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() terminate = False 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.Tab("Output and Settings") as tab_output_and_setting_selector: with gr.Row(): stop_htr_button = gr.Button( value="Stop HTR", variant="stop", ) htr_pipeline_button = gr.Button( "Run HTR", variant="primary", visible=True, elem_id="run_pipeline_button", ) htr_pipeline_button_api = gr.Button("Run pipeline", variant="primary", visible=False, scale=1) fast_file_downlod = gr.File( label="Download output file", visible=True, scale=1, height=100, elem_id="download_file" ) with gr.Tab("Image Viewer") as tab_image_viewer_selector: with gr.Row(): gr.Button( value="External Image Viewer", variant="secondary", link="https://huggingface.co/spaces/Riksarkivet/Viewer_demo", interactive=True, ) run_image_visualizer_button = gr.Button( value="Visualize results", variant="primary", interactive=True ) selection_text_from_image_viewer = gr.Textbox( interactive=False, label="Text Selector", info="Select a mask on Image Viewer to return text" ) with gr.Column(scale=4): with gr.Box(): with gr.Row(visible=True) as output_and_setting_tab: with gr.Column(scale=3): with gr.Row(): with gr.Group(): gr.Markdown("   ⚙️ Settings ") with gr.Row(): radio_file_input = gr.CheckboxGroup( choices=["Txt", "XML"], value=["XML"], label="Output file extension", # info="Only txt and page xml is supported for now!", scale=1, ) with gr.Row(): gr.Checkbox( value=True, label="Binarize image", info="Binarize image to reduce background noise", ) gr.Checkbox( value=True, label="Output prediction threshold", info="Output XML with prediction score", ) with gr.Row(): gr.Slider( value=0.6, minimum=0.5, maximum=1, label="HTR threshold", info="Prediction score threshold for transcribed lines", scale=1, ) gr.Slider( value=0.7, minimum=0.6, maximum=1, label="Avg threshold", info="Average prediction score for a region", scale=1, ) htr_tool_region_segment_model_dropdown = gr.Dropdown( choices=["Riksarkivet/RmtDet_region"], value="Riksarkivet/RmtDet_region", label="Region Segment models", info="Will add more models later!", ) # with gr.Accordion("Transcribe settings:", open=False): htr_tool_line_segment_model_dropdown = gr.Dropdown( choices=["Riksarkivet/RmtDet_lines"], value="Riksarkivet/RmtDet_lines", label="Line Segment models", info="Will add more models later!", ) htr_tool_transcriber_model_dropdown = gr.Dropdown( choices=["Riksarkivet/SATRN_transcriber", "microsoft/trocr-base-handwritten"], value="Riksarkivet/SATRN_transcriber", label="Transcriber models", info="Models will be continuously updated with future additions for specific cases.", ) with gr.Column(scale=2): 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(visible=False) as image_viewer_tab: text_polygon_dict = gr.Variable() fast_track_output_image = gr.Image( label="Image Viewer", type="numpy", height=600, interactive=False ) xml_rendered_placeholder_for_api = gr.Textbox(visible=False) htr_event_click_event = htr_pipeline_button.click( fast_track.segment_to_xml, inputs=[fast_track_input_region_image, radio_file_input], outputs=[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", ) def update_selected_tab_output_and_setting(): return gr.update(visible=True), gr.update(visible=False) def update_selected_tab_image_viewer(): return gr.update(visible=False), gr.update(visible=True) tab_output_and_setting_selector.select( fn=update_selected_tab_output_and_setting, outputs=[output_and_setting_tab, image_viewer_tab] ) tab_image_viewer_selector.select( fn=update_selected_tab_image_viewer, outputs=[output_and_setting_tab, image_viewer_tab] ) def stop_function(): from src.htr_pipeline.utils import pipeline_inferencer pipeline_inferencer.terminate = True gr.Info("The HTR execution was halted") stop_htr_button.click(fn=stop_function, inputs=None, outputs=None, cancels=[htr_event_click_event]) run_image_visualizer_button.click( fn=fast_track.visualize_image_viewer, inputs=fast_track_input_region_image, outputs=[fast_track_output_image, text_polygon_dict], ) fast_track_output_image.select( fast_track.get_text_from_coords, inputs=text_polygon_dict, outputs=selection_text_from_image_viewer )