Next.js Query Params inside GetStaticProps (Incremental Static Regeneration)? one Alternative...

Have you ever wondered how to access query parameters “context.req.query” inside getStaticProps???

In this video we will look at a possible “solution”. This technique will only work with a CDN. We will rely on the CDN with specific headers we send from our next.js responses.

This technique will also require that we have a next.js server, which means, it will not work with “next export”, which means we can’t deploy to places like GitHub Pages, for example.

GitHub Repository: https://github.com/bmvantunes/youtube-2021-april-getserversideprops-cdn

---- Timeline

• 00:00 Introduction
• 01:22 Introduction to ISR (Incremental Static Regeneration)
• 05:05 Solution
• 06:05 Headers that will form the solution
• 07:15 s-maxage timeline
• 08:16 s-maxage + stale-while-revalidate timeline
• 08:56 s-maxage + stale-while-revalidate=60 timeline
• 11:06 Problems to be aware - IMPORTANT!!!
• 15:00 With great power comes great responsibility
• 15:26 Problems Summary
• 15:54 Demo

#next #react #javascript

What is GEEK

Buddha Community

NBB: Ad-hoc CLJS Scripting on Node.js

Nbb

Not babashka. Node.js babashka!?

Ad-hoc CLJS scripting on Node.js.

Status

Experimental. Please report issues here.

Goals and features

Nbb's main goal is to make it easy to get started with ad hoc CLJS scripting on Node.js.

Additional goals and features are:

• Fast startup without relying on a custom version of Node.js.
• Small artifact (current size is around 1.2MB).
• First class macros.
• Support building small TUI apps using Reagent.
• Complement babashka with libraries from the Node.js ecosystem.

Requirements

Nbb requires Node.js v12 or newer.

How does this tool work?

CLJS code is evaluated through SCI, the same interpreter that powers babashka. Because SCI works with advanced compilation, the bundle size, especially when combined with other dependencies, is smaller than what you get with self-hosted CLJS. That makes startup faster. The trade-off is that execution is less performant and that only a subset of CLJS is available (e.g. no deftype, yet).

Usage

Install nbb from NPM:

$ npm install nbb -g

Omit -g for a local install.

Try out an expression:

$ nbb -e '(+ 1 2 3)'
6

And then install some other NPM libraries to use in the script. E.g.:

$ npm install csv-parse shelljs zx

Create a script which uses the NPM libraries:

(ns script
  (:require ["csv-parse/lib/sync$default" :as csv-parse]
            ["fs" :as fs]
            ["path" :as path]
            ["shelljs$default" :as sh]
            ["term-size$default" :as term-size]
            ["zx$default" :as zx]
            ["zx$fs" :as zxfs]
            [nbb.core :refer [*file*]]))

(prn (path/resolve "."))

(prn (term-size))

(println (count (str (fs/readFileSync *file*))))

(prn (sh/ls "."))

(prn (csv-parse "foo,bar"))

(prn (zxfs/existsSync *file*))

(zx/$ #js ["ls"])

Call the script:

$ nbb script.cljs
"/private/tmp/test-script"
#js {:columns 216, :rows 47}
510
#js ["node_modules" "package-lock.json" "package.json" "script.cljs"]
#js [#js ["foo" "bar"]]
true
$ ls
node_modules
package-lock.json
package.json
script.cljs

Macros

Nbb has first class support for macros: you can define them right inside your .cljs file, like you are used to from JVM Clojure. Consider the plet macro to make working with promises more palatable:

(defmacro plet
  [bindings & body]
  (let [binding-pairs (reverse (partition 2 bindings))
        body (cons 'do body)]
    (reduce (fn [body [sym expr]]
              (let [expr (list '.resolve 'js/Promise expr)]
                (list '.then expr (list 'clojure.core/fn (vector sym)
                                        body))))
            body
            binding-pairs)))

Using this macro we can look async code more like sync code. Consider this puppeteer example:

(-> (.launch puppeteer)
      (.then (fn [browser]
               (-> (.newPage browser)
                   (.then (fn [page]
                            (-> (.goto page "https://clojure.org")
                                (.then #(.screenshot page #js{:path "screenshot.png"}))
                                (.catch #(js/console.log %))
                                (.then #(.close browser)))))))))

Using plet this becomes:

(plet [browser (.launch puppeteer)
       page (.newPage browser)
       _ (.goto page "https://clojure.org")
       _ (-> (.screenshot page #js{:path "screenshot.png"})
             (.catch #(js/console.log %)))]
      (.close browser))

See the puppeteer example for the full code.

Since v0.0.36, nbb includes promesa which is a library to deal with promises. The above plet macro is similar to promesa.core/let.

Startup time

$ time nbb -e '(+ 1 2 3)'
6
nbb -e '(+ 1 2 3)'   0.17s  user 0.02s system 109% cpu 0.168 total

The baseline startup time for a script is about 170ms seconds on my laptop. When invoked via npx this adds another 300ms or so, so for faster startup, either use a globally installed nbb or use $(npm bin)/nbb script.cljs to bypass npx.

Dependencies

NPM dependencies

Nbb does not depend on any NPM dependencies. All NPM libraries loaded by a script are resolved relative to that script. When using the Reagent module, React is resolved in the same way as any other NPM library.

Classpath

To load .cljs files from local paths or dependencies, you can use the --classpath argument. The current dir is added to the classpath automatically. So if there is a file foo/bar.cljs relative to your current dir, then you can load it via (:require [foo.bar :as fb]). Note that nbb uses the same naming conventions for namespaces and directories as other Clojure tools: foo-bar in the namespace name becomes foo_bar in the directory name.

To load dependencies from the Clojure ecosystem, you can use the Clojure CLI or babashka to download them and produce a classpath:

$ classpath="$(clojure -A:nbb -Spath -Sdeps '{:aliases {:nbb {:replace-deps {com.github.seancorfield/honeysql {:git/tag "v2.0.0-rc5" :git/sha "01c3a55"}}}}}')"

and then feed it to the --classpath argument:

$ nbb --classpath "$classpath" -e "(require '[honey.sql :as sql]) (sql/format {:select :foo :from :bar :where [:= :baz 2]})"
["SELECT foo FROM bar WHERE baz = ?" 2]

Currently nbb only reads from directories, not jar files, so you are encouraged to use git libs. Support for .jar files will be added later.

Current file

The name of the file that is currently being executed is available via nbb.core/*file* or on the metadata of vars:

(ns foo
  (:require [nbb.core :refer [*file*]]))

(prn *file*) ;; "/private/tmp/foo.cljs"

(defn f [])
(prn (:file (meta #'f))) ;; "/private/tmp/foo.cljs"

Reagent

Nbb includes reagent.core which will be lazily loaded when required. You can use this together with ink to create a TUI application:

$ npm install ink

ink-demo.cljs:

(ns ink-demo
  (:require ["ink" :refer [render Text]]
            [reagent.core :as r]))

(defonce state (r/atom 0))

(doseq [n (range 1 11)]
  (js/setTimeout #(swap! state inc) (* n 500)))

(defn hello []
  [:> Text {:color "green"} "Hello, world! " @state])

(render (r/as-element [hello]))

Promesa

Working with callbacks and promises can become tedious. Since nbb v0.0.36 the promesa.core namespace is included with the let and do! macros. An example:

(ns prom
  (:require [promesa.core :as p]))

(defn sleep [ms]
  (js/Promise.
   (fn [resolve _]
     (js/setTimeout resolve ms))))

(defn do-stuff
  []
  (p/do!
   (println "Doing stuff which takes a while")
   (sleep 1000)
   1))

(p/let [a (do-stuff)
        b (inc a)
        c (do-stuff)
        d (+ b c)]
  (prn d))

$ nbb prom.cljs
Doing stuff which takes a while
Doing stuff which takes a while
3

Also see API docs.

Js-interop

Since nbb v0.0.75 applied-science/js-interop is available:

(ns example
  (:require [applied-science.js-interop :as j]))

(def o (j/lit {:a 1 :b 2 :c {:d 1}}))

(prn (j/select-keys o [:a :b])) ;; #js {:a 1, :b 2}
(prn (j/get-in o [:c :d])) ;; 1

Most of this library is supported in nbb, except the following:

• destructuring using :syms
• property access using .-x notation. In nbb, you must use keywords.

See the example of what is currently supported.

Examples

See the examples directory for small examples.

Also check out these projects built with nbb:

• c64core: retrocompute aesthetics twitter bot
• gallery.cljs: script to download walpapers from windowsonearth.org.
• nbb-action-example: example of a Github action built with nbb.

API

See API documentation.

Migrating to shadow-cljs

See this gist on how to convert an nbb script or project to shadow-cljs.

Build

Prequisites:

• babashka >= 0.4.0
• Clojure CLI >= 1.10.3.933
• Node.js 16.5.0 (lower version may work, but this is the one I used to build)

To build:

• Clone and cd into this repo
• bb release

Run bb tasks for more project-related tasks.

Download Details:
Author: borkdude
Download Link: Download The Source Code
Official Website: https://github.com/borkdude/nbb 
License: EPL-1.0

#node #javascript

Next.js Query Params inside GetStaticProps (Incremental Static Regeneration)? one Alternative...

Have you ever wondered how to access query parameters “context.req.query” inside getStaticProps???

In this video we will look at a possible “solution”. This technique will only work with a CDN. We will rely on the CDN with specific headers we send from our next.js responses.

This technique will also require that we have a next.js server, which means, it will not work with “next export”, which means we can’t deploy to places like GitHub Pages, for example.

GitHub Repository: https://github.com/bmvantunes/youtube-2021-april-getserversideprops-cdn

---- Timeline

• 00:00 Introduction
• 01:22 Introduction to ISR (Incremental Static Regeneration)
• 05:05 Solution
• 06:05 Headers that will form the solution
• 07:15 s-maxage timeline
• 08:16 s-maxage + stale-while-revalidate timeline
• 08:56 s-maxage + stale-while-revalidate=60 timeline
• 11:06 Problems to be aware - IMPORTANT!!!
• 15:00 With great power comes great responsibility
• 15:26 Problems Summary
• 15:54 Demo

#next #react #javascript

Google analytics Setup with Next JS, React JS using Router Events - 14

In this video, we are going to implement Google Analytics to our Next JS application. Tracking page views of an application is very important.

Google analytics will allow us to track analytics information.

Frontend: https://github.com/amitavroy/video-reviews
API: https://github.com/amitavdevzone/video-review-api
App link: https://video-reviews.vercel.app

You can find me on:
Twitter: https://twitter.com/amitavroy7​
Discord: https://discord.gg/Em4nuvQk

#next js #js #react js #react #next #google analytics

Laravel API and React Next JS frontend development - 28

In this video, I wanted to touch upon the functionality of adding Chapters inside a Course. The idea was to not think much and start the development and pick up things as they come.

There are places where I get stuck and trying to find answers to it up doing what every developer does - Google and get help. I hope this will help you understand the flow and also how developers debug while doing development.

App url: https://video-reviews.vercel.app
Github code links below:
Next JS App: https://github.com/amitavroy/video-reviews
Laravel API: https://github.com/amitavdevzone/video-review-api

You can find me on:
Twitter: https://twitter.com/amitavroy7​
Discord: https://discord.gg/Em4nuvQk

#next js #api #react next js #next #frontend #development

Focal Transformer | Official Implementation of Focal Transformer

Focal Transformer

This is the official implementation of our Focal Transformer -- "Focal Self-attention for Local-Global Interactions in Vision Transformers", by Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Xiyang Dai, Bin Xiao, Lu Yuan and Jianfeng Gao.

Introduction

Our Focal Transfomer introduced a new self-attention mechanism called focal self-attention for vision transformers. In this new mechanism, each token attends the closest surrounding tokens at fine granularity but the tokens far away at coarse granularity, and thus can capture both short- and long-range visual dependencies efficiently and effectively.

With our Focal Transformers, we achieved superior performance over the state-of-the-art vision Transformers on a range of public benchmarks. In particular, our Focal Transformer models with a moderate size of 51.1M and a larger size of 89.8M achieve 83.6 and 84.0 Top-1 accuracy, respectively, on ImageNet classification at 224x224 resolution. Using Focal Transformers as the backbones, we obtain consistent and substantial improvements over the current state-of-the-art methods for 6 different object detection methods trained with standard 1x and 3x schedules. Our largest Focal Transformer yields 58.7/58.9 box mAPs and 50.9/51.3 mask mAPs on COCO mini-val/test-dev, and 55.4 mIoU on ADE20K for semantic segmentation.

Benchmarking

Image Classification on ImageNet-1K

Model	Pretrain	Use Conv	Resolution	acc@1	acc@5	#params	FLOPs	Checkpoint	Config
Focal-T	IN-1K	No	224	82.2	95.9	28.9M	4.9G	download	yaml
Focal-T	IN-1K	Yes	224	82.7	96.1	30.8M	4.9G	download	yaml
Focal-S	IN-1K	No	224	83.6	96.2	51.1M	9.4G	download	yaml
Focal-B	IN-1K	No	224	84.0	96.5	89.8M	16.4G	download	yaml

Object Detection and Instance Segmentation on COCO

Mask R-CNN

Backbone	Pretrain	Lr Schd	#params	FLOPs	box mAP	mask mAP
Focal-T	ImageNet-1K	1x	49M	291G	44.8	41.0
Focal-T	ImageNet-1K	3x	49M	291G	47.2	42.7
Focal-S	ImageNet-1K	1x	71M	401G	47.4	42.8
Focal-S	ImageNet-1K	3x	71M	401G	48.8	43.8
Focal-B	ImageNet-1K	1x	110M	533G	47.8	43.2
Focal-B	ImageNet-1K	3x	110M	533G	49.0	43.7

RetinaNet

Backbone	Pretrain	Lr Schd	#params	FLOPs	box mAP
Focal-T	ImageNet-1K	1x	39M	265G	43.7
Focal-T	ImageNet-1K	3x	39M	265G	45.5
Focal-S	ImageNet-1K	1x	62M	367G	45.6
Focal-S	ImageNet-1K	3x	62M	367G	47.3
Focal-B	ImageNet-1K	1x	101M	514G	46.3
Focal-B	ImageNet-1K	3x	101M	514G	46.9

Other detection methods

Backbone	Pretrain	Method	Lr Schd	#params	FLOPs	box mAP
Focal-T	ImageNet-1K	Cascade Mask R-CNN	3x	87M	770G	51.5
Focal-T	ImageNet-1K	ATSS	3x	37M	239G	49.5
Focal-T	ImageNet-1K	RepPointsV2	3x	45M	491G	51.2
Focal-T	ImageNet-1K	Sparse R-CNN	3x	111M	196G	49.0

Semantic Segmentation on ADE20K

Backbone	Pretrain	Method	Resolution	Iters	#params	FLOPs	mIoU	mIoU (MS)
Focal-T	ImageNet-1K	UPerNet	512x512	160k	62M	998G	45.8	47.0
Focal-S	ImageNet-1K	UPerNet	512x512	160k	85M	1130G	48.0	50.0
Focal-B	ImageNet-1K	UPerNet	512x512	160k	126M	1354G	49.0	50.5
Focal-L	ImageNet-22K	UPerNet	640x640	160k	240M	3376G	54.0	55.4

Getting Started

• Please follow get_started_for_image_classification.md to get started for image classification.
• Please follow get_started_for_object_detection.md to get started for object detection.
• Please follow get_started_for_semantic_segmentation.md to get started for semantic segmentation.

Citation

If you find this repo useful to your project, please consider to cite it with following bib:

@misc{yang2021focal,
    title={Focal Self-attention for Local-Global Interactions in Vision Transformers}, 
    author={Jianwei Yang and Chunyuan Li and Pengchuan Zhang and Xiyang Dai and Bin Xiao and Lu Yuan and Jianfeng Gao},
    year={2021},
    eprint={2107.00641},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Acknowledgement

Our codebase is built based on Swin-Transformer. We thank the authors for the nicely organized code!

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Download Details:

Author: microsoft

Source Code: https://github.com/microsoft/Focal-Transformer