gradio-xterm-logger 0.0.2

Python library for easily interacting with trained machine learning models

gradio_xterm_logger

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_xterm_logger

Usage

import gradio as gr
from gradio_xterm_logger import GradioXtermLogger


with gr.Blocks() as demo:
    terminal_a = GradioXtermLogger(
        label="Terminal Output (Custom Log File)",
        log_path="inference_log.log",
        font_size=12,
        dark_mode=True,
        every=0.5
    )

    terminal_b = GradioXtermLogger(
        label="Terminal Output (Current Terminal)",
        show_current_terminal=True,
        font_size=12,
        dark_mode=False,
        every=0.5
    )

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

GradioXtermLogger

Initialization

name	type	default	description
log_path	str | None	None	None
dark_mode	bool	True	None
font_size	int	12	None
show_current_terminal	bool	False	None
n_lines	int	40	None
label	str | None	None	None
info	str | None	None	None
show_label	bool | None	None	None
container	bool	True	None
scale	int | None	None	None
min_width	int | None	None	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
load_fn	typing.Optional[typing.Callable[..., typing.Any]][ typing.Callable[..., typing.Any][Ellipsis, Any], None ]	None	None
every	float | None	0.5	None

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: Any
) -> Any:
    return value