input: Write a function that creates a machine learning program in Python to classify text data into 3 different categories. The function should load the data from a CSV file, vectorize the text data, convert the target labels to numerical values, split the data into training and testing sets, train a Naive Bayes model, test the model, and print out the classification report. If the data.csv file does not exist, the function should return "Data file not found" and stop execution.
  assert create_text_classifier() == None
  assert create_text_classifier("data_not_exists.csv") == "Data file not found"
output: def create_text_classifier(file_path="data.csv"):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
      
      try:
          data = pd.read_csv(file_path)
      except FileNotFoundError:
          return "Data file not found"
      
      X = data['text']
      y = data['category']
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
      
      y = y.astype('category').cat.codes
      
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      model = MultinomialNB()
      model.fit(X_train, y_train)
      
      y_pred = model.predict(X_test)
      
      print(classification_report(y_test, y_pred))