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✨ [Tasks] JSON Schema spec for Inference types + TS type generation #449
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@@ -24,9 +24,10 @@ | |
"format": "prettier --write .", | ||
"format:check": "prettier --check .", | ||
"prepublishOnly": "pnpm run build", | ||
"build": "tsup src/index.ts --format cjs,esm --clean --dts", | ||
"build": "tsup src/index.ts src/scripts/**.ts --format cjs,esm --clean --dts", | ||
"prepare": "pnpm run build", | ||
"check": "tsc" | ||
"check": "tsc", | ||
"inference-codegen": "pnpm run build && node dist/scripts/inference-codegen.js" | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Probably should include this directly in the build command |
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}, | ||
"files": [ | ||
"dist", | ||
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@@ -40,5 +41,8 @@ | |
], | ||
"author": "Hugging Face", | ||
"license": "MIT", | ||
"devDependencies": {} | ||
"devDependencies": { | ||
"@types/node": "^20.11.5", | ||
"quicktype-core": "^23.0.81" | ||
} | ||
} |
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Maybe the |
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@@ -0,0 +1,100 @@ | ||
import type { SerializedRenderResult } from "quicktype-core"; | ||
import { quicktype, InputData, JSONSchemaInput, FetchingJSONSchemaStore } from "quicktype-core"; | ||
import * as fs from "fs/promises"; | ||
import { existsSync as pathExists } from "fs"; | ||
import * as path from "path"; | ||
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const TYPESCRIPT_HEADER_FILE = ` | ||
/** | ||
* Inference code generated from the JSON schema spec in ./spec | ||
* | ||
* Generated on ${new Date().toISOString()} | ||
*/ | ||
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`; | ||
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const rootDirFinder = function (): string { | ||
const parts = __dirname.split("/"); | ||
let level = parts.length - 1; | ||
while (level > 0) { | ||
const currentPath = parts.slice(0, level).join("/"); | ||
console.debug(currentPath); | ||
try { | ||
require(`${currentPath}/package.json`); | ||
return path.normalize(currentPath); | ||
} catch (err) { | ||
/// noop | ||
} | ||
level--; | ||
} | ||
return ""; | ||
}; | ||
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async function buildInputData(taskId: string, taskSpecDir: string): Promise<InputData> { | ||
const schema = new JSONSchemaInput(new FetchingJSONSchemaStore()); | ||
await schema.addSource({ | ||
name: `${taskId}-input`, | ||
schema: await fs.readFile(`${taskSpecDir}/input.json`, { encoding: "utf-8" }), | ||
}); | ||
await schema.addSource({ | ||
name: `${taskId}-output`, | ||
schema: await fs.readFile(`${taskSpecDir}/output.json`, { encoding: "utf-8" }), | ||
}); | ||
const inputData = new InputData(); | ||
inputData.addInput(schema); | ||
return inputData; | ||
} | ||
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async function generateTypescript(inputData: InputData): Promise<SerializedRenderResult> { | ||
return await quicktype({ | ||
inputData, | ||
lang: "typescript", | ||
alphabetizeProperties: true, | ||
rendererOptions: { | ||
"just-types": true, | ||
"nice-property-names": true, | ||
"prefer-unions": true, | ||
"prefer-const-values": true, | ||
}, | ||
}); | ||
} | ||
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async function main() { | ||
const rootDir = rootDirFinder(); | ||
const tasksDir = path.join(rootDir, "src", "tasks"); | ||
const allTasks = await Promise.all( | ||
(await fs.readdir(tasksDir, { withFileTypes: true })) | ||
.filter((entry) => entry.isDirectory()) | ||
.map(async (entry) => ({ task: entry.name, dirPath: path.join(entry.path, entry.name) })) | ||
); | ||
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for (const { task, dirPath } of allTasks) { | ||
const taskSpecDir = path.join(dirPath, "spec"); | ||
if (!pathExists(taskSpecDir)) { | ||
console.debug(`No spec found for task ${task} - skipping`); | ||
continue; | ||
} | ||
console.debug(`✨ Generating types for task`, task); | ||
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console.debug(" 📦 Building input data"); | ||
const inputData = await buildInputData(task, taskSpecDir); | ||
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console.debug(" 🏭 Generating typescript code"); | ||
{ | ||
const { lines } = await generateTypescript(inputData); | ||
await fs.writeFile(`${dirPath}/inference.ts`, [TYPESCRIPT_HEADER_FILE, ...lines].join(`\n`), { | ||
flag: "w+", | ||
encoding: "utf-8", | ||
}); | ||
} | ||
} | ||
console.debug("✅ All done!"); | ||
} | ||
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let exit = 0; | ||
main() | ||
.catch((err) => { | ||
console.error("Failure", err); | ||
exit = 1; | ||
}) | ||
.finally(() => process.exit(exit)); |
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/** | ||
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* Inference code generated from the JSON schema spec in ./spec | ||
* | ||
* Generated on 2024-01-19T14:59:10.562Z | ||
*/ | ||
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/** | ||
* Inputs for Audio Classification inference | ||
*/ | ||
export interface AudioClassificationInput { | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Audio data is usually pass through https://huggingface.co/docs/api-inference/detailed_parameters#audio-classification-task There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Re-flagging this comment in case it was lost There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Same for images. The jsonschema cannot specify this since sending as raw data and sending as json are 2 different things. So for now it's kind of a blind spot. If we provide an openapi schema for our APIs in the future, then it will be possible to document it. Openapi easily integrates with jsonschema so having them is already a first good step. (difference between a jsonschema as in this PR and an openapi description is that this PR describes objects with their attributes while the openapi description with include stuff like server routes, accepted headers, etc.) (^ only my understanding of the specs, anyone feel free to correct me 😄) There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Yes - sorry for the delay in answering Leaving the image/audio data as
Yes that is correct, there will be some additional work necessary to generate an OpenAPI spec for an inference API (including actually specifying how we expect the binary data to be represented) |
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/** | ||
* On or several audio files to classify | ||
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*/ | ||
inputs: any; | ||
/** | ||
* Additional inference parameters | ||
*/ | ||
parameters?: AudioClassificationParameters; | ||
[property: string]: any; | ||
} | ||
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/** | ||
* Additional inference parameters | ||
* | ||
* Additional inference parameters for Audio Classification | ||
*/ | ||
export interface AudioClassificationParameters { | ||
/** | ||
* When specified, limits the output to the top K most probable classes. | ||
*/ | ||
topK?: number; | ||
[property: string]: any; | ||
} | ||
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/** | ||
* Outputs for Audio Classification inference | ||
*/ | ||
export interface AudioClassificationOutput { | ||
/** | ||
* The predicted class label (model specific). | ||
*/ | ||
label: string; | ||
/** | ||
* The corresponding probability. | ||
*/ | ||
score: number; | ||
[property: string]: any; | ||
} |
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tsup is used for files to be published.
If you just want to run the script, you can take inspiration from
doc-internal
:No need to add
ts-node
, it's already included in the root packageThere was a problem hiding this comment.
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^ that command fails with a syntax error (
cannot use import outside of a module
or smth)Is it OK tu use
tsc
to compile the script inpnpm run inference-codegen
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I think we can add
type: "module"
to thepackage.json