input: Write a function to develop a machine learning model in Python to identify the author of a given article using a pipeline with a TfidfVectorizer and Gaussian Naive Bayes classifier. Ensure the function trains the model and evaluates its performance. If the input is empty, the function should return a message indicating no data is available.
  assert identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])[0]>=0
  assert identify_author([], [])=='No data available to train the model.'
output: def identify_author(articles, authors):
      if not articles:
          return 'No data available to train the model.'
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      X_train, X_test, y_train, y_test = train_test_split(articles, authors, test_size=0.2, random_state=42)
      
      pipeline = make_pipeline(TfidfVectorizer(), GaussianNB())
      pipeline.fit(X_train, y_train)
      
      y_pred = pipeline.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy, pipeline