input: Write a function to create an AI model that classifies emails from the Enron Spam Dataset as either spam or non-spam using Python. The function should load the dataset, preprocess the text using a bag of words model, train a Support Vector Machine classifier, and evaluate the precision score of the model. If the dataset is not loaded correctly, the function should return an error message.
  
  assert classify_emails().startswith("Successfully trained the model with a precision score of")
  assert classify_emails() is None  # If dataset is not loaded correctly
output: def classify_emails():
      try:
          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
          
          # Load the dataset
          df = pd.read_csv('enron_spam_data.csv')
          
          # Preprocess the text using a bag of words model
          vectorizer = CountVectorizer()
          X = vectorizer.fit_transform(df['text'])
          y = df['label']
          
          # Split the dataset into training and testing sets
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          # Train a Support Vector Machine classifier
          clf = SVC()
          clf.fit(X_train, y_train)
          
          # Evaluate the precision score of the model
          y_pred = clf.predict(X_test)
          precision = precision_score(y_test, y_pred, average='binary')
          
          return f"Successfully trained the model with a precision score of {precision:.2f}"
      except Exception as e:
          return "Error: Dataset not loaded correctly"