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Visualise any type of files

Project description


tags: [gradio-custom-component, File] title: gradio_viewer short_description: Visualise files colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py

gradio_viewer

PyPI - Version

Visualise any type of files

Installation

pip install gradio_viewer

Usage

import gradio as gr
from gradio_viewer import Viewer


def set_interface():
    pdf = Viewer(
        value=[
            "./demo/data/Le_Petit_Chaperon_Rouge_Modifie.docx",
            "./demo/data/mermaid_graph-2.html",
            "./demo/data/graphique_couts_annuels.png",
            "./demo/data/Le_Petit_Chaperon_Rouge.zouzou",
        ],
        elem_classes=["visualisation"],
        n=1,
        height=300,
        visible=True,
    )

    pdf2 = Viewer(
        value=[
            "./demo/data/Le_Petit_Chaperon_Rouge_Modifie.docx",
            "./demo/data/mermaid_graph-2.html",
            "./demo/data/graphique_couts_annuels.png",
            "./demo/data/Le_Petit_Chaperon_Rouge.zouzou",
        ],
        elem_classes=["visualisation"],
        n=2,
        height=300,
        visible=True,
    )
    return pdf, pdf2


with gr.Blocks() as demo:
    with gr.Row():
        pdf = Viewer(visible=False)
        pdf2 = Viewer(visible=False)
    demo.load(set_interface, outputs=[pdf, pdf2])

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

Viewer

Initialization

name type default description
value
Any
None None
height
int | None
None 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
Callable[..., Any] | None
None None
every
Timer | float | None
None None
n
int
0 None
files_with_original_ext
list[str]
[] None
interface_language
str
"fr" None

Events

name description
change
upload

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 data after preprocessing, sent to the user's function in the backend.
  • As input: Should return, expects a str filepath or URL, or a list[str] of filepaths/URLs.
def predict(
    value: str
) -> str | list[str] | None:
    return value

Project details


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  • Uploaded via: twine/5.1.1 CPython/3.9.18

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