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import traceback
import gradio as gr
from utils.get_RGB_image import get_RGB_image, is_online_file, steam_online_file
from pdf2image import convert_from_path, convert_from_bytes
import layoutparser as lp
from PIL import Image
from utils.get_features import get_features
from imagehash import average_hash
from sklearn.metrics.pairwise import cosine_similarity
label_map = {0: 'Caption', 1: 'Footnote', 2: 'Formula', 3: 'List-item', 4: 'Page-footer', 5: 'Page-header', 6: 'Picture', 7: 'Section-header', 8: 'Table', 9: 'Text', 10: 'Title'}
label_names = list(label_map.values())
color_map = {'Caption': '#acc2d9', 'Footnote': '#56ae57', 'Formula': '#b2996e', 'List-item': '#a8ff04', 'Page-footer': '#69d84f', 'Page-header': '#894585', 'Picture': '#70b23f', 'Section-header': '#d4ffff', 'Table': '#65ab7c', 'Text': '#952e8f', 'Title': '#fcfc81'}
cache = {
'document_image_1_hash': None,
'document_image_2_hash': None,
'document_image_1_features': None,
'document_image_2_features': None,
}
pre_message_style = 'overflow: auto;border: 2px solid pink;padding: 4px;'
def similarity_fn(document_image_1: Image.Image, document_image_2: Image.Image, model: lp.Detectron2LayoutModel):
message = None
try:
document_image_1_hash = str(average_hash(document_image_1))
document_image_2_hash = str(average_hash(document_image_2))
if document_image_1_hash == cache['document_image_1_hash']:
document_image_1_features = cache['document_image_1_features']
else:
document_image_1_features = get_features(document_image_1, model, label_names)
cache['document_image_1_hash'] = document_image_1_hash
cache['document_image_1_features'] = document_image_1_features
if document_image_2_hash == cache['document_image_2_hash']:
document_image_2_features = cache['document_image_2_features']
else:
document_image_2_features = get_features(document_image_2, model, label_names)
cache['document_image_2_hash'] = document_image_2_hash
cache['document_image_2_features'] = document_image_2_features
[[similarity]] = cosine_similarity(
[
cache['document_image_1_features']['vectors']
],
[
cache['document_image_2_features']['vectors']
])
message = f'<pre style="{pre_message_style}">Similarity between the two documents is: {similarity}<pre>'
except Exception as e:
message = f'<pre style="{pre_message_style}">{traceback.format_exc()}<pre>'
return gr.HTML(message, visible=True)
def load_image(filename, page = 0):
try:
image = None
try:
if (is_online_file(filename)):
image = get_RGB_image(convert_from_bytes(steam_online_file(filename))[page])
else:
image = get_RGB_image(convert_from_path(filename)[page])
except:
image = get_RGB_image(filename)
return [
gr.Image(value=image, visible=True),
None
]
except:
error = traceback.format_exc()
return [None, gr.HTML(value=error, visible=True)]
def preview_url(url, page = 0):
[image, error] = load_image(url, page = page)
if image:
return [gr.Tabs(selected=0), image, error]
else:
return [gr.Tabs(selected=1), image, error]
def document_view(document_number: int):
gr.HTML(value=f'<h4>Load the {"first" if document_number == 1 else "second"} PDF or Document Image<h4>', elem_classes=['center'])
with gr.Tabs() as document_tabs:
with gr.Tab("From Image", id=0):
document = gr.Image(type="pil", label=f"Document {document_number}", visible=False)
document_error_message = gr.HTML(label="Error Message", visible=False)
document_preview = gr.UploadButton(
"Click to PDF or Document Image",
file_types=["image", ".pdf"],
file_count="single")
with gr.Tab("From URL", id=1):
document_url = gr.Textbox(
label=f"Document {document_number} URL",
info="Paste a Link/URL to PDF or Document Image",
placeholder="https://datasets-server.huggingface.co/.../image.jpg")
document_url_error_message = gr.HTML(label="Error Message", visible=False)
document_url_preview = gr.Button(value="Preview", variant="primary")
document_preview.upload(
fn = lambda file: load_image(file.name),
inputs = [document_preview],
outputs = [document, document_error_message])
document_url_preview.click(
fn = preview_url,
inputs = [document_url],
outputs = [document_tabs, document, document_url_error_message])
return document
def app(*, model_path, config_path, debug = False):
model: lp.Detectron2LayoutModel = lp.Detectron2LayoutModel(
config_path = config_path,
model_path = model_path,
label_map = label_map)
title = 'Document Similarity Search Using Visual Layout Features'
description = f"<h2>{title}<h2>"
css = '''
image { max-height="86vh" !important; }
.center { display: flex; flex: 1 1 auto; align-items: center; align-content: center; justify-content: center; justify-items: center; }
.hr { width: 100%; display: block; padding: 0; margin: 0; background: gray; height: 4px; border: none; }
'''
with gr.Blocks(title=title, css=css) as app:
with gr.Row():
gr.HTML(value=description, elem_classes=['center'])
with gr.Row(equal_height = False):
with gr.Column():
document_1_image = document_view(1)
with gr.Column():
document_2_image = document_view(2)
gr.HTML('<hr/>', elem_classes=['hr'])
with gr.Row(elem_classes=['center']):
with gr.Column():
submit = gr.Button(value="Similarity", variant="primary")
reset = gr.Button(value="Reset", variant="secondary")
with gr.Column():
similarity_output = gr.HTML(visible=False)
submit.click(
fn=lambda document_1_image, document_2_image: similarity_fn(
document_1_image,
document_2_image,
model),
inputs=[document_1_image, document_2_image],
outputs=[similarity_output])
return app.launch(debug=debug) |