input: Write a python function to train a logistic regression model using count vectorizer and evaluate its accuracy.
  assert train_and_evaluate_model('data.csv') > 80
  assert isinstance(train_and_evaluate_model('data.csv'), float)
output: def train_and_evaluate_model(csv_file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      
      # Load data
      data = pd.read_csv(csv_file_path)
      
      # Assuming the csv has two columns: 'text' and 'label'
      X = data['text']
      y = data['label']
      
      # Vectorize text data
      vectorizer = CountVectorizer()
      X_vec = vectorizer.fit_transform(X)
      
      # Split data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X_vec, y, test_size=0.2, random_state=42)
      
      # Train logistic regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
      
      # Predict and evaluate model
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      
      # Return accuracy as a percentage
      return accuracy * 100