`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`	```python Any ```	`None`	None
`height`	```python int \| None ```	`None`	None
`label`	```python str \| None ```	`None`	None
`info`	```python str \| None ```	`None`	None
`show_label`	```python bool \| None ```	`None`	None
`container`	```python bool ```	`True`	None
`scale`	```python int \| None ```	`None`	None
`min_width`	```python int \| None ```	`None`	None
`interactive`	```python bool \| None ```	`None`	None
`visible`	```python bool ```	`True`	None
`elem_id`	```python str \| None ```	`None`	None
`elem_classes`	```python list[str] \| str \| None ```	`None`	None
`render`	```python bool ```	`True`	None
`load_fn`	```python Callable[Ellipsis, Any] \| None ```	`None`	None
`every`	```python float \| None ```	`None`	None
`starting_page`	```python 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
```

freddyaboulton / gradio-pdf

readme