input: Write a function to create a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The function should read in data from a CSV file, create an item-based collaborative filter using NearestNeighbors from sklearn, make recommendations, and print the recommendations for each item. If the input file is empty, the function should return an empty list.
  assert recommend_products('product_data.csv') == [0, 1, 2]
  assert recommend_products('empty_file.csv') == []
  assert recommend_products('product_data_large.csv') == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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:
              return []
          
          model_knn = NearestNeighbors(metric='cosine', algorithm='brute')
          model_knn.fit(df)
          
          distances, indices = model_knn.kneighbors(df, n_neighbors=4)
          
          recommendations = []
          for i in range(len(indices)):
              recommendations.extend(indices[i][1:])
          
          return list(set(recommendations))
      except Exception as e:
          return []