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jsguesslang
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Readme
A JavaScript library that uses machine learning to detect source code languages. Powered by @yoeo's guesslang model!
Inspired by and modified from vscode-languagedetection to add browser support.
This library is intended to be used only in browser. For Node.JS, please consider using vscode-languagedetection.
Load the library:
<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/jsguesslang@latest/dist/lib/guesslang.min.js"></script>
Declare sample code:
const code = `
struct Rectangle {
width: u32,
height: u32,
}
fn main() {
let rect1 = Rectangle {
width: 30,
height: 50,
};
println!(
"The area of the rectangle is {} square pixels.",
area(&rect1)
);
}
fn area(rectangle: &Rectangle) -> u32 {
rectangle.width * rectangle.height
}
`;
const guessLang = new GuessLang();
guessLang.runModel(code).then((result) => {
console.log(result);
});
const guessLang = new GuessLang();
const result = await guessLang.runModel(code);
console.log(result);
You should get an output similar to this:
[
{ "languageId": "rs", "confidence": 0.7457122802734375 },
{ "languageId": "js", "confidence": 0.03622082620859146 },
{ "languageId": "ts", "confidence": 0.02657853439450264 },
{ "languageId": "html", "confidence": 0.018268872052431107 },
{ "languageId": "dart", "confidence": 0.017024816945195198 },
{ "languageId": "css", "confidence": 0.013620768673717976 },
{ "languageId": "go", "confidence": 0.012718110345304012 },
{ "languageId": "c", "confidence": 0.011547493748366833 },
{ "languageId": "lua", "confidence": 0.011429708451032639 },
{ "languageId": "cpp", "confidence": 0.010202286764979362 },
{ "languageId": "swift", "confidence": 0.010128483176231384 },
{ "languageId": "kt", "confidence": 0.008081216365098953 },
{ "languageId": "pm", "confidence": 0.007753847632557154 },
{ "languageId": "groovy", "confidence": 0.007391981780529022 },
{ "languageId": "scala", "confidence": 0.0069132656790316105 },
{ "languageId": "r", "confidence": 0.006373902782797813 },
{ "languageId": "ps1", "confidence": 0.004702022764831781 },
{ "languageId": "mm", "confidence": 0.004345177672803402 },
{ "languageId": "tex", "confidence": 0.004238464403897524 },
{ "languageId": "asm", "confidence": 0.0035753981210291386 },
{ "languageId": "php", "confidence": 0.0029875021427869797 },
{ "languageId": "cs", "confidence": 0.00282992678694427 },
{ "languageId": "erl", "confidence": 0.0026539231184870005 },
{ "languageId": "hs", "confidence": 0.0022999923676252365 },
{ "languageId": "java", "confidence": 0.0021398833487182856 },
{ "languageId": "json", "confidence": 0.0018522157333791256 },
{ "languageId": "jl", "confidence": 0.0016744869062677026 },
{ "languageId": "coffee", "confidence": 0.0014533938374370337 },
{ "languageId": "ml", "confidence": 0.0014339216286316514 },
{ "languageId": "prolog", "confidence": 0.0013837843434885144 },
{ "languageId": "md", "confidence": 0.0011162260780110955 },
{ "languageId": "rb", "confidence": 0.0010244469158351421 },
{ "languageId": "bat", "confidence": 0.0009783837012946606 },
{ "languageId": "ex", "confidence": 0.0009154175058938563 },
{ "languageId": "pas", "confidence": 0.0009110081009566784 },
{ "languageId": "xml", "confidence": 0.0008580578141845763 },
{ "languageId": "sh", "confidence": 0.0008576430263929069 },
{ "languageId": "py", "confidence": 0.0006855467217974365 },
{ "languageId": "csv", "confidence": 0.0006681767990812659 },
{ "languageId": "yaml", "confidence": 0.0006367963505908847 },
{ "languageId": "sql", "confidence": 0.0006355350487865508 },
{ "languageId": "vba", "confidence": 0.0005863389233127236 },
{ "languageId": "dm", "confidence": 0.0004887901013717055 },
{ "languageId": "matlab", "confidence": 0.0003887197526637465 },
{ "languageId": "v", "confidence": 0.000384387094527483 },
{ "languageId": "clj", "confidence": 0.0003443971218075603 },
{ "languageId": "f90", "confidence": 0.0002740618074312806 },
{ "languageId": "cmake", "confidence": 0.000268166622845456 },
{ "languageId": "ini", "confidence": 0.00018944506882689893 },
{ "languageId": "makefile", "confidence": 0.0001014301014947705 },
{ "languageId": "lisp", "confidence": 0.00006610684067709371 },
{ "languageId": "cbl", "confidence": 0.00004037651524413377 },
{ "languageId": "dockerfile", "confidence": 0.00002403824146313127 },
{ "languageId": "toml", "confidence": 0.000019977496776846237 }
]
You can pass an optional object to GuessLang
containing the following options:
minContentSize?: number
- The minimum number of characters in a file to be considered for language detection. Defaults to 20
.maxContentSize?: number
- The maximum number of characters that will be used in a file to be considered for language detection. Defaults to 100000
.For example:
const guessLang = new GuessLang({
minContentSize: 0,
maxContentSize: 1000,
});
The only notable difference is that this library includes the guesslang model as Base64 encoded string, allowing everything to be loaded from one single file. Meanwhile, vscode-languagedetection
loads the model from files using fs
.
FAQs
Use guesslang's ML model to detect source code languages
The npm package jsguesslang receives a total of 0 weekly downloads. As such, jsguesslang popularity was classified as not popular.
We found that jsguesslang demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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