input: Write a python function to train an SVM model to classify spam emails and return the precision score.
  assert train_svm_model("spam.csv") > 0.8
  assert isinstance(train_svm_model("spam.csv"), float)
output: def train_svm_model(csv_file):
      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 data
      data = pd.read_csv(csv_file)
      X = data['EmailText']
      y = data['Label']
  
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Transform the text data to TF-IDF features
      vectorizer = TfidfVectorizer()
      X_train_tfidf = vectorizer.fit_transform(X_train)
      X_test_tfidf = vectorizer.transform(X_test)
  
      # Train the SVM model
      model = SVC()
      model.fit(X_train_tfidf, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test_tfidf)
  
      # Calculate precision score
      precision = precision_score(y_test, y_pred, pos_label='spam')  # assuming 'spam' is the label for spam emails
  
      return precision