input: def classify_enron_emails() -> float:
      """ Create an AI model in Python to classify emails from the Enron Spam Dataset as either spam or non-spam.
      The model uses a bag-of-words representation and a linear SVM classifier. The precision score is returned.
      >>> classify_enron_emails()
      0.95
      """
output:     import os
      import numpy as np
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.svm import LinearSVC
      from sklearn.metrics import precision_score
  
      # Load the dataset
      def load_data(path):
          emails = []
          labels = []
          for label in ['spam', 'ham']:
              directory = os.path.join(path, label)
              for filename in os.listdir(directory):
                  with open(os.path.join(directory, filename), 'r', encoding='latin1') as file:
                      emails.append(file.read())
                      labels.append(label)
          return emails, labels
  
      # Path to the Enron Spam Dataset
      dataset_path = 'path_to_enron_dataset'
      emails, labels = load_data(dataset_path)
  
      # Vectorize the emails using bag-of-words
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(emails)
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
  
      # Train a linear SVM classifier
      classifier = LinearSVC()
      classifier.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = classifier.predict(X_test)
  
      # Calculate and return the precision score
      return precision_score(y_test, y_pred, pos_label='spam')