input: Write a function that creates a machine learning program in Python to classify text data into 3 different categories. The function should use the Multinomial Naive Bayes algorithm, and it should load data from a CSV file, vectorize the text, convert target labels to numerical values, split the data into training and test sets, train the model, predict on the test set, and print the classification report. The function should be named classify_text.
  
  assert classify_text("data.csv") is not None
  assert classify_text("data.csv").startswith("              precision    recall  f1-score   support")
output: def classify_text(file_path):
      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(file_path)
      X = data['text']
      y = data['category']
  
      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