How to use the pycorrector.correct function in pycorrector

def demo2():
    for i in x:
        print(i, pycorrector.detect(i))
        print(i, pycorrector.correct(i))

# -*- coding: utf-8 -*-
"""
@author:XuMing（xuming624@qq.com)
@description: 
"""

import pycorrector

corrected_sent, detail = pycorrector.correct('cv')
print(corrected_sent, detail)

def test1():
    for i in text:
        print(i, pycorrector.detect(i))
        print(i, pycorrector.correct(i))

'这是人工智能的一个分知',
             '我的家乡是有名的玉米之乡',
             '老师工作非常幸苦,我们要遵敬老师',
             'nihao, 耐得住欲妄',
             '一阙词牌名',
             '我兴高彩列地去公园游玩',
             '吹唐人记忆',
             ' 耐得住欲妄',
             '母子平爱',
             '不由的感叹道',
             ]

error_sentences.extend(text_list)
for line in error_sentences:
    print(pycorrector.detect(line))
    correct_sent = pycorrector.correct(line)
    print("original sentence:{} => correct sentence:{}".format(line, correct_sent))

def test2():
    for i in x:
        print(i, pycorrector.detect(i))
        print(i, pycorrector.correct(i))

'这纸厚度如何？质量怎么样',
    '双十一下单到现在还没发货的，',
    '一但工作效率提升，需要时间在工作上也减少',
    '可否送手机膜？送膜吗',
    '这水用来洗脸泡脚效果如何',
    '五香的不辣吧',
    '这款功率真有2000w吗',
    '我对于宠物出租得事非常认同，因为其实很多人喜欢宠物',  # 出租的事
    '有了宠物出租地方另一方面还可以题高人类对动物的了解，因为那些专业人氏可以指导我们对于动物的习惯。',  # 题高 => 提高 专业人氏 => 专业人士
    '三个凑皮匠胜过一个诸葛亮也有道理。',  # 凑
    '还有广告业是只要桌子前面坐者工作未必产生出来好的成果。',
]
pycorrector.set_custom_confusion_dict(path='./my_confusion.txt')
pycorrector.set_custom_word(path='./my_custom_word.txt')
for line in error_sentences:
    correct_sent = pycorrector.correct(line)
    print("original sentence:{} => correct sentence:{}".format(line, correct_sent))

print('*' * 53)

pycorrector.enable_char_error(enable=False)
# pycorrector.enable_word_error(enable=False)
for line in error_sentences:
    # idx_errors = pycorrector.detect(line)
    # print(idx_errors)
    correct_sent = pycorrector.correct(line)
    print("original sentence:{} => correct sentence:{}".format(line, correct_sent))

]
for line in error_sentences:
    idx_errors = pycorrector.detect(line)
    print(idx_errors)

    correct_sent = pycorrector.correct(line)
    print("original sentence:{} => correct sentence:{}".format(line, correct_sent))

print('*' * 53)
pycorrector.set_custom_confusion_dict(path='./my_confusion.txt')
pycorrector.set_custom_word(path='./my_custom_word.txt')
for line in error_sentences:
    idx_errors = pycorrector.detect(line)
    print(idx_errors)
    correct_sent = pycorrector.correct(line)
    print("original sentence:{} => correct sentence:{}".format(line, correct_sent))

'还有广告业是只要桌子前面坐者工作未必产生出来好的成果。',
]
pycorrector.set_custom_confusion_dict(path='./my_confusion.txt')
pycorrector.set_custom_word(path='./my_custom_word.txt')
for line in error_sentences:
    correct_sent = pycorrector.correct(line)
    print("original sentence:{} => correct sentence:{}".format(line, correct_sent))

print('*' * 53)

pycorrector.enable_char_error(enable=False)
# pycorrector.enable_word_error(enable=False)
for line in error_sentences:
    # idx_errors = pycorrector.detect(line)
    # print(idx_errors)
    correct_sent = pycorrector.correct(line)
    print("original sentence:{} => correct sentence:{}".format(line, correct_sent))

def eval_bcmi_data(data_path, verbose=False):
    sentence_size = 1
    right_count = 0
    right_result = dict()
    wrong_result = dict()
    with open(data_path, 'r', encoding='utf-8') as f:
        for line in f:
            line = line.strip()
            error_sentence, right_sentence, right_detail = get_bcmi_corpus(line)
            if not error_sentence:
                continue
            pred_sentence, pred_detail = correct(error_sentence)
            if verbose:
                print('input sentence:', error_sentence)
                print('pred sentence:', pred_sentence, pred_detail)
                print('right sentence:', right_sentence, right_detail)
            sentence_size += 1
            if right_sentence == pred_sentence:
                right_count += 1
                right_result[error_sentence] = [right_sentence, pred_sentence]
            else:
                wrong_result[error_sentence] = [right_sentence, pred_sentence]
    if verbose:
        print('right count:', right_count, ';sentence size:', sentence_size)
    return right_count / sentence_size, right_result, wrong_result