index.js

// IMPORT LIBRARIES TOOLS
import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// skip local model check
env.allowLocalModels = false;

// GLOBAL VARIABLES
var PROMPT_INPUT = `The neighbor has a job as a [MASK].` // a field for writing or changing a text value
var OUTPUT_LIST = [] // a blank array to store the results from the model


// RUN MODEL
async function fillInTask(){
  const pipe = await pipeline('fill-mask', 'Xenova/bert-base-uncased');
  
  var out = await pipe(PROMPT_INPUT);

  console.log(await out) // yields { score, sequence, token, token_str } for each result
  
  // await out.forEach(o => {
  //   console.log(o) // yields { score, sequence, token, token_str } for each result
  //   OUTPUT_LIST.push(o.sequence) // put only the full sequence in a list
  // })
  
  // console.log(OUTPUT_LIST)

  return await out
}

// PROCESS MODEL OUTPUT
// a generic function to pass in different model task functions
async function getOutputs(task){
  let output = await task

  await output.forEach(o => {
    OUTPUT_LIST.push(o.sequence) // put only the full sequence in a list
  })

  console.log(OUTPUT_LIST)

  return await OUTPUT_LIST
}

await getOutputs(fillInTask()) // getOutputs will later connect to the interface to display results



//// p5.js Instance

new p5(function (p5){
  p5.setup = function(){
      p5.noCanvas()
      console.log('p5 instance loaded')
      makeDisplayText()
      makeFields()
      makeButtons()
    }

  p5.draw = function(){
      // 
  }

  function makeDisplayText(){
    let title = p5.createElement('h1','Critical AI Prompt Battle')
    let subtitle = p5.createElement('h4','p5.js Critical AI Kit')
    let caption = p5.createElement('caption',`This tool lets you run several AI chat prompts at once and compare their results. Use it to explore what models 'know' about various concepts, communities, and cultures. For more information on prompt programming and critical AI, see [Tutorial & extra info][TO-DO][XXX]`)
  }

  function makeFields(){
    PROMPT_INPUT = p5.createInput(``) // access the text via PROMPT_INPUT.value()
    PROMPT_INPUT.size(700)
    PROMPT_INPUT.attribute('label', `Write a text prompt with at least one [MASK] that the model will fill in.`)
    p5.createP(PROMPT_INPUT.attribute('label'))
    PROMPT_INPUT.addClass("prompt")
  }

  function makeButtons(){
    let submitButton = p5.createButton("SUBMIT")
    submitButton.size(170)
    submitButton.class('submit')
    submitButton.mousePressed(makeResultsText)
  }

  // async 
  function makeResultsText(){
    console.log('button pressed')
    // let resultsText = p5.createElement('h5',"Results:")
    // let res = await getOutputs(fillInTask())
    // resultsText.html(str(res))
  }
  
});