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 data from a CSV file, vectorize the text using CountVectorizer, convert the target labels to numerical values, split the data into training and testing sets, train a Multinomial Naive Bayes model, test the model, and print out a classification report.
  assert classify_text_data("data.csv")==True
  assert classify_text_data("invalid_file.csv")==False
  assert classify_text_data("empty_file.csv")==False
  assert classify_text_data("incorrect_format.csv")==False
  assert classify_text_data("")==False
output: def classify_text_data(file_path):
      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
          
          df = pd.read_csv(file_path)
          if df.empty:
              return False
          
          X = df['text']
          y = df['category']
          
          y = pd.Categorical(y).codes
          
          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)
          
          print(classification_report(y_test, y_pred))
          
          return True
      except Exception as e:
          return False