app.py

import gradio as gr
from PIL import Image
import xml.etree.ElementTree as ET
import os
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, pipeline
import spaces

# --- Global Model and Processor ---
HF_PROCESSOR = None
HF_MODEL = None
HF_PIPE = None
MODEL_LOAD_ERROR_MSG = None

HF_PROCESSOR = AutoProcessor.from_pretrained("reducto/RolmOCR")
HF_MODEL = AutoModelForImageTextToText.from_pretrained(
    "reducto/RolmOCR", torch_dtype=torch.bfloat16, device_map="auto"
)
HF_PIPE = pipeline("image-text-to-text", model=HF_MODEL, processor=HF_PROCESSOR)


# --- Helper Functions ---


def get_xml_namespace(xml_file_path):
    """
    Dynamically gets the namespace from the XML file.
    Returns both the namespace and the format type (ALTO or PAGE).
    """
    try:
        tree = ET.parse(xml_file_path)
        root = tree.getroot()
        if "}" in root.tag:
            ns = root.tag.split("}")[0] + "}"
            # Determine format based on root element
            if "PcGts" in root.tag:
                return ns, "PAGE"
            elif "alto" in root.tag.lower():
                return ns, "ALTO"
    except ET.ParseError:
        print(f"Error parsing XML to find namespace: {xml_file_path}")
    return "", "UNKNOWN"


def parse_page_xml_for_text(xml_file_path):
    """
    Parses a PAGE XML file to extract text content.
    Returns:
        - full_text (str): All extracted text concatenated.
    """
    full_text_lines = []

    if not xml_file_path or not os.path.exists(xml_file_path):
        return "Error: XML file not provided or does not exist."

    try:
        ns_prefix, _ = get_xml_namespace(xml_file_path)
        tree = ET.parse(xml_file_path)
        root = tree.getroot()

        # Find all TextLine elements
        for text_line in root.findall(f".//{ns_prefix}TextLine"):
            # First try to get text from TextEquiv/Unicode
            text_equiv = text_line.find(f"{ns_prefix}TextEquiv/{ns_prefix}Unicode")
            if text_equiv is not None and text_equiv.text:
                full_text_lines.append(text_equiv.text)
                continue

            # If no TextEquiv, try to get text from Word elements
            line_text_parts = []
            for word in text_line.findall(f"{ns_prefix}Word"):
                word_text = word.find(f"{ns_prefix}TextEquiv/{ns_prefix}Unicode")
                if word_text is not None and word_text.text:
                    line_text_parts.append(word_text.text)

            if line_text_parts:
                full_text_lines.append(" ".join(line_text_parts))

        return "\n".join(full_text_lines)

    except ET.ParseError as e:
        return f"Error parsing XML: {e}"
    except Exception as e:
        return f"An unexpected error occurred during XML parsing: {e}"


def parse_alto_xml_for_text(xml_file_path):
    """
    Parses an ALTO XML file to extract text content.
    Returns:
        - full_text (str): All extracted text concatenated.
    """
    full_text_lines = []

    if not xml_file_path or not os.path.exists(xml_file_path):
        return "Error: XML file not provided or does not exist."

    try:
        ns_prefix, _ = get_xml_namespace(xml_file_path)
        tree = ET.parse(xml_file_path)
        root = tree.getroot()

        for text_line in root.findall(f".//{ns_prefix}TextLine"):
            line_text_parts = []
            for string_element in text_line.findall(f"{ns_prefix}String"):
                text = string_element.get("CONTENT")
                if text:
                    line_text_parts.append(text)
            if line_text_parts:
                full_text_lines.append(" ".join(line_text_parts))

        return "\n".join(full_text_lines)

    except ET.ParseError as e:
        return f"Error parsing XML: {e}"
    except Exception as e:
        return f"An unexpected error occurred during XML parsing: {e}"


def parse_xml_for_text(xml_file_path):
    """
    Main function to parse XML files, automatically detecting the format.
    """
    if not xml_file_path or not os.path.exists(xml_file_path):
        return "Error: XML file not provided or does not exist."

    try:
        _, xml_format = get_xml_namespace(xml_file_path)

        if xml_format == "PAGE":
            return parse_page_xml_for_text(xml_file_path)
        elif xml_format == "ALTO":
            return parse_alto_xml_for_text(xml_file_path)
        else:
            return f"Error: Unsupported XML format. Expected ALTO or PAGE XML."

    except Exception as e:
        return f"Error determining XML format: {str(e)}"


@spaces.GPU
def predict(pil_image):
    """Performs OCR prediction using the Hugging Face model."""
    global HF_PIPE, MODEL_LOAD_ERROR_MSG

    if HF_PIPE is None:
        error_to_report = (
            MODEL_LOAD_ERROR_MSG
            if MODEL_LOAD_ERROR_MSG
            else "OCR model could not be initialized."
        )
        raise RuntimeError(error_to_report)

    # Format the message in the expected structure
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": pil_image},
                {
                    "type": "text",
                    "text": "Return the plain text representation of this document as if you were reading it naturally.\n",
                },
            ],
        }
    ]

    # Use the pipeline with the properly formatted messages
    return HF_PIPE(messages, max_new_tokens=8096)


def run_hf_ocr(image_path):
    """
    Runs OCR on the provided image using the Hugging Face model (via predict function).
    """
    if image_path is None:
        return "No image provided for OCR."

    try:
        pil_image = Image.open(image_path).convert("RGB")
        ocr_results = predict(pil_image)  # predict handles model loading and inference

        # Parse the output based on the user's example structure
        if (
            isinstance(ocr_results, list)
            and ocr_results
            and "generated_text" in ocr_results[0]
        ):
            generated_content = ocr_results[0]["generated_text"]

            if isinstance(generated_content, str):
                return generated_content

            if isinstance(generated_content, list) and generated_content:
                if assistant_message := next(
                    (
                        msg["content"]
                        for msg in reversed(generated_content)
                        if isinstance(msg, dict)
                        and msg.get("role") == "assistant"
                        and "content" in msg
                    ),
                    None,
                ):
                    return assistant_message

                # Fallback if the specific assistant message structure isn't found but there's content
                if (
                    isinstance(generated_content[0], dict)
                    and "content" in generated_content[0]
                ):
                    if (
                        len(generated_content) > 1
                        and isinstance(generated_content[1], dict)
                        and "content" in generated_content[1]
                    ):
                        return generated_content[1][
                            "content"
                        ]  # Assuming second part is assistant
                    else:
                        return generated_content[0]["content"]

            print(f"Unexpected OCR output structure from HF model: {ocr_results}")
            return "Error: Could not parse OCR model output. Check console."

        else:
            print(f"Unexpected OCR output structure from HF model: {ocr_results}")
            return "Error: OCR model did not return expected output. Check console."

    except RuntimeError as e:  # Catch model loading/initialization errors from predict
        return str(e)
    except Exception as e:
        print(f"Error during Hugging Face OCR processing: {e}")
        return f"Error during Hugging Face OCR: {str(e)}"


# --- Gradio Interface Function ---


def process_files(image_path, xml_path):
    """
    Main function for the Gradio interface.
    Processes the image for display, runs OCR (Hugging Face model),
    and parses XML if provided.
    """
    img_to_display = None
    xml_text_output = "XML not provided or not processed."
    hf_ocr_text_output = "Image not provided or OCR not run."

    if image_path:
        try:
            img_to_display = Image.open(image_path).convert("RGB")
            hf_ocr_text_output = run_hf_ocr(image_path)
        except Exception as e:
            img_to_display = None  # Clear image if it failed to load
            hf_ocr_text_output = f"Error loading image or running HF OCR: {e}"
    else:
        hf_ocr_text_output = "Please upload an image to perform OCR."

    if xml_path:
        xml_text_output = parse_xml_for_text(xml_path)
    else:
        xml_text_output = "No XML file uploaded."

    # If only XML is provided without an image
    if not image_path and xml_path:
        img_to_display = None  # No image to display
        hf_ocr_text_output = "Upload an image to perform OCR."

    return img_to_display, xml_text_output, hf_ocr_text_output


# --- Create Gradio App ---

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# OCR Viewer and Extractor")
    gr.Markdown(
        "Upload an image to perform OCR using a Hugging Face model. "
        "Optionally, upload its corresponding ALTO or PAGE XML file to compare the extracted text."
    )

    with gr.Row():
        with gr.Column(scale=1):
            image_input = gr.File(
                label="Upload Image (PNG, JPG, etc.)", type="filepath"
            )
            xml_input = gr.File(
                label="Upload XML File (Optional, ALTO or PAGE format)", type="filepath"
            )
            submit_button = gr.Button("Process Image and XML", variant="primary")

    with gr.Row():
        with gr.Column(scale=1):
            output_image_display = gr.Image(
                label="Uploaded Image", type="pil", interactive=False
            )
        with gr.Column(scale=1):
            hf_ocr_output_textbox = gr.Markdown(
                label="OCR Output (Hugging Face Model)",
                show_copy_button=True,
            )
            xml_output_textbox = gr.Textbox(
                label="Text from XML",
                lines=15,
                interactive=False,
                show_copy_button=True,
            )

    submit_button.click(
        fn=process_files,
        inputs=[image_input, xml_input],
        outputs=[output_image_display, xml_output_textbox, hf_ocr_output_textbox],
    )

    gr.Markdown("---")
    gr.Markdown("### Example ALTO XML Snippet (for `String` element extraction):")
    gr.Code(
        value=(
            """<alto xmlns="http://www.loc.gov/standards/alto/v3/alto.xsd">
  <Description>...</Description>
  <Styles>...</Styles>
  <Layout>
    <Page ID="Page13" PHYSICAL_IMG_NR="13" WIDTH="2394" HEIGHT="3612">
      <PrintSpace>
        <TextLine WIDTH="684" HEIGHT="108" ID="p13_t1" HPOS="465" VPOS="196">
          <String ID="p13_w1" CONTENT="Introduction" HPOS="465" VPOS="196" WIDTH="684" HEIGHT="108" STYLEREFS="font0"/>
        </TextLine>
        <TextLine WIDTH="1798" HEIGHT="51" ID="p13_t2" HPOS="492" VPOS="523">
          <String ID="p13_w2" CONTENT="Britain" HPOS="492" VPOS="523" WIDTH="166" HEIGHT="51" STYLEREFS="font1"/>
          <SP WIDTH="24" VPOS="523" HPOS="658"/>
          <String ID="p13_w3" CONTENT="1981" HPOS="682" VPOS="523" WIDTH="117" HEIGHT="51" STYLEREFS="font1"/>
          <!-- ... more String and SP elements ... -->
        </TextLine>
        <!-- ... more TextLine elements ... -->
      </PrintSpace>
    </Page>
  </Layout>
</alto>"""
        ),
        interactive=False,
    )

if __name__ == "__main__":
    # Removed dummy file creation as it's less relevant for single file focus
    print("Attempting to launch Gradio demo...")
    print(
        "If the Hugging Face model is large, initial startup might take some time due to model download/loading (on first OCR attempt)."
    )
    demo.launch()