input: Write a function to implement a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The function should read product data from a CSV file, create an item-based collaborative filter using NearestNeighbors from sklearn, and print recommendations for each product. If the file is not found or the data is empty, the function should print an appropriate message.
  assert recommend_products('product_data.csv')==None
  assert recommend_products('nonexistent_file.csv')==print('File not found or data is empty.')
  assert recommend_products('empty_file.csv')==print('File not found or data is empty.')
output: def recommend_products(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      try:
          df = pd.read_csv(file_path)
          if df.empty:
              print('File not found or data is empty.')
              return
      except FileNotFoundError:
          print('File not found or data is empty.')
          return
      
      pivot_table = df.pivot_table(index='customer_id', columns='product_id', values='rating')
      pivot_table_norm = pivot_table.apply(lambda x: (x - np.mean(x)) / (np.max(x) - np.min(x)), axis=1)
      pivot_table_norm.fillna(0, inplace=True)
      
      knn = NearestNeighbors(metric='cosine', algorithm='brute')
      knn.fit(pivot_table_norm.values)
      
      for product in pivot_table_norm.columns:
          distances, indices = knn.kneighbors([pivot_table_norm[product].values], n_neighbors=6)
          for i in range(0, len(distances.flatten())):
              if i == 0:
                  print(f'Recommendations for product {product}:')
              else:
                  print(f'{i}: {pivot_table_norm.columns[indices.flatten()[i]]}, with distance of {distances.flatten()[i]}')