gradio-pdf-redaction 0.0.25

Easily display PDFs in Gradio. Forked from Freddy Boulton's gradio_pdf repo: https://github.com/freddyaboulton/gradio-pdf

gradio_pdf_redaction

Easily display PDFs in Gradio. Forked from Freddy Boulton's gradio_pdf repo: https://github.com/freddyaboulton/gradio-pdf

Installation

pip install gradio_pdf_redaction

Usage

import gradio as gr
from gradio_pdf_redaction import PDF
from pdf2image import convert_from_path
from transformers import pipeline
from pathlib import Path

dir_ = Path(__file__).parent

p = pipeline(
    "document-question-answering",
    model="impira/layoutlm-document-qa",
)

def qa(question: str, doc: str) -> str:
    img = convert_from_path(doc)[0]
    output = p(img, question)
    return sorted(output, key=lambda x: x["score"], reverse=True)[0]['answer']


EXAMPLES = [
    ["What is the total gross worth?", str(dir_ / "invoice_2.pdf")],
    ["Whos is being invoiced?", str(dir_ / "sample_invoice.pdf")],
]


def create_demo():
    return gr.Interface(
        qa,
        [gr.Textbox(label="Question"), PDF(label="Document")],
        gr.Textbox(),
        examples=EXAMPLES,
        cache_examples=False,
    )


# Required for `gradio cc dev` hot reload — must be a module-level Blocks/Interface.
demo = create_demo()

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

PDF

Initialization

name	type	default	description
value	typing.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	typing.Optional[typing.Callable[..., typing.Any]][ typing.Callable[..., typing.Any][Ellipsis, typing.Any], None, ]	None	None
every	float | None	None	None
starting_page	int | None	1	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 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: str
) -> str | None:
    return value