Update app.py

app.py

@@ -1,6 +1,7 @@
 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")
@@ -9,6 +10,16 @@ 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)