Update app.py

app.py

@@ -54,7 +54,7 @@ def preprocess(text):
     text=" ".join(text.split())
     return text
     
-def clean_tweet(tweet):
+def limpieza_datos(tweet):
     # Eliminar emojis
     tweet = re.sub(r'[\U0001F600-\U0001F64F]', '', tweet)
     tweet = re.sub(r'[\U0001F300-\U0001F5FF]', '', tweet)
@@ -98,14 +98,13 @@ with colT2:
     font-size:16px ; font-family: 'Times New Roman'; color: #3358ff;} 
     </style> """, unsafe_allow_html=True)
 
-def analizar_tweets(search_words, number_of_tweets):
+def tweets_usuario(usuario, cant_de_tweets):
   tabla = []
-  if(number_of_tweets > 0 and search_words != "" ):
+  if(cant_de_tweets > 0 and usuario != "" ):
       try:
           # Buscar la información del perfil de usuario
-          user = api.get_user(screen_name=search_words)
-          #st.text(f"La cuenta {search_words} existe.")
-          tweets = api.user_timeline(screen_name = search_words,tweet_mode="extended", count= number_of_tweets)
+          user = api.get_user(screen_name=usuario)
+          tweets = api.user_timeline(screen_name = usuario,tweet_mode="extended", count= cant_de_tweets)
           result = []
           for tweet in tweets:
               if (tweet.full_text.startswith('RT')):
@@ -115,7 +114,7 @@ def analizar_tweets(search_words, number_of_tweets):
                   try:
                       language = detect(text)
                       if language == 'es':
-                          datos=clean_tweet(text)
+                          datos=limpieza_datos(text)
                           if datos == "":
                               continue
                           else:
@@ -140,8 +139,7 @@ def analizar_tweets(search_words, number_of_tweets):
           tabla.append(muestra) 
   else:
       muestra= st.text("Ingrese los parametros correspondientes")
-      tabla.append(muestra)
-        
+      tabla.append(muestra)      
   return tabla
 
 def tweets_localidad(buscar_localidad):
@@ -158,7 +156,7 @@ def tweets_localidad(buscar_localidad):
             elif not tweet.full_text.strip():
                 continue
             else:
-                datos = preprocess(tweet.full_text)
+                datos = limpieza_datos(tweet.full_text)
                 prediction = pipeline_nlp(datos)
                 for predic in prediction:
                     etiqueta = {'Tweets': datos,'Prediccion': predic['label'], 'Probabilidad': predic['score']}
@@ -254,7 +252,7 @@ def run():
                   analizar_frase(search_words)
                     
                 elif (usuario):
-                    analizar_tweets(search_words,number_of_tweets)
+                    tweets_usuario(search_words,number_of_tweets)
                 elif (localidad):
                     tweets_localidad(search_words)