input: Write a function to create an AI model in Python that classifies emails from the Enron Spam Dataset as either spam or non-spam. The function should load the dataset, preprocess the text data, train a linear SVM model, and return the precision score of the model. If the dataset is not found, the function should raise a FileNotFoundError.
  assert create_spam_classifier("spam.csv")!=None
  assert isinstance(create_spam_classifier("spam.csv"), float)
  assert create_spam_classifier("non_existent_file.csv")==FileNotFoundError
  assert create_spam_classifier("spam_with_missing_label.csv")==KeyError
output: def create_spam_classifier(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.svm import LinearSVC
      from sklearn.metrics import precision_score
  
      try:
          df = pd.read_csv(file_path)
      except FileNotFoundError:
          raise FileNotFoundError
  
      try:
          X = df['text']
          y = df['label']
      except KeyError:
          raise KeyError
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      vectorizer = TfidfVectorizer()
      X_train_tfidf = vectorizer.fit_transform(X_train)
      X_test_tfidf = vectorizer.transform(X_test)
  
      model = LinearSVC()
      model.fit(X_train_tfidf, y_train)
  
      y_pred = model.predict(X_test_tfidf)
      return precision_score(y_test, y_pred, pos_label='spam')