input: Write a function to recommend new products to customers based on their past purchases using Pandas and the NearestNeighbors algorithm from sklearn. If the input data is empty, the function should return an empty list.
  assert product_recommender('product_data.csv')==['Recommendations for item 0: [1 2 3]', 'Recommendations for item 1: [0 3 4]', 'Recommendations for item 2: [0 1 4]', 'Recommendations for item 3: [1 2 4]', 'Recommendations for item 4: [3 2 1]']
  assert product_recommender('empty_data.csv')==[]
output: def product_recommender(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      data = pd.read_csv(file_path)
      
      if data.empty:
          return []
      
      nn = NearestNeighbors(n_neighbors=4, algorithm='auto').fit(data)
      distances, indices = nn.kneighbors(data)
      
      recommendations = []
      for i in range(len(data)):
          recommendations.append(f"Recommendations for item {i}: {indices[i][1:].tolist()}")
      
      return recommendations