input: Write a python function to read a CSV file, create a Naive Bayes model using sklearn, and predict labels based on text features.
  assert len(predicted_labels) == len(data)
  assert isinstance(predicted_labels, np.ndarray)
  assert set(predicted_labels).issubset(set(data.label))
output: def read_and_predict(csv_file):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Read the CSV file
      data = pd.read_csv(csv_file)
      
      # Extract features and labels
      X = data['text']
      y = data['label']
      
      # Convert text to a matrix of token counts
      vectorizer = CountVectorizer()
      X_counts = vectorizer.fit_transform(X)
      
      # Create and train the Naive Bayes model
      model = MultinomialNB()
      model.fit(X_counts, y)
      
      # Predict labels
      predicted_labels = model.predict(X_counts)
      
      return predicted_labels