app.py

# import torch
# from transformers import PegasusForConditionalGeneration, PegasusTokenizer

# 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

# from sentence_splitter import SentenceSplitter, split_text_into_sentences

# splitter = SentenceSplitter(language='en')

# def paraphraze(text):
#   sentence_list = splitter.split(text)
#   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 summarize(text):

#   paraphrased_text = paraphraze(text)
#   return paraphrased_text
########################################################################################################

import torch
from transformers import PegasusForConditionalGeneration, PegasusTokenizer

import streamlit as st
#from .paraphraser import get_paraphrased_sentences, model, tokenizer

hide_main_and_footer_style = """
        <style>
        #MainMenu {visibility: hidden;} footer{visibility: hidden;}
        #root>div:nth-child(1)>div>div>div>div>section>div{padding-top: .2rem;
        </style>
        """

st.markdown(hide_main_and_footer_style, unsafe_allow_html=True)


from transformers import *

model = PegasusForConditionalGeneration.from_pretrained("tuner007/pegasus_paraphrase")
tokenizer = PegasusTokenizerFast.from_pretrained("tuner007/pegasus_paraphrase")


def get_paraphrased_sentences(model, tokenizer, sentence, num_return_sequences=5, num_beams=5):
  # tokenize the text to be form of a list of token IDs
  inputs = tokenizer([sentence], truncation=True, padding="longest", return_tensors="pt")
  # generate the paraphrased sentences
  outputs = model.generate(
    **inputs,
    num_beams=num_beams,
    num_return_sequences=num_return_sequences,
  )
  # decode the generated sentences using the tokenizer to get them back to text
  return tokenizer.batch_decode(outputs, skip_special_tokens=True)


import streamlit as st
#from .paraphraser import get_paraphrased_sentences, model, tokenizer

#######
# from sentence_splitter import SentenceSplitter, split_text_into_sentences

# splitter = SentenceSplitter(language='en')

# def clean(text):
#   sentence_list = splitter.split(text)
#   paraphrase = []

#   for i in sentence_list:
#     paraphrase.append(i)
#     paraphrase2 = [' '.join(x) for x in paraphrase]
#     paraphrase3 = [' '.join(x for x in paraphrase2) ]
#   paraphrased_text = str(paraphrase3).strip('[]').strip("'")
#   return paraphrased_text


def app():
    st.title('Paraphraser')
    st.write('Please provide the text to be paraphrased')
    col1, spacer, col2 = st.columns([5,1,5]) #st.beta_columns((2,1,1,1))
    
    x = 0
    output = ['Result ']
    with col1:
        user_input = st.text_area('Enter text','', height=300)
        
        paraphraseNo = st.slider('Number of Parapharases',1,10,2)
        if st.button('Paraphrase'):
            with st.spinner(text="This may take a moment..."):
                output = get_paraphrased_sentences(model, tokenizer, user_input, num_beams=10, num_return_sequences=paraphraseNo)    
    
    #with spacer:
    
    with col2:
        for x, element in enumerate(output):
            user_output = st.text_area(label="", value=output[x], height=200 )

# st.markdown(
#     '''<style>
#     #MainMenu{visibility: hidden;} footer{visibility: hidden;}
#     #root>div:nth-child(1)>div>div>div>div>section>div{padding-top: .2rem;
#     </style>''', unsafe_allow_html=True
# )

if __name__ == '__main__':
	app()