input: from typing import Tuple
  
  def ai_email_classifier() -> float:
      """This function creates an AI model in Python to classify emails from the Enron Spam Dataset as either spam or non-spam. 
      The model uses a Support Vector Machine (SVM) classifier with a linear kernel and evaluates the performance using precision score.
      
      >>> ai_email_classifier()
      <Precision score of the model: a floating-point number between 0 and 1>
      """
output:     from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import TfidfVectorizer
      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')
      X = 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)
  
      # Vectorize the text data
      vectorizer = TfidfVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      # Train the SVM model
      model = SVC(kernel='linear')
      model.fit(X_train_vec, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test_vec)
  
      # Calculate the precision score
      precision = precision_score(y_test, y_pred, pos_label='spam')
  
      return precision