Spaces:
Running
Running
| import gradio as gr | |
| from typing import List | |
| from datasets import load_dataset | |
| class Space: | |
| def __init__(self, title, id): | |
| self.title = title | |
| self.id = id | |
| class News: | |
| def __init__(self, title, link): | |
| self.title = title | |
| self.link = link | |
| class Category: | |
| def __init__(self, category_id, title, description, news: List[News] = None, spaces=None): | |
| if news is None: | |
| news = [] | |
| if spaces is None: | |
| spaces = [] | |
| self.category_id = category_id | |
| self.title = title | |
| self.description = description | |
| self.news = news | |
| self.spaces = spaces | |
| client_side = Category( | |
| category_id="client_side", | |
| title="Client Side Libraries 🤝", | |
| description=""" | |
| ## These are client side libraries to easily interact or run training with models, datasets and Spaces on Hugging Face Hub 🤝 | |
| <br> | |
| """, | |
| news=[ | |
| News( | |
| title="We have launched huggingface.js to let developers interact with models on Hub in an API-like manner🚀", | |
| link="https://github.com/huggingface/huggingface.js" | |
| ), | |
| News( | |
| title="Xenova released transformers.js, to let you run powerful transformers easily inside browsers 🦾", | |
| link="https://github.com/xenova/transformers.js" | |
| ), | |
| News( | |
| title="Elixir 🤝 Hugging Face with Bumblebee", | |
| link="https://news.livebook.dev/announcing-bumblebee-gpt2-stable-diffusion-and-more-in-elixir-3Op73O" | |
| ) | |
| ], | |
| ) | |
| documentation = Category( | |
| category_id="documentation", | |
| title="Documentation 📚", | |
| description=""" | |
| ## These are our documentation efforts and blogs specifically targeted for software developers to get them started with building machine learning 🦾 | |
| <br> | |
| """, | |
| news=[ | |
| News( | |
| title="Tasks: Wikipedia of machine learning to easily find the model you need for your use case and get started with building! 📚 ", | |
| link="https://www.technologyreview.com/2023/03/22/1070167/these-news-tool-let-you-see-for-yourself-how-biased-ai-image-models-are/" | |
| ), | |
| News( | |
| title="huggingface.js Documentation", | |
| link="https://www.wired.com/story/welfare-state-algorithms/" | |
| ), | |
| News( | |
| title="Docker and Hugging Face Partner to Democratize AI", | |
| link="https://www.docker.com/blog/docker-and-hugging-face-partner-to-democratize-ai/" | |
| ), | |
| News( | |
| title="From GPT2 to Stable Diffusion: Hugging Face arrives to the Elixir community", | |
| link="https://huggingface.co./blog/elixir-bumblebee" | |
| ), | |
| News( | |
| title="Swift 🧨Diffusers: Fast Stable Diffusion for Mac", | |
| link="https://huggingface.co./blog/fast-mac-diffusers" | |
| ), | |
| News( | |
| title="Using Stable Diffusion with Core ML on Apple Silicon", | |
| link="https://huggingface.co./blog/diffusers-coreml" | |
| ), | |
| News( | |
| title="Tutorial: How Hugging Face achieved a 2x performance boost for Question Answering with DistilBERT in Node.js", | |
| link="https://blog.tensorflow.org/2020/05/how-hugging-face-achieved-2x-performance-boost-question-answering.html" | |
| ) | |
| ], | |
| ) | |
| use_cases = Category( | |
| category_id="use_cases", | |
| title="Use Cases 📖", | |
| description=""" | |
| ## These are resources compiled to demonstrate various use cases across different niches in software development. 👨💻 | |
| <br> | |
| """, | |
| news=[ | |
| News( | |
| title="AI for Game Development: Creating a Farming Game in 5 Days. Part 1", | |
| link="https://huggingface.co./blog/ml-for-games-1" | |
| ), | |
| News( | |
| title="AI for Game Development: Creating a Farming Game in 5 Days. Part 2", | |
| link="https://huggingface.co./blog/ml-for-games-2" | |
| ), | |
| News( | |
| title="3D Asset Generation: AI for Game Development #3", | |
| link="https://huggingface.co./blog/ml-for-games-3" | |
| ), | |
| News( | |
| title="Supercharged Customer Service with Machine Learning", | |
| link="https://huggingface.co./blog/supercharge-customer-service-with-machine-learning" | |
| ) | |
| ], | |
| ) | |
| cloud = Category( | |
| category_id="cloud", | |
| title="☁️ Cloud Deployment", | |
| description=""" | |
| ## This category includes resources on how to deploy machine learning models to cloud using various providers ☁️ | |
| <br> | |
| """, | |
| news=[ | |
| News( | |
| title="Deploying 🤗 ViT on Kubernetes with TF Serving", | |
| link="https://huggingface.co./blog/deploy-tfserving-kubernetes" | |
| ), | |
| News( | |
| title="An Overview of Inference Solutions on Hugging Face", | |
| link="https://huggingface.co./blog/inference-update" | |
| ), | |
| News( | |
| title="Hugging Face Collaborates with Microsoft to Launch Hugging Face Endpoints on Azure", | |
| link="https://huggingface.co./blog/hugging-face-endpoints-on-azure" | |
| ), | |
| News( | |
| title="Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it", | |
| link="https://www.youtube.com/watch?v=80ix-IyNnQI&ab_channel=AmazonWebServices" | |
| ), | |
| News( | |
| title="Getting Started with Hugging Face on AWS: Series of video tutorials", | |
| link="https://www.youtube.com/watch?v=80ix-IyNnQI&ab_channel=AmazonWebServices" | |
| ), | |
| ], | |
| ) | |
| categories = [client_side, documentation, cloud, use_cases] | |
| def news_card(news): | |
| with gr.Box(): | |
| with gr.Row(elem_id="news-row"): | |
| gr.Markdown(f"{news.title}") | |
| button = gr.Button(elem_id="article-button", value="Read more 🔗") | |
| button.click(fn=None, _js=f"() => window.open('{news.link}')") | |
| def space_card(space): | |
| with gr.Box(elem_id="space-card"): | |
| with gr.Row(elem_id="news-row"): | |
| gr.Markdown(f"{space.title}") | |
| button = gr.Button(elem_id="article-button", value="View 🔭") | |
| button.click(fn=None, _js=f"() => window.open('https://hf.space/{space.id}')") | |
| def category_tab(category): | |
| with gr.Tab(label=category.title, elem_id="news-tab"): | |
| with gr.Column(): | |
| gr.Markdown(category.description, elem_id="margin-top") | |
| with gr.Column(): | |
| [news_card(x) for x in category.news] | |
| # with gr.Tab(label="Hugging Face Projects"): | |
| # gr.Markdown("....") | |
| #with gr.Tab(label="Spaces"): | |
| # with gr.Row(elem_id="spaces-flex"): | |
| # [space_card(x) for x in category.spaces] | |
| # with gr.Tab(label="Models - Coming Soon!"): | |
| # gr.Markdown(elem_id="margin-top", value="#### Check back soon for featured models 🤗") | |
| # with gr.Tab(label="Datasets - Coming Soon!"): | |
| # gr.Markdown(elem_id="margin-top", value="#### Check back soon for featured datasets 🤗") | |
| with gr.Blocks(css="#margin-top {margin-top: 15px} #center {text-align: center;} #news-tab {padding: 15px;} #news-tab h3 {margin: 0px; text-align: center;} #news-tab p {margin: 0px;} #article-button {flex-grow: initial;} #news-row {align-items: center;} #spaces-flex {flex-wrap: wrap; justify-content: space-around;} #space-card { display: flex; min-width: calc(90% / 3); max-width:calc(100% / 3); box-sizing: border-box;} #event-tabs {margin-top: 0px;} #spaces-flex > #paper-tile {min-width: 30%; max-width: 30%;}") as demo: | |
| with gr.Row(elem_id="center"): | |
| gr.Markdown("# Hugging Face for Software Developers 🤗 💗 🧑💻") | |
| gr.Markdown(""" | |
| At Hugging Face, we are committed to democratize cutting-edge of machine learning for everyone. This page is dedicated to highlighting tools, documentation and projects – inside and outside Hugging Face – tailored to get software developers build with machine learning. | |
| """) | |
| with gr.Accordion(label="Events", open=False): | |
| with gr.Tab(label="Upcoming Events"): | |
| with gr.Row(elem_id="margin-top"): | |
| gr.Markdown("We'll be announcing more events soon!") | |
| with gr.Tab(label="Past Events"): | |
| with gr.Row(elem_id="margin-top"): | |
| with gr.Column(scale=1): | |
| with gr.Tabs(elem_id="event-tabs"): | |
| with gr.Tab("About the Event"): | |
| gr.Markdown(""" | |
| We have done a series of workshops for building, deploying and scaling models using AWS SageMaker. | |
| You can rewatch them [here](https://www.youtube.com/watch?v=pYqjCzoyWyo&ab_channel=HuggingFace). | |
| **Date:** October 26 2021 **Location:** YouTube | |
| """) | |
| with gr.Accordion(label="Visit us over on the Hugging Face Discord!", open=False): | |
| gr.Markdown(""" | |
| Follow these steps to join the discussion: | |
| 1. Go to [hf.co/join/discord](https://hf.co/join/discord) to join the Discord server. | |
| 2. Once you've registered, go to the `#role-assignment` channel. | |
| 3. Select the categories of your interest. Open Source ML is one that has different areas of machine learning. | |
| """, elem_id="margin-top") | |
| gr.Markdown(""" | |
| ### What can you achieve as a developer using Machine Learning? | |
| Following are different categories of interests that include tools and documentation for software developers. | |
| """) | |
| with gr.Column(): | |
| [category_tab(x) for x in categories] | |
| demo.launch() |