input: Write a function to implement a fuzzy search algorithm in Python that can match strings with near similarities using Levenshtein distance as the metric. The function should take a query string, a list of choices, and a threshold value as inputs. It should return a list of strings from choices that are close matches to the query based on the threshold.
  assert find_matches("apple", ["apples", "appel", "banana", "apricot"], 2) == ["apples", "appel"]
  assert find_matches("food", ["foot", "fool", "foodie", "good"], 1) == ["foot"]
  assert find_matches("python", ["typhon", "pythagoras", "pyth", "snoopy"], 3) == ["typhon", "pyth"]
  assert find_matches("hello", ["helo", "heillo", "heloos"], 2) == ["helo", "heillo"]
  assert find_matches("", ["", "hello", "world"], 0) == [""]
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]
  
      return [choice for choice in choices if levenshtein_distance(query, choice) <= threshold]