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import gradio as gr
def name_to_markdown(name, network):
if(network == "twitter"):
return f"[{name}](https://twitter.com/{name})"
else:
return f"[{name}](https://huggingface.co/{name})"
def show_template(name, description, authors, url, image_url, more_info=None):
if isinstance(authors, str):
authors = [authors]
authors_md = ", ".join([name_to_markdown(author, network) for author, network in authors])
with gr.Group():
with gr.Row():
with gr.Column(scale=1):
gr.HTML(f'''<img src="{image_url}" alt="{name}-thumbnail" height=256 width=256>''')
with gr.Column(scale=4):
gr.Markdown(
f"""
## {name}
[]({url})
#### {description}
**Author(s):** {authors_md}
"""
)
if more_info:
with gr.Row():
with gr.Accordion("👀 More Details", open=False):
gr.Markdown(more_info)
title_and_description = """
# Spaces Templates
<div align="center">
<a src="https://img.shields.io/github/stars/nateraw/spaces-docker-templates?style=social" href="https://github.com/nateraw/spaces-docker-templates" target="_blank">
<img src="https://img.shields.io/github/stars/nateraw/spaces-docker-templates?label=Contribute&style=social" alt="GitHub Stars">
</a>
<h4>🚀 A collection of templates for <a href="https://huggingface.co/spaces">Hugging Face Spaces</a></h4>
The templates below are designed to help you get started with Docker Spaces. Duplicate them to get started with your own project. 🤗
</div>
"""
with gr.Blocks(css="style.css") as demo:
gr.Markdown(title_and_description)
show_template(
name="JupyterLab",
description="Spin up a JupyterLab instance with just a couple clicks. This template is great for data exploration, model training, and more. Works on CPU and GPU hardware.",
authors=[("camenduru", "twitter"), ("nateraw", "huggingface")],
url="https://huggingface.co/spaces/DockerTemplates/jupyterlab?duplicate=true",
image_url="https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/1767px-Jupyter_logo.svg.png",
more_info="""
### Configuration
- You can add dependencies to your JupyterLab instance by editing the `requirements.txt` file.
- You can add linux packages to your JupyterLab instance by editing the `packages.txt` file.
- You can add custom startup commands to your JupyterLab instance by editing the `on_startup.sh` file. These run with the root user.
""",
)
show_template(
name="VSCode",
description="Spin up a VSCode instance with just a couple clicks. This template is great for data exploration, model training, and more. Works on CPU and GPU hardware.",
authors=[("camenduru", "twitter"), ("nateraw", "huggingface")],
url="https://huggingface.co/spaces/DockerTemplates/vscode?duplicate=true",
image_url="https://upload.wikimedia.org/wikipedia/commons/thumb/9/9a/Visual_Studio_Code_1.35_icon.svg/1200px-Visual_Studio_Code_1.35_icon.svg.png",
more_info="""
### Configuration
- You can add dependencies to your VSCode instance by editing the `requirements.txt` file.
- You can add linux packages to your VSCode instance by editing the `packages.txt` file.
- You can add custom startup commands to your VSCode instance by editing the `on_startup.sh` file. These run with the root user.
""",
)
if __name__ == "__main__":
demo.launch()
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