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, perform a train-test split, create a bag of words model, train an SVM classifier, and then return the precision score of the model. The input to the function should be the path to the dataset csv file. The function should use `train_test_split` to split the dataset into training and testing sets with a 80-20 split and a `linear` kernel for the SVM classifier.
  
  assert model_precision_score("spam.csv")==0.92
  assert model_precision_score("emails.csv")==0.88
  assert model_precision_score("nonspam.csv")==0.75
  assert model_precision_score("mixed.csv")==0.80
  assert model_precision_score("empty.csv")==0.00
output: def model_precision_score(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.svm import SVC
      from sklearn.metrics import precision_score
      
      df = pd.read_csv(file_path)
      X = df['text']
      y = df['label']
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
      
      svm_classifier = SVC(kernel='linear')
      svm_classifier.fit(X_train_vec, y_train)
      
      y_pred = svm_classifier.predict(X_test_vec)
      precision = precision_score(y_test, y_pred, pos_label='spam')
      
      return precision