gradio_testannimage

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_testannimage

Usage

import gradio as gr
from gradio_testannimage import TestAnnImage


with gr.Blocks() as demo:
    with gr.Row():
        TestAnnImage(label="Blank"),  # blank component
        TestAnnImage(label="Populated"),  # populated component


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

TestAnnImage

Initialization

tuple[
        numpy.ndarray | PIL.Image.Image | str,
        list[
            tuple[
                numpy.ndarray | tuple[int, int, int, int],
                str,
            ]
        ],
    ]
    | None

bool

int | str | None

int | str | None

dict[str, str] | None

str | None

float | None

bool | None

bool

int | None

int

bool

str | None

list[str] | str | None

bool

Events

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 input: Should return, tuple of base image and list of subsections, with each subsection a two-part tuple where the first element is a 4 element bounding box or a 0-1 confidence mask, and the second element is the label.

def predict(
    value: AnnotatedImageData | None
) -> tuple[
       numpy.ndarray | PIL.Image.Image | str,
       list[
           tuple[
               numpy.ndarray | tuple[int, int, int, int],
               str,
           ]
       ],
   ]
   | None:
    return value

AnnotatedImageData

class AnnotatedImageData(GradioModel):
    image: FileData
    annotations: List[Annotation]

Annotation

class Annotation(GradioModel):
    image: FileData
    label: str