| import gradio as gr | |
| from transformers import AutoModelForQuestionAnswering, pipeline,AutoTokenizer | |
| import torch | |
| def question_answer(context, question): | |
| AUTH_TOKEN = "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" | |
| model_checkpoint = "letrunglinh/qa_pnc" | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_auth_token=AUTH_TOKEN) | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint) | |
| model = pipeline('question-answering', model=model, | |
| tokenizer=tokenizer, use_auth_token=AUTH_TOKEN) | |
| to_predict = [ | |
| { | |
| "question": question, | |
| "context": context, | |
| } | |
| ] | |
| answers = model(to_predict) | |
| return answers['answer'], answers['score'] | |
| gr.Interface(fn=question_answer, inputs=["text", "text"], outputs=["textbox","textbox"], share = True).launch() |