input: Write a python function to create a Naive Bayes classifier using the given data and vectorizer.
  assert round(create_naive_bayes_classifier('./data.csv')[0], 2) == 0.85  # Example accuracy, replace with realistic values
  assert round(create_naive_bayes_classifier('./data.csv')[0], 2) > 0.80  # Example accuracy threshold
  assert isinstance(create_naive_bayes_classifier('./data.csv')[1], MultinomialNB)  # Classifier instance check
output: def create_naive_bayes_classifier(data_path):
      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 accuracy_score
  
      # Load data
      data = pd.read_csv(data_path)
  
      # Assuming the CSV has columns 'text' and 'label'
      texts = data['text']
      labels = data['label']
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
      
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
      
      # Create and train the Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train, y_train)
      
      # Predict and calculate accuracy
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy, clf