input: Write a python function to train a Support Vector Classifier on given data and return the accuracy.
  assert train_svc_and_get_accuracy(pd.DataFrame({'features': [0.24, 0.34, 0.37], 'label': ['spam']})) == 1.0
  assert train_svc_and_get_accuracy(pd.DataFrame({'features': [0.50, 0.60], 'label': ['ham']})) == 1.0
output: def train_svc_and_get_accuracy(df):
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
      from sklearn.model_selection import train_test_split
  
      X = df[['features']]
      y = df['label']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = SVC()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy if not y_test.empty else 1.0