input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories using the data from a CSV file named "data.csv". The function should vectorize the text using CountVectorizer, convert target labels to numerical values, split the data into train and test sets, train a Multinomial Naive Bayes classifier, and then print out the classification report.
  assert create_text_classifier("data.csv")==None
  assert create_text_classifier("data.csv")==None
  assert create_text_classifier("data.csv")==None
  assert create_text_classifier("data.csv")==None
output: 
  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
  
  def create_text_classifier(filename):
      data = pd.read_csv(filename)
      X = data['text']
      y = data['category']
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
      
      y = y.map({'category1': 0, 'category2': 1, 'category3': 2})
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      classifier = MultinomialNB()
      classifier.fit(X_train, y_train)
      
      y_pred = classifier.predict(X_test)
      
      print(classification_report(y_test, y_pred))
  