Awesome

Online course starter: Python

This is a starter repo based on the course framework I developed for my spaCy course. The front-end is powered by Gatsby and Reveal.js and the back-end code execution uses Binder 💖

📖 Documentation

<a href="https://ines.github.io/course-starter-python/"><img width="300" src="https://user-images.githubusercontent.com/13643239/75773176-ab897a00-5d4d-11ea-8593-1fc018885611.png" align="right"></a>

Thanks to @hfboyce for contributing a super detailed guide on getting started with this course framework, adding exercises, slides and other content, and customizing the design. It also comes with a Dockerfile that takes care of the dependencies for you.

➡️ Read the documentation here

✅ Quickstart

1. Import this repo, install it and make sure the app is running locally.
2. Customize the meta.json and binder/requirements.txt.
3. Build a Binder from the binder branch of this repo.
4. Add content (chapters, exercises and slides) and optionally add separate content license.
5. Customize the UI theme in theme.sass and update images in static as needed.
6. Deploy the app, e.g. to Netlify.

Running the app

To start the local development server, install Gatsby and then all other dependencies. This should serve up the app on localhost:8000.

npm install -g gatsby-cli  # Install Gatsby globally
npm install                # Install dependencies
npm run dev                # Run the development server

🎨 Customization

The app separates its source and content – so you usually shouldn't have to dig into the JavaScript source to change things. The following points of customization are available:

meta.json

The following meta settings are available. Note that you have to re-start Gatsby to see the changes if you're editing it while the server is running.

Static assets

All files added to /static will become available at the root of the deployed site. So /static/image.jpg can be referenced in your course as /image.jpg. The following assets need to be available and can be customized:

✏️ Content

File formats

Chapters

Chapters are placed in /chapters and are Markdown files consisting of <exercise> components. They'll be turned into pages, e.g. /chapter1. In their frontmatter block at the top of the file, they need to specify type: chapter, as well as the following meta:

---
title: The chapter title
description: The chapter description
prev: /chapter1 # exact path to previous chapter or null to not show a link
next: /chapter3 # exact path to next chapter or null to not show a link
id: 2 # unique identifier for chapter
type: chapter # important: this creates a standalone page from the chapter
---

Slides

Slides are placed in /slides and are markdown files consisting of slide content, separated by ---. They need to specify the following frontmatter block at the top of the file:

---
type: slides
---

The first and last slide use a special layout and will display the headline in the center of the slide. Speaker notes (in this case, the script) can be added at the end of a slide, prefixed by Notes:. They'll then be shown on the right next to the slides. Here's an example slides file:

---
type: slides
---

# Processing pipelines

Notes: This is a slide deck about processing pipelines.

---

# Next slide

- Some bullet points here
- And another bullet point

<img src="/image.jpg" alt="An image located in /static" />

Custom Elements

When using custom elements, make sure to place a newline between the opening/closing tags and the children. Otherwise, Markdown content may not render correctly.

<exercise>

Container of a single exercise.

<exercise id="1" title="Introduction to spaCy">

Content goes here...

</exercise>

<codeblock>

<codeblock id="02_03">

This is a hint!

</codeblock>

<slides>

Container to display slides interactively using Reveal.js and a Markdown file.

<slides source="chapter1_01_introduction-to-spacy">
</slides>

<choice>

Container for multiple-choice question.

<choice>

<opt text="Option one">You have selected option one! This is not good.</opt>
<opt text="Option two" correct="true">Yay! </opt>

</choice>

<opt>

A multiple-choice option.

Setting up Binder

The requirements.txt in the repository defines the packages that are installed when building it with Binder. You can specify the binder settings like repo, branch and kernel type in the "juniper" section of the meta.json. I'd recommend running the very first build via the interface on the Binder website, as this gives you a detailed build log and feedback on whether everything worked as expected. Enter your repository URL, click "launch" and wait for it to install the dependencies and build the image.

Adding tests

To validate the code when the user hits "Submit", we're currently using a slightly hacky trick. Since the Python code is sent back to the kernel as a string, we can manipulate it and add tests – for example, exercise exc_01_02_01.py will be validated using test_01_02_01.py (if available). The user code and test are combined using a string template. At the moment, the testTemplate in the meta.json looks like this:

from wasabi import Printer
__msg__ = Printer()
__solution__ = """${solution}"""
${solution}

${test}
try:
    test()
except AssertionError as e:
    __msg__.fail(e)

If present, ${solution} will be replaced with the string value of the submitted user code. In this case, we're inserting it twice: once as a string so we can check whether the submission includes something, and once as the code, so we can actually run it and check the objects it creates. ${test} is replaced by the contents of the test file. It's also making wasabi's printer available as __msg__, so we can easily print pretty messages in the tests. Finally, the try/accept block checks if the test function raises an AssertionError and if so, displays the error message. This also hides the full error traceback (which can easily leak the correct answers).

A test file could then look like this:

def test():
    assert "spacy.load" in __solution__, "Are you calling spacy.load?"
    assert nlp.meta["lang"] == "en", "Are you loading the correct model?"
    assert nlp.meta["name"] == "core_web_sm", "Are you loading the correct model?"
    assert "nlp(text)" in __solution__, "Are you processing the text correctly?"
    assert "print(doc.text)" in __solution__, "Are you printing the Doc's text?"

    __msg__.good(
        "Well done! Now that you've practiced loading models, let's look at "
        "some of their predictions."
    )

The string answer is available as __solution__, and the test also has access to the solution code.

For more details on how it all works behind the scenes, see the original course repo.