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import gradio as gr | |
from app import demo as app | |
import os | |
_docs = {'Model4DGS': {'description': 'Component allows users to upload or view 4D Gaussian Splatting files (.splat).', 'members': {'__init__': {'value': {'type': 'str | Callable | None', 'default': 'None', 'description': 'path to (.splat) file to show in model4DGS viewer. If callable, the function will be called whenever the app loads to set the initial value of the component.'}, 'height': {'type': 'int | None', 'default': 'None', 'description': 'height of the model4DGS component, in pixels.'}, 'label': {'type': 'str | None', 'default': 'None', 'description': None}, 'show_label': {'type': 'bool | None', 'default': 'None', 'description': None}, 'every': {'type': 'float | None', 'default': 'None', 'description': None}, 'container': {'type': 'bool', 'default': 'True', 'description': None}, 'scale': {'type': 'int | None', 'default': 'None', 'description': None}, 'min_width': {'type': 'int', 'default': '160', '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}}, 'postprocess': {'value': {'type': 'List[str] | str | None', 'description': "The output data received by the component from the user's function in the backend."}}, 'preprocess': {'return': {'type': 'List[str] | None', 'description': "The preprocessed input data sent to the user's function in the backend."}, 'value': None}}, 'events': {'change': {'type': None, 'default': None, 'description': 'Triggered when the value of the Model4DGS changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.'}, 'upload': {'type': None, 'default': None, 'description': 'This listener is triggered when the user uploads a file into the Model4DGS.'}, 'edit': {'type': None, 'default': None, 'description': 'This listener is triggered when the user edits the Model4DGS (e.g. image) using the built-in editor.'}, 'clear': {'type': None, 'default': None, 'description': 'This listener is triggered when the user clears the Model4DGS using the X button for the component.'}}}, '__meta__': {'additional_interfaces': {}, 'user_fn_refs': {'Model4DGS': []}}} | |
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_model4dgs` | |
<div style="display: flex; gap: 7px;"> | |
<a href="https://pypi.org/project/gradio_model4dgs/" target="_blank"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/gradio_model4dgs"></a> | |
</div> | |
Python library for easily interacting with trained machine learning models | |
""", elem_classes=["md-custom"], header_links=True) | |
app.render() | |
gr.Markdown( | |
""" | |
## Installation | |
```bash | |
pip install gradio_model4dgs | |
``` | |
## Usage | |
```python | |
import gradio as gr | |
from gradio_model4dgs import Model4DGS | |
import os | |
image_dir = os.path.join(os.path.dirname(__file__), "assets") | |
if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir): | |
examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)] | |
else: | |
examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()] | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
Model4DGS(value=examples, label="4D Model") | |
if __name__ == "__main__": | |
demo.launch(share=True) | |
``` | |
""", elem_classes=["md-custom"], header_links=True) | |
gr.Markdown(""" | |
## `Model4DGS` | |
### Initialization | |
""", elem_classes=["md-custom"], header_links=True) | |
gr.ParamViewer(value=_docs["Model4DGS"]["members"]["__init__"], linkify=[]) | |
gr.Markdown("### Events") | |
gr.ParamViewer(value=_docs["Model4DGS"]["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. | |
- **As input:** Is passed, the preprocessed input data sent to the user's function in the backend. | |
- **As output:** Should return, the output data received by the component from the user's function in the backend. | |
```python | |
def predict( | |
value: List[str] | None | |
) -> List[str] | str | None: | |
return value | |
``` | |
""", elem_classes=["md-custom", "Model4DGS-user-fn"], header_links=True) | |
demo.load(None, js=r"""function() { | |
const refs = {}; | |
const user_fn_refs = { | |
Model4DGS: [], }; | |
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() | |