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 a CSV file containing product data, create an item-based collaborative filter using the NearestNeighbors algorithm from scikit-learn, and print recommendations for each item in the data.
  assert recommend_products("product_data.csv").split('\n')[0] == "Recommendations for item 0: [0 1 2]"
  assert recommend_products("product_data.csv").split('\n')[1] == "Recommendations for item 1: [1 2 3]"
  assert recommend_products("product_data.csv").split('\n')[2] == "Recommendations for item 2: [2 3 4]"
  assert recommend_products("product_data.csv").split('\n')[-1] == "Recommendations for item 9: [9 0 1]"
output: def recommend_products(file_path):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      df = pd.read_csv(file_path)
      model_knn = NearestNeighbors(metric='cosine', algorithm='brute')
      model_knn.fit(df)
      
      recommendations = []
      for i in range(len(df)):
          distances, indices = model_knn.kneighbors([df.iloc[i]], n_neighbors=4)
          recommendations.append(f"Recommendations for item {i}: {indices.flatten()[1:].tolist()}")
      
      return '\n'.join(recommendations)