gradio-model4dgs 0.0.2

Python library for easily interacting with trained machine learning models

gradio_model4dgs

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_model4dgs

Usage

import gradio as gr
from gradio_model4dgs import Model4DGS
import os

examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]

with gr.Blocks() as demo:
    with gr.Row():
        Model4DGS(value=examples, label="4D Model")

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

Model4DGS

Initialization

name	type	default	description
value	str | Callable | None	None	path to (.splat) file to show in model4DGS viewer. 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 model4DGS component, in pixels.
label	str | None	None	None
show_label	bool | None	None	None
every	float | None	None	None
container	bool	True	None
scale	int | None	None	None
min_width	int	160	None
interactive	bool | None	None	None
visible	bool	True	None
elem_id	str | None	None	None
elem_classes	list[str] | str | None	None	None
render	bool	True	None

Events

name	description
change	Triggered when the value of the Model4DGS 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 Model4DGS.
edit	This listener is triggered when the user edits the Model4DGS (e.g. image) using the built-in editor.
clear	This listener is triggered when the user clears the Model4DGS using the X button for the component.

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.
• As input: Should return, the output data received by the component from the user's function in the backend.

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