Update app.py

Center chart title and update header and footer

app.py

@@ -36,15 +36,13 @@ def data_manipulation(df_redacted):
     return sentiment_counts, sentiment
 
 
-def display_header(tweet_url, sentiment):
+def display_header(sentiment):
     """
     Displays the header section of the app.
     """
-    st.markdown('**Objective:** Understand public sentiment of a Tweet by analyzing the sentiment of each reply.')
-    st.markdown('**Analysis:** This app runs sentiment analysis on 10,948 replies to a Facebook Tweet announcing '
-                'their rebranding to Meta on 10/28/2021. Link to Tweet: {}'.format(tweet_url))
-    st.markdown('**Results:** Most frequent sentiment category for the replies to this Tweet: **{}**'.format(sentiment))
-
+    st.header('This app runs a sentiment analysis of the replies to a Facebook Tweet '
+              'announcing their rebranding to Meta.')
+    st.header('RESULT: {}'.format(sentiment))
 
 def display_chart(sentiment_counts):
     """
@@ -54,20 +52,24 @@ def display_chart(sentiment_counts):
     fig = px.bar(sentiment_counts,
                  x='Sentiment',
                  y='Count',
-                 title='Tweet Replies Sentiment Counts by Category')
-
+                 title='Tweet Replies Count by Sentiment Category')
+    fig.update_layout(title_x=0.5)
     st.plotly_chart(fig, use_container_width=True)
 
 
-def display_footer():
+def display_footer(tweet_url, sentiment):
     """
     Displays the footer section of the app.
     """
+    st.markdown('**Objective:** Understand public sentiment of a Tweet by analyzing the sentiment of each reply.')
+    st.markdown('**Analysis:** This app runs sentiment analysis on 10,948 replies to a Facebook Tweet announcing '
+                'their rebranding to Meta on 10/28/2021. Link to Tweet: {}'.format(tweet_url))
+    st.markdown('**Results:** Most frequent sentiment category for the replies to this Tweet: **{}**'.format(sentiment))
     st.markdown('**Notes:** ')
     st.markdown('- The VADER model was used to analyze the sentiment of each reply: '
                 'https://github.com/cjhutto/vaderSentiment')
     st.markdown('- Due to Twitter developer policies, I am not able to share the data set of downloaded Tweet replies '
-                'so my DATA EXTRACTION and DATA CLEANSING steps are not shown at this time.')
+                'so my DATA EXTRACTION and DATA CLEANSING steps are not shown at this time but will be added soon!')
     st.markdown('**Plans for Version 2.0:**')
     st.markdown('- Formulate method for cleaning Tweet replies, such as removing those that are from bots or are spam.')
     st.markdown('- Analyze the sentiment of replies using the BERTweet model, which would be more appropriate for this '
@@ -85,9 +87,9 @@ def main():
     sentiment_counts, sentiment = data_manipulation(df_redacted)
 
     # DISPLAY DATA
-    display_header(tweet_url, sentiment)
+    display_header(sentiment)
     display_chart(sentiment_counts)
-    display_footer()
+    display_footer(tweet_url, sentiment)
 
 
 if __name__ == '__main__':