Create app.py

app.py

@@ -0,0 +1,233 @@
+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><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><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="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><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><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.Row():
+            with gr.Column():
+                gr.Markdown(category.description, elem_id="margin-top")
+            with gr.Column():
+                gr.Markdown("### Hugging Face News 📰")
+                [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="🤗 Hugging Face Papers"):
+            with gr.Row(elem_id="spaces-flex"):
+                [paper_tile(p) for p in papers.filter(lambda p: category.category_id in p["tags"])]
+        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 the 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):
+                    gr.Image(value="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/making-intelligence-banner.png", show_label=False)
+                with gr.Column(scale=2):
+                    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()