import gradio as gr


def gradio_inputs_for_MD_DLC(backends_list, md_models_list, dlc_models_list):
    # Input image
    gr_image_input = gr.Image(type="pil", label="Input Image")

    # Models
    gr_backend_input = gr.Radio(
        choices=backends_list,
        value=backends_list[0],
        label="Select backend",
    )

    gr_mega_model_input = gr.Dropdown(
        choices=md_models_list,
        value="md_v5a",
        type="value",
        label="Select Detector model (TensorFlow legacy only)",
        visible=gr_backend_input.value != "PyTorch",
    )

    gr_dlc_model_input = gr.Dropdown(
        choices=dlc_models_list,
        value="superanimal_quadruped",
        type="value",
        label="Select DeepLabCut model",
    )

    # Other inputs
    gr_dlc_only_checkbox = gr.Checkbox(
        value=False,
        label="Run DeepLabCut only, directly on input image?",
    )

    # Gradio Slider signature is (minimum, maximum, value, step, ...)
    gr_slider_conf_bboxes = gr.Slider(
        minimum=0,
        maximum=1,
        value=0.2,
        step=0.05,
        label="Set confidence threshold for animal detections",
    )

    gr_slider_conf_keypoints = gr.Slider(
        minimum=0,
        maximum=1,
        value=0.4,
        step=0.05,
        label="Set confidence threshold for keypoints",
    )

    # Data viz
    with gr.Accordion("Display options", open=False):
        gr_str_labels_checkbox = gr.Checkbox(
            value=True,
            label="Show bodypart labels?",
        )

        gr_color_by_confidence_checkbox = gr.Checkbox(
            value=True,
            label="Color keypoints by confidence? (otherwise by bodypart)",
        )

        gr_keypt_color = gr.ColorPicker(
            value="#862db7",
            label="Choose color for keypoint label",
        )

        gr_labels_font_style = gr.Dropdown(
            choices=["amiko", "animals", "nature", "painter", "zen"],
            value="amiko",
            type="value",
            label="Select keypoint label font",
        )

        gr_slider_font_size = gr.Slider(
            minimum=5,
            maximum=30,
            value=8,
            step=1,
            label="Set font size",
        )

        gr_slider_marker_size = gr.Slider(
            minimum=1,
            maximum=20,
            value=9,
            step=1,
            label="Set marker size",
        )

    return [
        gr_image_input,
        gr_backend_input,
        gr_mega_model_input,
        gr_dlc_model_input,
        gr_dlc_only_checkbox,
        gr_str_labels_checkbox,
        gr_slider_conf_bboxes,
        gr_slider_conf_keypoints,
        gr_labels_font_style,
        gr_slider_font_size,
        gr_keypt_color,
        gr_slider_marker_size,
        gr_color_by_confidence_checkbox,
    ]


def gradio_outputs_for_MD_DLC():
    gr_image_output = gr.Image(type="pil", label="Output Image")
    with gr.Row():
        gr_file_download = gr.File(label="Download JSON file")
        gr_image_download = gr.File(label="Download annotated image")
    gr_confidence_table = gr.Dataframe(
        headers=["animal", "bodypart", "confidence"],
        label="Keypoint confidence (lowest first)",
        interactive=False,
    )
    return [gr_image_output, gr_file_download, gr_image_download, gr_confidence_table]


def gradio_description_and_examples():
    title = "DeepLabCut Model Zoo: SuperAnimals"
    description = (
        "Estimate animal poses with the SuperAnimal models from the "
        "[DeepLabCut Model Zoo](http://www.mackenziemathislab.org/dlc-modelzoo) "
        "([paper](https://arxiv.org/abs/2203.07436)). "
        "Upload an image or pick an example below; to run on videos, see the Model Zoo page."
    )

    examples = [
        [image, "PyTorch", "md_v5a", "superanimal_quadruped", False, True, 0.5, 0.4, "amiko", 10, "#ff0000", 5, True]
        for image in ("examples/dog.jpeg", "examples/cat.jpg")
    ]

    return [title, description, examples]