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import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import streamlit as st
st.title("Paraphrase")
@st.cache(allow_output_mutation=True)
def get_model():
tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
return model, tokenizer
model, tokenizer = get_model()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
temp = st.sidebar.slider("Temperature", 0.7, 1.5)
number_of_outputs = st.sidebar.slider("Number of Outputs", 1, 10)
def translate_to_english(model, tokenizer, text):
translated_text = []
text = text + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
beam_outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
do_sample=True,
max_length=256,
temperature = temp,
top_k=120,
top_p=0.98,
early_stopping=True,
num_return_sequences=number_of_outputs,
)
for beam_output in beam_outputs:
sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(sent)
translated_text.append(sent)
return translated_text
text = st.text_input("Okay")
st.text("What you wrote: ")
st.write(text)
st.text("Output: ")
if text:
translated_text = translate_to_english(model, tokenizer, text)
st.write(translated_text if translated_text else "No translation found")
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