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 data from a CSV file, vectorize the text using CountVectorizer, convert target labels to numerical values, split data into train and test sets, train a Multinomial Naive Bayes model, and print out the classification report. The function should assume that the CSV file has columns "text" and "category".
  assert create_text_classifier("data.csv")=="""              precision    recall  f1-score   support
  
             1       0.85      0.91      0.88        21
             2       0.87      0.80      0.84        25
             3       0.89      0.92      0.90        31
  
      accuracy                           0.88        77
     macro avg       0.87      0.88      0.87        77
  weighted avg       0.88      0.88      0.88        77
  """
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(csv_file):
      data = pd.read_csv(csv_file)
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data['text'])
      y = data['category'].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)
      print(classification_report(y_test, y_pred, zero_division=0))
  