gradio-modifiablegallery 0.0.3

Python library for easily interacting with trained machine learning models

---

tags: [gradio-custom-component, Gallery] title: gradio_modifiablegallery short_description: colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py

gradio_modifiablegallery

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_modifiablegallery

Usage

from pathlib import Path

import gradio as gr
from gradio_modifiablegallery import ModifiableGallery

example = ModifiableGallery().example_value()


def delete_image(current_images, event: gr.EventData):

    image_to_delete_name = Path(event._data).name

    new_images = []
    for image, caption in current_images:
        if Path(image).name != image_to_delete_name:
            new_images.append((image, caption))

    return new_images


img = ("/home/ubuntu/gen-design-interface/tmp.jpg", "")
with gr.Blocks() as demo:
    with gr.Row():
        ModifiableGallery(label="Blank")  # blank component
        gallery = ModifiableGallery(
            value=[img, img, img], label="Populated", deletable=True
        )
        gallery.delete_image(fn=delete_image, inputs=gallery, outputs=gallery)


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

ModifiableGallery

Initialization

name	type	default	description
value	list[ numpy.ndarray | PIL.Image.Image | str | pathlib.Path | tuple ] | Callable | None	None	List of images to display in the gallery by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
format	str	"webp"	Format to save images before they are returned to the frontend, such as 'jpeg' or 'png'. This parameter only applies to images that are returned from the prediction function as numpy arrays or PIL Images. The format should be supported by the PIL library.
label	str | None	None	The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
every	float | None	None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
show_label	bool | None	None	if True, will display label.
container	bool	True	If True, will place the component in a container - providing some extra padding around the border.
scale	int | None	None	relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
min_width	int	160	minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
visible	bool	True	If False, component will be hidden.
elem_id	str | None	None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
elem_classes	list[str] | str | None	None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
render	bool	True	If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
key	int | str | None	None	if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.
columns	int | tuple | None	2	Represents the number of images that should be shown in one row, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints
rows	int | tuple | None	None	Represents the number of rows in the image grid, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints
height	int | float | None	None	The height of the gallery component, specified in pixels if a number is passed, or in CSS units if a string is passed. If more images are displayed than can fit in the height, a scrollbar will appear.
allow_preview	bool	False	If True, images in the gallery will be enlarged when they are clicked. Default is True.
preview	bool | None	False	If True, ModifiableGallery will start in preview mode, which shows all of the images as thumbnails and allows the user to click on them to view them in full size. Only works if allow_preview is True.
selected_index	int | None	None	The index of the image that should be initially selected. If None, no image will be selected at start. If provided, will set ModifiableGallery to preview mode unless allow_preview is set to False.
object_fit	"contain" | "cover" | "fill" | "none" | "scale-down" | None	None	CSS object-fit property for the thumbnail images in the gallery. Can be "contain", "cover", "fill", "none", or "scale-down".
show_share_button	bool | None	None	If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.
show_download_button	bool | None	True	If True, will show a download button in the corner of the selected image. If False, the icon does not appear. Default is True.
interactive	bool | None	None	If True, the gallery will be interactive, allowing the user to upload images. If False, the gallery will be static. Default is True.
type	"numpy" | "pil" | "filepath"	"filepath"	The format the image is converted to before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. If the image is SVG, the `type` is ignored and the filepath of the SVG is returned.
deletable	bool	True	None

Events

name	description
select	Event listener for when the user selects or deselects the ModifiableGallery. Uses event data gradio.SelectData to carry value referring to the label of the ModifiableGallery, and selected to refer to state of the ModifiableGallery. See EventData documentation on how to use this event data
upload	This listener is triggered when the user uploads a file into the ModifiableGallery.
change	Triggered when the value of the ModifiableGallery changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See .input() for a listener that is only triggered by user input.
delete_image

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As output: Is passed, passes the list of images as a list of (image, caption) tuples, or a list of (image, None) tuples if no captions are provided (which is usually the case). The image can be a str file path, a numpy array, or a PIL.Image object depending on type.
• As input: Should return, expects the function to return a list of images, or list of (image, str caption) tuples. Each image can be a str file path, a numpy array, or a PIL.Image object.

def predict(
    value: list[tuple[str, str | None]]
   | list[tuple[PIL.Image.Image, str | None]]
   | list[tuple[numpy.ndarray, str | None]]
   | None
) -> list[
       numpy.ndarray
       | PIL.Image.Image
       | pathlib.Path
       | str
       | tuple[
           numpy.ndarray
           | PIL.Image.Image
           | pathlib.Path
           | str,
           str,
       ]
   ]
   | None:
    return value