test docker & added singeltons

Dockerfile

@@ -33,4 +33,4 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["python", "app/app.py"]
+CMD ["python", "app.py"]

app.py

@@ -1,17 +1,9 @@
-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()
+from tabs.htr_tool import htr_tool_tab
+from tabs.stepwise_htr_tool import stepwise_htr_tool_tab
 
 with gr.Blocks(title="HTR Riksarkivet", theme=theme, css=css) as demo:
     gr.Markdown(TextApp.title_markdown)
@@ -21,239 +13,7 @@ with gr.Blocks(title="HTR Riksarkivet", theme=theme, css=css) as demo:
             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)
-
+            stepwise_htr_tool_tab.render()
         with gr.Tab("How to use"):
             with gr.Tabs():
                 with gr.Tab("HTR Tool"):
@@ -339,104 +99,6 @@ with gr.Blocks(title="HTR Riksarkivet", theme=theme, css=css) as demo:
     # 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)
 
 
@@ -445,5 +107,3 @@ 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)

helper/examples/examples.py

@@ -5,10 +5,18 @@ from PIL import Image
 
 
 class DemoImages:
-    def __init__(self, url="Riksarkivet/test_images_demo", cache_dir="./helper/examples/.cache_images") -> None:
-        self.images_datasets = datasets.load_dataset(url, cache_dir=cache_dir)
-        self.example_df = self.images_datasets["train"].to_pandas()
-        self.examples_list = self.convert_bytes_to_images()
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        if not cls._instance:
+            cls._instance = super(DemoImages, cls).__new__(cls, *args, **kwargs)
+        return cls._instance
+
+    def __init__(self, url="Riksarkivet/test_images_demo", cache_dir="./helper/examples/.cache_images"):
+        if not hasattr(self, "images_datasets"):
+            self.images_datasets = datasets.load_dataset(url, cache_dir=cache_dir)
+            self.example_df = self.images_datasets["train"].to_pandas()
+            self.examples_list = self.convert_bytes_to_images()
 
     def convert_bytes_to_images(self):
         examples_list = []

src/htr_pipeline/gradio_backend.py

@@ -16,8 +16,10 @@ class SingletonModelLoader:
         return cls._instance
 
     def __init__(self):
-        self.inferencer = Inferencer(local_run=True)
-        self.pipeline = Pipeline(self.inferencer)
+        if not hasattr(self, "inferencer"):
+            self.inferencer = Inferencer(local_run=True)
+        if not hasattr(self, "pipeline"):
+            self.pipeline = Pipeline(self.inferencer)
 
 
 # fast track

tabs/stepwise_htr_tool.py

@@ -0,0 +1,342 @@
+import gradio as gr
+
+from helper.examples.examples import DemoImages
+from src.htr_pipeline.gradio_backend import CustomTrack, SingletonModelLoader
+
+model_loader = SingletonModelLoader()
+
+custom_track = CustomTrack(model_loader)
+
+images_for_demo = DemoImages()
+
+with gr.Blocks() as stepwise_htr_tool_tab:
+    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)
+
+    # 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,
+        ],
+    )