Metadata-Version: 2.3
Name: gradio_pdf
Version: 0.0.10
Summary: Easily display PDFs in Gradio
Project-URL: repository, https://github.com/freddyaboulton/gradio-pdf
Project-URL: space, https://huggingface.co/spaces/freddyaboulton/gradio_pdf
Author-email: Freddy Boulton <alfonsoboulton@gmail.com>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: Document QA,Documents,PDF,gradio,gradio custom component,gradio-template-Fallback
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.8
Requires-Dist: gradio<5.0,>=4.0
Provides-Extra: dev
Requires-Dist: build; extra == 'dev'
Requires-Dist: twine; extra == 'dev'
Description-Content-Type: text/markdown


# `gradio_pdf`
<a href="https://pypi.org/project/gradio_pdf/" target="_blank"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/gradio_pdf"></a> <a href="https://github.com/freddyaboulton/gradio-pdf/issues" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/Issues-white?logo=github&logoColor=black"></a> <a href="https://huggingface.co/spaces/freddyaboulton/gradio_pdf/discussions" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/%F0%9F%A4%97%20Discuss-%23097EFF?style=flat&logoColor=black"></a>

Easily display PDFs in Gradio

## Installation

```bash
pip install gradio_pdf
```

## Usage

```python
import gradio as gr
from gradio_pdf import PDF
from fastapi import FastAPI
from fastapi.responses import FileResponse

app = FastAPI()

@app.get("/foo/pdf_worker")
def foo():
    return FileResponse("frontend/node_modules/pdfjs-dist/build/pdf.worker.mjs", headers={"Access-Control-Allow-Origin": "*",
                                                            'Content-Type': 'text/javascript'})




with gr.Blocks() as demo:
    PDF(interactive=True)


app = gr.mount_gradio_app(app, demo, "/")

import uvicorn
uvicorn.run(app, port=7860)
# import gradio as gr
# from _app import demo as app
# import os

# _docs = {'PDF': {'description': 'A base class for defining methods that all input/output components should have.', 'members': {'__init__': {'value': {'type': 'Any', 'default': 'None', 'description': None}, 'height': {'type': 'int | None', 'default': 'None', 'description': None}, 'label': {'type': 'str | None', 'default': 'None', 'description': None}, 'info': {'type': 'str | None', 'default': 'None', 'description': None}, 'show_label': {'type': 'bool | None', 'default': 'None', 'description': None}, 'container': {'type': 'bool', 'default': 'True', 'description': None}, 'scale': {'type': 'int | None', 'default': 'None', 'description': None}, 'min_width': {'type': 'int | None', 'default': 'None', 'description': None}, 'interactive': {'type': 'bool | None', 'default': 'None', 'description': None}, 'visible': {'type': 'bool', 'default': 'True', 'description': None}, 'elem_id': {'type': 'str | None', 'default': 'None', 'description': None}, 'elem_classes': {'type': 'list[str] | str | None', 'default': 'None', 'description': None}, 'render': {'type': 'bool', 'default': 'True', 'description': None}, 'load_fn': {'type': 'Callable[..., Any] | None', 'default': 'None', 'description': None}, 'every': {'type': 'float | None', 'default': 'None', 'description': None}}, 'postprocess': {'value': {'type': 'str | None', 'description': None}}, 'preprocess': {'return': {'type': 'str', 'description': None}, 'value': None}}, 'events': {'change': {'type': None, 'default': None, 'description': ''}, 'upload': {'type': None, 'default': None, 'description': ''}}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'PDF': []}}}
    
# abs_path = os.path.join(os.path.dirname(__file__), "css.css")

# with gr.Blocks(
#     css=abs_path,
#     theme=gr.themes.Default(
#         font_mono=[
#             gr.themes.GoogleFont("Inconsolata"),
#             "monospace",
#         ],
#     ),
# ) as demo:
#     gr.Markdown(
# """
# # `gradio_pdf`

# <div style="display: flex; gap: 7px;">
# <a href="https://pypi.org/project/gradio_pdf/" target="_blank"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/gradio_pdf"></a> <a href="https://github.com/freddyaboulton/gradio-pdf/issues" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/Issues-white?logo=github&logoColor=black"></a> <a href="https://huggingface.co/spaces/freddyaboulton/gradio_pdf/discussions" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/%F0%9F%A4%97%20Discuss-%23097EFF?style=flat&logoColor=black"></a>
# </div>

# Easily display PDFs in Gradio
# """, elem_classes=["md-custom"], header_links=True)
#     app.render()
#     gr.Markdown(
# """
# ## Installation

# ```bash
# pip install gradio_pdf
# ```

# ## Usage

# ```python

# 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()

# ```
# """, elem_classes=["md-custom"], header_links=True)


#     gr.Markdown("""
# ## `PDF`

# ### Initialization
# """, elem_classes=["md-custom"], header_links=True)

#     gr.ParamViewer(value=_docs["PDF"]["members"]["__init__"], linkify=[])


#     gr.Markdown("### Events")
#     gr.ParamViewer(value=_docs["PDF"]["events"], linkify=['Event'])




#     gr.Markdown("""

# ### 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.



#  ```python
# def predict(
#     value: str
# ) -> str | None:
#     return value
# ```
# """, elem_classes=["md-custom", "PDF-user-fn"], header_links=True)




#     demo.load(None, js=r"""function() {
#     const refs = {};
#     const user_fn_refs = {
#           PDF: [], };
#     requestAnimationFrame(() => {

#         Object.entries(user_fn_refs).forEach(([key, refs]) => {
#             if (refs.length > 0) {
#                 const el = document.querySelector(`.${key}-user-fn`);
#                 if (!el) return;
#                 refs.forEach(ref => {
#                     el.innerHTML = el.innerHTML.replace(
#                         new RegExp("\\b"+ref+"\\b", "g"),
#                         `<a href="#h-${ref.toLowerCase()}">${ref}</a>`
#                     );
#                 })
#             }
#         })
        
#         Object.entries(refs).forEach(([key, refs]) => {
#             if (refs.length > 0) {
#                 const el = document.querySelector(`.${key}`);
#                 if (!el) return;
#                 refs.forEach(ref => {
#                     el.innerHTML = el.innerHTML.replace(
#                         new RegExp("\\b"+ref+"\\b", "g"),
#                         `<a href="#h-${ref.toLowerCase()}">${ref}</a>`
#                     );
#                 })
#             }
#         })
#     })
# }

# """)

# demo.launch()

```

## `PDF`

### Initialization

<table>
<thead>
<tr>
<th align="left">name</th>
<th align="left" style="width: 25%;">type</th>
<th align="left">default</th>
<th align="left">description</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><code>value</code></td>
<td align="left" style="width: 25%;">

```python
Any
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>height</code></td>
<td align="left" style="width: 25%;">

```python
int | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>label</code></td>
<td align="left" style="width: 25%;">

```python
str | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>info</code></td>
<td align="left" style="width: 25%;">

```python
str | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>show_label</code></td>
<td align="left" style="width: 25%;">

```python
bool | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>container</code></td>
<td align="left" style="width: 25%;">

```python
bool
```

</td>
<td align="left"><code>True</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>scale</code></td>
<td align="left" style="width: 25%;">

```python
int | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>min_width</code></td>
<td align="left" style="width: 25%;">

```python
int | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>interactive</code></td>
<td align="left" style="width: 25%;">

```python
bool | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>visible</code></td>
<td align="left" style="width: 25%;">

```python
bool
```

</td>
<td align="left"><code>True</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>elem_id</code></td>
<td align="left" style="width: 25%;">

```python
str | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>elem_classes</code></td>
<td align="left" style="width: 25%;">

```python
list[str] | str | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>render</code></td>
<td align="left" style="width: 25%;">

```python
bool
```

</td>
<td align="left"><code>True</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>load_fn</code></td>
<td align="left" style="width: 25%;">

```python
Callable[Ellipsis, Any] | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>every</code></td>
<td align="left" style="width: 25%;">

```python
float | None
```

</td>
<td align="left"><code>None</code></td>
<td align="left">None</td>
</tr>

<tr>
<td align="left"><code>starting_page</code></td>
<td align="left" style="width: 25%;">

```python
int | None
```

</td>
<td align="left"><code>1</code></td>
<td align="left">None</td>
</tr>
</tbody></table>


### 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.

 ```python
 def predict(
     value: str
 ) -> str | None:
     return value
 ```
 
