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")
model=OPTForCausalLM.from_pretrained('pushkarraj/pushkar_OPT_paraphaser')
tokenizer=GPT2Tokenizer.from_pretrained('pushkarraj/pushkar_OPT_paraphaser',truncation=True)
generator=pipeline("text-generation",model=model,tokenizer=tokenizer,device=device)
def cleaned_para(input_sentence):
p=generator(''+input_sentence+ '>>>>
',do_sample=True,max_length=len(input_sentence.split(" "))+200,temperature = 0.8,repetition_penalty=1.2,top_p=0.4,top_k=1) return p[0]['generated_text'].split('>>>>
')[1].split('
')[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() return (".".join(x)) interface=gr.Interface(fn=paraphraser,inputs="text",outputs=["text"],title="Paraphraser",description="A paraphrasing tool") interface.launch()