Update app.py

app.py

@@ -54,6 +54,10 @@ def preprocess(text):
     text=re.sub(r"\)","",text)
     text=" ".join(text.split())
     return text
+    
+def highlight_survived(s):
+    return ['background-color: red']*len(s) if s.Sexista else ['background-color: green']*len(s)
+                
 
 st.set_page_config(layout="wide")
 st.markdown('<style>body{background-color: Blue;}</style>',unsafe_allow_html=True)
@@ -62,7 +66,7 @@ colT1,colT2 = st.columns([2,8])
 with colT2:
     #st.title('Analisis de comentarios sexistas en Twitter') 
     st.markdown(""" <style> .font {
-    font-size:40px ; font-family: 'Cooper Black'; color: #FF9633;} 
+    font-size:40px ; font-family: 'Cooper Black'; color: #FF9633;background-color: Blue;} 
     </style> """, unsafe_allow_html=True)
     st.markdown('<p class="font">Análisis de comentarios sexistas en Twitter</p>', unsafe_allow_html=True)
     
@@ -151,6 +155,8 @@ def run():
                 flat_predictions = np.argmax(flat_predictions, axis=1).flatten()#p = [i for i in classifier(tweet_list)]
                 df = pd.DataFrame(list(zip(tweet_list, flat_predictions)),columns =['Últimos '+ str(number_of_tweets)+' Tweets'+' de '+search_words, 'Sexista'])
                 df['Sexista']= np.where(df['Sexista']== 0, 'No Sexista', 'Sexista')
-                st.table(df)
+                
+                st.dataframe(df.style.apply(highlight_survived, axis=1))
+                #st.table(df)
             #st.write(df)
 run()