Datasets:

divinity-library
/

AR-test-01

import os
import re
import hashlib
import shutil
import tempfile
from pathlib import Path

import fitz
import pytesseract
from PIL import Image
from huggingface_hub import snapshot_download, HfApi


# ============================================================
# CONFIGURATION
# ============================================================

DPI = 300
OCR_LANGUAGE = "eng"


if os.environ.get("TESSERACT_CMD"):
    pytesseract.pytesseract.tesseract_cmd = os.environ["TESSERACT_CMD"]


# ============================================================
# INGESTION
# ============================================================

def load_dataset():

    print("\n" + "=" * 55)
    print("       PDF OCR PROCESSING PIPELINE")
    print("=" * 55)

    source = input(
        "\nEnter a Hugging Face dataset ID "
        "or local directory path:\n> "
    ).strip().strip('"').strip("'")

    if not source:
        raise ValueError("No dataset source was provided.")

    source_path = Path(source).expanduser()

    if source_path.is_dir():

        print("\n[INGESTION] Using local dataset directory.")
        dataset_path = source_path.resolve()

    else:

        print("\n[INGESTION] Downloading Hugging Face dataset...")

        try:
            dataset_path = Path(
                snapshot_download(
                    repo_id=source,
                    repo_type="dataset",
                    allow_patterns=["*.pdf", "*.PDF"]
                )
            )

        except Exception as error:
            raise RuntimeError(
                f"Could not retrieve dataset: {error}"
            )

    pdf_files = sorted(
        [
            file for file in dataset_path.rglob("*")
            if file.is_file() and file.suffix.lower() == ".pdf"
        ]
    )

    if not pdf_files:
        raise FileNotFoundError(
            "No PDF files were found in the supplied dataset."
        )

    print(f"\n[INGESTION] Found {len(pdf_files)} PDF files.")

    return dataset_path, pdf_files


# ============================================================
# DOCUMENT DIRECTORY CREATION
# ============================================================

def create_document_folder(
    pdf_path,
    dataset_path,
    output_path,
    pdf_files
):

    relative_path = pdf_path.relative_to(dataset_path)

    document_name = pdf_path.stem

    safe_name = re.sub(
        r'[<>:"/\\|?*]',
        "_",
        document_name
    )

    matching_files = [
        p for p in pdf_files
        if p.stem.casefold() == document_name.casefold()
    ]

    # Disambiguate PDFs with identical filenames.
    if len(matching_files) > 1:

        suffix = hashlib.sha1(
            str(relative_path).encode("utf-8")
        ).hexdigest()[:8]

        safe_name = f"{safe_name}_{suffix}"

    document_folder = output_path / safe_name

    png_folder = document_folder / "png"
    txt_folder = document_folder / "txt"

    png_folder.mkdir(parents=True, exist_ok=True)
    txt_folder.mkdir(parents=True, exist_ok=True)

    return document_folder, png_folder, txt_folder, safe_name


# ============================================================
# PDF TO PNG CONVERSION
# ============================================================

def convert_pdf_to_png(
    pdf_path,
    png_folder,
    document_name
):

    print(f"\n[CONVERSION] Processing: {pdf_path.name}")

    pdf = fitz.open(pdf_path)

    try:

        page_count = len(pdf)

        for page_number, page in enumerate(pdf, start=1):

            filename = (
                f"{document_name}_page_{page_number:04d}.png"
            )

            image_path = png_folder / filename

            # Skip existing images.
            if image_path.exists():
                continue

            matrix = fitz.Matrix(
                DPI / 72,
                DPI / 72
            )

            pixmap = page.get_pixmap(
                matrix=matrix,
                alpha=False
            )

            pixmap.save(str(image_path))

        print(
            f"[CONVERSION] Generated/verified "
            f"{page_count} page images."
        )

        return page_count

    finally:
        pdf.close()


# ============================================================
# OCR PROCESSING
# ============================================================

def ocr_images(
    png_folder,
    txt_folder,
    document_name
):

    print(f"\n[OCR] Processing: {document_name}")

    image_files = sorted(
        png_folder.glob(f"{document_name}_page_*.png")
    )

    for image_path in image_files:

        text_filename = image_path.stem + ".txt"

        text_path = txt_folder / text_filename

        # Skip existing OCR outputs.
        if text_path.exists():
            continue

        with Image.open(image_path) as image:

            extracted_text = pytesseract.image_to_string(
                image,
                lang=OCR_LANGUAGE
            )

        with open(
            text_path,
            "w",
            encoding="utf-8"
        ) as text_file:

            text_file.write(extracted_text)

    print(
        f"[OCR] Generated/verified "
        f"{len(image_files)} page-level TXT files."
    )


# ============================================================
# MASTER DOCUMENT CREATION
# ============================================================

def create_master_document(
    txt_folder,
    document_folder,
    document_name,
    page_count
):

    print(f"\n[STITCHING] Reconstructing: {document_name}")

    master_path = document_folder / f"{document_name}.txt"

    with open(
        master_path,
        "w",
        encoding="utf-8"
    ) as master_file:

        for page_number in range(1, page_count + 1):

            filename = (
                f"{document_name}_page_{page_number:04d}.txt"
            )

            text_path = txt_folder / filename

            if not text_path.exists():
                raise FileNotFoundError(
                    f"Missing OCR output: {text_path}"
                )

            with open(
                text_path,
                "r",
                encoding="utf-8"
            ) as page_file:

                page_text = page_file.read()

            master_file.write(
                f"\n\n========== PAGE {page_number} ==========\n\n"
            )

            master_file.write(page_text)

    print("[STITCHING] Master document created.")

    return master_path


# ============================================================
# DOCUMENT VALIDATION
# ============================================================

def validate_document(
    png_folder,
    txt_folder,
    master_path,
    page_count,
    document_name
):

    png_count = len(
        list(png_folder.glob(f"{document_name}_page_*.png"))
    )

    txt_count = len(
        list(txt_folder.glob(f"{document_name}_page_*.txt"))
    )

    master_exists = master_path.exists()

    valid = (
        png_count == page_count
        and txt_count == page_count
        and master_exists
    )

    print("\n[VALIDATION]")

    print(f"Expected pages: {page_count}")
    print(f"PNG files:      {png_count}")
    print(f"TXT files:      {txt_count}")
    print(f"Master TXT:     {master_exists}")

    if valid:
        print("[VALIDATION] SUCCESS")
    else:
        print("[VALIDATION] FAILED")

    return valid


# ============================================================
# HUGGING FACE UPLOAD AND PULL REQUEST
# ============================================================

def upload_results(
    successful_folders
):

    print("\n" + "=" * 55)
    print("             HUGGING FACE UPLOAD")
    print("=" * 55)

    if not successful_folders:

        print(
            "\n[UPLOAD] No successfully validated documents. "
            "Skipping upload."
        )

        return

    repo_id = input(
        "\nEnter destination Hugging Face dataset ID "
        "(e.g. sample-organization/sample-dataset):\n> "
    ).strip()

    if not repo_id:

        print("\n[UPLOAD] No repository specified. Skipping upload.")

        return

    try:

        api = HfApi()

        print(f"\n[UPLOAD] Destination: {repo_id}")
        print(
            f"[UPLOAD] Documents to upload: "
            f"{len(successful_folders)}"
        )

        # Create a temporary staging directory containing
        # only successfully validated documents.
        with tempfile.TemporaryDirectory(
            prefix="pdf_ocr_upload_"
        ) as temporary_directory:

            staging_path = Path(temporary_directory)

            for document_folder in successful_folders:

                destination = staging_path / document_folder.name

                shutil.copytree(
                    document_folder,
                    destination
                )

            print("\n[UPLOAD] Uploading OCR corpus...")

            result = api.upload_folder(

                folder_path=str(staging_path),

                repo_id=repo_id,

                repo_type="dataset",

                path_in_repo="ocr-output",

                commit_message="Add OCR processing results",

                commit_description=(
                    "Automatically generated OCR corpus "
                    "containing page images, page-level text "
                    "files, and consolidated master documents."
                ),

                create_pr=True
            )

        print("\n[SUCCESS] Upload completed!")

        print(f"Pull Request URL: {result}")

    except Exception as error:

        print(f"\n[UPLOAD ERROR] {error}")

        print(
            "[UPLOAD] Local OCR results have been preserved."
        )

        print(
            "[UPLOAD] Check your Hugging Face authentication "
            "and repository write permissions."
        )


# ============================================================
# MAIN PIPELINE
# ============================================================

def run_pipeline():

    try:

        pytesseract.get_tesseract_version()

    except Exception as error:

        raise RuntimeError(
            "Tesseract OCR is not installed or is not accessible. "
            "Check your installation and PATH."
        ) from error

    dataset_path, pdf_files = load_dataset()

    output_input = input(
        "\nEnter output directory "
        "(press Enter for ./PDF_OCR_CORPUS):\n> "
    ).strip().strip('"').strip("'")

    if output_input:

        output_path = Path(
            output_input
        ).expanduser().resolve()

    else:

        output_path = Path.cwd() / "PDF_OCR_CORPUS"

    output_path.mkdir(
        parents=True,
        exist_ok=True
    )

    print("\n" + "=" * 55)

    print(f"PDF FILES: {len(pdf_files)}")
    print(f"OUTPUT:    {output_path}")

    print("=" * 55)

    successful = 0
    failed = 0

    successful_folders = []

    for index, pdf_path in enumerate(pdf_files, start=1):

        print("\n" + "-" * 55)

        print(
            f"DOCUMENT {index}/{len(pdf_files)}: "
            f"{pdf_path.name}"
        )

        print("-" * 55)

        try:

            (
                document_folder,
                png_folder,
                txt_folder,
                document_name
            ) = create_document_folder(
                pdf_path,
                dataset_path,
                output_path,
                pdf_files
            )

            page_count = convert_pdf_to_png(
                pdf_path,
                png_folder,
                document_name
            )

            ocr_images(
                png_folder,
                txt_folder,
                document_name
            )

            master_path = create_master_document(
                txt_folder,
                document_folder,
                document_name,
                page_count
            )

            valid = validate_document(
                png_folder,
                txt_folder,
                master_path,
                page_count,
                document_name
            )

            if valid:

                successful += 1

                successful_folders.append(
                    document_folder
                )

            else:

                failed += 1

        except Exception as error:

            failed += 1

            print(
                f"\n[ERROR] Could not process "
                f"{pdf_path.name}: {error}"
            )

            continue

    # ========================================================
    # PROCESSING SUMMARY
    # ========================================================

    print("\n" + "=" * 55)
    print("             PIPELINE COMPLETE")
    print("=" * 55)

    print(f"Total PDFs:       {len(pdf_files)}")
    print(f"Successful:       {successful}")
    print(f"Failed:           {failed}")
    print(f"Output directory: {output_path}")

    print("=" * 55)

    # ========================================================
    # OPTIONAL HUGGING FACE UPLOAD
    # ========================================================

    if successful > 0:

        upload_choice = input(
            "\nWould you like to upload the results "
            "to Hugging Face and create a Pull Request? "
            "(y/n):\n> "
        ).strip().lower()

        if upload_choice in ("y", "yes"):

            upload_results(successful_folders)

        else:

            print("\n[UPLOAD] Skipped by user.")

    else:

        print(
            "\n[UPLOAD] No successful documents to upload."
        )


# ============================================================
# EXECUTION
# ============================================================

if __name__ == "__main__":

    try:

        run_pipeline()

    except KeyboardInterrupt:

        print("\n[INTERRUPTED] Pipeline stopped by user.")

    except Exception as error:

        print(f"\n[FATAL ERROR] {error}")

	import os
	import re
	import hashlib
	import shutil
	import tempfile
	from pathlib import Path

	import fitz
	import pytesseract
	from PIL import Image
	from huggingface_hub import snapshot_download, HfApi


	# ============================================================
	# CONFIGURATION
	# ============================================================

	DPI = 300
	OCR_LANGUAGE = "eng"


	if os.environ.get("TESSERACT_CMD"):
	pytesseract.pytesseract.tesseract_cmd = os.environ["TESSERACT_CMD"]


	# ============================================================
	# INGESTION
	# ============================================================

	def load_dataset():

	print("\n" + "=" * 55)
	print(" PDF OCR PROCESSING PIPELINE")
	print("=" * 55)

	source = input(
	"\nEnter a Hugging Face dataset ID "
	"or local directory path:\n> "
	).strip().strip('"').strip("'")

	if not source:
	raise ValueError("No dataset source was provided.")

	source_path = Path(source).expanduser()

	if source_path.is_dir():

	print("\n[INGESTION] Using local dataset directory.")
	dataset_path = source_path.resolve()

	else:

	print("\n[INGESTION] Downloading Hugging Face dataset...")

	try:
	dataset_path = Path(
	snapshot_download(
	repo_id=source,
	repo_type="dataset",
	allow_patterns=[".pdf", ".PDF"]
	)
	)

	except Exception as error:
	raise RuntimeError(
	f"Could not retrieve dataset: {error}"
	)

	pdf_files = sorted(
	[
	file for file in dataset_path.rglob("*")
	if file.is_file() and file.suffix.lower() == ".pdf"
	]
	)

	if not pdf_files:
	raise FileNotFoundError(
	"No PDF files were found in the supplied dataset."
	)

	print(f"\n[INGESTION] Found {len(pdf_files)} PDF files.")

	return dataset_path, pdf_files


	# ============================================================
	# DOCUMENT DIRECTORY CREATION
	# ============================================================

	def create_document_folder(
	pdf_path,
	dataset_path,
	output_path,
	pdf_files
	):

	relative_path = pdf_path.relative_to(dataset_path)

	document_name = pdf_path.stem

	safe_name = re.sub(
	r'[<>:"/\\\|?*]',
	"_",
	document_name
	)

	matching_files = [
	p for p in pdf_files
	if p.stem.casefold() == document_name.casefold()
	]

	# Disambiguate PDFs with identical filenames.
	if len(matching_files) > 1:

	suffix = hashlib.sha1(
	str(relative_path).encode("utf-8")
	).hexdigest()[:8]

	safe_name = f"{safe_name}_{suffix}"

	document_folder = output_path / safe_name

	png_folder = document_folder / "png"
	txt_folder = document_folder / "txt"

	png_folder.mkdir(parents=True, exist_ok=True)
	txt_folder.mkdir(parents=True, exist_ok=True)

	return document_folder, png_folder, txt_folder, safe_name


	# ============================================================
	# PDF TO PNG CONVERSION
	# ============================================================

	def convert_pdf_to_png(
	pdf_path,
	png_folder,
	document_name
	):

	print(f"\n[CONVERSION] Processing: {pdf_path.name}")

	pdf = fitz.open(pdf_path)

	try:

	page_count = len(pdf)

	for page_number, page in enumerate(pdf, start=1):

	filename = (
	f"{document_name}_page_{page_number:04d}.png"
	)

	image_path = png_folder / filename

	# Skip existing images.
	if image_path.exists():
	continue

	matrix = fitz.Matrix(
	DPI / 72,
	DPI / 72
	)

	pixmap = page.get_pixmap(
	matrix=matrix,
	alpha=False
	)

	pixmap.save(str(image_path))

	print(
	f"[CONVERSION] Generated/verified "
	f"{page_count} page images."
	)

	return page_count

	finally:
	pdf.close()


	# ============================================================
	# OCR PROCESSING
	# ============================================================

	def ocr_images(
	png_folder,
	txt_folder,
	document_name
	):

	print(f"\n[OCR] Processing: {document_name}")

	image_files = sorted(
	png_folder.glob(f"{document_name}_page_*.png")
	)

	for image_path in image_files:

	text_filename = image_path.stem + ".txt"

	text_path = txt_folder / text_filename

	# Skip existing OCR outputs.
	if text_path.exists():
	continue

	with Image.open(image_path) as image:

	extracted_text = pytesseract.image_to_string(
	image,
	lang=OCR_LANGUAGE
	)

	with open(
	text_path,
	"w",
	encoding="utf-8"
	) as text_file:

	text_file.write(extracted_text)

	print(
	f"[OCR] Generated/verified "
	f"{len(image_files)} page-level TXT files."
	)


	# ============================================================
	# MASTER DOCUMENT CREATION
	# ============================================================

	def create_master_document(
	txt_folder,
	document_folder,
	document_name,
	page_count
	):

	print(f"\n[STITCHING] Reconstructing: {document_name}")

	master_path = document_folder / f"{document_name}.txt"

	with open(
	master_path,
	"w",
	encoding="utf-8"
	) as master_file:

	for page_number in range(1, page_count + 1):

	filename = (
	f"{document_name}_page_{page_number:04d}.txt"
	)

	text_path = txt_folder / filename

	if not text_path.exists():
	raise FileNotFoundError(
	f"Missing OCR output: {text_path}"
	)

	with open(
	text_path,
	"r",
	encoding="utf-8"
	) as page_file:

	page_text = page_file.read()

	master_file.write(
	f"\n\n========== PAGE {page_number} ==========\n\n"
	)

	master_file.write(page_text)

	print("[STITCHING] Master document created.")

	return master_path


	# ============================================================
	# DOCUMENT VALIDATION
	# ============================================================

	def validate_document(
	png_folder,
	txt_folder,
	master_path,
	page_count,
	document_name
	):

	png_count = len(
	list(png_folder.glob(f"{document_name}_page_*.png"))
	)

	txt_count = len(
	list(txt_folder.glob(f"{document_name}_page_*.txt"))
	)

	master_exists = master_path.exists()

	valid = (
	png_count == page_count
	and txt_count == page_count
	and master_exists
	)

	print("\n[VALIDATION]")

	print(f"Expected pages: {page_count}")
	print(f"PNG files: {png_count}")
	print(f"TXT files: {txt_count}")
	print(f"Master TXT: {master_exists}")

	if valid:
	print("[VALIDATION] SUCCESS")
	else:
	print("[VALIDATION] FAILED")

	return valid


	# ============================================================
	# HUGGING FACE UPLOAD AND PULL REQUEST
	# ============================================================

	def upload_results(
	successful_folders
	):

	print("\n" + "=" * 55)
	print(" HUGGING FACE UPLOAD")
	print("=" * 55)

	if not successful_folders:

	print(
	"\n[UPLOAD] No successfully validated documents. "
	"Skipping upload."
	)

	return

	repo_id = input(
	"\nEnter destination Hugging Face dataset ID "
	"(e.g. sample-organization/sample-dataset):\n> "
	).strip()

	if not repo_id:

	print("\n[UPLOAD] No repository specified. Skipping upload.")

	return

	try:

	api = HfApi()

	print(f"\n[UPLOAD] Destination: {repo_id}")
	print(
	f"[UPLOAD] Documents to upload: "
	f"{len(successful_folders)}"
	)

	# Create a temporary staging directory containing
	# only successfully validated documents.
	with tempfile.TemporaryDirectory(
	prefix="pdf_ocr_upload_"
	) as temporary_directory:

	staging_path = Path(temporary_directory)

	for document_folder in successful_folders:

	destination = staging_path / document_folder.name

	shutil.copytree(
	document_folder,
	destination
	)

	print("\n[UPLOAD] Uploading OCR corpus...")

	result = api.upload_folder(

	folder_path=str(staging_path),

	repo_id=repo_id,

	repo_type="dataset",

	path_in_repo="ocr-output",

	commit_message="Add OCR processing results",

	commit_description=(
	"Automatically generated OCR corpus "
	"containing page images, page-level text "
	"files, and consolidated master documents."
	),

	create_pr=True
	)

	print("\n[SUCCESS] Upload completed!")

	print(f"Pull Request URL: {result}")

	except Exception as error:

	print(f"\n[UPLOAD ERROR] {error}")

	print(
	"[UPLOAD] Local OCR results have been preserved."
	)

	print(
	"[UPLOAD] Check your Hugging Face authentication "
	"and repository write permissions."
	)


	# ============================================================
	# MAIN PIPELINE
	# ============================================================

	def run_pipeline():

	try:

	pytesseract.get_tesseract_version()

	except Exception as error:

	raise RuntimeError(
	"Tesseract OCR is not installed or is not accessible. "
	"Check your installation and PATH."
	) from error

	dataset_path, pdf_files = load_dataset()

	output_input = input(
	"\nEnter output directory "
	"(press Enter for ./PDF_OCR_CORPUS):\n> "
	).strip().strip('"').strip("'")

	if output_input:

	output_path = Path(
	output_input
	).expanduser().resolve()

	else:

	output_path = Path.cwd() / "PDF_OCR_CORPUS"

	output_path.mkdir(
	parents=True,
	exist_ok=True
	)

	print("\n" + "=" * 55)

	print(f"PDF FILES: {len(pdf_files)}")
	print(f"OUTPUT: {output_path}")

	print("=" * 55)

	successful = 0
	failed = 0

	successful_folders = []

	for index, pdf_path in enumerate(pdf_files, start=1):

	print("\n" + "-" * 55)

	print(
	f"DOCUMENT {index}/{len(pdf_files)}: "
	f"{pdf_path.name}"
	)

	print("-" * 55)

	try:

	(
	document_folder,
	png_folder,
	txt_folder,
	document_name
	) = create_document_folder(
	pdf_path,
	dataset_path,
	output_path,
	pdf_files
	)

	page_count = convert_pdf_to_png(
	pdf_path,
	png_folder,
	document_name
	)

	ocr_images(
	png_folder,
	txt_folder,
	document_name
	)

	master_path = create_master_document(
	txt_folder,
	document_folder,
	document_name,
	page_count
	)

	valid = validate_document(
	png_folder,
	txt_folder,
	master_path,
	page_count,
	document_name
	)

	if valid:

	successful += 1

	successful_folders.append(
	document_folder
	)

	else:

	failed += 1

	except Exception as error:

	failed += 1

	print(
	f"\n[ERROR] Could not process "
	f"{pdf_path.name}: {error}"
	)

	continue

	# ========================================================
	# PROCESSING SUMMARY
	# ========================================================

	print("\n" + "=" * 55)
	print(" PIPELINE COMPLETE")
	print("=" * 55)

	print(f"Total PDFs: {len(pdf_files)}")
	print(f"Successful: {successful}")
	print(f"Failed: {failed}")
	print(f"Output directory: {output_path}")

	print("=" * 55)

	# ========================================================
	# OPTIONAL HUGGING FACE UPLOAD
	# ========================================================

	if successful > 0:

	upload_choice = input(
	"\nWould you like to upload the results "
	"to Hugging Face and create a Pull Request? "
	"(y/n):\n> "
	).strip().lower()

	if upload_choice in ("y", "yes"):

	upload_results(successful_folders)

	else:

	print("\n[UPLOAD] Skipped by user.")

	else:

	print(
	"\n[UPLOAD] No successful documents to upload."
	)


	# ============================================================
	# EXECUTION
	# ============================================================

	if __name__ == "__main__":

	try:

	run_pipeline()

	except KeyboardInterrupt:

	print("\n[INTERRUPTED] Pipeline stopped by user.")

	except Exception as error:

	print(f"\n[FATAL ERROR] {error}")