input: Write a function that trains an AI model to classify emails from the Enron Spam Dataset as either spam or non-spam using a linear SVM. The function should load the dataset, preprocess it with a bag of words model, split the data into training and testing sets, train the SVM model, and then print the precision score of the model.
  assert classify_emails_as_spam_or_non_spam("spam.csv", encoding='iso-8859-1', test_size=0.20, random_state=42, average='binary') == 0.95
  assert classify_emails_as_spam_or_non_spam("spam.csv", encoding='iso-8859-1', test_size=0.30, random_state=42, average='binary') == 0.94
  assert classify_emails_as_spam_or_non_spam("spam.csv", encoding='iso-8859-1', test_size=0.10, random_state=42, average='binary') == 0.96
  assert classify_emails_as_spam_or_non_spam("spam.csv", encoding='iso-8859-1', test_size=0.25, random_state=42, average='binary') == 0.93
  assert classify_emails_as_spam_or_non_spam("spam.csv", encoding='iso-8859-1', test_size=0.15, random_state=42, average='binary') == 0.95
output: def classify_emails_as_spam_or_non_spam(file_path, encoding='iso-8859-1', test_size=0.20, random_state=42, average='binary'):
      import pandas as pd
      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
      
      df = pd.read_csv(file_path, encoding=encoding)
      X = df['text']
      y = df['label']
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
      
      model = LinearSVC()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      precision = precision_score(y_test, y_pred, average=average)
      
      print(precision)
      return precision