input: Write a function that creates a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The function should use Nearest Neighbors from scikit-learn to determine the recommendations. If the data file 'product_data.csv' is not available, the function should raise a FileNotFoundError.
  assert recommend_products()==None
output: 
  def recommend_products():
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      try:
          df = pd.read_csv('product_data.csv')
      except FileNotFoundError:
          raise FileNotFoundError("The file 'product_data.csv' was not found.")
      
      df_pivot = df.pivot_table(index='customer_id', columns='product_id', values='purchase_count')
      df_pivot = df_pivot.fillna(0)
      
      model_knn = NearestNeighbors(metric='cosine', algorithm='brute')
      model_knn.fit(df_pivot)
      
      distances, indices = model_knn.kneighbors(df_pivot, n_neighbors=6)
      
      recommendations = {}
      for customer_index in range(len(indices)):
          customer_id = df_pivot.index[customer_index]
          similar_customers = indices[customer_index][1:]
          recommended_products = set()
          for similar_customer in similar_customers:
              similar_customer_products = df_pivot.iloc[similar_customer]
              recommended_products.update(similar_customer_products[similar_customer_products > 0].index)
          recommended_products -= set(df_pivot.iloc[customer_index][df_pivot.iloc[customer_index] > 0].index)
          recommendations[customer_id] = list(recommended_products)[:5]
      
      return recommendations
  