input: from typing import List, Tuple
  
  def sentiment_analysis(filename: str) -> Tuple[str, float]:
      """ Create a Python program to perform sentiment analysis on product reviews. The program 
      should use natural language processing techniques like tokenization and n-grams to generate 
      features for sentiment analysis and use a classification model to assign sentiments to the 
      reviews.
      >>> sentiment_analysis('reviews.csv')
      ('precision', 0.85)
      """
output:     import pandas as pd
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import precision_score
  
      # Load the dataset
      df = pd.read_csv(filename)
      reviews = df['review'].tolist()
      sentiments = df['sentiment'].tolist()
  
      # Tokenization and n-grams
      vectorizer = TfidfVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(reviews)
  
      # Split the dataset
      X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and evaluate
      y_pred = model.predict(X_test)
      precision = precision_score(y_test, y_pred, average='weighted')
  
      return ('precision', precision)