Spaces:
Running
on
T4
Running
on
T4
File size: 4,175 Bytes
c9a1c2d deef83a c9a1c2d deef83a c9a1c2d deef83a |
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 |
import gradio as gr
from helper.examples.examples import DemoImages
from src.htr_pipeline.gradio_backend import FastTrack, SingletonModelLoader
model_loader = SingletonModelLoader()
fast_track = FastTrack(model_loader)
images_for_demo = DemoImages()
with gr.Blocks() as htr_tool_tab:
with gr.Row(equal_height=True):
with gr.Column(scale=2):
with gr.Row():
fast_track_input_region_image = gr.Image(
label="Image to run HTR on", type="numpy", tool="editor", elem_id="image_upload", height=395
)
with gr.Row():
# with gr.Group():
# callback = gr.CSVLogger()
# # hf_writer = gr.HuggingFaceDatasetSaver(HF_API_TOKEN, "htr_pipelin_flags")
# flagging_button = gr.Button(
# "Flag",
# variant="secondary",
# visible=True,
# ).style(full_width=True)
# radio_file_input = gr.Radio(
# value="Text file", choices=["Text file ", "Page XML file "], label="What kind file output?"
# )
radio_file_input = gr.CheckboxGroup(
choices=["Txt", "XML"],
value=["Txt"],
label="Output file extension",
# info="Only txt and page xml is supported for now!",
)
htr_pipeline_button = gr.Button(
"Run HTR",
variant="primary",
visible=True,
elem_id="run_pipeline_button",
).style(full_width=False)
with gr.Group():
with gr.Row():
fast_file_downlod = gr.File(label="Download output file", visible=False)
with gr.Row():
with gr.Accordion("Example images to use:", open=False) as fast_example_accord:
fast_name_files_placeholder = gr.Markdown(visible=False)
gr.Examples(
examples=images_for_demo.examples_list,
inputs=[fast_name_files_placeholder, fast_track_input_region_image],
label="Example images",
examples_per_page=5,
)
with gr.Row():
gr.Markdown(
"""
Image viewer for xml output:
<p align="center">
<a href="https://huggingface.co/spaces/Riksarkivet/Viewer_demo">
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg" alt="Badge 1">
</a>
</p>
"""
)
with gr.Column(scale=4):
fast_track_output_image = gr.Image(label="HTR results visualizer", type="numpy", tool="editor", height=650)
with gr.Row(visible=False) as api_placeholder:
htr_pipeline_button_api = gr.Button(
"Run pipeline",
variant="primary",
visible=False,
).style(full_width=False)
xml_rendered_placeholder_for_api = gr.Textbox(visible=False)
htr_pipeline_button.click(
fast_track.segment_to_xml,
inputs=[fast_track_input_region_image, radio_file_input],
outputs=[fast_track_output_image, fast_file_downlod, fast_file_downlod],
)
htr_pipeline_button_api.click(
fast_track.segment_to_xml_api,
inputs=[fast_track_input_region_image],
outputs=[xml_rendered_placeholder_for_api],
api_name="predict",
)
# 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)
|