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space.py
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import gradio as gr
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from app import demo as app
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import os
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_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': []}}}
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abs_path = os.path.join(os.path.dirname(__file__), "css.css")
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with gr.Blocks(
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css=abs_path,
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theme=gr.themes.Default(
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font_mono=[
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gr.themes.GoogleFont("Inconsolata"),
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"monospace",
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],
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),
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) as demo:
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gr.Markdown(
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"""
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# `gradio_model4dgs`
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<div style="display: flex; gap: 7px;">
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<a href="https://pypi.org/project/gradio_model4dgs/" target="_blank"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/gradio_model4dgs"></a>
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</div>
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Python library for easily interacting with trained machine learning models
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""", elem_classes=["md-custom"], header_links=True)
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app.render()
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gr.Markdown(
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"""
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## Installation
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```bash
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pip install gradio_model4dgs
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```
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## Usage
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```python
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import gradio as gr
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from gradio_model4dgs import Model4DGS
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import os
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image_dir = os.path.join(os.path.dirname(__file__), "assets")
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if os.path.exists(image_dir) and os.path.isdir(image_dir) and os.listdir(image_dir):
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examples = [os.path.join(image_dir, file) for file in os.listdir(image_dir)]
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else:
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examples = [os.path.join(os.path.dirname(__file__), example) for example in Model4DGS().example_inputs()]
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with gr.Blocks() as demo:
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with gr.Row():
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Model4DGS(value=examples, label="4D Model")
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if __name__ == "__main__":
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demo.launch(share=True)
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```
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""", elem_classes=["md-custom"], header_links=True)
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gr.Markdown("""
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## `Model4DGS`
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### Initialization
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""", elem_classes=["md-custom"], header_links=True)
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gr.ParamViewer(value=_docs["Model4DGS"]["members"]["__init__"], linkify=[])
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gr.Markdown("### Events")
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gr.ParamViewer(value=_docs["Model4DGS"]["events"], linkify=['Event'])
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gr.Markdown("""
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### User function
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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).
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- When used as an Input, the component only impacts the input signature of the user function.
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- When used as an output, the component only impacts the return signature of the user function.
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The code snippet below is accurate in cases where the component is used as both an input and an output.
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- **As input:** Is passed, the preprocessed input data sent to the user's function in the backend.
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- **As output:** Should return, the output data received by the component from the user's function in the backend.
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```python
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def predict(
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value: List[str] | None
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) -> List[str] | str | None:
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return value
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```
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""", elem_classes=["md-custom", "Model4DGS-user-fn"], header_links=True)
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demo.load(None, js=r"""function() {
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const refs = {};
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const user_fn_refs = {
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Model4DGS: [], };
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requestAnimationFrame(() => {
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Object.entries(user_fn_refs).forEach(([key, refs]) => {
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if (refs.length > 0) {
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const el = document.querySelector(`.${key}-user-fn`);
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if (!el) return;
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refs.forEach(ref => {
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el.innerHTML = el.innerHTML.replace(
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new RegExp("\\b"+ref+"\\b", "g"),
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`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
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);
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})
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}
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})
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Object.entries(refs).forEach(([key, refs]) => {
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if (refs.length > 0) {
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const el = document.querySelector(`.${key}`);
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if (!el) return;
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refs.forEach(ref => {
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el.innerHTML = el.innerHTML.replace(
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new RegExp("\\b"+ref+"\\b", "g"),
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`<a href="#h-${ref.toLowerCase()}">${ref}</a>`
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);
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})
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}
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})
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})
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}
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""")
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demo.launch()
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