scripts/pdfeditor.py

import base64
import pymupdf

# from agentic_doc.parse import parse
from scripts.llm_nlp_preprocessing import llm_regulatory_change_detector
from scripts.llm_no_nlp_preprocessing import (
    llm_regulatory_change_detector_without_nlp_insights,
)
from scripts.pymupdf_nlp_preprocessing import (
    pymupdf_regulatory_change_detector_with_nlp_insights,
)
from scripts.pymupdf_no_nlp_preprocessing import (
    pymupdf_regulatory_change_detector_without_nlp_insights,
)
from scripts.pdf_text_extractor import (
    create_hierarchical_structure_by_llm,
    create_hierarchical_structure_by_pymupdf,
)


# Define hex colors as RGB tuples (0–1 range)
color_mapping = {
    "addition": (0, 1, 0),  # green
    "deletion": (1, 0, 0),  # red
    "modification": (0, 0.6, 1),  # blue
}


def add_infos_to_pdf(doc, analysis_summary, extraction_method, do_nlp_preprocessing):
    """
    Doc is edited in place.
    Adds metadata to the PDF document.
    Adds a summary of the analysis to the first page of the PDF.

    :param doc: The PyMuPDF document object.
    :type doc: pymupdf.Document
    :param analysis_summary: The summary of the analysis results.
    :type analysis_summary: dict
    :param extraction_method: The method used for text extraction from the PDF. Options are "PyMuPDF" or "LLM".
    :type extraction_method: str
    :param do_nlp_preprocessing: Flag indicating whether NLP preprocessing was used.
    :type do_nlp_preprocessing: bool
    """
    changes_by_type = analysis_summary.get("changes_by_type", {})

    additions = changes_by_type.get("addition") or changes_by_type.get("additions") or 0
    deletions = changes_by_type.get("deletion") or changes_by_type.get("deletions") or 0
    modifications = (
        changes_by_type.get("modification") or changes_by_type.get("modifications") or 0
    )

    summary_text = (
        "Regulatory Summary:\n"
        f"- Extraction Method: {extraction_method}, NLP Preprocessing: {'yes' if do_nlp_preprocessing else 'no'}\n"
        f"- Total Changes: {analysis_summary.get('total_changes_detected', '0')}, Successful Annotations: {analysis_summary.get('successful_annotations', '0')}\n"
        f"- Additions: {additions}\n"
        f"- Deletions: {deletions}\n"
        f"- Modifications: {modifications}\n"
    )
    page = doc.load_page(0)
    rect = pymupdf.Rect(10, 10, 550, 150)
    page.insert_textbox(
        rect,
        summary_text,
        fontsize=9,
        fontname="helv",
        align=pymupdf.TEXT_ALIGN_LEFT,
        color=(0, 0, 0.7),
        overlay=True,
    )

    metadata = doc.metadata
    metadata["title"] = "Annotated " + (
        metadata["title"] if metadata["title"] else "PDF"
    )
    metadata["author"] = "Fortiss Regulatory Change Detector" + (
        " & " + metadata["author"] if metadata["author"] else ""
    )
    metadata["subject"] = "Annotated PDF with regulatory changes"
    metadata["keywords"] = "regulatory, changes, annotations, pdf"
    doc.set_metadata(metadata)


def add_failed_annotations_to_pdf(doc, failed_annotations):
    """
    Doc is edited in place.
    Adds failed annotations to the end of the PDF document.

    :param doc: The PyMuPDF document object.
    :type doc: pymupdf.Document
    :param failed_annotations: The failed annotations to be added.
    :type failed_annotations: array
    """
    if not failed_annotations:
        return
    page = doc.new_page(pno=-1)
    annotation_str = "Failed Annotations:\n"
    for failed_annotation in failed_annotations:
        text = failed_annotation["change"]["relevant_text"]
        change_type = failed_annotation["change"]["change_type"]
        change_str = failed_annotation["change"]["change"]
        page_num = failed_annotation["page"]
        annotation_str += (
            f"Page {page_num}: {text} ({change_type}) Change: {change_str}\n"
        )

    rect = pymupdf.Rect(20, 20, 580, 822)
    page.insert_textbox(
        rect,
        annotation_str,
        fontsize=9,
        fontname="helv",
        align=pymupdf.TEXT_ALIGN_LEFT,
        color=(0, 0, 0.7),
    )


def get_data_dict_pymupdf(pdf_input: str, do_nlp_preprocessing: bool = True):
    try:
        pymupdf_structure = create_hierarchical_structure_by_pymupdf(pdf_input)
    except Exception as e:
        raise Exception(f"Error extracting text from PDF: {e}")
    try:
        if do_nlp_preprocessing:
            data_dict, _ = pymupdf_regulatory_change_detector_with_nlp_insights(
                pymupdf_structure
            )
        else:
            data_dict, _ = pymupdf_regulatory_change_detector_without_nlp_insights(
                pymupdf_structure
            )
        return data_dict
    except Exception as e:
        raise Exception(f"Error querying the pymupdf: {e}")


def extract_document_pymupdf(uploaded_document: bytes, do_nlp_preprocessing=True):
    data = get_data_dict_pymupdf(uploaded_document, do_nlp_preprocessing)
    if not data:
        return [], ""
    flattened_changes = []
    for page_num_str, changes in data.get("changes_by_page", {}).items():
        for change in changes:
            flattened_changes.append(
                {
                    "text": change.get("relevant_text", ""),
                    "validated": False,
                    "confirmed": False,
                    "category": change.get("change", ""),
                    "type": change.get("change_type", ""),
                    "context": change.get("explanation", ""),
                    "grounding": [{"page": int(page_num_str), "line": -1}],
                }
            )
    markdown = "" # parse(uploaded_document.read())[0].model_dump_json().get("markdown", "")
    return flattened_changes, markdown


def pymupdf_pdf_annotator(pdf_path, do_nlp_preprocessing=True):
    """
    Annotates a PDF document by applying highlights and comments based on the changes
    it gets from querying the llm with nlp preprocessing.
    The text is extracted using PyMuPDF.
    The annotations involve identifying specific text passages within the PDF and assigning an appropriate highlight color and comment
    based on the change type (addition, deletion, or modification).

    :param pdf_path: The file path to the PDF document that will be annotated.
    :type pdf_path: str
    :param do_nlp_preprocessing: Flag indicating whether to use NLP preprocessing for text extraction. Default is True.
    :type do_nlp_preprocessing: bool

    :return: Base64-encoded string of the annotated PDF document suitable for embedding in HTML.
    :rtype: str
    """
    try:
        doc = pymupdf.open(pdf_path)
    except Exception as e:
        raise Exception(f"Error opening PDF file: {e}")
    data = get_data_dict_pymupdf(pdf_path, do_nlp_preprocessing)
    if not data:
        raise Exception("No data found in the PDF document. Please check the file.")
    successful_annotations = 0
    failed_annotations = []

    for page_num_str, changes in data.get("changes_by_page", {}).items():
        page_num = int(page_num_str)
        doc_page = doc.load_page(page_num - 1)
        # Sort by length of relevant_text in descending order to avoid overlapping highlights
        changes = sorted(changes, key=lambda c: -len(c["relevant_text"]))
        annotated_areas = []

        for change in changes:
            text = change["relevant_text"]
            change_type = change["change_type"]
            change_str = change["change"]
            comment = change["explanation"]

            # Search for the relevant text on the page
            results = doc_page.search_for(text)
            # we only want the results that do not overlap with already annotated areas
            results = list(
                filter(
                    lambda result: not any(
                        result.intersects(area) for area in annotated_areas
                    ),
                    results,
                )
            )
            if not results:
                print(
                    f"No non-overlapping match found on page {page_num} for: '{text}'"
                )
                failed_annotations.append({"change": change, "page": page_num})
                continue

            color = color_mapping.get(change_type, (1, 1, 0))

            annotated_areas.append(results[0])
            highlight = doc_page.add_highlight_annot(results[0])
            highlight.set_colors({"stroke": color})
            highlight.set_info(
                info={
                    "title": "Comment",
                    "content": f"{change_type} - {change_str}\n{comment}",
                    "name": change_type,
                }
            )
            highlight.update()
            successful_annotations += 1

            # if the resulting rects contain anything other than our search text we know it is a multiline highlight because for each line
            # we will have a new result rect. We need to check if the text in the rect is not equal to our search text but is inside of it
            # TODO test with multiple instances of multiline text on same page
            for result in results[1:]:
                resulttext = doc_page.get_textbox(result)
                if (
                    (resulttext.strip() != text.strip())
                    & (resulttext.strip() in text.strip())
                    & (not any(result.intersects(area) for area in annotated_areas))
                ):
                    highlight = doc_page.add_highlight_annot(result)
                    highlight.set_colors({"stroke": color})
                    highlight.update()
                    annotated_areas.append(result)

    data["analysis_summary"]["successful_annotations"] = successful_annotations
    add_infos_to_pdf(doc, data["analysis_summary"], "PyMuPDF", do_nlp_preprocessing)
    add_failed_annotations_to_pdf(doc, failed_annotations)
    base64_pdf = base64.b64encode(doc.tobytes()).decode("utf-8")
    doc.saveIncr()
    doc.close()
    return base64_pdf


def extract_document_llm(uploaded_document: bytes, do_nlp_preprocessing=True):
    try:
        llm_structure = create_hierarchical_structure_by_llm(uploaded_document)
    except Exception as e:
        raise Exception(f"Error extracting text from PDF: {e}")
    try:
        if do_nlp_preprocessing:
            data_dict = llm_regulatory_change_detector(llm_structure)
        else:
            data_dict = llm_regulatory_change_detector_without_nlp_insights(
                llm_structure
            )
    except Exception as e:
        raise Exception(f"Error querying the LLM: {e}")
    data = data_dict
    flattened_changes = []
    for _, changes in data.get("results", {}).items():
        for change in changes:
            flattened_changes.append(
                {
                    "text": change.get("relevant_text", ""),
                    "validated": False,
                    "confirmed": False,
                    "category": change.get("change", ""),
                    "type": change.get("change_type", ""),
                    "context": change.get("explanation", ""),
                    "grounding": [{"page": -1, "line": -1}],
                }
            )
    markdown = "" # parse(uploaded_document.read())[0].model_dump_json().get("markdown", "")
    return flattened_changes, markdown


def llm_pdf_annotator(pdf_path, do_nlp_preprocessing=True):
    """
    Annotates a PDF document by applying highlights and comments based on the changes
    it gets from querying the llm with nlp preprocessing.
    The text is extracted uing an LLM.
    The annotations involve identifying specific text passages within the PDF and assigning an appropriate highlight color and comment
    based on the change type (addition, deletion, or modification).

    :param pdf_path: The file path to the PDF document that will be annotated.
    :type pdf_path: str
    :param do_nlp_preprocessing: Flag indicating whether to use NLP preprocessing for text extraction. Default is True.
    :type do_nlp_preprocessing: bool

    :return: Base64-encoded string of the annotated PDF document suitable for embedding in HTML.
    :rtype: str
    """
    try:
        doc = pymupdf.open(pdf_path)
    except Exception as e:
        raise Exception(f"Error opening PDF file: {e}")

    try:
        llm_structure = create_hierarchical_structure_by_llm(pdf_path)
    except Exception as e:
        raise Exception(f"Error extracting text from PDF: {e}")
    try:
        if do_nlp_preprocessing:
            data_dict = llm_regulatory_change_detector(llm_structure)
        else:
            data_dict = llm_regulatory_change_detector_without_nlp_insights(
                llm_structure
            )
    except Exception as e:
        raise Exception(f"Error querying the LLM: {e}")
    data = data_dict
    successful_annotations = 0
    failed_annotations = []

    for _, changes in data.get("results", {}).items():
        # Sort by length of relevant_text in descending order to avoid overlapping highlights
        changes = sorted(changes, key=lambda c: -len(c["relevant_text"]))
        annotated_areas = []

        for change in changes:
            text = change["relevant_text"]
            change_type = change["change_type"]
            comment = change["explanation"]
            change_str = change["change"]
            results = []
            # search entire document for the text because we dont have the page index in the llm output
            for page_num in range(len(doc)):
                page = doc.load_page(page_num)
                text_instances = page.search_for(text)

                for inst in text_instances:
                    results.append({"page": page_num, "bbox": inst})
            # we only want the results that do not overlap with already annotated areas
            results = list(
                filter(
                    lambda result: not any(
                        result["bbox"].intersects(area) for area in annotated_areas
                    ),
                    results,
                )
            )
            if not results:
                print(
                    f"No non-overlapping match found on page {page_num} for: '{text}'"
                )
                failed_annotations.append({"change": change, "page": page_num})
                continue

            color = color_mapping.get(change_type, (1, 1, 0))
            ## we only want the first result because we will add highlights for each line of the multiline text
            doc_page = doc.load_page(results[0]["page"])
            bbox = results[0]["bbox"]
            annotated_areas.append(bbox)
            highlight = doc_page.add_highlight_annot(bbox)
            highlight.set_colors({"stroke": color})
            highlight.set_info(
                info={
                    "title": "Comment",
                    "content": f"{change_type} - {change_str}\n{comment}",
                    "name": change_type,
                }
            )
            highlight.update()
            successful_annotations += 1

            # if the resulting rects contain anything other than our search text we know it is a multiline highlight because for each line
            # we will have a new result rect. We need to check if the text in the rect is not equal to our search text but is inside of it
            for result in results[1:]:
                resulttext = doc_page.get_textbox(bbox)
                if (
                    (resulttext.strip() != text.strip())
                    & (resulttext.strip() in text.strip())
                    & (
                        not any(
                            result["bbox"].intersects(area) for area in annotated_areas
                        )
                    )
                ):
                    highlight = doc_page.add_highlight_annot(result["bbox"])
                    highlight.set_colors({"stroke": color})
                    highlight.update()
                    annotated_areas.append(result["bbox"])

    data["analysis_summary"]["successful_annotations"] = successful_annotations
    add_infos_to_pdf(doc, data["analysis_summary"], "LLM", do_nlp_preprocessing)
    add_failed_annotations_to_pdf(doc, failed_annotations)
    base64_pdf = base64.b64encode(doc.tobytes()).decode("utf-8")
    doc.saveIncr()
    doc.close()
    return base64_pdf