Update README.md

README.md

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    <p class="lg:col-span-2">
     We are happy to invite you to the Gradio Blocks Party - a community event in which we will create <b>interactive demos</b> for state-of-the-art machine learning models. Demos are powerful because they allow anyone — not just ML engineers — to try out models in the browser, give feedback on predictions, identify trustworthy models. The event will take place from May 16th to 31st. We will be organizing this event on <a href="https://github.com/huggingface/community-events" target="_blank" style="text-decoration: underline">Github</a> and the <a href="https://discord.com/invite/feTf9x3ZSB" target="_blank" style="text-decoration: underline">Hugging Face discord channel</a>. Prizes will be given at the end of the event, see: <a href="#Prizes" target="_blank" style="text-decoration: underline">Prizes</a> </p><br />
    
-   <p class="lg:col-span-2"> We will be building demos using the new Gradio Blocks API. Blocks allows you to build web-based demos in a flexible way using the <a href="https://www.gradio.app">Gradio library</a>. Gradio is a popular choice for building demos for machine learning models, as it allows you to create UIs from Python. For example, here is a UI for Dall-E Mini using Gradio Blocks:
+   <p class="lg:col-span-2"> We will be building demos using the new Gradio <a "#what-blocks">Blocks API</a>. Blocks allows you to build web-based demos in a flexible way using the <a href="https://www.gradio.app">Gradio library</a>. Gradio is a popular choice for building demos for machine learning models, as it allows you to create UIs from Python. For example, here is a UI for Dall-E Mini using Gradio Blocks:
    </p>
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    <p class="lg:col-span-2"><code>pip install gradio</code> to install gradio locally</p>
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-   <h3  class="my-8 lg:col-span-2" style="font-size:20px; font-weight:bold">What is Blocks?</h3>
+   <h3  class="my-8 lg:col-span-2" style="font-size:20px; font-weight:bold" id="what-blocks">What is Blocks?</h3>
    <p class="lg:col-span-2">
      <code>gradio.Blocks</code> is a low-level API that allows you to have full control over the data flows and layout of your application. You can build very complex, multi-step applications using Blocks.
      If you have already used gradio.Interface, you know that you can easily create fully-fledged machine learning demos with just a few lines of code. The Interface API is very convenient but in some cases may not be sufficiently flexible for your needs. For example, you might want to: