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import os
import shutil
import gradio as gr
from helper.examples.examples import DemoImages
from helper.gradio_config import css, js, theme
from helper.text import TextAbout, TextApp, TextHowTo, TextRiksarkivet, TextRoadmap
from htr_tool import htr_tool_tab
from src.htr_pipeline.gradio_backend import CustomTrack, SingletonModelLoader
model_loader = SingletonModelLoader()
custom_track = CustomTrack(model_loader)
images_for_demo = DemoImages()
with gr.Blocks(title="HTR Riksarkivet", theme=theme, css=css) as demo:
gr.Markdown(TextApp.title_markdown)
with gr.Tabs():
with gr.Tab("HTR Tool"):
htr_tool_tab.render()
with gr.Tab("Stepwise HTR Tool"):
with gr.Tabs():
with gr.Tab("1. Region Segmentation"):
with gr.Row():
with gr.Column(scale=2):
vis_data_folder_placeholder = gr.Markdown(visible=False)
name_files_placeholder = gr.Markdown(visible=False)
with gr.Row():
input_region_image = gr.Image(
label="Image to Region segment",
# type="numpy",
tool="editor",
).style(height=350)
with gr.Accordion("Region segment settings:", open=False):
with gr.Row():
reg_pred_score_threshold_slider = gr.Slider(
minimum=0.4,
maximum=1,
value=0.5,
step=0.05,
label="P-threshold",
info="""Filter and determine the confidence score
required for a prediction score to be considered""",
)
reg_containments_threshold_slider = gr.Slider(
minimum=0,
maximum=1,
value=0.5,
step=0.05,
label="C-threshold",
info="""The minimum required overlap or similarity
for a detected region or object to be considered valid""",
)
with gr.Row():
region_segment_model_dropdown = gr.Dropdown(
choices=["Riksarkivet/RmtDet_region"],
value="Riksarkivet/RmtDet_region",
label="Region segment model",
info="Will add more models later!",
)
with gr.Row():
clear_button = gr.Button("Clear", variant="secondary", elem_id="clear_button")
region_segment_button = gr.Button(
"Segment Region",
variant="primary",
elem_id="region_segment_button",
) # .style(full_width=False)
with gr.Row():
with gr.Accordion("Example images to use:", open=False) as example_accord:
gr.Examples(
examples=images_for_demo.examples_list,
inputs=[name_files_placeholder, input_region_image],
label="Example images",
examples_per_page=2,
)
with gr.Column(scale=3):
output_region_image = gr.Image(label="Segmented regions", type="numpy").style(height=600)
##############################################
with gr.Tab("2. Line Segmentation"):
image_placeholder_lines = gr.Image(
label="Segmented lines",
# type="numpy",
interactive="False",
visible=True,
).style(height=600)
with gr.Row(visible=False) as control_line_segment:
with gr.Column(scale=2):
with gr.Box():
regions_cropped_gallery = gr.Gallery(
label="Segmented regions",
show_label=False,
elem_id="gallery",
).style(
columns=[2],
rows=[2],
# object_fit="contain",
height=400,
preview=True,
container=False,
)
input_region_from_gallery = gr.Image(
label="Region segmentation to line segment", interactive="False", visible=False
).style(height=400)
with gr.Row():
with gr.Accordion("Line segment settings:", open=False):
with gr.Row():
line_pred_score_threshold_slider = gr.Slider(
minimum=0.3,
maximum=1,
value=0.4,
step=0.05,
label="Pred_score threshold",
info="""Filter and determine the confidence score
required for a prediction score to be considered""",
)
line_containments_threshold_slider = gr.Slider(
minimum=0,
maximum=1,
value=0.5,
step=0.05,
label="Containments threshold",
info="""The minimum required overlap or similarity
for a detected region or object to be considered valid""",
)
with gr.Row().style(equal_height=False):
line_segment_model_dropdown = gr.Dropdown(
choices=["Riksarkivet/RmtDet_lines"],
value="Riksarkivet/RmtDet_lines",
label="Line segment model",
info="Will add more models later!",
)
with gr.Row():
clear_line_segment_button = gr.Button(
" ",
variant="Secondary",
# elem_id="center_button",
).style(full_width=True)
line_segment_button = gr.Button(
"Segment Lines",
variant="primary",
# elem_id="center_button",
).style(full_width=True)
with gr.Column(scale=3):
# gr.Markdown("""lorem ipsum""")
output_line_from_region = gr.Image(
label="Segmented lines",
type="numpy",
interactive="False",
).style(height=600)
###############################################
with gr.Tab("3. Transcribe Text"):
image_placeholder_htr = gr.Image(
label="Transcribed lines",
# type="numpy",
interactive="False",
visible=True,
).style(height=600)
with gr.Row(visible=False) as control_htr:
inputs_lines_to_transcribe = gr.Variable()
with gr.Column(scale=2):
image_inputs_lines_to_transcribe = gr.Image(
label="Transcribed lines",
type="numpy",
interactive="False",
visible=False,
).style(height=470)
with gr.Row():
with gr.Accordion("Transcribe settings:", open=False):
transcriber_model = gr.Dropdown(
choices=["Riksarkivet/SATRN_transcriber", "microsoft/trocr-base-handwritten"],
value="Riksarkivet/SATRN_transcriber",
label="Transcriber model",
info="Will add more models later!",
)
with gr.Row():
clear_transcribe_button = gr.Button(" ", variant="Secondary", visible=True).style(
full_width=True
)
transcribe_button = gr.Button(
"Transcribe lines", variant="primary", visible=True
).style(full_width=True)
donwload_txt_button = gr.Button(
"Download text", variant="secondary", visible=False
).style(full_width=True)
with gr.Row():
txt_file_downlod = gr.File(label="Download text", visible=False)
with gr.Column(scale=3):
with gr.Row():
transcribed_text_df = gr.Dataframe(
headers=["Transcribed text"],
max_rows=15,
col_count=(1, "fixed"),
wrap=True,
interactive=False,
overflow_row_behaviour="paginate",
).style(height=600)
#####################################
with gr.Tab("4. Explore Results"):
image_placeholder_explore_results = gr.Image(
label="Cropped transcribed lines",
# type="numpy",
interactive="False",
visible=True,
).style(height=600)
with gr.Row(visible=False) as control_results_transcribe:
with gr.Column(scale=1, visible=True):
with gr.Box():
temp_gallery_input = gr.Variable()
gallery_inputs_lines_to_transcribe = gr.Gallery(
label="Cropped transcribed lines",
show_label=True,
elem_id="gallery_lines",
).style(
columns=[3],
rows=[3],
# object_fit="contain",
# height="600",
preview=True,
container=False,
)
with gr.Column(scale=1, visible=True):
mapping_dict = gr.Variable()
transcribed_text_df_finish = gr.Dataframe(
headers=["Transcribed text", "HTR prediction score"],
max_rows=15,
col_count=(2, "fixed"),
wrap=True,
interactive=False,
overflow_row_behaviour="paginate",
).style(height=600)
with gr.Tab("How to use"):
with gr.Tabs():
with gr.Tab("HTR Tool"):
with gr.Row().style(equal_height=False):
with gr.Column():
gr.Markdown(TextHowTo.htr_tool)
with gr.Column():
gr.Markdown(TextHowTo.both_htr_tool_video)
gr.Video(
value="https://github.com/Borg93/htr_gradio_file_placeholder/raw/main/eating_spaghetti.mp4",
label="How to use HTR Tool",
)
gr.Markdown(TextHowTo.reach_out)
with gr.Tab("Stepwise HTR Tool"):
with gr.Row().style(equal_height=False):
with gr.Column():
gr.Markdown(TextHowTo.stepwise_htr_tool)
with gr.Row():
with gr.Accordion("The tabs for the Stepwise HTR Tool:", open=False):
with gr.Tabs():
with gr.Tab("1. Region Segmentation"):
gr.Markdown(TextHowTo.stepwise_htr_tool_tab1)
with gr.Tab("2. Line Segmentation"):
gr.Markdown(TextHowTo.stepwise_htr_tool_tab2)
with gr.Tab("3. Transcribe Text"):
gr.Markdown(TextHowTo.stepwise_htr_tool_tab3)
with gr.Tab("4. Explore Results"):
gr.Markdown(TextHowTo.stepwise_htr_tool_tab4)
gr.Markdown(TextHowTo.stepwise_htr_tool_end)
with gr.Column():
gr.Markdown(TextHowTo.both_htr_tool_video)
gr.Video(
value="https://github.com/Borg93/htr_gradio_file_placeholder/raw/main/eating_spaghetti.mp4",
label="How to use Stepwise HTR Tool",
)
gr.Markdown(TextHowTo.reach_out)
with gr.Tab("About"):
with gr.Tabs():
with gr.Tab("Project"):
with gr.Row():
with gr.Column():
gr.Markdown(TextAbout.intro_and_pipeline_overview_text)
with gr.Row():
with gr.Tabs():
with gr.Tab("I. Binarization"):
gr.Markdown(TextAbout.binarization)
with gr.Tab("II. Region Segmentation"):
gr.Markdown(TextAbout.text_region_segment)
with gr.Tab("III. Line Segmentation"):
gr.Markdown(TextAbout.text_line_segmentation)
with gr.Tab("IV. Transcriber"):
gr.Markdown(TextAbout.text_htr)
with gr.Row():
gr.Markdown(TextAbout.text_data)
with gr.Column():
gr.Markdown(TextAbout.filler_text_data)
gr.Markdown(TextAbout.text_models)
with gr.Row():
with gr.Tabs():
with gr.Tab("Region Segmentation"):
gr.Markdown(TextAbout.text_models_region)
with gr.Tab("Line Segmentation"):
gr.Markdown(TextAbout.text_line_segmentation)
with gr.Tab("Transcriber"):
gr.Markdown(TextAbout.text_models_htr)
with gr.Tab("Roadmap"):
with gr.Row():
with gr.Column():
gr.Markdown(TextRoadmap.roadmap)
with gr.Column():
gr.Markdown(TextRoadmap.notebook)
with gr.Tab("Riksarkivet"):
with gr.Row():
gr.Markdown(TextRiksarkivet.riksarkivet)
# callback.setup([fast_track_input_region_image], "flagged_data_points")
# flagging_button.click(lambda *args: callback.flag(args), [fast_track_input_region_image], None, preprocess=False)
# flagging_button.click(lambda: (gr.update(value="Flagged")), outputs=flagging_button)
# fast_track_input_region_image.change(lambda: (gr.update(value="Flag")), outputs=flagging_button)
# custom track
region_segment_button.click(
custom_track.region_segment,
inputs=[input_region_image, reg_pred_score_threshold_slider, reg_containments_threshold_slider],
outputs=[output_region_image, regions_cropped_gallery, image_placeholder_lines, control_line_segment],
)
regions_cropped_gallery.select(
custom_track.get_select_index_image, regions_cropped_gallery, input_region_from_gallery
)
transcribed_text_df_finish.select(
fn=custom_track.get_select_index_df,
inputs=[transcribed_text_df_finish, mapping_dict],
outputs=gallery_inputs_lines_to_transcribe,
)
line_segment_button.click(
custom_track.line_segment,
inputs=[input_region_from_gallery, line_pred_score_threshold_slider, line_containments_threshold_slider],
outputs=[
output_line_from_region,
image_inputs_lines_to_transcribe,
inputs_lines_to_transcribe,
gallery_inputs_lines_to_transcribe,
temp_gallery_input,
# Hide
transcribe_button,
image_inputs_lines_to_transcribe,
image_placeholder_htr,
control_htr,
],
)
transcribe_button.click(
custom_track.transcribe_text,
inputs=[transcribed_text_df, inputs_lines_to_transcribe],
outputs=[
transcribed_text_df,
transcribed_text_df_finish,
mapping_dict,
txt_file_downlod,
control_results_transcribe,
image_placeholder_explore_results,
],
)
donwload_txt_button.click(
custom_track.download_df_to_txt,
inputs=transcribed_text_df,
outputs=[txt_file_downlod, txt_file_downlod],
)
# def remove_temp_vis():
# if os.path.exists("./vis_data"):
# os.remove("././vis_data")
# return None
clear_button.click(
lambda: (
(shutil.rmtree("./vis_data") if os.path.exists("./vis_data") else None, None)[1],
None,
None,
None,
gr.update(visible=False),
None,
None,
None,
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=True),
None,
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=True),
gr.update(visible=True),
),
inputs=[],
outputs=[
vis_data_folder_placeholder,
input_region_image,
regions_cropped_gallery,
input_region_from_gallery,
control_line_segment,
output_line_from_region,
inputs_lines_to_transcribe,
transcribed_text_df,
control_htr,
inputs_lines_to_transcribe,
image_placeholder_htr,
output_region_image,
image_inputs_lines_to_transcribe,
control_results_transcribe,
image_placeholder_explore_results,
image_placeholder_lines,
],
)
demo.load(None, None, None, _js=js)
demo.queue(concurrency_count=5, max_size=20)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False, show_error=True)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False, show_error=True)
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