input: Write a function to create a recommendation system in Python using Pandas that recommends new products to customers based on their past purchases. If the provided 'product_data.csv' is empty, the function should return an empty list. Also ensure that the function can handle scenarios where there are not enough similar items to recommend.
  
  assert recommend_products('product_data.csv')==['Recommendations for item 0:', [1, 4, 2], 'Recommendations for item 1:', [3, 0, 4], 'Recommendations for item 2:', [1, 4, 5]]
  assert recommend_products('empty_data.csv')==[]
  assert recommend_products('sparse_data.csv')==['Recommendations for item 0:', [1], 'Recommendations for item 1:', [0], 'Recommendations for item 2:', []]
output: import pandas as pd
  
  def recommend_products(file_path):
      try:
          df = pd.read_csv(file_path)
          if df.empty:
              return []
          
          recommendations = []
          for item in df.columns:
              similar_items = df.nlargest(3, item)[item].index.tolist()
              recommendations.append(f'Recommendations for item {item}:')
              recommendations.append(similar_items)
          
          return recommendations
      except pd.errors.EmptyDataError:
          return []