gradio_tiff

Custom Gradio component for multi-page TIFF images

Installation

pip install gradio_tiff

Usage

import gradio as gr
from gradio_tiff import Tiff

demo = gr.Interface(
    lambda x: x,
    inputs=Tiff(value=Tiff().example_value()),
    outputs=Tiff(),
)


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

Tiff

Initialization

name	type	default	description
value	str | None	None	A path or URL to the TIFF image.
label	str | I18nData | 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	Timer | float | None	None	Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
inputs	Component | Sequence[Component] | set[Component] | None	None	Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.
show_label	bool | None	None	if True, will display label.
show_download_button	bool	True	If True, shows a button to download the original TIFF file.
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.
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 | Literal["hidden"]	True	If False, component will be hidden. If "hidden", component will be visually hidden and not take up space in the layout but still exist in the DOM
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 | tuple[int | str, ...] | None	None	in a gr.render, Components with the same key across re-renders are treated as the same component, not a new component. Properties set in 'preserved_by_key' are not reset across a re-render.
preserved_by_key	list[str] | str | None	"value"	A list of parameters from this component's constructor. Inside a gr.render() function, if a component is re-rendered with the same key, these (and only these) parameters will be preserved in the UI (if they have been changed by the user or an event listener) instead of re-rendered based on the values provided during constructor.

Events

name	description
clear	This listener is triggered when the user clears the Tiff using the clear button for the component.
change	Triggered when the value of the Tiff 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.
upload	This listener is triggered when the user uploads a file into the Tiff.

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.

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