Update app.py

app.py

@@ -125,7 +125,7 @@ def analizar_tweets(search_words, number_of_tweets ):
     logits1 = logits1.detach().cpu().numpy()
     # Store predictions and true labels
     predictions.append(logits1)
-
+      
   #flat_predictions = [item for sublist in predictions for item in sublist]
   flat_predictions = [item for sublist in predictions for item in sublist]
   
@@ -139,7 +139,7 @@ def analizar_tweets(search_words, number_of_tweets ):
   df['Tweets'] = df['Tweets'].str.replace('RT|@', '')   
   #df['Tweets'] = df['Tweets'].apply(lambda x: re.sub(r'[:;][-o^]?[)\]DpP3]|[(/\\]|[\U0001f600-\U0001f64f]|[\U0001f300-\U0001f5ff]|[\U0001f680-\U0001f6ff]|[\U0001f1e0-\U0001f1ff]','', x))
 
-st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
+  st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
 
   return df
 
@@ -194,7 +194,7 @@ def analizar_frase(frase):
   text= pd.DataFrame({'Frase': [frase], 'Prediccion':[flat_predictions], 'Probabilidad':[probabilidad_sexista]})
   text['prediccion'] = np.where(text['prediccion'] == 0 , 'No Sexista', 'Sexista')
 
-st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
+  st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
   
   return text