input: Write a function to create a machine learning program that classifies text data into 3 different categories using a Naive Bayes classifier. The function should load data from a CSV file, vectorize the text data, convert target labels to numerical values, split the data into training and testing sets, train the model, and print a classification report. If the input file is empty, the function should return a message stating "No data available to train the model.".
  
  assert ml_text_classifier("data.csv")==print(classification_report(y_test, y_pred))
  assert ml_text_classifier("empty_data.csv")=="No data available to train the model."
output: def ml_text_classifier(file_path):
      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
  
      try:
          data = pd.read_csv(file_path)
          if data.empty:
              return "No data available to train the model."
          
          X = data['text']
          y = data['label']
          
          vectorizer = CountVectorizer()
          X = vectorizer.fit_transform(X)
          
          y = y.map({'label1': 0, 'label2': 1, 'label3': 2})
          
          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))
      except Exception as e:
          return str(e)