Customizing your GitHub profile - Scripting

Recently, GitHub allowed users to customize the appearance of their profile page. This is different from GitHub Pages: it allows to add extra content to one's GitHub profile.

Since that feature is available, a lot of developers have decided to customize their page. I think this is fun, so I also wanted to do the same, in my own way.

I wanted to describe it, so fellow developers can take inspiration. Since the whole process is a bit long, I've split this post in two parts. This part is about creating the build script, while the second is about automating its execution.

Starting on the right foot

A lot of developers have written on how to customize one's profile. It consists of two steps:

1. Create a repository with the same name as your GitHub account
2. Add a README at the root. The README can be of any type that GitHub renders (Markdown or Asciidoctor)

From that point on, when you go to your profile's page, GitHub will render the README at the top. To give you a taste, here's the final preview:

Here's a summary of my requirements:

• The README MUST display the following data:

  • Bio
  • Last 3 blog posts
  • Next 5 planned talks
  • Last YouTube recording

• The README must not duplicate data from any existing data source

• The README must should update at regular intervals

• The README template must use the Asciidoctor format

• The system must be free to use

• The system must be an occasion to learn new things

• The system must not require too much learning time

With that in mind, the design I came up with is straightforward: a template with placeholders filled by the response of different HTTP calls.

Laying out the work

With the previous requirements list, I chose to use the following components:

1. GitHub Actions
2. Kotlin Scripting
3. Freemarker

Kotlin Scripting

A lot of developers have used Python to write the script that updates their profile page. It's not an option for me, as it would require too much investment on my part. On the other side, I'm quite familiar with Kotlin. But I did known nothing about Kotlin scripting.

The first hurdle I encountered was to understand there are two ways to use Kotlin in scripts:

• the official but still-experimental Kotlin Scripting
• and the community-led kscript effort.

While the latter seems to be more mature, I chose the former because it's backed by JetBrains, the company behind Kotlin.

Here are a couple of tips to help one start:

• Install Kotlin Scripting with SDKMAN.
• Suffix the script's name with main.kts e.g. myscript.main.kts
• The script's shebang is #!/usr/bin/env kotlin
• To use dependencies:
  1. Configure the required Maven repository(ies) i.e. @file:Repository("https://jcenter.bintray.com") for Bintray
  2. Use @file:DependsOn to add the dependency e.g. @file:DependsOn("org.json:json:20200518")

The script should:

1. Make the calls to each data source to get the relevant data
2. Load the README template
3. Fill in the template placeholders with the relevant bits of data
4. Write the README.adoc file with the completed template

Templating

Initially, I thought I would read the template from a document, and fill placeholders using Kotlin's String interpolation. I learned that interpolation runs at compile-time, not at run-time.

Then, I searched for a JVM-based templating engine. I set my eyes on Apache Freemarker. It's Open Source, easy to set up, and gets the job done.

Basically, Freemarker defines a simple template syntax with placeholders, loops, and conditionals. The code loads the template, evaluates it against a provided model, and writes the result back into a Writer.

The associated code looks like that:

val template = Configuration(Configuration.VERSION_2_3_29)                // 1
  .apply {
    setDirectoryForTemplateLoading(File("."))
    defaultEncoding = "UTF-8"
    templateExceptionHandler = TemplateExceptionHandler.RETHROW_HANDLER
    logTemplateExceptions = false
    wrapUncheckedExceptions = true
    fallbackOnNullLoopVariable = false
  }.getTemplate("template.adoc")

val root = mapOf(                                                         // 2
    "bio" to bio,
    "posts" to posts,
    "talks" to talks,
    "videoId" to videoId
)

template.process(root, FileWriter("README.adoc"))                         // 3

1. Create the Freemarker configuration
2. Create the model as a map
3. Merge the model with the template, and write down the result in the README.adoc file

If you're more visual, here's a partial view of class diagram:

HTTP calls

In the past, I've used the Fuel HTTP client. The API takes advantage of the Kotlin language, offers several syntax flavors, and has a solid community behind it.

But when I started to use it, I faced an issue. In a traditional Maven project, the workaround would be to add the dependency in the available version, and thus override the default one. Yet, it doesn't work with Kotlin Scripting - it's not possible to override versions in the same way.

I had to pivot. I chose OkHttp as an alternative. The benefit of OkHttp is its fluent API.

Here's a simplified view of the class diagram:

The code looks something like this:

val client = OkHttpClient()                 // 1

val request = Request.Builder()
  .url("https://blog.frankel.ch/feed.xml")
  .addHeader("User-Agent", "Mozilla/5.0")   // 2
  .build()                                  // 3

val content = client
  .newCall(request)
  .execute()                                // 4
  .body
  ?.string()                                // 5

1. The client is stateless, you can reuse it across calls
2. A proxy protects some sites by checking that requests come from "proper" browsers
3. Create the request object
4. Execute the call, it returns a Response object
5. Get the response's content as a String, if it exists

Reading data sources

All data I want to display already exist in one form or another. Here are the desired data, their sources and their respective format:

Data	Source	Format
Bio	Blog GitLab repository*	YAML
Blog posts	RSS feed	XML
Planned talks	Blog GitLab repository*	HTML
Talks description	Papercall.io	YAML
YouTube recording	YouTube	JSON

^* The GitLab repository is private

The common parts

Across all calls to the different data sources, the sequence is always the same:

1. Create the request object
2. Execute the call
3. Transform the response results into the required object e.g. a string or a list of objects

For that reason, I created an utility function to take care of the common parts in the sequence:

fun <T> execute(builder: Request.Builder,           // 1
                extractor: (String?) -> T): T {     // 2
  val body = client.newCall(builder.build())
    .execute()
    .body
    ?.string()
  return extractor(body)
}

1. The request parameter, including the URL, is different for each data source
2. The extractor function takes care of the transform

A lazy property defines each data source. Then, each fills the corresponding key-value pair in the Freemarker model map:

val bio: String by lazy { }
val posts: List<Post> by lazy { }
val talks: List<Talk> by lazy { }
val videoId: String by lazy { }

val root = mapOf(
  "bio" to bio,
  "posts" to posts,
  "talks" to talks,
  "videoId" to videoId
)

Bio

My private GitLab repository hosts a YAML data file that contains the bio. To parse YAML, I use the popular SnakeYAML library.

The returned YAML looks like:

main:
    firstName: Nicolas
    lastName: Fränkel
    bio: >
      blah blah

The corresponding extractor function is straightforward:

val extractBio = { body: String? ->
  val authors = Yaml().load<Map<String, Any>>(body)     // 1
  val main = authors["main"] as Map<*, *>               // 2
  main["bio"] as String                                 // 3
}

1. Loads the YAML as a map
2. Get the main key as another map
3. Get the bio key as a string

Next talks

The talks are also read from a YAML data file from the same private repo. Hence, the code is pretty similar from the previous section. Yet, there's a twist: each talk's description is quite long, and I'd prefer something more concise.

All my talks are already set on Papercall. A talk has different characteristics, including a short "elevator pitch". I could pre-load all talks in a map, with the title as the key, and the "elevator pitch" as the value. But it works only if the title matches letter-by-letter, case included. This is hard to guarantee, because I (or the conference) sometimes customize the title.

The chosen approach is a bit different. The map is still pre-loaded, but the flow is akin to:

1. IF the title matches, RETURN the pitch

2. ELSE

   1. FOR each title, compute the Jaro Winkler distance. For that, use the JaroWinklerSimilarity class from Apache Commons Text.
      1. IF none is higher than a set threshold e.g. 0.5, there's no corresponding pitch. RETURN the description from the YAML.
      2. ELSE RETURN the pitch with the highest similarity

Since Papercall offers no API, I read the HTML page, and parse it using JSoup. This is admittedly fragile, but I found no other way. In the worst case, the build breaks, GitHub sends a notification, and doesn't update the profile.

Latest blog posts

The RSS feed contains all blog posts. RSS is XML, and XML parsing is ages-old in Java.

The required steps are:

1. Load the RSS over the web
2. Deserialize the returned XML to a DOM Document using the out-of-the-box Java API
3. Extract the latest 3 blog post nodes to a NodeList using XPath
4. Transform the NodeList object to a List<Post>

A data class models the Post entity:

data class Post(val published: LocalDate,
                val title: String,
                val link: String,
                val excerpt: String)

One hurdle I faced is that the excerpt of each talk might contain HTML entities. I use StringEscapeUtils.unescapeHtml4() from Apache Commons Text to cope with that.

Latest recording

Recordings of my talks are available on a manually-curated Youtube playlist. YouTube offers an API. One needs to activate - and configure - it on the Google Cloud Console. The API is free up to 10k calls per day. That is more than enough for this use-case.

The exact API to call is the Playlist Items. One needs to call it with the following parameters:

Parameter	Description	Value
playlistId	Unique ID of the playlist	PL0EuBuKK-s1EL-K3okpYwR0QZbAPRVmEG
maxResults	Highest number of items returned in the result set	1
part	Specifies a comma-separated list of one or more playlistItem resource properties that the API response will include	snippet

The returned JSON looks like the following:

{
  "kind": "youtube#playlistItemListResponse",
  "etag": "mHM75p3nyuatm7RG27LD-EajROI",
  "nextPageToken": "CAEQAA",
  "items": [
    {
      "kind": "youtube#playlistItem",
      "etag": "AK-w0AZhAbntxHmSsp8X0_tLG-k",
      "id": "UEwwRXVCdUtLLXMxRUwtSzNva3BZd1IwUVpiQVBSVm1FRy4xRDJGOTc4RkUxMzUwMUFG",
      "snippet": {
        "publishedAt": "2020-08-07T17:58:15Z",
        "channelId": "UC5wHWZfEjZChkgVzg2jnbhQ",
        "title": "Shorten all URLs: Quarkus, Kotlin, Hazelcast and GraalVM",
        "description": "Nicolas Frankel Developer Advocate, Hazelcast...",
        "thumbnails": {
          "default": {
            "url": "https://i.ytimg.com/vi/rJ9erRb5Fg8/default.jpg",
            "width": 120,
            "height": 90
          },
        "channelTitle": "Nicolas Frankel",
        "playlistId": "PL0EuBuKK-s1EL-K3okpYwR0QZbAPRVmEG",
        "position": 0,
        "resourceId": {
          "kind": "youtube#video",
          "videoId": "rJ9erRb5Fg8"                                   // 1
        }
      }
    }
  ],
  "pageInfo": {
    "totalResults": 113,
    "resultsPerPage": 1
  }
}

1. The extractor function needs to return this ID

Between OkHttp plus the generic JSON-parsing library, and the specific YouTube one, I decided to favor the former over the latter. IMHO, there's no benefit in using Google objects in this context.

Conclusion

GitHub profiles are a cool way to customize your GitHub landing page. In this post, we set up a script that gets bits and pieces from different data sources, and write a custom README. In opposite to most existing projects, I showed how to do this with a JVM-based stack.

You can check the full script on GitHub, as well as the complete source code.

In the next post, I will explain how create the build pipeline using GitHub actions.

Originally published at A Java Geek on September 13^th, 2020