|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
""" |
|
Convert a directory of PDF files to a Hugging Face dataset. |
|
|
|
This script uses the built-in PDF support in the datasets library to create |
|
a dataset from PDF files. Each PDF is converted to images (one per page). |
|
|
|
Example usage: |
|
# Basic usage - convert PDFs in a directory |
|
uv run pdf-to-dataset.py /path/to/pdfs username/my-dataset |
|
|
|
# Create a private dataset |
|
uv run pdf-to-dataset.py /path/to/pdfs username/my-dataset --private |
|
|
|
# Organize by subdirectories (creates labels) |
|
# folder/invoice/doc1.pdf -> label: invoice |
|
# folder/receipt/doc2.pdf -> label: receipt |
|
uv run pdf-to-dataset.py /path/to/organized-pdfs username/categorized-pdfs |
|
""" |
|
|
|
import logging |
|
import os |
|
import sys |
|
from argparse import ArgumentParser, RawDescriptionHelpFormatter |
|
from pathlib import Path |
|
|
|
from datasets import load_dataset |
|
from huggingface_hub import login |
|
|
|
logging.basicConfig( |
|
level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" |
|
) |
|
logger = logging.getLogger(__name__) |
|
|
|
|
|
def validate_directory(directory: Path) -> int: |
|
"""Validate directory and count PDF files.""" |
|
if not directory.exists(): |
|
raise ValueError(f"Directory does not exist: {directory}") |
|
|
|
if not directory.is_dir(): |
|
raise ValueError(f"Path is not a directory: {directory}") |
|
|
|
|
|
pdf_count = len(list(directory.rglob("*.pdf"))) |
|
|
|
if pdf_count == 0: |
|
raise ValueError(f"No PDF files found in directory: {directory}") |
|
|
|
return pdf_count |
|
|
|
|
|
def main(): |
|
parser = ArgumentParser( |
|
description="Convert PDF files to Hugging Face datasets", |
|
formatter_class=RawDescriptionHelpFormatter, |
|
epilog=__doc__, |
|
) |
|
|
|
parser.add_argument("directory", type=Path, help="Directory containing PDF files") |
|
parser.add_argument( |
|
"repo_id", |
|
type=str, |
|
help="Hugging Face dataset repository ID (e.g., 'username/dataset-name')", |
|
) |
|
parser.add_argument( |
|
"--private", action="store_true", help="Create a private dataset repository" |
|
) |
|
parser.add_argument( |
|
"--hf-token", |
|
type=str, |
|
default=None, |
|
help="Hugging Face API token (can also use HF_TOKEN environment variable)", |
|
) |
|
|
|
args = parser.parse_args() |
|
|
|
|
|
hf_token = args.hf_token or os.environ.get("HF_TOKEN") |
|
if hf_token: |
|
login(token=hf_token) |
|
else: |
|
logger.info("No HF token provided. Will attempt to use cached credentials.") |
|
|
|
try: |
|
|
|
pdf_count = validate_directory(args.directory) |
|
logger.info(f"Found {pdf_count} PDF files to process") |
|
|
|
|
|
logger.info("Loading PDFs as dataset (this may take a while for large PDFs)...") |
|
dataset = load_dataset("pdffolder", data_dir=str(args.directory)) |
|
|
|
|
|
logger.info("\nDataset created successfully!") |
|
logger.info(f"Structure: {dataset}") |
|
|
|
if "train" in dataset: |
|
train_size = len(dataset["train"]) |
|
logger.info(f"Training examples: {train_size}") |
|
|
|
|
|
if train_size > 0: |
|
sample = dataset["train"][0] |
|
logger.info(f"\nSample structure: {list(sample.keys())}") |
|
if "label" in sample: |
|
logger.info("Labels found - PDFs are organized by category") |
|
|
|
|
|
logger.info(f"\nPushing to Hugging Face Hub: {args.repo_id}") |
|
dataset.push_to_hub(args.repo_id, private=args.private) |
|
|
|
logger.info("โ
Dataset uploaded successfully!") |
|
logger.info(f"๐ Available at: https://huggingface.co/datasets/{args.repo_id}") |
|
|
|
|
|
logger.info("\nTo use your dataset:") |
|
logger.info(f' dataset = load_dataset("{args.repo_id}")') |
|
|
|
except Exception as e: |
|
logger.error(f"Failed to create dataset: {e}") |
|
sys.exit(1) |
|
|
|
|
|
if __name__ == "__main__": |
|
if len(sys.argv) == 1: |
|
|
|
print(__doc__) |
|
sys.exit(0) |
|
|
|
main() |
|
|