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import gradio as gr
import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
from sentence_splitter import SentenceSplitter, split_text_into_sentences
model_name = 'tuner007/pegasus_paraphrase'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
def get_response(input_text, num_return_sequences):
batch = tokenizer.prepare_seq2seq_batch([input_text], truncation=True, padding='longest', max_length=60,
return_tensors="pt").to(torch_device)
translated = model.generate(**batch, max_length=60, num_beams=10, num_return_sequences=num_return_sequences,
temperature=1.5)
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
return tgt_text
def get_response_from_text(
context="I am a student at the University of Washington. I am taking a course called Data Science."):
splitter = SentenceSplitter(language='en')
sentence_list = splitter.split(context)
paraphrase = []
for i in sentence_list:
a = get_response(i, 1)
paraphrase.append(a)
paraphrase2 = [' '.join(x) for x in paraphrase]
paraphrase3 = [' '.join(x for x in paraphrase2)]
paraphrased_text = str(paraphrase3).strip('[]').strip("'")
return paraphrased_text
def greet(context):
paragraphs = context.split("\n")
paraphrased_text = []
for paragraph in paragraphs:
try:
paraphrased_paragraph = get_response_from_text(paragraph)
paraphrased_text.append(paraphrased_paragraph)
except:
pass
response = ' '.join(paraphrased_text)
return str(response)
iface = gr.Interface(fn=greet, inputs="text", outputs="text", title="Developed by Farhan Siddiqui")
iface.launch()
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