Update app.py

app.py

@@ -493,7 +493,7 @@ st.write(f'''
     Lets review all the tweets and how they fall into the categories of finance, politics, technology, and wildlife.  
     ''')
 
-st.dataframe(zero_shot_results.style.highlight_max(axis=1, subset=['finance', 'politics', 'technology', 'wildlife'], props='color:white; background-color:green;').format(precision=2))
+st.dataframe(zero_shot_results.style.format(precision=2))
 
 st.write(f'''
     We can observe that the model does not have strong confidence in predicting the categories for some of the tweets.  
@@ -526,10 +526,7 @@ st.write(f'''
 classification_sentiment_df = pd.merge(zero_shot_results_clean, sentiment_results[['sentiment']], how='left', left_index=True, right_index=True)
 classification_sentiment_df = classification_sentiment_df[['tweet', 'category', 'score', 'sentiment']]
 
-def highlight_sentiment(value):
-    color = 'green' if value >= 0.5 else 'red'
-    return 'color:{}'.format(color)
-st.dataframe(classification_sentiment_df.style.applymap(highlight_sentiment, subset=['sentiment']).format(precision=2))
+st.dataframe(classification_sentiment_df.style.format(precision=2))
 
 st.write(f'''
     The difficult part for zero-shot classification is defining the right set of categories for each business case.  
@@ -571,5 +568,13 @@ fig.update_yaxes(range=[0, 1])
 fig.add_hline(y=0.5, line_width=3, line_color='darkgreen')
 st.plotly_chart(fig, use_container_width=True)
 
+st.markdown('''---''')
+st.write('\n')
+st.write('\n')
+
+st.write('''
+    That's the end of the this demo 😎, the source code can be found on [Github](https://github.com/Greco1899/survey_analytics).  
+    ''')
 st.write('\n')
-st.markdown('''---''')
+st.image('https://images.unsplash.com/photo-1620712943543-bcc4688e7485?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=2565&q=80')
+st.caption('Photo by [Andrea De Santis](https://unsplash.com/@santesson89) on [Unsplash](https://unsplash.com).')