Datasets:

danish-foundation-models
/

danish-dynaword

# /// script
# requires-python = ">=3.12"
# dependencies = [
#     "beautifulsoup4==4.13.3",
#     "datasets>=3.0.0",
#     "transformers",
#     "dynaword"
# ]
# [tool.uv.sources]
# dynaword = { git = "https://huggingface.co/datasets/danish-foundation-models/danish-dynaword", rev = "00e7f2aee7f7ad2da423419f77ecbb9c0536de0d" }
# ///
"""
Danske Taler API Downloader
This script downloads speeches/articles from the Danske Taler API: https://www.dansketaler.dk/api/v1

It saves it into the following structure:

```
{
  "text": "Lav et referat af nedenstående tekst:\n\nTekst:\nOpdatering: Manden er nu fundet af Nordjyllands Politi[...]",
  "source": "nordjyllandnews",
  "id": "nordjyllandnews_0",
  "added": "2024-12-16",
  "created": "2000-01-01, 2024-01-01",
  "license": "Creative Commons Legal Code\n\nCC0 1.0 Universal",
  "domain": "News",
  "metadata": {
    "source-pretty": "Nordjylland News"
  }
}
```

Note: To run this script, you need to set `GIT_LFS_SKIP_SMUDGE=1` to be able to install dynaword:

```bash
GIT_LFS_SKIP_SMUDGE=1 uv run data/memo/create.py
```

This second version fixed previous issues with the download and processing of the Danish Memo repository:
https://huggingface.co/datasets/danish-foundation-models/danish-dynaword/discussions/67
"""

import logging
import time
from datetime import date
from pathlib import Path
from typing import Any

from datasets import Dataset
import pandas as pd
import requests
from bs4 import BeautifulSoup, NavigableString
from tqdm import tqdm

from dynaword.process_dataset import (
    add_token_count,
    ensure_column_order,
    remove_duplicate_text,
    remove_empty_texts,
)

logger = logging.getLogger(__name__)

# Configuration
API_BASE_URL = "https://www.dansketaler.dk/api/v1"

KNOWN_HTML_TAGS = {
    "html",
    "head",
    "body",
    "title",
    "meta",
    "link",
    "script",
    "style",
    "div",
    "span",
    "p",
    "a",
    "ul",
    "ol",
    "li",
    "table",
    "tr",
    "td",
    "th",
    "img",
    "h1",
    "h2",
    "h3",
    "h4",
    "h5",
    "h6",
    "strong",
    "em",
    "br",
    "hr",
    "form",
    "input",
    "button",
    "label",
    "select",
    "option",
    "textarea",
    "iframe",
    "nav",
    "footer",
    "header",
    "main",
    "section",
    "article",
}


def contains_html_tags(text):
    soup = BeautifulSoup(str(text), "html.parser")
    return any(tag.name in KNOWN_HTML_TAGS for tag in soup.find_all())


def get_all_speeches() -> list[dict[str, Any]]:
    # fetch first page, notably the total number of pages
    url = f"{API_BASE_URL}/speeches?per_page=50"
    response = requests.get(url)
    response.raise_for_status()
    speeches = response.json()
    meta = speeches["meta"]
    total_pages = meta["total_pages"]

    # fetch all pages
    all_speeches = []
    for page in range(1, total_pages + 1):
        url = f"{API_BASE_URL}/speeches?per_page=50&page={page}"
        response = requests.get(url)
        response.raise_for_status()
        speeches = response.json()
        all_speeches.extend(speeches["speeches"])

    return all_speeches


def fetch_speech_content(
    url: str, max_retries: int = 3, backoff_factor: float = 0.5
) -> tuple[str | None, str]:
    """
    Fetches the license div from the page with retry logic.

    Args:
        url: The URL to fetch the license div from
        max_retries: Maximum number of retry attempts
        backoff_factor: Factor to determine exponential backoff time between retries

    Returns:
        The text content of the license div if found, None otherwise
    """
    retries = 0

    while retries <= max_retries:
        try:
            response = requests.get(url, timeout=10)
            response.raise_for_status()

            soup = BeautifulSoup(response.text, "html.parser")
            license_div = soup.find("div", class_="speech-copyright")
            speech_div = soup.find("div", class_="speech-article-content")
            speech = ""
            if speech_div:
                # Iterate over the children of the found div
                for child_div in speech_div.children:  # type: ignore
                    if child_div.name == "div":  # type: ignore
                        current_paragraph = []
                        for content in child_div.contents:  # type: ignore
                            if isinstance(content, NavigableString):
                                # Append text content
                                current_paragraph.append(str(content).strip())
                            elif content.name == "br":
                                # If a <br> is encountered, join and print the current paragraph, then reset
                                if current_paragraph:
                                    speech += "".join(current_paragraph)
                                    speech += "\n"  # Add a newline for paragraph break
                                    current_paragraph = []
                        # Print any remaining text in the current_paragraph list
                        if current_paragraph:
                            speech += "".join(current_paragraph)
                            speech += "\n"  # Add a newline for paragraph break

            return (license_div.text if license_div else None, speech)

        except (requests.RequestException, AttributeError) as e:
            retries += 1

            if retries > max_retries:
                logger.info(
                    f"Failed to fetch license after {max_retries} attempts: {str(e)}"
                )
                return (None, "")

            # Calculate backoff time using exponential backoff
            wait_time = backoff_factor * (2 ** (retries - 1))
            logger.info(
                f"Attempt {retries} failed. Retrying in {wait_time:.2f} seconds..."
            )
            time.sleep(wait_time)

    return (None, "")


def convert_to_license(license_information: str | None) -> str | None:
    """checks if "Materialet er fri af ophavsret" is in the page"""

    if license_information and (
        ("Materialet er fri af ophavsret" in license_information)
        or ("Materialet er fri af ophvasret" in license_information)
        or ("Ophavsretten er bortfaldet" in license_information)
        or ("Manuskriptet er fri af ophavsret" in license_information)
        or ("Offentlig " == license_information)
    ):
        return "cc0"

    return license_information


def convert_to_row(speech_meta: dict[str, Any]) -> dict[str, Any]:
    speech_id = speech_meta["id"]

    date_of_speech = speech_meta["date"]["iso_date"]
    date_of_speech_start = f"{date_of_speech}"
    date_of_speech_end = f"{date_of_speech}"

    (license_information, speech) = fetch_speech_content(speech_meta["url"])

    row = {
        "id": f"danske-taler_{speech_id}",
        "text": speech,
        "source": "danske-taler",
        # current date
        "added": date.today().isoformat(),
        "created": f"{date_of_speech_start}, {date_of_speech_end}",
        "license_information": license_information,
        "domain": "Spoken",
        "metadata": {"source-pretty": "Danske Taler"},
    }

    return row


def download_speeches() -> pd.DataFrame:
    logger.info("Fetching all speeches from Danske Taler API")
    speeches = get_all_speeches()
    logger.info(f"Found {len(speeches)} speeches")

    rows = []
    for speech in tqdm(speeches):
        row = convert_to_row(speech)
        rows.append(row)

    logger.info(f"Saving {len(rows)} speeches to dataset")
    df = pd.DataFrame(rows)
    return df


def main():
    save_path = Path(__file__).parent / "danske-taler.parquet"
    save_path_all = Path(__file__).parent / "tmp" / "danske-taler-all.parquet"
    save_path_all.parent.mkdir(parents=False, exist_ok=True)

    if save_path_all.exists():
        logger.info(f"Loading dataset from {save_path_all}")
        df = pd.read_parquet(save_path_all)
    else:
        logger.info(f"Downloading speeches and saving to {save_path_all}")
        df = download_speeches()
        df.to_parquet(save_path_all)

    licenses = [convert_to_license(license) for license in df["license_information"]]
    df["license"] = licenses

    uniques_licenses = set(df["license"].tolist())
    logger.info("Unique licenses:")
    for license in uniques_licenses:
        logger.info(f"\t{license}")

    # remove documents without a cc0 license
    len_df = len(df)
    df = df[df["license"] == "cc0"]
    logger.info(f"Removed {len_df - len(df)} documents without a cc0 license")

    dataset = Dataset.from_pandas(df, preserve_index=False)

    dataset = remove_empty_texts(dataset)  # remove rows with empty text
    dataset = remove_duplicate_text(dataset)  # remove rows with duplicate text
    dataset = add_token_count(dataset)
    dataset = ensure_column_order(dataset)

    assert len(set(dataset["id"])) == len(dataset), "IDs are not unique"
    assert len(set(dataset["text"])) == len(dataset), "Texts are not unique"
    assert len(set(df["license"])) == 1, "Multiple licenses found"

    # check for html tags in text
    assert not df["text"].apply(contains_html_tags).any(), "HTML tags found in text"

    dataset.to_parquet(save_path)


if __name__ == "__main__":
    log_path = Path(__file__).parent / "danske-taler.log"
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s - %(levelname)s - %(message)s",
        handlers=[
            logging.StreamHandler(),
            logging.FileHandler(log_path),
        ],
    )
    main()