input: def classify_emails(data: pd.DataFrame) -> float:
      """ Create an AI model in Python to classify emails as either spam or non-spam.
      The function loads the dataset, preprocesses the data, trains a Linear SVM model on it,
      and then tests the model to compute precision.
      >>> import pandas as pd
      >>> data = pd.DataFrame({'EmailText': ['win free tokens', 'urgent meeting'], 'Label': ['spam', 'non-spam']})
      >>> classify_emails(data)
      1.0
      """
      X = data['EmailText']
      y = data['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
output:     from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn import svm
      from sklearn.metrics import precision_score