app.py

import streamlit as st
import torch
import sacremoses
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import FSMTForConditionalGeneration, FSMTTokenizer

st.title("Paraphraser Three -- Back Translation")

user_input = st.text_area("Input sentence.")

def load_en2de():
  en2de = pipeline("translation_en_to_de", model="t5-base")
  return en2de
  
def load_de2en():
  model_name = "facebook/wmt19-de-en"
  tokenizer = FSMTTokenizer.from_pretrained(model_name)
  model_de_to_en = FSMTForConditionalGeneration.from_pretrained(model_name)
  return tokenizer, model_de_to_en
  
en2de = load_en2de()
tokenizer_de2en, de2en = load_de2en()

en_to_de_output = en2de(user_input)
translated_text = en_to_de_output[0]['translation_text']

input_ids = tokenizer_de2en.encode(translated_text, return_tensors="pt")
output_ids = de2en.generate(input_ids)[0]
augmented_text = tokenizer_de2en.decode(output_ids, skip_special_tokens=True)

st.write("Paraphrased text using back translation: ", augmented_text)