# 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()