Update app.py

app.py

@@ -86,7 +86,7 @@ def analizar_tweets(search_words, number_of_tweets ):
   for tweet in tweets:
       if (tweet.full_text.startswith('RT')):
           continue
-      elif not  tweet.full_text.strip():
+      elif not tweet.full_text.strip():
           continue
       else:
           datos = preprocess(tweet.full_text)
@@ -123,9 +123,9 @@ def tweets_localidad(buscar_localidad):
         tweets = api.search_tweets(q="",lang="es",geocode=f"{location.latitude},{location.longitude},{radius}", count = 500, tweet_mode="extended")
         result = []
         for tweet in tweets:
-            if (tweet.full_text.startswith):
+            if (tweet.full_text.startswith('RT')):
                 continue
-            elif not  tweet.full_text.strip():
+            elif not tweet.full_text.strip:
                 continue
             else:
                 datos = preprocess(tweet.full_text)
@@ -134,11 +134,12 @@ def tweets_localidad(buscar_localidad):
                     etiqueta = {'Tweets': datos,'Prediccion': predic['label'], 'Probabilidad': predic['score']}
                     result.append(etiqueta)
         df = pd.DataFrame(result)
-        #df['Prediccion'] = np.where(df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
-        #df = df[df["Prediccion"] == 'Sexista']
-        #df = df[df["Probabilidad"] > 0.5]
+        df['Prediccion'] = np.where(df['Prediccion'] == 'LABEL_1', 'Sexista', 'No Sexista')
+        df = df[df["Prediccion"] == 'Sexista']
+        df = df[df["Probabilidad"] > 0.5]
         df = df.sort_values(by='Probabilidad', ascending=False)
         muestra = st.table(df.reset_index(drop=True).head(5).style.applymap(color_survived, subset=['Prediccion']))
+        
         if df.empty:
             st.text("No se encontraron tweets sexistas dentro de la localidad")
         else: