`gradio_freddytb`

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_freddytb

Usage

import gradio as gr
from gradio_freddytb import FreddyTb


example = FreddyTb().example_inputs()

demo = gr.Interface(
    lambda x:x,
    FreddyTb(),  # interactive version of your component
    FreddyTb(),  # static version of your component
    # examples=[[example]],  # uncomment this line to view the "example version" of your component
)


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

`FreddyTb`

Initialization

name	type	default	description
`value`	str \| None	`None`	A path or URL for the default value that FreddyTb component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.
`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.
`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.

Events

name	description
`clear`	This listener is triggered when the user clears the FreddyTb using the X button for the component.
`change`	Triggered when the value of the FreddyTb 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 FreddyTb.

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, a string containing the path to the image.
As input: Should return, a string or pathlib.Path object containing the path to the image.

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

Metadata

Release files for gradio-freddytb 0.0.1

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Source distribution for gradio-freddytb 0.0.1
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Table of built distributions (wheels) for gradio-freddytb 0.0.1
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Download URL	gradio_freddytb-0.0.1.tar.gz
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Uploaded via	`twine/4.0.2 CPython/3.9.18`

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Size	87.4 kB
Tags	Python 3
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Upload date	Jan 31, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.18`

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0.0.1 This release

Jan 31, 2024 2 release files

gradio-freddytb 0.0.1