基于哈夫曼的文本文件压缩代码

您好！关于基于哈夫曼的文本文件压缩代码，以下是一个简单的示例：

import heapq
import os


class HuffmanNode:
    def __init__(self, char, freq):
        self.char = char
        self.freq = freq
        self.left = None
        self.right = None

    def __lt__(self, other):
        return self.freq < other.freq


def build_frequency_dict(text):
    frequency_dict = {}
    for char in text:
        if char in frequency_dict:
            frequency_dict[char] += 1
        else:
            frequency_dict[char] = 1
    return frequency_dict


def build_huffman_tree(frequency_dict):
    priority_queue = []
    for char, freq in frequency_dict.items():
        heapq.heappush(priority_queue, HuffmanNode(char, freq))

    while len(priority_queue) > 1:
        left_node = heapq.heappop(priority_queue)
        right_node = heapq.heappop(priority_queue)
        merged_node = HuffmanNode(None, left_node.freq + right_node.freq)
        merged_node.left = left_node
        merged_node.right = right_node
        heapq.heappush(priority_queue, merged_node)

    return priority_queue[0]


def build_encoding_dict(huffman_tree):
    encoding_dict = {}

    def traverse_tree(node, code):
        if node.char:
            encoding_dict[node.char] = code
        else:
            traverse_tree(node.left, code + '0')
            traverse_tree(node.right, code + '1')

    traverse_tree(huffman_tree, '')
    return encoding_dict


def compress(text):
    frequency_dict = build_frequency_dict(text)
    huffman_tree = build_huffman_tree(frequency_dict)
    encoding_dict = build_encoding_dict(huffman_tree)

    compressed_text = ''
    for char