input: Write a function to create a recommendation system in Python using Pandas that recommends new products to customers based on their past purchases. The function should read data from a CSV file containing past purchase information, create an item-based collaborative filter using NearestNeighbors, and then make recommendations for each item based on its similarity to other items. The function should print out the recommendations for each item. If no recommendations can be made, it should print an appropriate message.
  
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def recommend_products(file_path):
  # 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])
      if indices is None:
          print('No recommendations available.')
  
  assert recommend_products('test_product_data.csv') # No output expected, only check function call validity
output: 
  def recommend_products(file_path):
      data = pd.read_csv(file_path)
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])
      if indices is None:
          print('No recommendations available.')
  