Datasets:

LLParallax
/

kanji-dataset

import xml.etree.ElementTree as ET
from pathlib import Path
from PIL import Image
import pandas as pd
from datasets import Dataset, Features, Image as HFImage, Value
from huggingface_hub import HfApi


def parse_kanjidic(xml_file_path):
    """
    Parses the kanjidic2.xml file to extract kanji literals and their
    English meanings.
    Args:
        xml_file_path (str or Path): Path to the kanjidic2.xml file.
    Returns:
        dict: A dictionary mapping each kanji character to a list of its
              English meanings.
    """
    print(f"Parsing {xml_file_path}...")
    try:
        tree = ET.parse(xml_file_path)
        root = tree.getroot()
    except (ET.ParseError, FileNotFoundError) as e:
        print(f"Error reading or parsing XML file: {e}")
        return {}

    kanji_meanings = {}
    # The kanjidic2.xml file contains <character> tags for each kanji[6]
    for char_element in root.findall('character'):
        literal = char_element.find('literal').text
        meanings = []
        
        # Meanings are found within reading_meaning -> rmgroup -> meaning
        rmgroup = char_element.find('reading_meaning/rmgroup')
        if rmgroup is not None:
            # We select meanings without an 'm_lang' attribute, as they are English by default
            for meaning_element in rmgroup.findall('meaning'):
                if 'm_lang' not in meaning_element.attrib:
                    meanings.append(meaning_element.text)
        
        if literal and meanings:
            kanji_meanings[literal] = meanings
            
    print(f"Successfully parsed {len(kanji_meanings)} kanji characters.")
    return kanji_meanings

def create_kanji_dataset(image_dir, kanji_data):
    """
    Creates a Hugging Face Dataset from a directory of kanji images and
    parsed kanji meanings.
    Args:
        image_dir (str or Path): Directory containing the rendered kanji images.
        kanji_data (dict): Dictionary of kanji meanings from parse_kanjidic.
    Returns:
        datasets.Dataset: The final Hugging Face Dataset object.
    """
    image_dir = Path(image_dir)
    records = []

    print(f"Processing images from {image_dir.resolve()}...")
    image_paths = list(image_dir.glob('*.png'))
    if not image_paths:
        print("No PNG images found in the specified directory.")
        return None

    for img_path in image_paths:
        # Your rendering script saves files as U+<hex_codepoint>.png
        hex_codepoint = img_path.stem.split('U+')[-1]
        try:
            # Convert hex codepoint to the actual kanji character
            kanji_char = chr(int(hex_codepoint, 16))
        except ValueError:
            print(f"Skipping invalid filename: {img_path.name}")
            continue

        if kanji_char in kanji_data:
            # Concatenate meanings into a single descriptive string
            text_description = ", ".join(kanji_data[kanji_char])
            records.append({
                "image": str(img_path),
                "text": text_description
            })

    if not records:
        print("No matching data found. Ensure image filenames correspond to kanji in the XML.")
        return None
        
    print(f"Created {len(records)} records for the dataset.")
    
    # Create a DataFrame first, then convert to a Dataset
    df = pd.DataFrame(records)
    
    # Define the dataset features to ensure correct type handling, especially for images[7]
    features = Features({
        'image': HFImage(decode=True),
        'text': Value(dtype='string'),
    })
    
    # Create the dataset from the pandas DataFrame
    dataset = Dataset.from_pandas(df, features=features)
    
    return dataset

def main():
    image_directory = "kanji_images"
    kanjidic_xml_path = "kanjidic2.xml"
    repo_id = "LLParallax/kanji-dataset" 

    # --- Step 1: Parse Kanji Meanings ---
    kanji_meanings_data = parse_kanjidic(kanjidic_xml_path)
    if not kanji_meanings_data:
        return

    # --- Step 2: Create the Dataset Object ---
    kanji_dataset = create_kanji_dataset(image_directory, kanji_meanings_data)
    if kanji_dataset is None:
        return
        
    print("\nDataset created successfully. Sample record:")
    print(kanji_dataset[0])

    # --- Step 3: Push to Hugging Face Hub ---
    # This will automatically convert the dataset to the Parquet format[5][7]
    print(f"\nPushing dataset to Hugging Face Hub at {repo_id}...")
    try:
        kanji_dataset.push_to_hub(repo_id, private=False) # Set to True for a private dataset
        print("Dataset pushed successfully!")
        print(f"View your dataset at: https://huggingface.co/datasets/{repo_id}")
    except Exception as e:
        print(f"Failed to push dataset to Hub: {e}")

if __name__ == "__main__":
    main()

	import xml.etree.ElementTree as ET
	from pathlib import Path
	from PIL import Image
	import pandas as pd
	from datasets import Dataset, Features, Image as HFImage, Value
	from huggingface_hub import HfApi


	def parse_kanjidic(xml_file_path):
	"""
	Parses the kanjidic2.xml file to extract kanji literals and their
	English meanings.

	Args:
	xml_file_path (str or Path): Path to the kanjidic2.xml file.

	Returns:
	dict: A dictionary mapping each kanji character to a list of its
	English meanings.
	"""
	print(f"Parsing {xml_file_path}...")
	try:
	tree = ET.parse(xml_file_path)
	root = tree.getroot()
	except (ET.ParseError, FileNotFoundError) as e:
	print(f"Error reading or parsing XML file: {e}")
	return {}

	kanji_meanings = {}
	# The kanjidic2.xml file contains <character> tags for each kanji[6]
	for char_element in root.findall('character'):
	literal = char_element.find('literal').text
	meanings = []

	# Meanings are found within reading_meaning -> rmgroup -> meaning
	rmgroup = char_element.find('reading_meaning/rmgroup')
	if rmgroup is not None:
	# We select meanings without an 'm_lang' attribute, as they are English by default
	for meaning_element in rmgroup.findall('meaning'):
	if 'm_lang' not in meaning_element.attrib:
	meanings.append(meaning_element.text)

	if literal and meanings:
	kanji_meanings[literal] = meanings

	print(f"Successfully parsed {len(kanji_meanings)} kanji characters.")
	return kanji_meanings

	def create_kanji_dataset(image_dir, kanji_data):
	"""
	Creates a Hugging Face Dataset from a directory of kanji images and
	parsed kanji meanings.

	Args:
	image_dir (str or Path): Directory containing the rendered kanji images.
	kanji_data (dict): Dictionary of kanji meanings from parse_kanjidic.

	Returns:
	datasets.Dataset: The final Hugging Face Dataset object.
	"""
	image_dir = Path(image_dir)
	records = []

	print(f"Processing images from {image_dir.resolve()}...")
	image_paths = list(image_dir.glob('*.png'))
	if not image_paths:
	print("No PNG images found in the specified directory.")
	return None

	for img_path in image_paths:
	# Your rendering script saves files as U+<hex_codepoint>.png
	hex_codepoint = img_path.stem.split('U+')[-1]
	try:
	# Convert hex codepoint to the actual kanji character
	kanji_char = chr(int(hex_codepoint, 16))
	except ValueError:
	print(f"Skipping invalid filename: {img_path.name}")
	continue

	if kanji_char in kanji_data:
	# Concatenate meanings into a single descriptive string
	text_description = ", ".join(kanji_data[kanji_char])
	records.append({
	"image": str(img_path),
	"text": text_description
	})

	if not records:
	print("No matching data found. Ensure image filenames correspond to kanji in the XML.")
	return None

	print(f"Created {len(records)} records for the dataset.")

	# Create a DataFrame first, then convert to a Dataset
	df = pd.DataFrame(records)

	# Define the dataset features to ensure correct type handling, especially for images[7]
	features = Features({
	'image': HFImage(decode=True),
	'text': Value(dtype='string'),
	})

	# Create the dataset from the pandas DataFrame
	dataset = Dataset.from_pandas(df, features=features)

	return dataset

	def main():
	image_directory = "kanji_images"
	kanjidic_xml_path = "kanjidic2.xml"
	repo_id = "LLParallax/kanji-dataset"

	# --- Step 1: Parse Kanji Meanings ---
	kanji_meanings_data = parse_kanjidic(kanjidic_xml_path)
	if not kanji_meanings_data:
	return

	# --- Step 2: Create the Dataset Object ---
	kanji_dataset = create_kanji_dataset(image_directory, kanji_meanings_data)
	if kanji_dataset is None:
	return

	print("\nDataset created successfully. Sample record:")
	print(kanji_dataset[0])

	# --- Step 3: Push to Hugging Face Hub ---
	# This will automatically convert the dataset to the Parquet format[5][7]
	print(f"\nPushing dataset to Hugging Face Hub at {repo_id}...")
	try:
	kanji_dataset.push_to_hub(repo_id, private=False) # Set to True for a private dataset
	print("Dataset pushed successfully!")
	print(f"View your dataset at: https://huggingface.co/datasets/{repo_id}")
	except Exception as e:
	print(f"Failed to push dataset to Hub: {e}")

	if __name__ == "__main__":
	main()