app.py

import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

import functools
from PIL import Image, ImageDraw
import gradio as gr

import torch
from docquery.pipeline import get_pipeline
from docquery.document import load_bytes, load_document, ImageDocument


def ensure_list(x):
    if isinstance(x, list):
        return x
    else:
        return [x]


CHECKPOINTS = {
    "LayoutLMv1 🦉": "impira/layoutlm-document-qa",
    "Donut 🍩": "naver-clova-ix/donut-base-finetuned-docvqa",
}

PIPELINES = {}


def construct_pipeline(model):
    global PIPELINES
    if model in PIPELINES:
        return PIPELINES[model]

    device = "cuda" if torch.cuda.is_available() else "cpu"
    ret = get_pipeline(checkpoint=CHECKPOINTS[model], device=device)
    PIPELINES[model] = ret
    return ret


@functools.lru_cache(1024)
def run_pipeline(model, question, document, top_k):
    pipeline = construct_pipeline(model)
    return pipeline(question=question, **document.context, top_k=top_k)


# TODO: Move into docquery
# TODO: Support words past the first page (or window?)
def lift_word_boxes(document, page):
    return document.context["image"][page][1]


def expand_bbox(word_boxes):
    if len(word_boxes) == 0:
        return None

    min_x, min_y, max_x, max_y = zip(*[x[1] for x in word_boxes])
    min_x, min_y, max_x, max_y = [min(min_x), min(min_y), max(max_x), max(max_y)]
    return [min_x, min_y, max_x, max_y]


# LayoutLM boxes are normalized to 0, 1000
def normalize_bbox(box, width, height, padding=0.005):
    min_x, min_y, max_x, max_y = [c / 1000 for c in box]
    if padding != 0:
        min_x = max(0, min_x - padding)
        min_y = max(0, min_y - padding)
        max_x = min(max_x + padding, 1)
        max_y = min(max_y + padding, 1)
    return [min_x * width, min_y * height, max_x * width, max_y * height]


examples = [
    [
        "invoice.png",
        "What is the invoice number?",
    ],
    [
        "contract.jpeg",
        "What is the purchase amount?",
    ],
    [
        "statement.png",
        "What are net sales for 2020?",
    ],
]


def process_path(path):
    if path:
        try:
            document = load_document(path)
            return (
                document,
                gr.update(visible=True, value=document.preview),
                gr.update(visible=True),
                gr.update(visible=False, value=None),
            )
        except Exception:
            pass
    return (
        None,
        gr.update(visible=False, value=None),
        gr.update(visible=False),
        gr.update(visible=False, value=None),
    )


def process_upload(file):
    if file:
        return process_path(file.name)
    else:
        return (
            None,
            gr.update(visible=False, value=None),
            gr.update(visible=False),
            gr.update(visible=False, value=None),
        )


colors = ["#64A087", "green", "black"]


def process_question(question, document, model=list(CHECKPOINTS.keys())[0]):
    if document is None:
        return None, None

    predictions = run_pipeline(model, question, document, 3)
    pages = [x.copy().convert("RGB") for x in document.preview]
    for i, p in enumerate(ensure_list(predictions)):
        if i > 0:
            # Keep the code around to produce multiple boxes, but only show the top
            # prediction for now
            break

        if "start" in p and "end" in p:
            image = pages[p["page"]]
            draw = ImageDraw.Draw(image, "RGBA")
            x1, y1, x2, y2 = normalize_bbox(
                expand_bbox(
                    lift_word_boxes(document, p["page"])[p["start"] : p["end"] + 1]
                ),
                image.width,
                image.height,
            )
            draw.rectangle(((x1, y1), (x2, y2)), fill=(0, 255, 0, int(0.4 * 255)))

    return gr.update(visible=True, value=pages), gr.update(
        visible=True, value=predictions
    )


def load_example_document(img, question, model):
    if img is not None:
        document = ImageDocument(Image.fromarray(img))
        preview, answer = process_question(question, document, model)
        return document, question, preview, gr.update(visible=True), answer
    else:
        return None, None, None, gr.update(visible=False), None


CSS = """
#question input {
    font-size: 16px;
}
#url-textbox {
    padding: 0 !important;
}
#short-upload-box .w-full {
    min-height: 10rem !important;
}
/* I think something like this can be used to re-shape
 * the table
 */
/*
.gr-samples-table tr {
    display: inline;
}
.gr-samples-table .p-2 {
    width: 100px;
}
*/
#select-a-file {
    width: 100%;
}
#file-clear {
    padding-top: 2px !important;
    padding-bottom: 2px !important;
    padding-left: 8px !important;
    padding-right: 8px !important;
}
.gradio-container.light .gr-button-primary {
    background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%);
    border: 1px solid #B0DCCC;
    border-radius: 8px;
    color: #1B8700;
}
.gradio-container.dark button#submit-button {
    background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%);
    border: 1px solid #B0DCCC;
    border-radius: 8px;
    color: #1B8700
}
"""

with gr.Blocks(css=CSS) as demo:
    gr.Markdown("# DocQuery: Query Documents w/ NLP")
    gr.Markdown(
        "DocQuery uses LayoutLMv1 fine-tuned on DocVQA, a document visual question"
        " answering dataset, as well as SQuAD, which boosts its English-language comprehension."
        " To use it, simply upload an image or PDF, type a question, and click 'submit', or "
        " click one of the examples to load them."
        " [Github Repo](https://github.com/impira/docquery)"
    )

    document = gr.Variable()
    example_question = gr.Textbox(visible=False)
    example_image = gr.Image(visible=False)

    with gr.Row(equal_height=True):
        with gr.Column():
            with gr.Row():
                gr.Markdown("## 1. Select a file", elem_id="select-a-file")
                img_clear_button = gr.Button(
                    "Clear", variant="secondary", elem_id="file-clear", visible=False
                )
            image = gr.Gallery(visible=False)
            with gr.Row(equal_height=True):
                url = gr.Textbox(
                    show_label=False,
                    placeholder="URL",
                    lines=1,
                    max_lines=1,
                    elem_id="url-textbox",
                )
                submit = gr.Button("Get")
            gr.Markdown("— or —")
            upload = gr.File(
                label=" - or -", interactive=True, elem_id="short-upload-box"
            )
            gr.Examples(
                examples=examples,
                inputs=[example_image, example_question],
            )

        with gr.Column() as col:
            gr.Markdown("## 2. Ask a question")
            question = gr.Textbox(
                label="Question",
                placeholder="e.g. What is the invoice number?",
                lines=1,
                max_lines=1,
            )
            model = gr.Radio(
                choices=list(CHECKPOINTS.keys()),
                value=list(CHECKPOINTS.keys())[0],
                label="Model",
            )

            with gr.Row():
                clear_button = gr.Button("Clear", variant="secondary")
                submit_button = gr.Button(
                    "Submit", variant="primary", elem_id="submit-button"
                )
            with gr.Column():
                output = gr.JSON(label="Output", visible=False)

    img_clear_button.click(
        lambda _: (
            gr.update(visible=False, value=None),
            None,
            gr.update(visible=False, value=None),
            gr.update(visible=False),
            None,
            None,
            None,
        ),
        inputs=img_clear_button,
        outputs=[image, document, output, img_clear_button, example_image, upload, url],
    )
    clear_button.click(
        lambda _: (
            gr.update(visible=False, value=None),
            None,
            None,
            gr.update(visible=False, value=None),
            None,
            None,
            None,
        ),
        inputs=clear_button,
        outputs=[image, document, question, output, example_image, upload, url],
    )

    upload.change(
        fn=process_upload,
        inputs=[upload],
        outputs=[document, image, img_clear_button, output],
    )
    url.change(
        fn=process_path,
        inputs=[url],
        outputs=[document, image, img_clear_button, output],
    )

    question.submit(
        fn=process_question,
        inputs=[question, document, model],
        outputs=[image, output],
    )

    submit_button.click(
        process_question,
        inputs=[question, document, model],
        outputs=[image, output],
    )

    model.change(
        process_question, inputs=[question, document, model], outputs=[image, output]
    )

    example_image.change(
        fn=load_example_document,
        inputs=[example_image, example_question, model],
        outputs=[document, question, image, img_clear_button, output],
    )

if __name__ == "__main__":
    demo.launch()