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| import gradio as gr | |
| import random | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| from question_generation import question_generation_sampling | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| g1_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer") | |
| g1_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer") | |
| g2_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-race-Distractor") | |
| g2_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-Distractor") | |
| g1_model.eval() | |
| g2_model.eval() | |
| g1_model.to(device) | |
| g2_model.to(device) | |
| def generate_multiple_choice_question( | |
| context | |
| ): | |
| num_questions = 1 | |
| question_item = question_generation_sampling( | |
| g1_model, g1_tokenizer, | |
| g2_model, g2_tokenizer, | |
| context, num_questions, device | |
| )[0] | |
| question = question_item['question'] | |
| options = question_item['options'] | |
| options[0] = f"{options[0]} [ANSWER]" | |
| random.shuffle(options) | |
| output_string = f"Question: {question}\n[A] {options[0]}\n[B] {options[1]}\n[C] {options[2]}\n[D] {options[3]}" | |
| return output_string | |
| demo = gr.Interface( | |
| fn=generate_multiple_choice_question, | |
| inputs=gr.Textbox(lines=8, placeholder="Context Here..."), | |
| outputs=gr.Textbox(lines=5, placeholder="Question: \n[A] \n[B] \n[C] \n[D] "), | |
| title="Multiple-choice Question Generator", | |
| description="Provide some context (e.g. news article or any passage) in the context box and click **Submit**. The models currently support English only. This demo is a part of MQAG - https://github.com/potsawee/mqag0.", | |
| allow_flagging='never' | |
| ) | |
| demo.launch() | |