sketch.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
let PROMPT_INPUT = `Happy people are better than [MASK].` // a field for writing or changing a text value
let OUTPUT_LIST = [] // a blank array to store the results from the model
let pField

// RUN MODEL
async function fillInTask(input){
  console.log('fill-in task initiated')

  const pipe = await pipeline('fill-mask', 'Xenova/bert-base-uncased');
  
  var out = await pipe(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(await OUTPUT_LIST)

  // displayResults(await OUTPUT_LIST)

  console.log('fill-in task completed')

  return await out
  // return await OUTPUT_LIST
}

// 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')
      makeTextDisplay()
      makeFields()
      makeButtons()
    }

  p5.draw = function(){
      // 
  }

  function makeTextDisplay(){
    let title = p5.createElement('h1','p5.js Critical AI Prompt Battle')
    let intro = p5.createP(`This tool lets you explore several AI prompts results at once.`) 
    p5.createP(`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(){
    pField = p5.createInput(PROMPT_INPUT) // turns the string into an input; now access the text via PROMPT_INPUT.value()
    pField.size(700)
    pField.attribute('label', `Write a text prompt with one [MASK] that the model will fill in.`)
    p5.createP(pField.attribute('label'))
    pField.addClass("prompt")
    // pField.value(PROMPT_INPUT)
    // console.log(pField.value())
  }

  function makeButtons(){
    let submitButton = p5.createButton("SUBMIT")
    submitButton.size(170)
    submitButton.class('submit')
    submitButton.mousePressed(displayResults)
    let outHeader = p5.createElement('h3',"Results") 
  }

  async function displayResults(){
    console.log('displayed, pressed')

    PROMPT_INPUT = pField.value() // updates prompt if it's changed
    console.log("latest prompt: ", PROMPT_INPUT)

    let fillIn = await fillInTask(PROMPT_INPUT)

    let outs = await getOutputs(fillIn)

    console.log(outs)
  
    // text = str(outs)
    
    let outText = p5.createP('')
    await outText.html(outs, false)
  }

  // async function makeOutputDisplay(){
  //   console.log('button pressed')
  //   let outHead = p5.createElement('h4',"Results:")

  //   let out = await fillInTask() //just model no parsing
  //   // let out = await getOutputs(fillInTask()) // model and parsing

  //   out = str(await out)
  //   console.log(out)
    
  //   let outText = p5.createP('')
  //   await outText.html(out)
  // }

});