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 text data from a CSV file, vectorize the text, convert the target labels to numerical values, split the data into training and testing sets, train a Multinomial Naive Bayes model, test the model, and print out the classification report. The function should be named `classify_text` and should take the path to the CSV file as its input parameter.
  
  assert classify_text("data1.csv")
  assert classify_text("data2.csv")
  assert classify_text("data3.csv")
  assert classify_text("data4.csv")
  assert classify_text("data5.csv")
output: 
  def classify_text(csv_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
  
      # Load the data
      data = pd.read_csv(csv_path)
      X = data['text']
      y = data['category']
  
      # Convert text to numerical data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
  
      # Convert target labels to numerical values
      y = y.astype('category').cat.codes
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Test the model
      y_pred = model.predict(X_test)
  
      # Print the classification report
      print(classification_report(y_test, y_pred))
  