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import torch
from transformers import (
AutoTokenizer,
AutoModelForSeq2SeqLM,
AdamW,
get_linear_schedule_with_warmup
)
model = AutoModelForSeq2SeqLM.from_pretrained("ADELIB/ANQG")
tokenizer = AutoTokenizer.from_pretrained("ADELIB/ANQG")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device",device)
model=model.to(device)
import gradio as gr
def generate__questions(cnt,ans):
text="context: " +cnt + " " + "answer: " + ans + " </s>"
text_encoding = tokenizer.encode_plus(
text,max_length=512,padding=True,return_tensors="pt"
)
model.eval()
generated_ids = model.generate(
input_ids=text_encoding['input_ids'].to(device),
attention_mask=text_encoding['attention_mask'].to(device),
max_length=72,
early_stopping=True,
num_beams=5,
num_return_sequences=1
)
preds = [
tokenizer.decode(gen_id,skip_special_tokens=True,clean_up_tokenization_spaces=True)
for gen_id in generated_ids
]
t=' '.join(preds)
st=t.replace('question: ',' ')
return(st)
demo = gr.Interface(fn=generate__questions, inputs=[gr.Textbox(label='Context'),
gr.Textbox(label='Answer')] ,
outputs=gr.Textbox(label='Question'),
title="Arabic Question Generation",
description="Get the Question from given Context and a Answer")
demo.launch()
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