gradio-pdf 0.0.13

Description:

gradiopdf 0.0.13

gradio_pdf

Easily display PDFs in Gradio
Installation
pip install gradio_pdf

Usage
import gradio as gr
from gradio_pdf 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']


demo = gr.Interface(
qa,
[gr.Textbox(label="Question"), PDF(label="Document")],
gr.Textbox(),
examples=[["What is the total gross worth?", str(dir_ / "invoice_2.pdf")],
["Whos is being invoiced?", str(dir_ / "sample_invoice.pdf")]]
)

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

PDF
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[Ellipsis, 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