input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories. The function should load the data from a CSV file, vectorize the text, convert target labels to numerical values, split the data into training and testing sets, train a Naive Bayes model, test the model, and print out a classification report. The function should have the following parameters: `data_path` (the path to the CSV file), `text_column` (the column name containing the text data), and `category_column` (the column name containing the category labels). The function should return the classification report.
  assert create_text_classifier("data.csv", "text", "category") == classification_report
  assert create_text_classifier("data_empty.csv", "text", "category") == classification_report
output: def create_text_classifier(data_path, text_column, category_column):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
      
      data = pd.read_csv(data_path)
      X = data[text_column]
      y = data[category_column]
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
      
      y = y.astype('category').cat.codes
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = MultinomialNB()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      
      report = classification_report(y_test, y_pred)
      print(report)
      return report