from collections import defaultdict from io import BytesIO from pathlib import Path import requests from zipfile import ZipFile import pandas as pd URL = "https://sprogtek-ressources.digst.govcloud.dk/nota/Nota-txt_only.zip" column_order = [ "text", "source", "id", "added", "created", "license", "domain", "metadata", ] def convert_sample(id: str, text: str) -> dict: year = id[4:8] new_example = dict( text=text, id=id.split("_")[0], source="nota", domain="Readaloud", license="Creative Commons Legal Code\n\nCC0 1.0 Universal", added="2025-02-03", created=f"{year}-01-01, {year}-12-31", # assuming v2018 metadata={"source-pretty": "Nota lyd- og tekstdata"}, ) return new_example def download_and_process_zip(url): response = requests.get(url) response.raise_for_status() # Ensure we got a valid response with ZipFile(BytesIO(response.content), "r") as z: file_groups = defaultdict(list) # Read all text files from the ZIP for file_name in z.namelist(): if file_name.endswith(".txt"): # Process only text files prefix = file_name.split("/")[1].split("_")[0] with z.open(file_name) as f: file_groups[prefix].append(f.read().decode("utf-8")) # Combine files with the same prefix combined_files = { f"{prefix}_combined.txt": "\n".join(contents) for prefix, contents in file_groups.items() } return combined_files # Dictionary with combined file names and contents def main(): combined_results = download_and_process_zip(URL) dataset = [] for filename, content in combined_results.items(): sample = convert_sample(filename, content) dataset.append(sample) df = pd.DataFrame(dataset) df = df.drop_duplicates(keep="first", subset=["text"]) save_path = Path(__file__).parent / "nota.parquet" df.to_parquet(save_path) if __name__ == "__main__": main()