有没有办法制作一个自动纠正扫描文件的脚本？

import nltk
from nltk.corpus import wordnet
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from sklearn.metrics.pairwise import cosine_similarity

# 定义纠正规则
rules = {
    "misspelled": {"wordnet.synsets('misspelled.word'): [wordnet.synset('spelled.word')]",
                   "wordnet.synsets('spelled.word'): [wordnet.synset('misspelled.word')]"},
    "incorrectly_spelled": {
        "wordnet.synsets('incorrectly_spelled.word'): [wordnet.synset('spelled.word')]",
        "wordnet.synsets('spelled.word'): [wordnet.synset('incorrectly_spelled.word')]"}
}

# 分词
def tokenize(text):
    tokens = word_tokenize(text)
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(token) for token in tokens]
    return tokens

# 拼写和语法纠正
def correct_spelling(tokens, rules):
    corrected = []
    for token in tokens:
        if token in rules["misspelled"]:
            synonyms = rules["misspelled"][token]
            for synonym in synonyms:
                if synonym not in corrected:
                    corrected.append(synonym)
        elif token in rules["incorrectly_spelled"]:
            synonyms = rules["incorrectly_spelled"][token]
            for synonym in synonyms:
                if synonym not in corrected:
                    corrected.append(synonym)
        corrected.append(token)
    return corrected

# 计算相似度
def calculate_similarity(corrected):
    tokens = corrected[0].split()
    corrected_tokens = [token.lower() for token in tokens]
    sim = cosine_similarity([tokens], [corrected_tokens])
    return sim

# 输出结果
def output_results(corrected, similarity):
    for i in range(len(similarity)):
        for j in range(len(similarity[i])):
            print(f"{similarity[i][j]:.2f} {corrected[i][j]}")

# 示例
text = "This is an example sentence to demonstrate the power of natural language processing"
corrected = correct_spelling(tokenize(text), rules)
similarity = calculate_similarity(corrected)
output_results(corrected, similarity)