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| <!-- DISABLE-FRONTMATTER-SECTIONS --> | ||
| # Gradio, check! | ||
|
|
||
| # End-of-chapter quiz | ||
| This wraps up the chapter on building cool ML demos with Gradio - we hope you enjoyed it! To recap, in this chapter we learned: | ||
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|
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| Let's test what you learned in this chapter! | ||
| - How to create Gradio demos with the high-level `Interface` API, and how to configure different input and output modalities. | ||
| - Different ways to share Gradio demos, through temporary links and hosting on [Hugging Face Spaces](https://huggingface.co/spaces). | ||
| - How to integrate Gradio demos with models and Spaces on the Hugging Face Hub. | ||
| - Advanced features like storing state in a demo or providing authentication. | ||
| - How to have full control of the data flow and layout of your demo with Gradio Blocks. | ||
|
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||
| ### 1. What can you use Gradio to do? | ||
| If you'd like to test your understanding of the concepts covered in this chapter, check out the quiz in the next section! | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "Create a demo for your machine learning model", | ||
| explain: "With a few lines of python code you can generate a demo for your ML model using our library of pre-built components.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Share your machine learning model with others", | ||
| explain: "Using the share=True parameter in the launch method, you can generate a share link to send to anyone.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Debug your model", | ||
| explain: "One advantage of a gradio demo is being able to test your model with real data which you can change and observe the model's predictions change in real time, helping you debug your model.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Train your model", | ||
| explain: "Gradio is designed to be used for model inference, AFTER your model is trained.", | ||
| } | ||
| ]} | ||
| /> | ||
| ## Gradio blocks party 🥳 | ||
|
|
||
| ### 2. Gradio ONLY works with PyTorch models | ||
| If you want to put the knowledge from this chapter to good use, come join the Gradio blocks party! This is a community event that's hosted by Hugging Face on **May 16-31**. During this event, you'll build cool machine learning demos with Gradio and be in the running to win Hugging Face swag and prizes! | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "True", | ||
| explain: "Gradio works with pytorch models, but also works for any type of machine learning model!" | ||
| }, | ||
| { | ||
| text: "False", | ||
| explain: "Gradio is model agnostic, meaning you can create a demo for any type of machine learning model.", | ||
| correct: true | ||
| } | ||
| ]} | ||
| /> | ||
|
|
||
| ### 3. Where can you launch a Gradio demo from? | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "Standard python IDEs", | ||
| explain: "Gradio works great with your favorite IDE.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Google Colab notebooks", | ||
| explain: "You can create and launch a demo within your Google colab notebook.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Jupyter notebooks", | ||
| explain: "Good choice - You can create and launch a demo within your Jupyter notebook.", | ||
| correct: true | ||
| } | ||
| ]} | ||
| /> | ||
|
|
||
| ### 4. Gradio is designed primarily for NLP models | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "True", | ||
| explain: "Gradio works with pretty much any data type, not just NLP." | ||
| }, | ||
| { | ||
| text: "False", | ||
| explain: "Gradio supplies developers with a library of pre-built components for pretty much all data types.", | ||
| correct: true | ||
| } | ||
| ]} | ||
| /> | ||
|
|
||
| ### 5. Which of the following features are supported by Gradio? | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "Multiple inputs and outputs", | ||
| explain: "Multiple inputs and outputs is possible with gradio. All you need to do is pass in a list of inputs and outputs to their corresponding parameters", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "State for data persistance", | ||
| explain: "Gradio is capable of adding state to your interface.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Username and passwords authentication", | ||
| explain: "Pass in a list of username/password tuples to the launch method to add authentication.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Automatic analytics for who uses your gradio demo", | ||
| explain: "Try again - Gradio does not supply developers analytics on who uses their demos." | ||
| }, | ||
| { | ||
| text: "Loading a model from Hugging Face's model hub or Hugging Face Spaces", | ||
| explain: "Absolutely - load any Hugging Face model using the gr.Interface.load() method", | ||
| correct: true | ||
| } | ||
| ]} | ||
| /> | ||
|
|
||
| ### 6. Which of the following are valid ways of loading a Hugging Face model from Hub or Spaces? | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "gr.Interface.load('huggingface/{user}/{model_name}')", | ||
| explain: "This is a valid method of loading a Hugging Face model from the Hub", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "gr.Interface.load('model/{user}/{model_name}')", | ||
| explain: "This is a valid method of loading a Hugging Face model from the Hub", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "gr.Interface.load('demos/{user}/{model_name}')", | ||
| explain: "Try again -- you cannot load a model by using the 'demos' prefix." | ||
| }, | ||
| { | ||
| text: "gr.Interface.load('spaces/{user}/{model_name}')", | ||
| explain: "This is a valid method of loading a Hugging Face model from Spaces", | ||
| correct: true | ||
| } | ||
| ]} | ||
| /> | ||
|
|
||
| ### 7. Select all the steps necessary for adding state to your Gradio interface | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "Pass in an extra parameter into your prediction function, which represents the state of the interface.", | ||
| explain: "An extra parameter storing history or state of your interface is necessary.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "At the end of the prediction function, return the updated value of the state as an extra return value.", | ||
| explain: "This history or state value needs to be returned by your function.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Add the state input and state output components when creating your Interface", | ||
| explain: "Gradio provides a state input and output component to persist data.", | ||
| correct: true | ||
| } | ||
| ]} | ||
| /> | ||
|
|
||
| ### 8. Which of the following are components included in the Gradio library? | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "Textbox.", | ||
| explain: "Yes, you can create textboxes with the Textbox component.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Graph.", | ||
| explain: "There is currently no Graph component.", | ||
| }, | ||
| { | ||
| text: "Image.", | ||
| explain: "Yes, you can create an image upload widget with the Image component.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Audio.", | ||
| explain: "Yes, you can create an audio upload widget with the Audio component.", | ||
| correct: true | ||
| }, | ||
| ]} | ||
| /> | ||
|
|
||
| ### 9. What does Gradio `Blocks` allow you to do? | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "Combine multiple demos into one web app", | ||
| explain: "You can use the `with gradio.Tabs():` to add tabs for multiple demos", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Assign event triggers such as clicked/changed/etc to `Blocks` components", | ||
| explain: "When you assign an event, you pass in three parameters: fn: the function that should be called, inputs: the (list) of input component(s), and outputs: the (list) of output components that should be called.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Automatically determine which `Blocks` component should be interactive vs. static", | ||
| explain: "Based on the event triggers you define, `Blocks` automatically figures out whether a component should accept user input or not.", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "Create multi-step demos; meaning allowing you to reuse the output of one component as the input to the next", | ||
| explain: "You can use a component for the input of one event trigger but the output of another.", | ||
| correct: true | ||
| }, | ||
| ]} | ||
| /> | ||
|
|
||
| ### 10. You can share a public link to a `Blocks` demo and host a `Blocks` demo on Hugging Face spaces. | ||
|
|
||
| <Question | ||
| choices={[ | ||
| { | ||
| text: "True", | ||
| explain: "Just like `Interface`, all of the sharing and hosting capabilities are the same for `Blocks` demos!", | ||
| correct: true | ||
| }, | ||
| { | ||
| text: "False", | ||
| explain: "Just like `Interface`, all of the sharing and hosting capabilities are the same for `Blocks` demos!", | ||
| correct: false | ||
| } | ||
| ]} | ||
| /> | ||
| Check out the [event description](https://github.com/AK391/community-events/blob/main/gradio-blocks/README.md) for details on how to participate - we can't wait to see what you'll build 🤗! |
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