gradio-legacyimage

gradio_legacyimage

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_legacyimage

Usage

import numpy as np

import gradio as gr
from gradio_legacyimage import LegacyImage

def process(x):
    flip = x.copy()
    flip["back"] = np.fliplr(flip["back"])
    mask = x.copy()
    mask["back"] = mask["mask"]
    return x, flip, mask

with gr.Blocks() as demo:
    with gr.Column():
        im1 = LegacyImage(source="upload", type="pil", tool="sketch")
        im2 = LegacyImage()
        im3 = LegacyImage()
        im4 = LegacyImage()

    btn = gr.Button()
    btn.click(process, inputs=im1, outputs=[im2, im3, im4])

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

LegacyImage

Initialization

name	type	default	description
value	str | PIL.Image.Image | numpy.ndarray | None	None	A PIL LegacyImage, numpy array, path or URL for the default value that LegacyImage component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.
height	int | None	None	Height of the displayed image in pixels.
width	int | None	None	Width of the displayed image in pixels.
image_mode	"1" | "L" | "P" | "RGB" | "RGBA" | "CMYK" | "YCbCr" | "LAB" | "HSV" | "I" | "F"	"RGB"	"RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning.
type	"numpy" | "pil" | "filepath"	"numpy"	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.
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. Queue must be enabled. 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.
show_download_button	bool	True	If True, will display button to download image.
container	bool	True	If True, will place the component in a container - providing some extra padding around the border.
scale	int | None	None	relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
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.
interactive	bool | None	None	if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.
visible	bool	True	If False, component will be hidden.
streaming	bool	False	If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'.
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.
mirror_webcam	bool	True	If True webcam will be mirrored. Default is True.
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.
source	"upload" | "webcam" | "canvas"	"upload"	None
invert_colors	bool	False	None
shape	tuple[int, int] | None	None	None
tool	"editor" | "select" | "sketch" | "color-sketch" | None	None	None
brush_radius	float | None	None	None
brush_color	str	"#000000"	None
mask_opacity	float	0.7	None

Events

name	description
clear	This listener is triggered when the user clears the LegacyImage using the X button for the component.
change	Triggered when the value of the LegacyImage 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.
stream	This listener is triggered when the user streams the LegacyImage.
select	Event listener for when the user selects or deselects the LegacyImage. Uses event data gradio.SelectData to carry value referring to the label of the LegacyImage, and selected to refer to state of the LegacyImage. See EventData documentation on how to use this event data
upload	This listener is triggered when the user uploads a file into the LegacyImage.
edit	This listener is triggered when the user edits the LegacyImage (e.g. image) using the built-in editor.

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, the preprocessed input data sent to the user's function in the backend.

def predict(
    value: PreprocessData | None
) -> PreprocessData | None:
    return value

PreprocessData

class PreprocessData(TypedDict):
    back: Optional[Union[np.ndarray, _Image.Image, str]]
    mask: Optional[Union[np.ndarray, _Image.Image, str]]