input: Write a function to implement a fuzzy search algorithm in Python that can match strings with near similarities. The function should take a query string, a list of choices, and a threshold for the maximum Levenshtein distance allowed between the query and a match. The function should return a list of choices that are close matches to the query based on the given threshold.
  
  ### Function Definition:
  
  def find_matches(query, choices, threshold):
      # Convert query and choices to lower case
      query, choices = query.lower(), [choice.lower() for choice in choices]
      # Initialize empty list to store matches
      matches = []
      
      # Loop through each choice
      for choice in choices:
          # Calculate Levenshtein distance between query and choice
          distance = levenshtein_distance(query, choice)
          # If distance is less than threshold, append choice to matches
          if distance <= threshold:
              matches.append(choice)
      
      return matches
  
  
  ### Test Cases:
  
  assert find_matches("apple", ["apple", "apples", "appetite", "orange"], 2) == ["apple", "apples", "appetite"]
  assert find_matches("python", ["Javascript", "Java", "Python3", "PHP"], 3) == ["Python3"]
  assert find_matches("dog", ["cat", "dog", "dot", "god"], 1) == ["dog"]
  assert find_matches("hello", ["helo", "hell", "hello", "help"], 0) == ["hello"]
  assert find_matches("fuzzy", [], 3) == []
  
output: def find_matches(query, choices, threshold):
      query, choices = query.lower(), [choice.lower() for choice in choices]
      matches = []
      
      for choice in choices:
          distance = levenshtein_distance(query, choice)
          if distance <= threshold:
              matches.append(choice)
      
      return matches