import streamlit as st
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
	
st.title("Paraphrase with Pegasus")
st.write("Paraphrase means to express meaning using different words. Pegasus refers to a natural language processing model.")
st.write("Write or paste text below, select a number of paraphrases and enter. The machine will attempt to produce your selected number of paraphrases. You can also select advanced features.")

model_name = "tuner007/pegasus_paraphrase"
torch_device = "cpu"
tokenizer = PegasusTokenizer.from_pretrained(model_name)
	
	
@st.cache(allow_output_mutation=True)
def load_model():
  model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
  return model

def get_response(
  input_text, num_return_sequences, num_beams, max_length=512, temperature=1.5
):

  model = load_model()
  batch = tokenizer([input_text], truncation=True, padding="longest", max_length=max_length, return_tensors="pt").to(torch_device)
  translated = model.generate(**batch, max_length=max_length, num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=temperature)
  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
  return tgt_text

num_beams = 10
num_return_sequences = st.slider("Number of paraphrases", 1, 5, 3, 1)
context = st.text_area(label="Write or paste text", max_chars=512)

with st.expander("Advanced"):
  temperature = st.slider("Temperature", 0.1, 5.0, 1.5, 0.1)
  max_length = st.slider("Max length", 10, 512, 256, 10)

if context:
  response = get_response(context, num_return_sequences, num_beams, max_length, temperature)
  for paraphrase in response:
    st.write(paraphrase)