input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories. The program should load data from a CSV file, vectorize the text data, convert target labels to numerical values, split the data into training and testing sets, train a Naive Bayes model, and print a classification report.
  assert create_text_classifier() is None
  assert type(create_text_classifier()) == NoneType
  assert "classification_report" in create_text_classifier()
  assert "accuracy" in create_text_classifier()
  assert "weighted avg" in create_text_classifier()
output: def create_text_classifier():
      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('text_data.csv')
      X = data['text']
      y = data['category']
  
      y = y.map({'category1': 0, 'category2': 1, 'category3': 2})
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
  
      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)