input: Write a function to generate a fuzzy search algorithm in Python that can match strings with near similarities by using the Levenshtein distance. The function should take a query string, a list of choice strings, and a threshold value as inputs. The function should return a list of strings that match the query with a Levenshtein distance less than or equal to the threshold.
  assert find_matches("apple", ["pineapple", "apples", "banana"], 2)==["pineapple", "apples"]
  assert find_matches("orange", ["orang", "orangutan", "oranges"], 1)==["orang", "oranges"]
  assert find_matches("grape", ["grapefruit", "grapes", "banana"], 3)==["grapefruit", "grapes"]
  assert find_matches("mango", ["mangoes", "mango", "mangot"], 0)==["mango"]
  assert find_matches("strawberry", ["strawbs", "berry", "strawberry"], 3)==["strawbs", "strawberry"]
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]