md2memos.py

import hashlib
import re
import random
import string
import time
import requests
import os
import csv
import secrets
import string
import argparse
from datetime import datetime

memos_parser = argparse.ArgumentParser(description="markdowk parser for memos")
memos_parser.add_argument('--md',help='makrdown file',default="")

def get_arg_by_name(parser_args,ArgName):
    args_pairs = parser_args.__dict__
    for key,value in args_pairs.items():
        if key == ArgName:
            return value
        
global_resource_id_counter = 1  # 图片ID计数器

# 函数：将日期字符串转换为UNIX时间戳
def convert_to_timestamp(date_str):
    # 定义不同的日期格式
    formats = [
        ("%Y-%m-%d %H:%M", re.compile(r'^\d{4}-\d{2}-\d{2} \d{2}:\d{2}$')),
        ("%Y.%m.%d %H:%M", re.compile(r'^\d{4}\.\d{2}\.\d{2} \d{2}:\d{2}$'))
    ]
    
    # 尝试匹配日期字符串并转换
    for fmt, pattern in formats:
        if pattern.match(date_str):
            dt = datetime.strptime(date_str, fmt)
            return int(time.mktime(dt.timetuple()))
    
    raise ValueError(f"Date format for '{date_str}' is not supported.")

# 函数：生成随机的resource_name
def generate_resource_name(length=25):
    alphabet = string.ascii_letters + string.digits
    return ''.join(secrets.choice(alphabet) for i in range(length))


# 函数：处理图片链接，并返回剔除图片链接的内容和图片信息列表
def process_image_links(entry_content, created_ts,entry_id_counter):
    image_link_pattern = re.compile(r'!\[.*?\]\((.*?)\)')
    image_info_list = []
    
    global global_resource_id_counter
    # 查找所有图片链接
    matches = image_link_pattern.findall(entry_content)
    for match in matches:
        # 获取图片文件名和链接
        external_link = match
        filename = os.path.basename(external_link)
        
        # 获取图片类型
        file_type = f"image/{os.path.splitext(filename)[1].lstrip('.')}"
        
        # 尝试获取图片大小
        try:
            response = requests.head(external_link)
            size = response.headers.get('content-length', 0)
        except Exception as e:
            print(f"Failed to get size for image {external_link}: {e}")
            size = 0

        # 构建图片信息字典
        image_info = {
            'id': global_resource_id_counter,
            'resource_name': generate_resource_name(),
            'creator_id': 1,
            'created_ts': created_ts,
            'updated_ts': created_ts,
            'filename': filename,
            'blob': '',
            'external_link': external_link,
            'type': file_type,
            'size': size,
            'internal_path': '',
            'memo_id': entry_id_counter # 这里假设memo_id与图片id相同，根据实际情况调整
        }

        image_info_list.append(image_info)
        global_resource_id_counter += 1

    # 移除原内容中的图片链接
    entry_content_cleaned = image_link_pattern.sub('', entry_content)

    return entry_content_cleaned, image_info_list

# 更新后的 parse_markdown_to_csv 函数和其他相关函数...

# 函数：解析Markdown内容并生成CSV格式的数据
def parse_markdown_to_csv(markdown_content):
    entries = markdown_content.strip().split('## ')[1:]  # 分割Markdown内容
    csv_data = []
    resource_data = []
    id_counter = 1  # ID计数器

    for entry in reversed(entries):
        lines = entry.split('\n')
        non_empty_lines = [line for line in lines if line.strip() != '']  # 去除空行
        timestamp_str = non_empty_lines[0].strip()
        content = '\n'.join(non_empty_lines[1:]).strip()
        timestamp = convert_to_timestamp(timestamp_str)

        resource_name = generate_resource_name()
        clean_conent,resource_content_data= process_image_links(content,timestamp,id_counter)
        
        if content == "" and resource_content_data:
            continue

        # 生成CSV格式的行数据
        csv_row = {
            'id': id_counter,
            'resource_name': resource_name,
            'creator_id': 1,
            'created_ts': timestamp,
            'updated_ts': timestamp,
            'row_status': 'NORMAL',
            'content': clean_conent,
            'visibility': 'PRIVATE'
        }

        csv_data.append(csv_row)
        if resource_content_data:
            resource_data.extend(resource_content_data)
        id_counter += 1
    return csv_data,resource_data

# 函数：将CSV数据写入文件
def write_csv_data(csv_data, file_path):
    headers = ['id', 'resource_name', 'creator_id', 'created_ts', 'updated_ts', 'row_status', 'content', 'visibility']
    
    with open(file_path, 'w', newline='', encoding='utf-8') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=headers)
        writer.writeheader()
        for row in csv_data:
            writer.writerow(row)

# 新函数：将图片信息写入CSV文件
def write_image_info_csv(image_info_list, file_path):
    headers = ['id', 'resource_name', 'creator_id', 'created_ts', 'updated_ts', 'filename', 'blob', 'external_link', 'type', 'size', 'internal_path', 'memo_id']
    
    with open(file_path, 'w', newline='', encoding='utf-8') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=headers)
        writer.writeheader()
        for image_info in image_info_list:
            writer.writerow(image_info)

# 主程序
def main():

    parser_args = memos_parser.parse_args()
    md_file = get_arg_by_name(parser_args,"md")
    if md_file != "" and os.path.exists(md_file):
        md_file = os.path.normpath(md_file)
    
    md_path = os.path.dirname(md_file)
    md_filename = os.path.basename(md_file)
    file_base_name, file_extension = os.path.splitext(md_filename)

    # 读取Markdown文件
    with open(md_file, 'r', encoding='utf-8') as file:
        markdown_content = file.read()

    # 解析Markdown并生成CSV数据
    csv_data,resource_data = parse_markdown_to_csv(markdown_content)
    # 写入CSV文件
    csv_file_path = os.path.join(md_path,file_base_name + '_memo.csv')  # 输出CSV文件的路径
    write_csv_data(csv_data, csv_file_path)
    print(f'memo.csv file has been created at {csv_file_path}')
    
    resource_csv_file_path = os.path.join(md_path,file_base_name + '_resource.csv') 
    write_image_info_csv(resource_data,resource_csv_file_path)
    print(f'memo resource.csv file has been created at {resource_csv_file_path}')

# 运行主程序
if __name__ == "__main__":
    main()