files added

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.sav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,33 @@
+import streamlit as st
+import pickle
+from PIL import Image
+
+
+
+with st.sidebar:
+    st.subheader('Text Summarization Using BERT')
+    st.divider()
+    st.write('This is a text summarization app using BERT. It is a state of the art model for text summarization. It is a pretrained model which is trained on a large dataset of news articles. It can be used for summarizing any text. It is a very powerful model and is very fast. It is also very accurate.')
+    image = Image.open('NextSentencePrediction.jpg')
+    st.image(image, caption='Bert Model')
+    st.code('App Built by Ambuj Raj',language='python')
+
+
+
+def summary(txt):
+    with st.spinner('Summarizing...'):
+        loaded_model = pickle.load(open('bert.sav', 'rb'))
+        summary = loaded_model(txt, min_length=50)
+    st.success('Your Summary is ready and is given below ⬇️')
+    st.caption(summary)
+
+st.title('Text Summarization Using BERT')
+st.divider()
+txt = st.text_area('Enter the Text to extract Summary', '''''')
+if st.button('Summarize'):
+    summary(txt)
+
+
+
+
+

bert.sav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ded2c906eeb91f7685021fff34062eaafa6dd3389a28df9e968d8639ab37d32
+size 1341515428

requirements.txt

@@ -0,0 +1 @@
+streamlit=1.21.0