Spaces:
Build error
Build error
File size: 10,615 Bytes
bc12901 2359223 e62060d 2359223 ab36703 bc12901 2359223 bc6a638 bc12901 bc6a638 225fcc2 2359223 225fcc2 2359223 8171e8e 225fcc2 2359223 8171e8e bc6a638 225fcc2 1af0b6d 87500f1 1af0b6d fcfd908 1af0b6d 0b2b653 1af0b6d fcfd908 bc12901 bc6a638 2359223 bc6a638 d229b67 2359223 15fad86 d207d63 15fad86 194858a 15fad86 2359223 e62060d 15fad86 d207d63 15fad86 194858a 15fad86 bc6a638 87ad231 2359223 15fad86 d207d63 15fad86 194858a 15fad86 225fcc2 bc6a638 0b2b653 bc6a638 2359223 194858a bc6a638 d1e1ea7 2359223 99d94a6 2359223 d1e1ea7 2359223 bc6a638 87500f1 2359223 87500f1 2359223 0b2b653 2359223 194858a d1e1ea7 194858a 15fad86 2359223 d207d63 194858a d207d63 a83fad4 2359223 27d0a44 d207d63 27d0a44 d207d63 3a4d71f d207d63 37a2f41 d207d63 27d0a44 37a2f41 3a4d71f 42081d7 27d0a44 194858a d207d63 b403624 d207d63 b403624 d207d63 2359223 d207d63 4472e08 d207d63 27d0a44 2359223 d207d63 2359223 d207d63 4472e08 d207d63 2359223 4472e08 15fad86 6d0b7db 194858a 6d0b7db 66c9f11 6d0b7db 194858a 6d0b7db bc6a638 177edb5 194858a 177edb5 2359223 194858a 2359223 bc6a638 177edb5 194858a 177edb5 d229b67 2359223 194858a 177edb5 2359223 e62060d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 |
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from PIL import Image, ImageDraw
import traceback
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
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),
gr.update(visible=False, value=None),
)
except Exception:
traceback.print_exc()
return (
None,
gr.update(visible=False, value=None),
gr.update(visible=False),
gr.update(visible=False, value=None),
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),
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, None
text_value = 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:
text_value = p["answer"]
else:
# 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),
gr.update(
visible=True,
value=text_value,
),
)
def load_example_document(img, question, model):
if img is not None:
document = ImageDocument(Image.fromarray(img))
preview, answer, answer_text = process_question(question, document, model)
return document, question, preview, gr.update(visible=True), answer, answer_text
else:
return None, None, None, gr.update(visible=False), None, 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;
margin-top: 10px;
}
.gradio-container .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
}
table.gr-samples-table tr td {
border: none;
outline: none;
}
table.gr-samples-table tr td:first-of-type {
width: 0%;
}
div#short-upload-box div.absolute {
display: none !important;
}
gradio-app > div > div > div > div.w-full > div, .gradio-app > div > div > div > div.w-full > div {
gap: 0px 2%;
}
gradio-app div div div div.w-full, .gradio-app div div div div.w-full {
gap: 0px;
}
gradio-app h2, .gradio-app h2 {
padding-top: 10px;
}
#answer {
overflow-y: scroll;
color: white;
background: #666;
border-color: #666;
font-size: 20px;
font-weight: bold;
}
#answer span {
color: white;
}
#answer textarea {
color:white;
background: #777;
border-color: #777;
font-size: 18px;
}
"""
with gr.Blocks(css=CSS) as demo:
gr.Markdown("# DocQuery: Document Query Engine")
gr.Markdown(
"DocQuery (created by [Impira](https://impira.com)) 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."
" DocQuery is MIT-licensed and available on [Github](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=None, 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_text = gr.Textbox(
label="Top Answer", visible=False, elem_id="answer"
)
output = gr.JSON(label="Output", visible=False)
for cb in [img_clear_button, clear_button]:
cb.click(
lambda _: (
gr.update(visible=False, value=None),
None,
gr.update(visible=False, value=None),
gr.update(visible=False, value=None),
gr.update(visible=False),
None,
None,
None,
None,
),
inputs=clear_button,
outputs=[
image,
document,
output,
output_text,
img_clear_button,
example_image,
upload,
url,
question,
],
)
upload.change(
fn=process_upload,
inputs=[upload],
outputs=[document, image, img_clear_button, output, output_text],
)
submit.click(
fn=process_path,
inputs=[url],
outputs=[document, image, img_clear_button, output, output_text],
)
question.submit(
fn=process_question,
inputs=[question, document, model],
outputs=[image, output, output_text],
)
submit_button.click(
process_question,
inputs=[question, document, model],
outputs=[image, output, output_text],
)
model.change(
process_question,
inputs=[question, document, model],
outputs=[image, output, output_text],
)
example_image.change(
fn=load_example_document,
inputs=[example_image, example_question, model],
outputs=[document, question, image, img_clear_button, output, output_text],
)
if __name__ == "__main__":
demo.launch(enable_queue=False)
|