn=get_out,inputs=[question,context],outputs=res)
# demo.launch()
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
import gradio as gr
model = AutoModelForQuestionAnswering.from_pretrained('sundea/Work-QA')
tokenizer = AutoTokenizer.from_pretrained('sundea/Work-QA')
QA = pipeline('question-answering', model=model, tokenizer=tokenizer)
def get_out(text1, text2):
QA_input = {'question': text1, 'context': text2}
res = QA(QA_input)
return res['answer']
# 添加示例
examples = [
['李理居住在哪','李理住在南京，他养了只小狗，名字叫丢丢，它是棕色毛色。'],
[ '李理的小狗叫什么','李理住在南京，他养了只小狗，名字叫丢丢，它是棕色毛色。'],
['李理的小狗是什么颜色的','李理住在南京，他养了只小狗，名字叫丢丢，它是棕色毛色。']
]
# 创建Gradio应用程序
with gr.Interface(fn=get_out,
inputs=[gr.inputs.Textbox(label='question'), gr.inputs.Textbox(label='context')],
outputs=gr.outputs.Textbox(label='answer'),
title='Question Answering',
examples=examples) as app:
app.launch()
|