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)