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. The function should load the dataset, preprocess the data, train an SVM model, and return the precision score of the model. If the dataset is not available or cannot be loaded, the function should return 'Dataset not found'.
  import pandas as pd
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.model_selection import train_test_split
  from sklearn import svm
  from sklearn.metrics import precision_score
  
  def classify_spam_emails(csv_path):
      try:
          # Load the dataset
          df = pd.read_csv(csv_path, encoding='iso-8859-1')
          # Load label and data
          X = df['EmailText']
          y = df['Label']
  
          # Split data into train-test sets
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
  
          # Create bag of words model
          count_vectorizer = CountVectorizer()
          X_train_counts = count_vectorizer.fit_transform(X_train)
  
          # Train the model
          clf = svm.SVC(kernel='linear')
          clf.fit(X_train_counts, y_train)
  
          # Test the model
          X_test_counts = count_vectorizer.transform(X_test)
          predictions = clf.predict(X_test_counts)
          score = precision_score(y_test, predictions, average='binary')
  
          return score
      except FileNotFoundError:
          return 'Dataset not found'
  
  assert classify_spam_emails("spam.csv") == 0.96  # Assuming the model achieves 96% precision
  assert classify_spam_emails("nonexistent.csv") == 'Dataset not found'
output: def classify_spam_emails(csv_path):
      try:
          df = pd.read_csv(csv_path, encoding='iso-8859-1')
          X = df['EmailText']
          y = df['Label']
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
          count_vectorizer = CountVectorizer()
          X_train_counts = count_vectorizer.fit_transform(X_train)
          clf = svm.SVC(kernel='linear')
          clf.fit(X_train_counts, y_train)
          X_test_counts = count_vectorizer.transform(X_test)
          predictions = clf.predict(X_test_counts)
          score = precision_score(y_test, predictions, average='binary')
          return score
      except FileNotFoundError:
          return 'Dataset not found'