input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories using a Naive Bayes classifier. If the given data is successfully classified, return a classification report. If the data is empty or there is an error in the process, return a string "Error occurred".
  assert classify_text_data("data.csv")==classification_report(y_test, y_pred)
  assert classify_text_data("empty_data.csv")=="Error occurred"
  assert classify_text_data("incorrect_format.csv")=="Error occurred"
output: def classify_text_data(file_path):
      try:
          import pandas as pd
          from sklearn.feature_extraction.text import CountVectorizer
          from sklearn.naive_bayes import MultinomialNB
          from sklearn.model_selection import train_test_split
          from sklearn.metrics import classification_report
          
          data = pd.read_csv(file_path)
          if data.empty:
              return "Error occurred"
          
          X = data['text']
          y = data['category']
          
          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)
          
          classifier = MultinomialNB()
          classifier.fit(X_train_vec, y_train)
          
          y_pred = classifier.predict(X_test_vec)
          
          return classification_report(y_test, y_pred)
      except Exception:
          return "Error occurred"