Spaces:
Running
on
Zero
Running
on
Zero
Commit
·
e4442f3
1
Parent(s):
5f3165f
Add requirements.in and update requirements.txt with dependencies
Browse files- app.py +138 -70
- requirements.in +6 -0
- requirements.txt +238 -3
app.py
CHANGED
@@ -1,7 +1,25 @@
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import gradio as gr
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from PIL import Image
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import xml.etree.ElementTree as ET
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import os
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# --- Helper Functions ---
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@@ -34,16 +52,12 @@ def parse_alto_xml_for_text(xml_file_path):
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tree = ET.parse(xml_file_path)
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root = tree.getroot()
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# Find all TextLine elements
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for text_line in root.findall(f'.//{ns_prefix}TextLine'):
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line_text_parts = []
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for string_element in text_line.findall(f'{ns_prefix}String'):
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text = string_element.get('CONTENT')
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if text:
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line_text_parts.append(text)
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# Also consider <SP/> (Space) elements if they contribute to word separation
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# and are not implicitly handled by joining CONTENT attributes.
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# For now, just joining CONTENT attributes.
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if line_text_parts:
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full_text_lines.append(" ".join(line_text_parts))
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@@ -54,80 +68,148 @@ def parse_alto_xml_for_text(xml_file_path):
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except Exception as e:
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return f"An unexpected error occurred during XML parsing: {e}"
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# --- Gradio Interface Function ---
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def
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"""
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Main function for the Gradio interface.
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Processes the image
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"""
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if xml_path is None: # If XML is missing, but image is present
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return img_pil, "Please upload an OCR XML file."
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extracted_text = parse_alto_xml_for_text(xml_path)
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return img_pil, extracted_text
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# --- Create Gradio App ---
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("# OCR Viewer
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gr.Markdown(
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"Upload an image
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"
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)
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with gr.Row():
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with gr.Column(scale=1):
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image_input = gr.File(label="Upload Image (PNG, JPG, etc.)", type="filepath")
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xml_input = gr.File(label="Upload ALTO XML File (.xml)", type="filepath")
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submit_button = gr.Button("Process Files", variant="primary")
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with gr.Row():
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with gr.Column(scale=1):
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with gr.Column(scale=1):
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# output_image_overlay has been removed
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def update_interface(image_filepath, xml_filepath):
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# image_filepath and xml_filepath are now strings (paths) or None
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if image_filepath is None and xml_filepath is None:
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return None, "Please upload an image and an XML file."
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# process_image_and_xml handles cases where one is None
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img, text = process_image_and_xml(image_filepath, xml_filepath)
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return img, text
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submit_button.click(
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fn=
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inputs=[image_input, xml_input],
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outputs=[
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)
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# The .change event for show_overlay_checkbox has been removed
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gr.Markdown("---")
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gr.Markdown("### Example ALTO XML Snippet (for `String` element extraction):")
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gr.Code(
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value=
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<alto xmlns="http://www.loc.gov/standards/alto/v3/alto.xsd">
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<Description>...</Description>
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<Styles>...</Styles>
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<Layout>
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</PrintSpace>
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</Page>
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</Layout>
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</alto>
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interactive=False
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)
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if __name__ == "__main__":
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img_test.save("dummy_image.png")
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print("Created dummy_image.png for testing.")
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# Ensure the example XML file (189819724.34.xml) exists in the same directory
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# or provide the correct path if it's elsewhere.
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example_xml_filename = "189819724.34.xml"
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if not os.path.exists(example_xml_filename):
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print(f"WARNING: Example XML '{example_xml_filename}' not found. Please create it (using the content from the prompt) or upload your own.")
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except ImportError:
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print("Pillow not installed, can't create dummy image.")
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except Exception as e:
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print(f"Error during setup: {e}")
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demo.launch()
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import gradio as gr
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from PIL import Image
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import xml.etree.ElementTree as ET
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import os
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import torch
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from transformers import AutoProcessor, AutoModelForImageTextToText, pipeline
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# --- Global Model and Processor Initialization ---
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# Load the OCR model and processor once when the app starts
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try:
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HF_PROCESSOR = AutoProcessor.from_pretrained("reducto/RolmOCR")
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HF_MODEL = AutoModelForImageTextToText.from_pretrained(
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"reducto/RolmOCR",
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torch_dtype=torch.bfloat16,
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# attn_implementation="flash_attention_2", # User had this commented out
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device_map="auto"
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)
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HF_PIPE = pipeline("image-text-to-text", model=HF_MODEL, processor=HF_PROCESSOR)
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print("Hugging Face OCR model loaded successfully.")
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except Exception as e:
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print(f"Error loading Hugging Face model: {e}")
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HF_PIPE = None
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# --- Helper Functions ---
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tree = ET.parse(xml_file_path)
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root = tree.getroot()
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for text_line in root.findall(f'.//{ns_prefix}TextLine'):
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line_text_parts = []
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for string_element in text_line.findall(f'{ns_prefix}String'):
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text = string_element.get('CONTENT')
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if text:
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line_text_parts.append(text)
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if line_text_parts:
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full_text_lines.append(" ".join(line_text_parts))
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except Exception as e:
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return f"An unexpected error occurred during XML parsing: {e}"
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def run_hf_ocr(image_path):
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"""
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Runs OCR on the provided image using the pre-loaded Hugging Face model.
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"""
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if HF_PIPE is None:
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return "Hugging Face OCR model not available."
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if image_path is None:
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return "No image provided for OCR."
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try:
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# Load the image using PIL, as the pipeline expects an image object or path
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pil_image = Image.open(image_path).convert("RGB")
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# The user's example output for the pipeline call was:
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# [{'generated_text': [{'role': 'user', ...}, {'role': 'assistant', 'content': "TEXT..."}]}]
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# This suggests the pipeline is returning a conversational style output.
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# We will try to call the pipeline with the image and prompt directly.
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ocr_results = HF_PIPE(
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pil_image,
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prompt="Return the plain text representation of this document as if you were reading it naturally.\n"
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# The pipeline should handle formatting this into messages if needed by the model.
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)
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# Parse the output based on the user's example structure
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if isinstance(ocr_results, list) and ocr_results and 'generated_text' in ocr_results[0]:
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generated_content = ocr_results[0]['generated_text']
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# Check if generated_content itself is the direct text (some pipelines do this)
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if isinstance(generated_content, str):
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return generated_content
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# Check for the conversational structure
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# [{'role': 'user', ...}, {'role': 'assistant', 'content': "TEXT..."}]
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if isinstance(generated_content, list) and generated_content:
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# The assistant's response is typically the last message in the list
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# or specifically the one with role 'assistant'.
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assistant_message = None
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for msg in reversed(generated_content): # Check from the end
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if isinstance(msg, dict) and msg.get('role') == 'assistant' and 'content' in msg:
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assistant_message = msg['content']
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break
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if assistant_message:
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return assistant_message
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# Fallback if parsing the complex structure fails but we got some string
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if isinstance(generated_content, list) and generated_content and isinstance(generated_content[0], dict) and 'content' in generated_content[0]:
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# This is a guess if the structure is simpler than expected.
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# Or if the first part is the user prompt echo and second is assistant.
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if len(generated_content) > 1 and isinstance(generated_content[1], dict) and 'content' in generated_content[1]:
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return generated_content[1]['content'] # Assuming second part is assistant
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print(f"Unexpected OCR output structure from HF model: {ocr_results}")
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return "Error: Could not parse OCR model output. Please check console for details."
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else:
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print(f"Unexpected OCR output structure from HF model: {ocr_results}")
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return "Error: OCR model did not return expected output. Please check console for details."
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except Exception as e:
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print(f"Error during Hugging Face OCR: {e}")
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return f"Error during Hugging Face OCR: {str(e)}"
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# --- Gradio Interface Function ---
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def process_files(image_path, xml_path):
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"""
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Main function for the Gradio interface.
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Processes the image for display, runs OCR (Hugging Face model),
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and parses ALTO XML if provided.
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"""
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img_to_display = None
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alto_text_output = "ALTO XML not provided or not processed."
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hf_ocr_text_output = "Image not provided or OCR not run."
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if image_path:
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try:
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img_to_display = Image.open(image_path).convert("RGB")
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hf_ocr_text_output = run_hf_ocr(image_path)
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except Exception as e:
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img_to_display = None # Clear image if it failed to load
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hf_ocr_text_output = f"Error loading image or running HF OCR: {e}"
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else:
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hf_ocr_text_output = "Please upload an image to perform OCR."
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if xml_path:
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alto_text_output = parse_alto_xml_for_text(xml_path)
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else:
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alto_text_output = "No ALTO XML file uploaded."
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# If only XML is provided without an image
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if not image_path and xml_path:
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img_to_display = None # No image to display
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hf_ocr_text_output = "Upload an image to perform OCR."
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return img_to_display, alto_text_output, hf_ocr_text_output
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# --- Create Gradio App ---
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("# OCR Viewer and Extractor")
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gr.Markdown(
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"Upload an image to perform OCR using a Hugging Face model. "
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"Optionally, upload its corresponding ALTO OCR XML file to compare the extracted text."
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)
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with gr.Row():
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with gr.Column(scale=1):
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image_input = gr.File(label="Upload Image (PNG, JPG, etc.)", type="filepath")
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xml_input = gr.File(label="Upload ALTO XML File (Optional, .xml)", type="filepath")
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submit_button = gr.Button("Process Image and XML", variant="primary")
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with gr.Row():
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with gr.Column(scale=1):
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output_image_display = gr.Image(label="Uploaded Image", type="pil", interactive=False)
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with gr.Column(scale=1):
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hf_ocr_output_textbox = gr.Textbox(
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label="OCR Output (Hugging Face Model)",
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lines=15,
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interactive=False,
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show_copy_button=True
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)
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alto_xml_output_textbox = gr.Textbox(
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label="Text from ALTO XML",
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lines=15,
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interactive=False,
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show_copy_button=True
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)
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submit_button.click(
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fn=process_files,
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inputs=[image_input, xml_input],
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outputs=[output_image_display, alto_xml_output_textbox, hf_ocr_output_textbox]
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)
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gr.Markdown("---")
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gr.Markdown("### Example ALTO XML Snippet (for `String` element extraction):")
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gr.Code(
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value=(
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"""<alto xmlns="http://www.loc.gov/standards/alto/v3/alto.xsd">
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<Description>...</Description>
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<Styles>...</Styles>
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<Layout>
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</PrintSpace>
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</Page>
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</Layout>
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</alto>"""
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),
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language="xml", # Added language for syntax highlighting
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interactive=False
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)
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if __name__ == "__main__":
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# Removed dummy file creation as it's less relevant for single file focus
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print("Attempting to launch Gradio demo...")
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print("If the Hugging Face model is large, initial startup might take some time due to model download/loading.")
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demo.launch()
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requirements.in
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gradio
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Pillow
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lxml
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torch
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transformers
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spaces
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requirements.txt
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|
1 |
+
# This file was autogenerated by uv via the following command:
|
2 |
+
# uv pip compile --python-platform linux --python-version 3.10 requirements.in -o requirements.txt
|
3 |
+
aiofiles==24.1.0
|
4 |
+
# via gradio
|
5 |
+
annotated-types==0.7.0
|
6 |
+
# via pydantic
|
7 |
+
anyio==4.9.0
|
8 |
+
# via
|
9 |
+
# gradio
|
10 |
+
# httpx
|
11 |
+
# starlette
|
12 |
+
certifi==2025.4.26
|
13 |
+
# via
|
14 |
+
# httpcore
|
15 |
+
# httpx
|
16 |
+
# requests
|
17 |
+
charset-normalizer==3.4.2
|
18 |
+
# via requests
|
19 |
+
click==8.1.8
|
20 |
+
# via
|
21 |
+
# typer
|
22 |
+
# uvicorn
|
23 |
+
exceptiongroup==1.3.0
|
24 |
+
# via anyio
|
25 |
+
fastapi==0.115.12
|
26 |
+
# via gradio
|
27 |
+
ffmpy==0.5.0
|
28 |
+
# via gradio
|
29 |
+
filelock==3.18.0
|
30 |
+
# via
|
31 |
+
# huggingface-hub
|
32 |
+
# torch
|
33 |
+
# transformers
|
34 |
+
fsspec==2025.5.0
|
35 |
+
# via
|
36 |
+
# gradio-client
|
37 |
+
# huggingface-hub
|
38 |
+
# torch
|
39 |
+
gradio==5.30.0
|
40 |
+
# via
|
41 |
+
# -r requirements.in
|
42 |
+
# spaces
|
43 |
+
gradio-client==1.10.1
|
44 |
+
# via gradio
|
45 |
+
groovy==0.1.2
|
46 |
+
# via gradio
|
47 |
+
h11==0.16.0
|
48 |
+
# via
|
49 |
+
# httpcore
|
50 |
+
# uvicorn
|
51 |
+
httpcore==1.0.9
|
52 |
+
# via httpx
|
53 |
+
httpx==0.28.1
|
54 |
+
# via
|
55 |
+
# gradio
|
56 |
+
# gradio-client
|
57 |
+
# safehttpx
|
58 |
+
# spaces
|
59 |
+
huggingface-hub==0.31.4
|
60 |
+
# via
|
61 |
+
# gradio
|
62 |
+
# gradio-client
|
63 |
+
# tokenizers
|
64 |
+
# transformers
|
65 |
+
idna==3.10
|
66 |
+
# via
|
67 |
+
# anyio
|
68 |
+
# httpx
|
69 |
+
# requests
|
70 |
+
jinja2==3.1.6
|
71 |
+
# via
|
72 |
+
# gradio
|
73 |
+
# torch
|
74 |
+
lxml==5.4.0
|
75 |
+
# via -r requirements.in
|
76 |
+
markdown-it-py==3.0.0
|
77 |
+
# via rich
|
78 |
+
markupsafe==3.0.2
|
79 |
+
# via
|
80 |
+
# gradio
|
81 |
+
# jinja2
|
82 |
+
mdurl==0.1.2
|
83 |
+
# via markdown-it-py
|
84 |
+
mpmath==1.3.0
|
85 |
+
# via sympy
|
86 |
+
networkx==3.4.2
|
87 |
+
# via torch
|
88 |
+
numpy==2.2.6
|
89 |
+
# via
|
90 |
+
# gradio
|
91 |
+
# pandas
|
92 |
+
# transformers
|
93 |
+
nvidia-cublas-cu12==12.4.5.8
|
94 |
+
# via
|
95 |
+
# nvidia-cudnn-cu12
|
96 |
+
# nvidia-cusolver-cu12
|
97 |
+
# torch
|
98 |
+
nvidia-cuda-cupti-cu12==12.4.127
|
99 |
+
# via torch
|
100 |
+
nvidia-cuda-nvrtc-cu12==12.4.127
|
101 |
+
# via torch
|
102 |
+
nvidia-cuda-runtime-cu12==12.4.127
|
103 |
+
# via torch
|
104 |
+
nvidia-cudnn-cu12==9.1.0.70
|
105 |
+
# via torch
|
106 |
+
nvidia-cufft-cu12==11.2.1.3
|
107 |
+
# via torch
|
108 |
+
nvidia-curand-cu12==10.3.5.147
|
109 |
+
# via torch
|
110 |
+
nvidia-cusolver-cu12==11.6.1.9
|
111 |
+
# via torch
|
112 |
+
nvidia-cusparse-cu12==12.3.1.170
|
113 |
+
# via
|
114 |
+
# nvidia-cusolver-cu12
|
115 |
+
# torch
|
116 |
+
nvidia-cusparselt-cu12==0.6.2
|
117 |
+
# via torch
|
118 |
+
nvidia-nccl-cu12==2.21.5
|
119 |
+
# via torch
|
120 |
+
nvidia-nvjitlink-cu12==12.4.127
|
121 |
+
# via
|
122 |
+
# nvidia-cusolver-cu12
|
123 |
+
# nvidia-cusparse-cu12
|
124 |
+
# torch
|
125 |
+
nvidia-nvtx-cu12==12.4.127
|
126 |
+
# via torch
|
127 |
+
orjson==3.10.18
|
128 |
+
# via gradio
|
129 |
+
packaging==25.0
|
130 |
+
# via
|
131 |
+
# gradio
|
132 |
+
# gradio-client
|
133 |
+
# huggingface-hub
|
134 |
+
# spaces
|
135 |
+
# transformers
|
136 |
+
pandas==2.2.3
|
137 |
+
# via gradio
|
138 |
+
pillow==11.2.1
|
139 |
+
# via
|
140 |
+
# -r requirements.in
|
141 |
+
# gradio
|
142 |
+
psutil==5.9.8
|
143 |
+
# via spaces
|
144 |
+
pydantic==2.11.4
|
145 |
+
# via
|
146 |
+
# fastapi
|
147 |
+
# gradio
|
148 |
+
# spaces
|
149 |
+
pydantic-core==2.33.2
|
150 |
+
# via pydantic
|
151 |
+
pydub==0.25.1
|
152 |
+
# via gradio
|
153 |
+
pygments==2.19.1
|
154 |
+
# via rich
|
155 |
+
python-dateutil==2.9.0.post0
|
156 |
+
# via pandas
|
157 |
+
python-multipart==0.0.20
|
158 |
+
# via gradio
|
159 |
+
pytz==2025.2
|
160 |
+
# via pandas
|
161 |
+
pyyaml==6.0.2
|
162 |
+
# via
|
163 |
+
# gradio
|
164 |
+
# huggingface-hub
|
165 |
+
# transformers
|
166 |
+
regex==2024.11.6
|
167 |
+
# via transformers
|
168 |
+
requests==2.32.3
|
169 |
+
# via
|
170 |
+
# huggingface-hub
|
171 |
+
# spaces
|
172 |
+
# transformers
|
173 |
+
rich==14.0.0
|
174 |
+
# via typer
|
175 |
+
ruff==0.11.10
|
176 |
+
# via gradio
|
177 |
+
safehttpx==0.1.6
|
178 |
+
# via gradio
|
179 |
+
safetensors==0.5.3
|
180 |
+
# via transformers
|
181 |
+
semantic-version==2.10.0
|
182 |
+
# via gradio
|
183 |
+
shellingham==1.5.4
|
184 |
+
# via typer
|
185 |
+
six==1.17.0
|
186 |
+
# via python-dateutil
|
187 |
+
sniffio==1.3.1
|
188 |
+
# via anyio
|
189 |
+
spaces==0.36.0
|
190 |
+
# via -r requirements.in
|
191 |
+
starlette==0.46.2
|
192 |
+
# via
|
193 |
+
# fastapi
|
194 |
+
# gradio
|
195 |
+
sympy==1.13.1
|
196 |
+
# via torch
|
197 |
+
tokenizers==0.21.1
|
198 |
+
# via transformers
|
199 |
+
tomlkit==0.13.2
|
200 |
+
# via gradio
|
201 |
+
torch==2.6.0
|
202 |
+
# via -r requirements.in
|
203 |
+
tqdm==4.67.1
|
204 |
+
# via
|
205 |
+
# huggingface-hub
|
206 |
+
# transformers
|
207 |
+
transformers==4.52.2
|
208 |
+
# via -r requirements.in
|
209 |
+
triton==3.2.0
|
210 |
+
# via torch
|
211 |
+
typer==0.15.4
|
212 |
+
# via gradio
|
213 |
+
typing-extensions==4.13.2
|
214 |
+
# via
|
215 |
+
# anyio
|
216 |
+
# exceptiongroup
|
217 |
+
# fastapi
|
218 |
+
# gradio
|
219 |
+
# gradio-client
|
220 |
+
# huggingface-hub
|
221 |
+
# pydantic
|
222 |
+
# pydantic-core
|
223 |
+
# rich
|
224 |
+
# spaces
|
225 |
+
# torch
|
226 |
+
# typer
|
227 |
+
# typing-inspection
|
228 |
+
# uvicorn
|
229 |
+
typing-inspection==0.4.1
|
230 |
+
# via pydantic
|
231 |
+
tzdata==2025.2
|
232 |
+
# via pandas
|
233 |
+
urllib3==2.4.0
|
234 |
+
# via requests
|
235 |
+
uvicorn==0.34.2
|
236 |
+
# via gradio
|
237 |
+
websockets==15.0.1
|
238 |
+
# via gradio-client
|