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 in data from a CSV file, create an item-based collaborative filter using NearestNeighbors, and make recommendations. If no data is provided, the function should return an empty list.
  
  
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def recommend_products(file_path):
      if not file_path:
          return []
      
      data = pd.read_csv(file_path)
      
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      
      recommendations = []
      for i in range(len(data)):
          recommendations.append('Recommendations for item {}: {}'.format(i, indices[i]))
      
      return recommendations
  
  assert recommend_products('')==[]
  assert recommend_products('product_data.csv')==['Recommendations for item 0: [0 1 2]', 'Recommendations for item 1: [1 2 0]', 'Recommendations for item 2: [2 0 1]']
  
output: def recommend_products(file_path):
      if not file_path:
          return []
      
      data = pd.read_csv(file_path)
      
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      
      recommendations = []
      for i in range(len(data)):
          recommendations.append('Recommendations for item {}: {}'.format(i, indices[i]))
      
      return recommendations