Update app.py

app.py

@@ -10,15 +10,12 @@ auth = tw.OAuthHandler(consumer_key, consumer_secret)
 auth.set_access_token(access_token, access_token_secret)
 api = tw.API(auth, wait_on_rate_limit=True)
 classifier = pipeline('sentiment-analysis')
-
-st.title('Live Twitter Sentiment Analysis with Tweepy and HuggingFace Transformers')
-st.markdown('This app uses tweepy to get tweets from twitter based on the input name/phrase. It then processes the tweets through HuggingFace transformers pipeline function for sentiment analysis. The resulting sentiments and corresponding tweets are then put in a dataframe for display which is what you see as result.')
-
+st.title('Sentiment Analysis')
+st.markdown('Live Real Time Twitter sentiment enter:  @TwitterAccount to see last N sentiments on mentions .')
 def run():
-
     with st.form(key='Enter name'):
         search_words = st.text_input('Enter the name for which you want to know the sentiment')
-        number_of_tweets = st.number_input('Enter the number of latest tweets for which you want to know the sentiment(Maximum 50 tweets)', 0,50,10)
+        number_of_tweets = st.number_input('Enter the number of latest tweets', 0,100,100)
         submit_button = st.form_submit_button(label='Submit')
     if submit_button:
         tweets =tw.Cursor(api.search_tweets,q=search_words,lang="en").items(number_of_tweets)
@@ -27,6 +24,5 @@ def run():
         q=[p[i]['label'] for i in range(len(p))]
         df = pd.DataFrame(list(zip(tweet_list, q)),columns =['Latest '+str(number_of_tweets)+' Tweets'+' on '+search_words, 'sentiment'])
         st.write(df)
-
 if __name__=='__main__':
     run()