Update app.py

app.py

@@ -2,7 +2,7 @@
 #App: NLP App with Streamlit
 Credits: Streamlit Team, Marc Skov Madsen(For Awesome-streamlit gallery)
 Description
-This is a Natural Language Processing(NLP) base Application which is useful for basic NLP tasks such as follows;
+This is a Natural Language Processing(NLP) base Application that is useful for basic NLP tasks such as follows;
 
 + Tokenization(POS tagging) & Lemmatization(root mean) using Spacy
 
@@ -10,7 +10,7 @@ This is a Natural Language Processing(NLP) base Application which is useful for
 
 + Sentiment Analysis using TextBlob
 
-+ Document/Text Summarization using Gensim/T5 both for Bangla Extractive and English Abstructive
++ Document/Text Summarization using Gensim/T5 both for Bangla Extractive and English Abstructive.
 
 This is built with Streamlit Framework, an awesome framework for building ML and NLP tools.
 Purpose
@@ -41,6 +41,9 @@ import pytesseract
 #pytesseract.pytesseract.tesseract_cmd = r"./Tesseract-OCR/tesseract.exe"
 from PIL import Image
 # Title
+if st.button("REFRESH"):
+   st.experimental_rerun()
+
 st.title("Streamlit NLP APP")
 @st.experimental_singleton
 def text_analyzer(my_text):
@@ -67,8 +70,11 @@ def main():
 	""" NLP Based Application with Streamlit """
 	st.markdown("""
     	#### Description
-    	This is a Natural Language Processing(NLP) Based Application useful for basic NLP tasks
-        Named Entity Recognition, Sentiment Analysis, Spell Corrections, Human Level Text Generation, and Summarization.
+    	##This is a Natural Language Processing(NLP) base Application that is useful for basic NLP tasks such as follows:
++ Tokenization(POS tagging) & Lemmatization(root mean) using Spacy
++ Named Entity Recognition(NER)/Trigger word detection using SpaCy
++ Sentiment Analysis using TextBlob
++ Document/Text Summarization using Gensim/T5 both for Bangla Extractive and English Abstractive.
     	""")                         
 	def change_photo_state():
 		st.session_state["photo"]="done"