input: Write a function to create an AI model in Python to classify emails from the Enron Spam Dataset as either spam or non-spam. The function should return the precision score of the model. If the dataset is not found, it should return "Dataset not found".
  assert classify_spam("spam.csv")==0.95
  assert classify_spam("nonexistent.csv")=="Dataset not found"
  assert classify_spam("spam.csv")==0.93
  assert classify_spam("")=="Dataset not found"
output: def classify_spam(filename):
      if not filename:
          return "Dataset not found"
      try:
          import pandas as pd
          from sklearn.model_selection import train_test_split
          from sklearn.feature_extraction.text import CountVectorizer
          from sklearn.naive_bayes import MultinomialNB
          from sklearn.metrics import precision_score
          
          df = pd.read_csv(filename)
          X = df['text']
          y = df['label']
          
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          vectorizer = CountVectorizer()
          X_train_vec = vectorizer.fit_transform(X_train)
          X_test_vec = vectorizer.transform(X_test)
          
          model = MultinomialNB()
          model.fit(X_train_vec, y_train)
          
          y_pred = model.predict(X_test_vec)
          precision = precision_score(y_test, y_pred, pos_label='spam')
          
          return precision
      except FileNotFoundError:
          return "Dataset not found"