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 Initialization ---
# Load the OCR model and processor once when the app starts
try:
    HF_PROCESSOR = AutoProcessor.from_pretrained("reducto/RolmOCR")
    HF_MODEL = AutoModelForImageTextToText.from_pretrained(
        "reducto/RolmOCR",
        torch_dtype=torch.bfloat16,
        # attn_implementation="flash_attention_2", # User had this commented out
        device_map="auto"
    )
    HF_PIPE = pipeline("image-text-to-text", model=HF_MODEL, processor=HF_PROCESSOR)
    print("Hugging Face OCR model loaded successfully.")
except Exception as e:
    print(f"Error loading Hugging Face model: {e}")
    HF_PIPE = None

# --- Helper Functions ---

def get_alto_namespace(xml_file_path):
    """
    Dynamically gets the ALTO namespace from the XML file.
    """
    try:
        tree = ET.parse(xml_file_path)
        root = tree.getroot()
        if '}' in root.tag:
            return root.tag.split('}')[0] + '}'
    except ET.ParseError:
        print(f"Error parsing XML to find namespace: {xml_file_path}")
    return ''

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_alto_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 run_hf_ocr(image_path):
    """
    Runs OCR on the provided image using the pre-loaded Hugging Face model.
    """
    if HF_PIPE is None:
        return "Hugging Face OCR model not available."
    if image_path is None:
        return "No image provided for OCR."
    
    try:
        # Load the image using PIL, as the pipeline expects an image object or path
        pil_image = Image.open(image_path).convert("RGB")

        # The user's example output for the pipeline call was:
        # [{'generated_text': [{'role': 'user', ...}, {'role': 'assistant', 'content': "TEXT..."}]}]
        # This suggests the pipeline is returning a conversational style output.
        # We will try to call the pipeline with the image and prompt directly.
        ocr_results = predict(pil_image)
        
        # 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']
            
            # Check if generated_content itself is the direct text (some pipelines do this)
            if isinstance(generated_content, str):
                return generated_content

            # Check for the conversational structure
            # [{'role': 'user', ...}, {'role': 'assistant', 'content': "TEXT..."}]
            if isinstance(generated_content, list) and generated_content:
                # The assistant's response is typically the last message in the list
                # or specifically the one with role 'assistant'.
                assistant_message = None
                for msg in reversed(generated_content): # Check from the end
                    if isinstance(msg, dict) and msg.get('role') == 'assistant' and 'content' in msg:
                        assistant_message = msg['content']
                        break
                if assistant_message:
                    return assistant_message
            
            # Fallback if parsing the complex structure fails but we got some string
            if isinstance(generated_content, list) and generated_content and isinstance(generated_content[0], dict) and 'content' in generated_content[0]:
                 # This is a guess if the structure is simpler than expected.
                 # Or if the first part is the user prompt echo and second is assistant.
                 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

            print(f"Unexpected OCR output structure from HF model: {ocr_results}")
            return "Error: Could not parse OCR model output. Please check console for details."
            
        else:
            print(f"Unexpected OCR output structure from HF model: {ocr_results}")
            return "Error: OCR model did not return expected output. Please check console for details."

    except Exception as e:
        print(f"Error during Hugging Face OCR: {e}")
        return f"Error during Hugging Face OCR: {str(e)}"
@spaces.GPU
def predict(pil_image):
    ocr_results = HF_PIPE(
            pil_image, 
            prompt="Return the plain text representation of this document as if you were reading it naturally.\n"
            # The pipeline should handle formatting this into messages if needed by the model.
        )
    
    return ocr_results

# --- 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 ALTO XML if provided.
    """
    img_to_display = None
    alto_text_output = "ALTO 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:
        alto_text_output = parse_alto_xml_for_text(xml_path)
    else:
        alto_text_output = "No ALTO 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, alto_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 OCR 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 ALTO XML File (Optional, .xml)", 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.Textbox(
                label="OCR Output (Hugging Face Model)", 
                lines=15, 
                interactive=False,
                show_copy_button=True
            )
            alto_xml_output_textbox = gr.Textbox(
                label="Text from ALTO XML", 
                lines=15, 
                interactive=False,
                show_copy_button=True
            )
    
    submit_button.click(
        fn=process_files,
        inputs=[image_input, xml_input],
        outputs=[output_image_display, alto_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.")
    demo.launch()