input: Write a function to create a product recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The function should read in a product data CSV file, create an item-based collaborative filter using the NearestNeighbors class from scikit-learn, and print recommendations for each item in the dataset.
  
  def product_recommendation(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      # Read in data
      data = pd.read_csv(file_path)
  
      # Create an item-based collaborative filter
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      
      # Make recommendations
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      
      # Print results 
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])
  
  assert product_recommendation('empty_file.csv') == None
  assert product_recommendation('sample_data.csv') == """Recommendations for item 0: [0 4 7]
  Recommendations for item 1: [1 2 5]
  Recommendations for item 2: [2 1 3]"""
output: def product_recommendation(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      # Read in data
      data = pd.read_csv(file_path)
  
      # Create an item-based collaborative filter
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      
      # Make recommendations
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      
      # Print results 
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])