SegmentAnything2AssistApp.py

import gradio
import gradio_image_annotation
import gradio_imageslider
import spaces
import torch

import src.SegmentAnything2Assist.SegmentAnything2Assist as SegmentAnything2Assist

example_image_annotation = {
    "image": "assets/cars.jpg",
    "boxes": [
        {
            "label": "+",
            "color": (0, 255, 0),
            "xmin": 886,
            "ymin": 551,
            "xmax": 886,
            "ymax": 551,
        },
        {
            "label": "-",
            "color": (255, 0, 0),
            "xmin": 1239,
            "ymin": 576,
            "xmax": 1239,
            "ymax": 576,
        },
        {
            "label": "-",
            "color": (255, 0, 0),
            "xmin": 610,
            "ymin": 574,
            "xmax": 610,
            "ymax": 574,
        },
        {
            "label": "",
            "color": (0, 0, 255),
            "xmin": 254,
            "ymin": 466,
            "xmax": 1347,
            "ymax": 1047,
        },
    ],
}


VERBOSE = True
DEBUG = False


segment_anything2assist = SegmentAnything2Assist.SegmentAnything2Assist(
    sam_model_name="sam2_hiera_tiny", device=torch.device("cuda")
)


def change_base_model(model_name, device):
    global segment_anything2assist
    gradio.Info(f"Changing model to {model_name} on {device}", duration=3)
    try:
        segment_anything2assist = SegmentAnything2Assist.SegmentAnything2Assist(
            model_name=model_name, device=torch.device(device)
        )
        gradio.Info(f"Model has been changed to {model_name} on {device}", duration=5)
    except:
        gradio.Error(f"Model could not be changed", duration=5)


def __post_process_annotator_inputs(value):
    if VERBOSE:
        print("SegmentAnything2AssistApp::____post_process_annotator_inputs::Called.")
    __current_mask, __current_segment = None, None
    new_boxes = []
    __image_point_coords = []
    __image_point_labels = []
    __image_box = []

    b_has_box = False
    for box in value["boxes"]:
        if box["label"] == "":
            if not b_has_box:
                new_box = box.copy()
                new_box["color"] = (0, 0, 255)
                new_boxes.append(new_box)
                b_has_box = True
            __image_box = [box["xmin"], box["ymin"], box["xmax"], box["ymax"]]

        elif box["label"] == "+" or box["label"] == "-":
            new_box = box.copy()
            new_box["color"] = (0, 255, 0) if box["label"] == "+" else (255, 0, 0)
            new_box["xmin"] = int((box["xmin"] + box["xmax"]) / 2)
            new_box["ymin"] = int((box["ymin"] + box["ymax"]) / 2)
            new_box["xmax"] = new_box["xmin"]
            new_box["ymax"] = new_box["ymin"]
            new_boxes.append(new_box)

            __image_point_coords.append([new_box["xmin"], new_box["ymin"]])
            __image_point_labels.append(1 if box["label"] == "+" else 0)

    if len(__image_box) == 0:
        __image_box = None

    if len(__image_point_coords) == 0:
        __image_point_coords = None

    if len(__image_point_labels) == 0:
        __image_point_labels = None

    if VERBOSE:
        print("SegmentAnything2AssistApp::____post_process_annotator_inputs::Done.")

    return __image_point_coords, __image_point_labels, __image_box


@spaces.GPU(duration=60)
def generate_image_mask(
    value,
    mask_threshold,
    max_hole_area,
    max_sprinkle_area,
    image_output_mode,
):
    global segment_anything2assist

    # Force post processing of annotated image
    image_point_coords, image_point_labels, image_box = __post_process_annotator_inputs(
        value
    )

    if VERBOSE:
        print("SegmentAnything2AssistApp::generate_image_mask::Called.")
    mask_chw, mask_iou = segment_anything2assist.generate_masks_from_image(
        value["image"],
        image_point_coords,
        image_point_labels,
        image_box,
        mask_threshold,
        max_hole_area,
        max_sprinkle_area,
    )

    if VERBOSE:
        print("SegmentAnything2AssistApp::generate_image_mask::Masks generated.")

    __current_mask, __current_segment = segment_anything2assist.apply_mask_to_image(
        value["image"], mask_chw[0]
    )

    if VERBOSE:
        print(
            "SegmentAnything2AssistApp::generate_image_mask::Masks and Segments created."
        )

    __image_box = gradio.DataFrame(value=[[]])
    __image_point_coords = gradio.DataFrame(value=[[]])
    if DEBUG:
        __image_box = gradio.DataFrame(
            value=[image_box],
            label="Box",
            interactive=False,
            headers=["XMin", "YMin", "XMax", "YMax"],
        )
        x = []
        for i, _ in enumerate(image_point_coords):
            x.append(
                [
                    image_point_labels[i],
                    image_point_coords[i][0],
                    image_point_coords[i][1],
                ]
            )
        __image_point_coords = gradio.DataFrame(
            value=x,
            label="Point Coords",
            interactive=False,
            headers=["Label", "X", "Y"],
        )

    if image_output_mode == "Mask":
        return (
            [value["image"], __current_mask],
            __image_point_coords,
            __image_box,
            __current_mask,
            __current_segment,
        )
    elif image_output_mode == "Segment":
        return (
            [value["image"], __current_segment],
            __image_point_coords,
            __image_box,
            __current_mask,
            __current_segment,
        )
    else:
        gradio.Warning("This is an issue, please report the problem!", duration=5)
        return (
            gradio_imageslider.ImageSlider(render=True),
            __image_point_coords,
            __image_box,
            __current_mask,
            __current_segment,
        )


def on_image_output_mode_change(image_input, radio, __current_mask, __current_segment):
    if VERBOSE:
        print("SegmentAnything2AssistApp::generate_image_mask::Called.")
    if __current_mask is None or __current_segment is None:
        gradio.Warning("Configuration was changed, generate the mask again", duration=5)
        return gradio_imageslider.ImageSlider(render=True)
    if radio == "Mask":
        return [image_input["image"], __current_mask]
    elif radio == "Segment":
        return [image_input["image"], __current_segment]
    else:
        gradio.Warning("This is an issue, please report the problem!", duration=5)
        return gradio_imageslider.ImageSlider(render=True)


def __generate_auto_mask(image, auto_list, auto_mode, auto_bbox_mode, masks, bboxes):
    global segment_anything2assist

    # When value from gallery is called, it is a tuple
    if type(masks[0]) == tuple:
        masks = [mask[0] for mask in masks]

    image_with_bbox, mask, segment = segment_anything2assist.apply_auto_mask_to_image(
        image, [int(i) - 1 for i in auto_list], masks, bboxes
    )

    output_1 = image_with_bbox if auto_bbox_mode else image
    output_2 = mask if auto_mode == "Mask" else segment
    return [output_1, output_2]


@spaces.GPU(duration=60)
def generate_auto_mask(
    image,
    points_per_side,
    points_per_batch,
    pred_iou_thresh,
    stability_score_thresh,
    stability_score_offset,
    mask_threshold,
    box_nms_thresh,
    crop_n_layers,
    crop_nms_thresh,
    crop_overlay_ratio,
    crop_n_points_downscale_factor,
    min_mask_region_area,
    use_m2m,
    multimask_output,
    output_mode,
):
    global segment_anything2assist
    if VERBOSE:
        print("SegmentAnything2AssistApp::generate_auto_mask::Called.")

    masks, bboxes, predicted_iou, stability_score = (
        segment_anything2assist.generate_automatic_masks(
            image,
            points_per_side,
            points_per_batch,
            pred_iou_thresh,
            stability_score_thresh,
            stability_score_offset,
            mask_threshold,
            box_nms_thresh,
            crop_n_layers,
            crop_nms_thresh,
            crop_overlay_ratio,
            crop_n_points_downscale_factor,
            min_mask_region_area,
            use_m2m,
            multimask_output,
        )
    )

    if len(masks) == 0:
        gradio.Warning(
            "No masks generated, please tweak the advanced parameters.", duration=5
        )
        return (
            gradio_imageslider.ImageSlider(),
            gradio.CheckboxGroup([], value=[], label="Mask List", interactive=False),
            gradio.Checkbox(value=False, label="Show Bounding Box", interactive=False),
            gradio.Gallery(
                None, label="Output Gallery", interactive=False, type="numpy"
            ),
            gradio.DataFrame(
                value=[[]],
                label="Box",
                interactive=False,
                headers=["XMin", "YMin", "XMax", "YMax"],
            ),
        )
    else:
        choices = [str(i) for i in range(len(masks))]

        returning_image = __generate_auto_mask(
            image, ["0"], output_mode, False, masks, bboxes
        )
        return (
            returning_image,
            gradio.CheckboxGroup(
                choices, value=["0"], label="Mask List", interactive=True
            ),
            gradio.Checkbox(value=False, label="Show Bounding Box", interactive=True),
            gradio.Gallery(
                masks, label="Output Gallery", interactive=True, type="numpy"
            ),
            gradio.DataFrame(
                value=bboxes,
                label="Box",
                interactive=False,
                headers=["XMin", "YMin", "XMax", "YMax"],
                type="array",
            ),
        )


def __generate_yolo_mask(
    image,
    yolo_mask,
    output_mode,
):
    global segment_anything2assist
    if VERBOSE:
        print("SegmentAnything2AssistApp::generate_yolo_mask::Called.")

    mask = yolo_mask[4]

    if output_mode == "Mask":
        return [image, mask]

    mask, output_image = segment_anything2assist.apply_mask_to_image(image, mask)

    if output_mode == "Segment":
        return [image, output_image]


@spaces.GPU(duration=60)
def generate_yolo_mask(
    image,
    yolo_model_choice,
    mask_threshold,
    max_hole_area,
    max_sprinkle_area,
    output_mode,
):
    global segment_anything2assist
    if VERBOSE:
        print("SegmentAnything2AssistApp::generate_yolo_mask::Called.")

    results = segment_anything2assist.generate_mask_from_image_with_yolo(
        image,
        YOLOv10ModelName=yolo_model_choice,
        mask_threshold=mask_threshold,
        max_hole_area=max_hole_area,
        max_sprinkle_area=max_sprinkle_area,
    )

    if len(results) > 0:
        if VERBOSE:
            print("SegmentAnything2AssistApp::generate_yolo_mask::Masks generated.")

        yolo_masks = []
        for result in results:
            yolo_mask = [
                result["name"],
                result["class"],
                result["confidence"],
                [result["box"]],
                result["mask_chw"],
                result["mask_iou"][0].item(),
            ]
            yolo_masks.append(yolo_mask)

        return __generate_yolo_mask(image, yolo_masks[0], output_mode), gradio.Dataset(
            label="YOLOv10 Assisted Masks", type="values", samples=yolo_masks
        )

    else:
        if VERBOSE:
            print("SegmentAnything2AssistApp::generate_yolo_mask::No masks generated.")

        return gradio.ImageSlider(), gradio.Dataset()


with gradio.Blocks() as base_app:
    gradio.Markdown(
        """
        <h1 style="text-align: center;">Segment Anything 2 Assist 🚀</h1>
        <p style="text-align: center;">A tool for advanced image segmentation and annotation. 🖼️✏️</p>
        """
    )
    with gradio.Row():
        with gradio.Column():
            base_model_choice = gradio.Dropdown(
                [
                    "sam2_hiera_large",
                    "sam2_hiera_small",
                    "sam2_hiera_base_plus",
                    "sam2_hiera_tiny",
                ],
                value="sam2_hiera_tiny",
                label="Model Choice",
            )
        with gradio.Column():
            base_gpu_choice = gradio.Dropdown(
                ["cpu", "cuda"], value="cuda", label="Device Choice"
            )
    base_model_choice.change(
        change_base_model, inputs=[base_model_choice, base_gpu_choice]
    )
    base_gpu_choice.change(
        change_base_model, inputs=[base_model_choice, base_gpu_choice]
    )

    # Image Segmentation
    with gradio.Tab(label="🌆 Image Segmentation", id="image_tab") as image_tab:
        gradio.Markdown("Image Segmentation", render=True)
        with gradio.Column():
            with gradio.Accordion("Image Annotation Documentation", open=False):
                gradio.Markdown(
                    """
                    ### 🖼️ Image Annotation Documentation

                    Image annotation allows you to mark specific regions of an image with labels. 
                    In this app, you can annotate an image by drawing bounding boxes and/or making points on the image. 
                    The labels can be either '+' or '-'. 

                    **📝 How to Annotate an Image:**
                    - Bounding Box: Click and drag to draw a box around the desired region.
                    - Positive or Negative Points: Draw a small box (note that the center point will be used for the annotation) and add either "+" or "-" as the label respectively.

                    **🎨 Generating Masks:**
                    - Once you have annotated the image, click the 'Generate Mask' button to generate a mask based on the annotations.
                    - The mask can be either a binary mask or a segmented mask, depending on the selected output mode.
                    - You can switch between the output modes using the radio buttons.
                    - If you make any changes to the annotations or the output mode, you need to regenerate the mask by clicking the button again.

                    **⚙️ Advanced Options:**
                    - The advanced options allow you to adjust the SAM mask threshold, maximum hole area, and maximum sprinkle area.
                    - These options control the sensitivity and accuracy of the segmentation process.
                    - Experiment with different settings to achieve the desired results.
                    """
                )
            image_input = gradio_image_annotation.image_annotator(
                example_image_annotation
            )
            with gradio.Accordion("Advanced Options", open=False):
                image_generate_SAM_mask_threshold = gradio.Slider(
                    0.0, 1.0, 0.0, label="SAM Mask Threshold"
                )
                image_generate_SAM_max_hole_area = gradio.Slider(
                    0, 1000, 0, label="SAM Max Hole Area"
                )
                image_generate_SAM_max_sprinkle_area = gradio.Slider(
                    0, 1000, 0, label="SAM Max Sprinkle Area"
                )
            image_generate_mask_button = gradio.Button("Generate Mask")
            with gradio.Row():
                with gradio.Column():
                    image_output_mode = gradio.Radio(
                        ["Segment", "Mask"], value="Segment", label="Output Mode"
                    )
                with gradio.Column(scale=3):
                    image_output = gradio_imageslider.ImageSlider()

            with gradio.Accordion("Debug", open=DEBUG, visible=DEBUG):
                __image_point_coords = gradio.DataFrame(
                    value=[["+", 886, 551], ["-", 1239, 576]],
                    label="Point Coords",
                    interactive=False,
                    headers=["Label", "X", "Y"],
                )
                __image_box = gradio.DataFrame(
                    value=[[254, 466, 1347, 1047]],
                    label="Box",
                    interactive=False,
                    headers=["XMin", "YMin", "XMax", "YMax"],
                )
                __current_mask = gradio.Image(label="Current Mask", interactive=False)
                __current_segment = gradio.Image(
                    label="Current Segment", interactive=False
                )

            # image_input.change(__post_process_annotator_inputs, inputs = [image_input])
            image_generate_mask_button.click(
                generate_image_mask,
                inputs=[
                    image_input,
                    image_generate_SAM_mask_threshold,
                    image_generate_SAM_max_hole_area,
                    image_generate_SAM_max_sprinkle_area,
                    image_output_mode,
                ],
                outputs=[
                    image_output,
                    __image_point_coords,
                    __image_box,
                    __current_mask,
                    __current_segment,
                ],
            )
            image_output_mode.change(
                on_image_output_mode_change,
                inputs=[
                    image_input,
                    image_output_mode,
                    __current_mask,
                    __current_segment,
                ],
                outputs=[image_output],
            )

    # Auto Segmentation
    with gradio.Tab(label="🤖 Auto Segmentation", id="auto_tab"):
        gradio.Markdown("Auto Segmentation", render=True)
        with gradio.Column():
            with gradio.Accordion("Auto Annotation Documentation", open=False):
                gradio.Markdown(
                    """
                    ### 🖼️ Auto Annotation Documentation

                    Auto annotation allows you to automatically generate masks for an image based on advanced parameters. 
                    In this app, you can configure various settings to control the mask generation process.

                    **📝 How to Use Auto Annotation:**
                    - Upload or select an image.
                    - Adjust the advanced options to fine-tune the mask generation process.
                    - Click the 'Generate Auto Mask' button to generate masks automatically.

                    **⚙️ Advanced Options:**
                    - **Points Per Side:** Number of points to sample per side of the image.
                    - **Points Per Batch:** Number of points to process in each batch.
                    - **Pred IOU Threshold:** Threshold for the predicted Intersection over Union (IOU) score.
                    - **Stability Score Threshold:** Threshold for the stability score.
                    - **Stability Score Offset:** Offset for the stability score.
                    - **Mask Threshold:** Threshold for the mask generation.
                    - **Box NMS Threshold:** Non-Maximum Suppression (NMS) threshold for boxes.
                    - **Crop N Layers:** Number of layers to crop.
                    - **Crop NMS Threshold:** NMS threshold for crops.
                    - **Crop Overlay Ratio:** Overlay ratio for crops.
                    - **Crop N Points Downscale Factor:** Downscale factor for the number of points in crops.
                    - **Min Mask Region Area:** Minimum area for mask regions.
                    - **Use M2M:** Whether to use M2M (Mask-to-Mask) refinement.
                    - **Multi Mask Output:** Whether to generate multiple masks.

                    **🎨 Generating Masks:**
                    - Once you have configured the advanced options, click the 'Generate Auto Mask' button.
                    - The masks will be generated automatically based on the selected parameters.
                    - You can view the generated masks and adjust the settings if needed.
                    """
                )
            auto_input = gradio.Image("assets/cars.jpg")
            with gradio.Accordion("Advanced Options", open=False):
                auto_generate_SAM_points_per_side = gradio.Slider(
                    1, 64, 12, 1, label="Points Per Side", interactive=True
                )
                auto_generate_SAM_points_per_batch = gradio.Slider(
                    1, 64, 32, 1, label="Points Per Batch", interactive=True
                )
                auto_generate_SAM_pred_iou_thresh = gradio.Slider(
                    0.0, 1.0, 0.8, 1, label="Pred IOU Threshold", interactive=True
                )
                auto_generate_SAM_stability_score_thresh = gradio.Slider(
                    0.0, 1.0, 0.95, label="Stability Score Threshold", interactive=True
                )
                auto_generate_SAM_stability_score_offset = gradio.Slider(
                    0.0, 1.0, 1.0, label="Stability Score Offset", interactive=True
                )
                auto_generate_SAM_mask_threshold = gradio.Slider(
                    0.0, 1.0, 0.0, label="Mask Threshold", interactive=True
                )
                auto_generate_SAM_box_nms_thresh = gradio.Slider(
                    0.0, 1.0, 0.7, label="Box NMS Threshold", interactive=True
                )
                auto_generate_SAM_crop_n_layers = gradio.Slider(
                    0, 10, 0, 1, label="Crop N Layers", interactive=True
                )
                auto_generate_SAM_crop_nms_thresh = gradio.Slider(
                    0.0, 1.0, 0.7, label="Crop NMS Threshold", interactive=True
                )
                auto_generate_SAM_crop_overlay_ratio = gradio.Slider(
                    0.0, 1.0, 512 / 1500, label="Crop Overlay Ratio", interactive=True
                )
                auto_generate_SAM_crop_n_points_downscale_factor = gradio.Slider(
                    1, 10, 1, label="Crop N Points Downscale Factor", interactive=True
                )
                auto_generate_SAM_min_mask_region_area = gradio.Slider(
                    0, 1000, 0, label="Min Mask Region Area", interactive=True
                )
                auto_generate_SAM_use_m2m = gradio.Checkbox(
                    label="Use M2M", interactive=True
                )
                auto_generate_SAM_multimask_output = gradio.Checkbox(
                    value=True, label="Multi Mask Output", interactive=True
                )
            auto_generate_button = gradio.Button("Generate Auto Mask")
            with gradio.Row():
                with gradio.Column():
                    auto_output_mode = gradio.Radio(
                        ["Segment", "Mask"],
                        value="Segment",
                        label="Output Mode",
                        interactive=True,
                    )
                    auto_output_list = gradio.CheckboxGroup(
                        [], value=[], label="Mask List", interactive=False
                    )
                    auto_output_bbox = gradio.Checkbox(
                        value=False, label="Show Bounding Box", interactive=False
                    )
                with gradio.Column(scale=3):
                    auto_output = gradio_imageslider.ImageSlider()
            with gradio.Accordion("Debug", open=DEBUG, visible=DEBUG):
                __auto_output_gallery = gradio.Gallery(
                    None, label="Output Gallery", interactive=False, type="numpy"
                )
                __auto_bbox = gradio.DataFrame(
                    value=[[]],
                    label="Box",
                    interactive=False,
                    headers=["XMin", "YMin", "XMax", "YMax"],
                )

            auto_generate_button.click(
                generate_auto_mask,
                inputs=[
                    auto_input,
                    auto_generate_SAM_points_per_side,
                    auto_generate_SAM_points_per_batch,
                    auto_generate_SAM_pred_iou_thresh,
                    auto_generate_SAM_stability_score_thresh,
                    auto_generate_SAM_stability_score_offset,
                    auto_generate_SAM_mask_threshold,
                    auto_generate_SAM_box_nms_thresh,
                    auto_generate_SAM_crop_n_layers,
                    auto_generate_SAM_crop_nms_thresh,
                    auto_generate_SAM_crop_overlay_ratio,
                    auto_generate_SAM_crop_n_points_downscale_factor,
                    auto_generate_SAM_min_mask_region_area,
                    auto_generate_SAM_use_m2m,
                    auto_generate_SAM_multimask_output,
                    auto_output_mode,
                ],
                outputs=[
                    auto_output,
                    auto_output_list,
                    auto_output_bbox,
                    __auto_output_gallery,
                    __auto_bbox,
                ],
            )
            auto_output_list.change(
                __generate_auto_mask,
                inputs=[
                    auto_input,
                    auto_output_list,
                    auto_output_mode,
                    auto_output_bbox,
                    __auto_output_gallery,
                    __auto_bbox,
                ],
                outputs=[auto_output],
            )
            auto_output_bbox.change(
                __generate_auto_mask,
                inputs=[
                    auto_input,
                    auto_output_list,
                    auto_output_mode,
                    auto_output_bbox,
                    __auto_output_gallery,
                    __auto_bbox,
                ],
                outputs=[auto_output],
            )
            auto_output_mode.change(
                __generate_auto_mask,
                inputs=[
                    auto_input,
                    auto_output_list,
                    auto_output_mode,
                    auto_output_bbox,
                    __auto_output_gallery,
                    __auto_bbox,
                ],
                outputs=[auto_output],
            )

    # YOLOv10 assisted Segmentation.
    with gradio.Tab("🤙 YOLOv10 assisted Segmentation"):
        gradio.Markdown("YOLOv10 assisted Segmentation")
        with gradio.Column():
            with gradio.Accordion("YOLOv10 Documentation", open=False):
                gradio.Markdown(
                    """
                    ### 🖼️ YOLOv10 Assisted Segmentation Documentation

                    YOLOv10 assisted segmentation allows you to generate masks for an image using the YOLOv10 model. 
                    In this app, you can configure various settings to control the mask generation process.

                    **📝 How to Use YOLOv10 Assisted Segmentation:**
                    - Upload or select an image.
                    - Choose the desired YOLOv10 model from the dropdown.
                    - Adjust the advanced settings to fine-tune the mask generation process.
                    - Click the 'Generate YOLOv10 Mask' button to generate masks.

                    **⚙️ Advanced Settings:**
                    - **SAM Mask Threshold:** Threshold for the SAM mask generation.
                    - **Max Hole Area:** Maximum area for holes in the mask.
                    - **Max Sprinkle Area:** Maximum area for sprinkled regions in the mask.

                    **🎨 Generating Masks:**
                    - Once you have configured the settings, click the 'Generate YOLOv10 Mask' button.
                    - The masks will be generated based on the selected parameters.
                    - You can view the generated masks and adjust the settings if needed.
                    """
                )

            yolo_input = gradio.Image("assets/cars.jpg")
            yolo_model_choice = gradio.Dropdown(
                choices=["nano", "small", "medium", "base", "large", "xlarge"],
                value="nano",
                label="YOLOv10 Model Choice",
            )
            with gradio.Accordion("Advanced Settings", open=False):
                yolo_generate_SAM_mask_threshold = gradio.Slider(
                    0.0, 1.0, 0.0, label="SAM Mask Threshold"
                )
                yolo_generate_SAM_max_hole_area = gradio.Slider(
                    0, 1000, 0, label="SAM Max Hole Area"
                )
                yolo_generate_SAM_max_sprinkle_area = gradio.Slider(
                    0, 1000, 0, label="SAM Max Sprinkle Area"
                )

            yolo_generate_mask_button = gradio.Button("Generate YOLOv10 Mask")
            with gradio.Row():
                with gradio.Column():
                    yolo_output_mode = gradio.Radio(
                        ["Segment", "Mask"], value="Segment", label="Output Mode"
                    )
                with gradio.Column(scale=3):
                    yolo_output = gradio_imageslider.ImageSlider()

            with gradio.Accordion("Debug 1", open=DEBUG, visible=DEBUG):
                __yolo_name = gradio.Textbox(
                    label="Name", interactive=DEBUG, visible=DEBUG
                )
                __yolo_class = gradio.Number(
                    label="Class", interactive=DEBUG, visible=DEBUG
                )
                __yolo_confidence = gradio.Number(
                    label="Confidence", interactive=DEBUG, visible=DEBUG
                )
                __yolo_box = gradio.DataFrame(
                    value=[[1, 2, 3, 4]], label="Box", interactive=DEBUG, visible=DEBUG
                )
                __yolo_mask = gradio.Image(
                    label="Mask", interactive=DEBUG, visible=DEBUG
                )
                __yolo_mask_iou = gradio.Number(
                    label="Mask IOU", interactive=DEBUG, visible=DEBUG
                )

            with gradio.Row():
                yolo_masks = gradio.Dataset(
                    label="YOLOv10 Assisted Masks",
                    type="values",
                    components=[
                        __yolo_name,
                        __yolo_class,
                        __yolo_confidence,
                        __yolo_box,
                        __yolo_mask,
                        __yolo_mask_iou,
                    ],
                )

            yolo_generate_mask_button.click(
                generate_yolo_mask,
                inputs=[
                    yolo_input,
                    yolo_model_choice,
                    yolo_generate_SAM_mask_threshold,
                    yolo_generate_SAM_max_hole_area,
                    yolo_generate_SAM_max_sprinkle_area,
                    yolo_output_mode,
                ],
                outputs=[yolo_output, yolo_masks],
            )

            yolo_masks.click(
                __generate_yolo_mask,
                inputs=[yolo_input, yolo_masks, yolo_output_mode],
                outputs=[yolo_output],
            )

            yolo_output_mode.change(
                __generate_yolo_mask,
                inputs=[yolo_input, yolo_masks, yolo_output_mode],
                outputs=[yolo_output],
            )


if __name__ == "__main__":
    base_app.launch()