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)
    # 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(server_port=9090)