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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()
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