input: Write a function to generate 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 allowable Levenshtein distance. The function should return a list of choices that match the query string within the specified threshold. If the list of choices is empty, the function should return an empty list.
  assert find_matches("apple", ["apple", "apples", "app", "aple"], 2)==["apple", "apples", "app", "aple"]
  assert find_matches("apple", ["apple", "apples", "app", "aple"], 1)==["apple", "apples", "app"]
  assert find_matches("apple", ["banana", "orange", "grape"], 2)==[]
  assert find_matches("apple", [], 2)==[]
  assert find_matches("", ["apple", "banana", "orange"], 2)=[]
output: def find_matches(query, choices, threshold):
      def levenshtein_distance(s1, s2):
          if len(s1) < len(s2):
              return levenshtein_distance(s2, s1)
          if len(s2) == 0:
              return len(s1)
          previous_row = range(len(s2) + 1)
          for i, c1 in enumerate(s1):
              current_row = [i + 1]
              for j, c2 in enumerate(s2):
                  insertions = previous_row[j + 1] + 1
                  deletions = current_row[j] + 1
                  substitutions = previous_row[j] + (c1 != c2)
                  current_row.append(min(insertions, deletions, substitutions))
              previous_row = current_row
          return previous_row[-1]
      
      if not choices:
          return []
      
      return [choice for choice in choices if levenshtein_distance(query, choice) <= threshold]