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import streamlit as st
import joblib,torch
import time
from PIL import Image
device = 'cuda' if torch.cuda.is_available() else 'cpu'
loaded_tokenizer = joblib.load("finalized_tokenizer.sav")
loaded_model = joblib.load("finalized_model.sav")
st.title('Text Summarization using Pegasus')
txt = st.text_area('Enter Text to summarize here', '')
with st.sidebar:
st.subheader("Text Summarization using Pegasus")
st.write("PEGASUS uses an encoder-decoder model for sequence-to-sequence learning. In such a model, the encoder will first take into consideration the context of the whole input text and encode the input text into something called context vector, which is basically a numerical representation of the input text. This numerical representation will then be fed to the decoder whose job is decode the context vector to produce the summary.")
image =Image.open("Pegasus_model.png")
st.image(image, caption='Pegasus Model')
st.code("App built by Srishti Pandey",language="python")
if st.button('Summarize'):
with st.spinner('Summarizing..'):
batch = loaded_tokenizer(txt, truncation=True, padding='longest', return_tensors="pt").to(device)
translated = loaded_model.generate(**batch)
tgt_text = loaded_tokenizer.batch_decode(translated, skip_special_tokens=True)
st.success('Summarized Text')
st.subheader(tgt_text[0])
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