input: Write a function to create a recommendation system using Pandas in Python that recommends new products to customers based on their past purchases. The function should read in a CSV file containing product data, fit a NearestNeighbors model using cosine similarity, and then make recommendations for each item. If the input CSV file is not provided, the function should return an error message.
  
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def recommend_products(product_data_csv):
      if not product_data_csv:
          return "Error: No product data CSV file provided."
      data = pd.read_csv(product_data_csv)
      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(indices[i].tolist())
      return recommendations
  
  assert recommend_products('product_data.csv')[0] == [0, 1, 2]
  assert recommend_products('') == "Error: No product data CSV file provided."
output: def recommend_products(product_data_csv):
      if not product_data_csv:
          return "Error: No product data CSV file provided."
      data = pd.read_csv(product_data_csv)
      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(indices[i].tolist())
      return recommendations