input: def classify_emails(dataset_path: str) -> float:
      """ Given the path to a CSV dataset of emails, classify each email as either spam or non-spam using an AI model. 
      The model is trained using the Support Vector Machine (SVM) algorithm with a linear kernel. 
      The precision score of the model for the test set is returned.
      >>> classify_emails("spam.csv")
      0.95
      """
output:     import pandas as pd
      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
  
      # Load the dataset
      df = pd.read_csv(dataset_path)
  
      # Assuming the dataset has columns 'text' and 'label'
      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