Update app.py

app.py

@@ -102,7 +102,7 @@ def analizar_tweets(search_words, number_of_tweets ):
   tweets = api.user_timeline(screen_name = search_words,tweet_mode="extended", count= number_of_tweets)
   tweet_list = [i.full_text for i in tweets]
   text= pd.DataFrame(tweet_list)
-  text[0] = text[0].apply(preprocess_tweet)
+  text[0] = text[0].apply(preprocess)
   text_list = text[0].tolist()
   result = []
   for text in text_list:
@@ -121,14 +121,10 @@ def analizar_tweets(search_words, number_of_tweets ):
   return tabla
 
 def analizar_frase(frase):
-    
-  #palabra = frase.split()
-  #palabra = frase
-  predictions = pipeline_nlp(frase)
 
+  predictions = pipeline_nlp(frase)
   # convierte las predicciones en una lista de diccionarios
   data = [{'Texto': frase, 'Prediccion': prediction['label'], 'Probabilidad': prediction['score']} for prediction in predictions]
-  
   # crea un DataFrame a partir de la lista de diccionarios
   df = pd.DataFrame(data)
   df['Prediccion'] = np.where( df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
@@ -139,7 +135,6 @@ def analizar_frase(frase):
   return tabla
 
 def tweets_localidad(buscar_localidad):
-    
     tabla = pd.DataFrame()
     try:
         geolocator = Nominatim(user_agent="nombre_del_usuario")
@@ -162,8 +157,6 @@ def tweets_localidad(buscar_localidad):
                     result.append(etiqueta)
         df = pd.DataFrame(result)
         df['Prediccion'] = np.where( df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
-        #tabla = st.table(df.reset_index(drop=True).head(30).style.applymap(color_survived, subset=['Prediccion']))
-        #df['Tweets'] = df['Tweets'].str.replace('RT|@', '')
         df=df[df["Prediccion"] == 'Sexista']
         tabla = st.table(df.reset_index(drop=True).head(50).style.applymap(color_survived, subset=['Prediccion']))