input: Write a function to predict the class labels of a given set of data. The function should load the dataset from a provided CSV URL, split the dataset into features and label, create and fit a DecisionTreeClassifier model, and then predict the class label for a given data point. The function should return the predicted class label.
  
  assert predict([5.1, 3.5, 1.4, 0.2]) == ['Iris-setosa']
  assert predict([6.2, 2.9, 4.3, 1.3]) == ['Iris-versicolor']
  assert predict([7.7, 3.8, 6.7, 2.2]) == ['Iris-virginica']
output: import pandas as pd
  from sklearn.tree import DecisionTreeClassifier
  
  def predict(data_point):
      url = "https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv"
      df = pd.read_csv(url)
      X = df.iloc[:, :-1]
      y = df.iloc[:, -1]
      model = DecisionTreeClassifier()
      model.fit(X, y)
      prediction = model.predict([data_point])
      return prediction