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| import gradio as gr | |
| import pandas as pd | |
| import os | |
| import time | |
| import torch | |
| from transformers import pipeline, GPT2Tokenizer, OPTForCausalLM | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| torch.cuda.empty_cache() | |
| model=OPTForCausalLM.from_pretrained("pushkarraj/opt_paraphraser") | |
| tokenizer=GPT2Tokenizer.from_pretrained("facebook/opt-1.3b",truncation=True) | |
| generator=pipeline("text-generation",model=model,tokenizer=tokenizer,device=device) | |
| def cleaned_para(input_sentence): | |
| p=generator('<s>'+input_sentence+ '</s>>>>><p>',do_sample=True,max_length=len(input_sentence.split(" "))+200,temperature = 0.9,repetition_penalty=1.2) | |
| return p[0]['generated_text'].split('</s>>>>><p>')[1].split('</p>')[0] | |
| from spacy.lang.en import English # updated | |
| def sentensizer(raw_text): | |
| nlp = English() | |
| nlp.add_pipe("sentencizer") # updated | |
| doc = nlp(raw_text) | |
| sentences = [sent for sent in doc.sents] | |
| print(sentences) | |
| return sentences | |
| def paraphraser(text): | |
| begin=time.time() | |
| x=[cleaned_para(str(i)) for i in sentensizer(text)] | |
| end=time.time() | |
| print(end-begin) | |
| return ("".join(x)) | |
| interface=gr.Interface(fn=paraphraser,inputs="text",outputs=["text"],title="Paraphraser",description="A paraphrasing tool") | |
| interface.launch() |