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()