input: Write a function to create an AI model in Python that classifies emails from the Enron Spam Dataset as either spam or non-spam using a bag of words model and SVM classifier. The function should return the precision score of the model.
  assert classify_emails() > 0.9
output: 
  def classify_emails():
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import precision_score
      import pandas as pd
      import os
  
      # Load the dataset
      data = pd.read_csv('enron_spam_data.csv')
      X = data['text']
      y = data['label']
  
      # Split the dataset
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Create a bag of words model
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      # Train an SVM classifier
      svm = SVC(kernel='linear')
      svm.fit(X_train_vec, y_train)
  
      # Predict and calculate precision score
      y_pred = svm.predict(X_test_vec)
      precision = precision_score(y_test, y_pred, pos_label='spam')
  
      return precision
  