pushkarraj's picture
Update app.py
fa6261f
raw history blame
No virus
1.26 kB
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('<s>'+input_sentence+ '</s>>>>><p>',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('</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()
return (".".join(x))
interface=gr.Interface(fn=paraphraser,inputs="text",outputs=["text"],title="Paraphraser",description="A paraphrasing tool")
interface.launch()