|
from transformers import AutoModelForQuestionAnswering,AutoTokenizer,pipeline |
|
import gradio as gr |
|
|
|
model = AutoModelForQuestionAnswering.from_pretrained('uer/roberta-base-chinese-extractive-qa') |
|
tokenizer = AutoTokenizer.from_pretrained('uer/roberta-base-chinese-extractive-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'] |
|
|
|
|
|
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(server_port=9090) |
|
|
|
|
|
|
|
|
