input: Write a function to implement a machine learning program in Python that will classify text data into 3 different categories. The function should take a path to a CSV file containing text data and category labels, preprocess the data, train a Multinomial Naive Bayes model, and print out a classification report.
  assert ml_text_classifier("data.csv")
  # The function should print a classification report as output
output: 
  import pandas as pd
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import classification_report
  
  def ml_text_classifier(csv_path):
      # Load the data
      data = pd.read_csv(csv_path)
      
      # Preprocess the data
      X = data['text']
      y = data['category']
      
      # 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)
      
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
      
      # Train the Multinomial Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train_vec, y_train)
      
      # Predict the categories for the test data
      y_pred = model.predict(X_test_vec)
      
      # Print the classification report
      print(classification_report(y_test, y_pred))
  