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 the data, vectorize the text, convert target labels to numerical values, split the data into train and test sets, train a Naive Bayes model, test the model, and print out the classification report.
  assert create_text_classifier("data.csv")=="              precision    recall  f1-score   support\n\n           1       0.89      0.88      0.88       100\n           2       0.84      0.85      0.85       100\n           3       0.92      0.91      0.91       100\n\n    accuracy                           0.88       300\n   macro avg       0.88      0.88      0.88       300\nweighted avg       0.88      0.88      0.88       300\n"
  assert create_text_classifier("empty_data.csv")==None
  assert create_text_classifier("no_category_data.csv")==ValueError("No 'category' column in the data")
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
      
      try:
          data = pd.read_csv(file_path)
      except pd.errors.EmptyDataError:
          return None
      
      if 'category' not in data.columns:
          raise ValueError("No 'category' column in the data")
      
      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)
      
      report = classification_report(y_test, y_pred, output_dict=False)
      return report