Merge pull request #1027 from honzajavorek/honzajavorek/language-markers

style: use consistent code example language markers

Author: honzajavorek

sources/academy/platform/deploying_your_code/index.md

@@ -47,7 +47,7 @@ For this section, we'll be turning this example project into an actor:
 <Tabs groupId="main">
 <TabItem value="JavaScript" label="JavaScript">
 
-```javascript
+```js
 // index.js
 const addAllNumbers = (...nums) => nums.reduce((total, curr) => total + curr, 0);
 
@@ -57,7 +57,7 @@ console.log(addAllNumbers(1, 2, 3, 4)); // -> 10
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 # index.py
 def add_all_numbers (nums):
     total = 0

sources/academy/platform/deploying_your_code/inputs_outputs.md

@@ -90,7 +90,7 @@ Cool! When we run `node index.js`, we see **20**.
 
 Alternatively, when writing in a language other than JavaScript, we can create our own `get_input()` function which utilizes the Apify API when the actor is running on the platform. For this example, we are using the [Apify Client](../getting_started/apify_client.md) for Python to access the API.
 
-```Python
+```py
 # index.py
 from apify_client import ApifyClient
 from os import environ
@@ -164,7 +164,7 @@ Just as with the custom `get_input()` utility function, you can write a custom `
 
 > You can read and write your output anywhere; however, it is standard practice to use a folder named **storage**.
 
-```Python
+```py
 # index.py
 from apify_client import ApifyClient
 from os import environ

sources/academy/platform/getting_started/apify_client.md

@@ -43,15 +43,15 @@ After installing the package, let's make a file named **client** and import the
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 // client.js
 import { ApifyClient } from 'apify-client';
 ```
 
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 # client.py
 from apify_client import ApifyClient
 
@@ -69,7 +69,7 @@ Before we can use the client though, we must create a new instance of the `Apify
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 const client = new ApifyClient({
     token: 'YOUR_TOKEN',
 });
@@ -78,7 +78,7 @@ const client = new ApifyClient({
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 client = ApifyClient(token='YOUR_TOKEN')
 
 ```
@@ -93,7 +93,7 @@ Now that we've got our instance, we can point to an Actor using the [`client.act
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 const run = await client.actor('YOUR_USERNAME/adding-actor').call({
     num1: 4,
     num2: 2,
@@ -103,7 +103,7 @@ const run = await client.actor('YOUR_USERNAME/adding-actor').call({
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 run = client.actor('YOUR_USERNAME/adding-actor').call(run_input={
     'num1': 4,
     'num2': 2
@@ -127,14 +127,14 @@ The `run` variable we created in the last section points to the **run info** obj
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 const dataset = client.dataset(run.defaultDatasetId);
 ```
 
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 dataset = client.dataset(run['defaultDatasetId'])
 
 ```
@@ -147,7 +147,7 @@ Finally, we can download the items in the dataset by using the **list items** fu
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 const { items } = await dataset.listItems();
 
 console.log(items);
@@ -156,7 +156,7 @@ console.log(items);
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 items = dataset.list_items().items
 
 print(items)
@@ -171,7 +171,7 @@ The final code for running the actor and fetching its dataset items looks like t
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 // client.js
 import { ApifyClient } from 'apify-client';
 
@@ -194,7 +194,7 @@ console.log(items);
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 # client.py
 from apify_client import ApifyClient
 
@@ -227,14 +227,14 @@ First, we'll create a pointer to our actor, similar to before (except this time,
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 const actor = client.actor('YOUR_USERNAME/adding-actor');
 ```
 
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 actor = client.actor('YOUR_USERNAME/adding-actor')
 
 ```
@@ -247,7 +247,7 @@ Then, we'll just call the `.update()` method on the `actor` variable we created
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 await actor.update({
     defaultRunOptions: {
         build: 'latest',
@@ -260,7 +260,7 @@ await actor.update({
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 actor.update(default_run_build='latest', default_run_memory_mbytes=256, default_run_timeout_secs=20)
 
 ```

sources/academy/platform/getting_started/creating_actors.md

@@ -70,11 +70,11 @@ When you click on the **Use locally** button, you'll be presented with instructi
 
 With the Apify CLI installed, you can simply run the following commands in your terminal:
 
-```bash
+```shell
 apify create my-actor -t getting_started_node
 ```
 
-```bash
+```shell
 cd my-actor
 apify run
 ```

sources/academy/tutorials/api/retry_failed_requests.md

@@ -17,7 +17,7 @@ The additional complication is that the [Request](https://crawlee.dev/api/core/c
 
 A simplified code example can look like this:
 
-```typescript
+```ts
 // The code is similar for both Crawlee-only but uses a different API
 import { Actor } from 'apify';
 

sources/academy/tutorials/api/run_actor_and_retrieve_data_via_api.md

@@ -94,7 +94,7 @@ You can skip most of this tutorial by following this code example that calls Goo
 <Tabs groupId="main">
 <TabItem value="Node.js" label="Node.js">
 
-```javascript
+```js
 import { ApifyClient } from 'apify-client';
 
 const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });
@@ -116,7 +116,7 @@ items.forEach((item) => {
 </TabItem>
 <TabItem value="Python" label="Python">
 
-```python
+```py
 from apify_client import ApifyClient
 client = ApifyClient(token='YOUR_API_TOKEN')
 

sources/academy/tutorials/apify_scrapers/puppeteer_scraper.md

@@ -387,7 +387,7 @@ returns `true`.
 > See [`page.waitFor()`](https://pptr.dev/#?product=Puppeteer&show=api-pagewaitforselectororfunctionortimeout-options-args)
 in the Puppeteer documentation.
 
-```javascript
+```js
 // Waits for 2 seconds.
 await page.waitFor(2000);
 // Waits until an element with id "my-id" appears in the page.
@@ -401,7 +401,7 @@ The selector may never be found and the function might never return `true`, so t
 a timeout. The default is `30` seconds. You can override it by providing an options object as the second parameter,
 with a `timeout` property.
 
-```javascript
+```js
 await page.waitFor('.bad-class', { timeout: 5000 });
 ```
 
@@ -433,7 +433,7 @@ div.show-more > button
 
 Now that we know what to wait for, we just plug it into the `waitFor()` function.
 
-```javascript
+```js
 await page.waitFor('div.show-more > button');
 ```
 
@@ -443,7 +443,7 @@ We have a unique selector for the button and we know that it's already rendered
 of cake. We'll use the Puppeteer `page` again to issue the click. Puppeteer will actually simulate dragging the mouse
 and making a left mouse click in the element.
 
-```javascript
+```js
 await page.click('div.show-more > button');
 ```
 
@@ -453,7 +453,7 @@ This will show the next page of actors.
 
 We've shown two function calls, but how do we make this work together in the `pageFunction`?
 
-```javascript
+```js
 async function pageFunction(context) {
 
     // ...

sources/academy/tutorials/node_js/avoid_eacces_error_in_actor_builds.md

@@ -7,27 +7,27 @@ slug: /node-js/avoid-eacces-error-in-actor-builds
 
 Sometimes when building an actor using a custom Dockerfile, you might receive errors like:
 
-```Bash
+```shell
 Missing write access to ...
 ```
 
 or
 
-```Bash
+```shell
 EACCES: permission denied
 ```
 
 This problem is usually caused by the fact that by default, the `COPY` Dockerfile instruction copies files as the root user (with UID and GID of 0), while your Dockerfile probably uses another user to copy files and run commands.
 
 To fix this problem, make sure the `COPY`  instruction in Dockerfile uses the `--chown` flag. For example, instead of
 
-```Bash
+```shell
 COPY . ./
 ```
 
 use
 
-```Bash
+```shell
 COPY --chown=myuser:myuser . ./
 ```
 

sources/academy/tutorials/node_js/multiple-runs-scrape.md

@@ -40,7 +40,7 @@ The Orchestrator Actor orchestrates the parallel execution of scraper Actor runs
 It runs multiple instances of the scraper Actor and passes the request queue and dataset to them.
 For the Actor's base structure, we use Apify CLI and create a new Actor with the following command and use the [Empty TypeScript Actor template](https://apify.com/templates/ts-empty).
 
-```bash
+```shell
 apify create orchestrator-actor
 ````
 
@@ -60,7 +60,7 @@ Here’s the breakdown of the necessary input:
 </TabItem>
 <TabItem value="main.ts" label="main.ts">
 
-```typescript
+```ts
 import { Actor, log } from 'apify';
 
 interface Input {
@@ -73,7 +73,7 @@ interface Input {
 await Actor.init();
 
 const {
-    parallelRunsCount= 1,
+    parallelRunsCount = 1,
     targetActorId,
     targetActorInput = {},
     targetActorRunOptions = {},
@@ -90,7 +90,7 @@ if (!targetActorId) throw new Error('Missing the "targetActorId" input!');
 
 The Orchestrator Actor will reuse its default dataset and request queue. The dataset stores the results of the scraping process, and the request queue is used as shared storage for processing requests.
 
-```typescript
+```ts
 import { Actor } from 'apify';
 
 const requestQueue = await Actor.openRequestQueue();
@@ -103,7 +103,7 @@ const dataset = await Actor.openDataset();
 The Orchestrator Actor will maintain the state of the scraping runs to track progress and manage continuity. It will record the state of Actor runs, initializing this tracking with the first run.
 This persistent state ensures that, in migration or restart (resurrection) cases, the Actor can resume the same runs without losing progress.
 
-```typescript
+```ts
 import { Actor, log } from 'apify';
 
 const { apifyClient } = Actor;
@@ -154,7 +154,7 @@ Additionally, by registering for abort events, the Actor can terminate all paral
 
 Once you have the Orchestrator Actor ready, you can push it to Apify using the following command from the root directory of the Actor project:
 
-```bash
+```shell
 apify push
 ```
 
@@ -173,7 +173,7 @@ By running this command, you will be prompted to provide the Actor ID, which you
 The Scraper Actor performs website scraping. It operates using the request queue and dataset provided by the Orchestrator Actor.
 You will need to integrate your chosen scraper logic into this framework. The only thing you need to do is utilize the request queue and dataset initialized by the Orchestrator Actor.
 
-```typescript
+```ts
 import { Actor } from 'apify';
 
 interface Input {
@@ -207,7 +207,7 @@ You can check [full code example](https://github.com/apify/apify-docs/tree/maste
 
 You need to push the Scraper Actor to Apify using the following command from the root directory of the Actor project:
 
-```bash
+```shell
 apify push
 ```