create_meta_data_csv_md.py

import os
import json
import gzip
import csv
from multiprocessing import Pool, cpu_count
import time

def process_json_file(file_path):
    with gzip.open(file_path, 'rt', encoding='utf-8') as gz_file:
        data = json.load(gz_file)
        return data.get('meta', {})

def get_file_size_mb(file_path):
    return round(os.path.getsize(file_path) / (1024 * 1024), 2)

def write_to_csv_and_md(output_csv, output_md, headers, data):
    with open(output_csv, 'w', newline='', encoding='utf-8') as csv_file:
        writer = csv.DictWriter(csv_file, fieldnames=headers)
        writer.writeheader()
        writer.writerows(data)

    with open(output_md, 'w', encoding='utf-8') as md_file:
        md_file.write("| " + " | ".join(headers) + " |\n")
        md_file.write("|" + "|".join([" --- " for _ in headers]) + "|\n")

        for row in data:
            md_file.write("| " + " | ".join([str(row[header]) for header in headers]) + " |\n")

def process_file(file_name, input_directory, base_url):
    file_path = os.path.join(input_directory, file_name)
    meta_data = process_json_file(file_path)
    file_size_mb = get_file_size_mb(file_path)

    row_data = {
        "filesize": file_size_mb,
        "filename": file_name,
        "URL": f"{base_url}{file_name.replace('.json.gz', '')}",
        **meta_data
    }

    return row_data

def main(input_directory, output_csv, output_md, base_url="https://do-me.github.io/SemanticFinder/?hf="):
    headers = [
        "filesize", "textTitle", "textAuthor", "textYear", "textLanguage", "URL",
        "modelName", "quantized", "splitParam", "splitType", "characters", "chunks",
        "wordsToAvoidAll", "wordsToCheckAll", "wordsToAvoidAny", "wordsToCheckAny",
        "exportDecimals", "lines", "textNotes", "textSourceURL", "filename"
    ]

    all_data = []
    
    start_time = time.time()

    file_list = [file_name for file_name in os.listdir(input_directory) if file_name.endswith('.json.gz')]

    with Pool(cpu_count()) as pool:
        all_data = pool.starmap(process_file, [(file_name, input_directory, base_url) for file_name in file_list])

    write_to_csv_and_md(output_csv, output_md, headers, all_data)

    end_time = time.time()
    processing_time = end_time - start_time
    print(f"Processing time: {round(processing_time, 2)} seconds")

if __name__ == "__main__":
    input_directory = "."
    output_csv = "meta_data.csv"
    output_md = "meta_data.md"

    main(input_directory, output_csv, output_md)