Update app.py

app.py

@@ -198,7 +198,7 @@ def analizar_frase(frase):
   text['Prediccion'] = np.where(text['Prediccion'] == 0 , 'No Sexista', 'Sexista')
 
 
-  tabla = st.table(text.reset_index(drop=True).head(30).style.applymap(color_survived, subset=['Prediccion']))
+  tabla = st.table(text.reset_index(drop=True).head(50).style.applymap(color_survived, subset=['Prediccion']))
     
   return tabla
 
@@ -255,10 +255,10 @@ def tweets_localidad(buscar_localidad):
   probability = np.amax(logits1,axis=1).flatten()
   Tweets =['Últimos 50 Tweets'+' de '+ buscar_localidad]
   df = pd.DataFrame(list(zip(text1, localidad,username, flat_predictions,probability)), columns = ['Tweets' ,'Localidad' , 'Usuario','Prediccion','Probabilidad'])
-  df_filtrado = df[df["Prediccion"] == 1 ]
+  
   df['Prediccion']= np.where(df['Prediccion']== 0, 'No Sexista', 'Sexista')
   #df['Tweets'] = df['Tweets'].str.replace('RT|@', '')
-  
+  df_filtrado = df[df["Prediccion"]=="Sexista" ]
   #df['Tweets'] = df['Tweets'].apply(lambda x: re.sub(r'[:;][-o^]?[)\]DpP3]|[(/\\]|[\U0001f600-\U0001f64f]|[\U0001f300-\U0001f5ff]|[\U0001f680-\U0001f6ff]|[\U0001f1e0-\U0001f1ff]','', x))
   
   tabla = st.table(df.reset_index(drop=True).head(50).style.applymap(color_survived, subset=['Prediccion']))