input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories. The function should load a dataset 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 classifier, predict the categories for the test set, and print out the classification report. The function should return the trained model.
  assert create_text_classifier("data.csv")
  assert create_text_classifier("empty.csv")
output: def create_text_classifier(file_path):
      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
  
      df = pd.read_csv(file_path)
      if df.empty:
          return None
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['text'])
      y = df['category']
      
      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))
      
      return model