input: Write a python function to load a dataset, preprocess the text data using CountVectorizer, split the data, train a Bernoulli Naive Bayes model, and return the accuracy.
  assert isinstance(load_and_train_model('Dataset.csv'), float)
  assert 0 <= load_and_train_model('Dataset.csv') <= 1
output: def load_and_train_model(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 BernoulliNB
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      df = pd.read_csv(file_path)
      
      # Assuming the dataset has 'text' and 'label' columns
      X = df['text']
      y = df['label']
      
      # Preprocess the text data
      vectorizer = CountVectorizer()
      X_vectorized = vectorizer.fit_transform(X)
      
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)
      
      # Train the model
      model = BernoulliNB()
      model.fit(X_train, y_train)
      
      # Predict and calculate accuracy
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy