input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories. The program should use the Bag-of-Words model to transform text data into vectors, split the data into training and testing sets, train a Multinomial Naive Bayes classifier, and print a classification report to evaluate the model's performance. If the given data file is not found, the function should return an error message indicating that the file could not be loaded.
  assert classify_text_data()=="classification_report"
  assert classify_text_data("invalid_file.csv")=="Error: File could not be loaded."
output: def classify_text_data(file_path="data.csv"):
      try:
          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
          
          data = pd.read_csv(file_path)
          X = data['text']
          y = data['category']
          
          vectorizer = CountVectorizer()
          X = vectorizer.fit_transform(X)
          
          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)
          
          return report
      except FileNotFoundError:
          return "Error: File could not be loaded."