Update app.py

app.py

@@ -121,6 +121,7 @@ def analizar_tweets(search_words, number_of_tweets ):
   return tabla
 
 def analizar_frase(frase):
+    
   #palabra = frase.split()
   #palabra = frase
   predictions = pipeline_nlp(frase)
@@ -138,54 +139,55 @@ def analizar_frase(frase):
   return tabla
 
 def tweets_localidad(buscar_localidad):
-  geolocator = Nominatim(user_agent="nombre_del_usuario")
-  location = geolocator.geocode(buscar_localidad)
-  radius = "10km"
-  tweets = api.search_tweets(q="",lang="es",geocode=f"{location.latitude},{location.longitude},{radius}", count = 50, tweet_mode="extended")
-  tweet_list = [i.full_text for i in tweets]
-  text= pd.DataFrame(tweet_list)
-  text[0] = text[0].apply(preprocess_tweet)
-  text_list = text[0].tolist()
-  result = []
-  for text in text_list:
-      if (text.startswith('RT')):
-          continue
-      else:
-          prediction = pipeline_nlp(text)
-          for predic in prediction:
-              etiqueta = {'Tweets': text,'Prediccion': predic['label'], 'Probabilidad': predic['score']}
-              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']))
+    try:
+        geolocator = Nominatim(user_agent="nombre_del_usuario")
+        location = geolocator.geocode(buscar_localidad)
+        radius = "10km"
+        tweets = api.search_tweets(q="",lang="es",geocode=f"{location.latitude},{location.longitude},{radius}", count = 50, tweet_mode="extended")
+        tweet_list = [i.full_text for i in tweets]
+        text= pd.DataFrame(tweet_list)
+        text[0] = text[0].apply(preprocess_tweet)
+        text_list = text[0].tolist()
+        result = []
+        for text in text_list:
+            if (text.startswith('RT')):
+                continue
+            else:
+                prediction = pipeline_nlp(text)
+                for predic in prediction:
+                    etiqueta = {'Tweets': text,'Prediccion': predic['label'], 'Probabilidad': predic['score']}
+                    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']))
+        
+        df_sexista = df[df['Prediccion']=="Sexista"]
+        df_no_sexista = df[df['Probabilidad'] > 0]
+        sexista = len(df_sexista)
+        no_sexista = len(df_no_sexista)
     
-  df_sexista = df[df['Prediccion']=="Sexista"]
-  df_no_sexista = df[df['Probabilidad'] > 0]
-  sexista = len(df_sexista)
-  no_sexista = len(df_no_sexista)
-
-  # Crear un gráfico de barras
-  labels = ['Sexista  ', ' No sexista']
-  counts = [sexista, no_sexista]
-  plt.bar(labels, counts)
-  plt.xlabel('Categoría')
-  plt.ylabel('Cantidad de tweets')
-  plt.title('Cantidad de tweets sexistas y no sexistas')
-  plt.figure(figsize=(10,6))
-  plt.show()
-  st.pyplot()
-  
-  st.set_option('deprecation.showPyplotGlobalUse', False)
-  
-  return tabla
-
+      # Crear un gráfico de barras
+        labels = ['Sexista  ', ' No sexista']
+        counts = [sexista, no_sexista]
+        plt.bar(labels, counts)
+        plt.xlabel('Categoría')
+        plt.ylabel('Cantidad de tweets')
+        plt.title('Cantidad de tweets sexistas y no sexistas')
+        plt.figure(figsize=(10,6))
+        plt.show()
+        st.pyplot()
+      
+        st.set_option('deprecation.showPyplotGlobalUse', False)
+      
+        
+    except error:
+        st.text¨("Error")
+        
+    return tabla    
 
-
-    
 def run():   
  with st.form("my_form"):
    col,buff1, buff2 = st.columns([2,2,1])