Update app.py

app.py

@@ -97,23 +97,23 @@ with colT2:
 
 
 def analizar_tweets(search_words, number_of_tweets ):
-  tweets = api.user_timeline(screen_name = search_words, count= number_of_tweets)
-  for tweet in tweets:
-      tweet_list = tweet.text
-      
-  #tweet_list = [i.text for i in 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= text.apply(preprocess_tweet)
-  #text1=text[0].values
-  predictions = pipeline_nlp(text1)
-  data = [{'Texto': text, 'Prediccion': prediction['label'], 'Probabilidad': prediction['score']} for prediction in predictions]
-  # crea un DataFrame a partir de la lista de diccionarios
+  text[0] = text[0].apply(preprocess_tweet)
+  text_list = text[0].tolist()
+  result = []
+  for text in text_list:
+      prediction = pipeline_nlp(text)
+      etiqueta= [{'Tweets': text,'Prediccion': predic['label'], 'Probabilidad': predic['score']} for predic in prediction]
+      result.append(etiqueta)
+  
+  #df = pd.DataFrame(result)
   df = pd.DataFrame(data)
   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']))
-
+    
   return tabla
 
 def analizar_frase(frase):