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| # https://huggingface.co/tuner007/pegasus_paraphrase | |
| import nltk | |
| from nltk import sent_tokenize | |
| nltk.download('punkt') | |
| import gradio as gr | |
| import torch | |
| from transformers import PegasusForConditionalGeneration, PegasusTokenizer | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| 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 paraphraser(input_text,num_return_sequences=1): | |
| sentence_list = sent_tokenize(input_text) | |
| output = [] | |
| for sentence in sentence_list: | |
| batch = tokenizer.prepare_seq2seq_batch([sentence],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) | |
| output.extend(tgt_text) | |
| paraphrase = [' '.join(x for x in output)] | |
| paraphrased_text = str(paraphrase).strip('[]').strip("'") | |
| return paraphrased_text | |
| paraphraseUI = gr.Interface(fn=paraphraser, inputs='textbox', outputs='text', title="ParaPhraser", theme='dark') | |
| paraphraseUI.launch(inbrowser=True, share=True) |