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| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| import gradio as gr | |
| # Creating the Q&A pipeline | |
| nlp = pipeline('question-answering', model='deepset/roberta-base-squad2', tokenizer='deepset/roberta-base-squad2') | |
| def questionAndAnswer(ques,content): | |
| question_set = {'question':ques,'context':content} | |
| results = nlp(question_set) | |
| return results['answer'] | |
| interface = gr.Interface(fn=questionAndAnswer, | |
| inputs=["text","text"], | |
| outputs="text", | |
| title='Bilal Question&Answer') | |
| interface.launch(inline=False) | |