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 using CountVectorizer, convert target labels to numerical values, split the data into training and testing sets, train a MultinomialNB model, predict on the test set, and print out the classification report.
  
  assert classify_text_data().split('\n')[-1] == ' 3       0.00      0.00      0.00       1'
  assert classify_text_data().split('\n')[-2] == ' 2       1.00      0.67      0.80       3'
  assert classify_text_data().split('\n')[-3] == ' 1       0.25      1.00      0.40       1'
  assert classify_text_data().split('\n')[-4] == 'accuracy                           0.50       5'
  
output: def classify_text_data():
      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.csv')
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data['text'])
      y = data['category'].map({'cat1': 1, 'cat2': 2, 'cat3': 3})
  
      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, target_names=['cat1', 'cat2', 'cat3'])
      return report