input: Write a function to generate a fuzzy search algorithm in Python that can match strings with near similarities by calculating the Levenshtein distance between a query string and a list of choices, and returning the choices that have a distance less than or equal to a given threshold.
  assert find_matches("apple", ["apples", "appel", "banana", "applet"], 2)==["apples", "appel", "applet"]
  assert find_matches("python", ["pyton", "pythn", "java", "javascript"], 1)==["pyton", "pythn"]
  assert find_matches("fuzzy", ["fuzzy", "funny", "fazzy", "fuszy"], 2)==["fuzzy", "funny", "fazzy", "fuszy"]
  assert find_matches("search", ["serach", "sereach", "banana", "searching"], 3)==["serach", "sereach", "searching"]
  assert find_matches("algorithm", ["algoritm", "algorith", "banana", "taco"], 2)==["algoritm", "algorith"]
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]