input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories using Naive Bayes. The function should load the data, vectorize the text, convert target labels to numerical values, split the data into training and testing sets, train the model, test the model, and print the classification report.
  assert create_ml_program("")==None
  assert create_ml_program("data.csv")==None
output: def create_ml_program(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
  
      if not file_path:
          return None
  
      data = pd.read_csv(file_path)
      X = data['text']
      y = data['category']
  
      y = y.map({'category1': 0, 'category2': 1, 'category3': 2})
  
      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 None