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 in a CSV file containing product data, create an item-based collaborative filter using cosine similarity, and recommend top 3 products for each item in the dataset. The function should return the recommendations in the form of an array of tuples, where each tuple represents the top 3 recommended items for a given item.
  
  assert create_recommendations('product_data.csv') == [([1, 3, 5], [0.1, 0.2, 0.3]), ([0, 2, 4], [0.3, 0.4, 0.5]), ([1, 2, 3], [0.2, 0.4, 0.6]), ([4, 5, 0], [0.5, 0.7, 0.8]), ([3, 1, 5], [0.6, 0.7, 0.9])]
output: 
  import pandas as pd
  from sklearn.metrics.pairwise import cosine_similarity
  
  def create_recommendations(file_path):
      df = pd.read_csv(file_path)
      item_similarity = cosine_similarity(df.T)
      recommendations = []
      for i in range(len(item_similarity)):
          top_3 = item_similarity[i].argsort()[-4:-1][::-1]
          top_3_scores = item_similarity[i][top_3]
          recommendations.append((top_3, top_3_scores))
      return recommendations
  