input: Write a function to create a recommendation system in Python using Pandas and the NearestNeighbors algorithm from scikit-learn. The function should read a CSV file containing product data, fit a collaborative filter based on cosine similarity, and then make recommendations for each product, returning the indices of the recommended products. If the CSV file is not found, the function should return an error message.
  assert recommendation_system('product_data.csv')==[0, 1, 2]
  assert recommendation_system('non_existent_file.csv')=='File not found'
  assert recommendation_system('')=='File not found'
output: def recommendation_system(csv_file):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      from sklearn.metrics.pairwise import cosine_similarity
  
      try:
          df = pd.read_csv(csv_file)
      except FileNotFoundError:
          return 'File not found'
  
      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(indices[0][1:].tolist())
  
      return recommendations[0]