input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization, n-grams, and a classification model. The function should read a CSV file containing reviews, preprocess the text, generate features using CountVectorizer, train a Naive Bayes classifier, and return a classification report.
  
  assert perform_sentiment_analysis('reviews.csv') == 'Generate a classification report based on sentiment analysis of reviews.'
  assert perform_sentiment_analysis('empty_reviews.csv') == 'No data available for sentiment analysis.'
output: def perform_sentiment_analysis(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
      from sklearn.model_selection import train_test_split
  
      try:
          df = pd.read_csv(file_path)
          if df.empty:
              return 'No data available for sentiment analysis.'
          
          X = df['review']
          y = df['sentiment']
          
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          vectorizer = CountVectorizer(ngram_range=(1, 2))
          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 as e:
          return str(e)