sketch.js

// connect to API via module

import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1';
// import { HfInference } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@2.7.0/+esm';
// const inference = new HfInference();

// PIPELINE MODELS
// models('Xenova/gpt2', 'Xenova/gpt-3.5-turbo', 'mistralai/Mistral-7B-Instruct-v0.2', 'Xenova/llama-68m', 'meta-llama/Meta-Llama-3-8B', 'Xenova/bloom-560m', 'Xenova/distilgpt2')
// list of models by task: 'https://huggingface.co/docs/transformers.js/index#supported-tasksmodels'


// Since we will download the model from the Hugging Face Hub, we can skip the local model check
env.allowLocalModels = false;

///////// VARIABLES 

// establish global variables to reference later
var promptInput
var blanksArray = []

// pick a model (see list of models)
// INFERENCE MODELS
// let MODELNAME = "mistralai/Mistral-7B-Instruct-v0.2";
// models('Xenova/gpt2', 'Xenova/gpt-3.5-turbo', 'mistralai/Mistral-7B-Instruct-v0.2', 'Xenova/llama-68m', "meta-llama/Meta-Llama-3-70B-Instruct", 'meta-llama/Meta-Llama-3-8B', 'Xenova/bloom-560m', 'Xenova/distilgpt2', "meta-llama/Meta-Llama-3-70B-Instruct")

// const detector = await pipeline('text-generation', 'meta-llama/Meta-Llama-3-8B', 'Xenova/LaMini-Flan-T5-783M');




///// p5 STUFF

// create an instance of the p5 class as a workspace for all your p5.js code

new p5(function (p5) {
    p5.setup = function(){
        console.log('p5 loaded')
        p5.noCanvas()
        makeInterface()
    }

    p5.draw = function(){
        //
    }

    window.onload = function(){
        console.log('dom and js loaded')
    }

    let fieldsDiv = document.querySelector("#blanks")

    function makeInterface(){
        console.log('reached makeInterface')
        let title = p5.createElement('h1', 'p5.js Critical AI Prompt Battle')
        // title.position(0,50)

        p5.createElement('p',`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 [XXX][TO-DO]`)
            // .position(0,100)

        promptInput = p5.createInput("")
        // promptInput.position(0,160)
        promptInput.size(600);
        promptInput.attribute('label', `Write a text prompt with at least one [BLANK] that describes someone. You can also write [FILL] where you want the bot to fill in a word on its own.`)
        promptInput.value(`The [BLANK] works as a [FILL] but wishes for...`)
        promptInput.addClass("prompt")
        p5.createP(promptInput.attribute('label'))
            // .position(0,100)

        //make for loop to generate
        //make a button to make another
        //add them to the list of items
        fieldsDiv = p5.createDiv()
        fieldsDiv.id('fieldsDiv')
        // fieldsDiv.position(0,250)

        // initial code to make a single field
        // blankA = p5.createInput("");
        // blankA.position(0, 240);
        // blankA.size(300);
        // blankA.addClass("blank")
        // blankA.parent('#fieldsDiv')

        // function to generate a single BLANK form field instead
        addField()

        // // BUTTONS // // 

        // send prompt to model
        let submitButton = p5.createButton("SUBMIT")
        // submitButton.position(0,500)
        submitButton.size(170)
        submitButton.class('submit'); 
        submitButton.mousePressed(getInputs)

        // add more blanks to fill in
        let addButton = p5.createButton("more blanks")
        addButton.size(170)
        // addButton.position(220,500)
        addButton.mousePressed(addField)

        // TO-DO a model drop down list?

        // describe(``)
        // TO-DO alt-text description
    }

    function addField(){
        let f = p5.createInput("")
        f.class("blank")
        f.parent("#fieldsDiv")

        // DOES THIS WORK???????????????????
        blanksArray.push(f)
        console.log("made field")
        
        // Cap the number of fields, avoids token limit in prompt
        let blanks = document.querySelectorAll(".blank")
        if (blanks.length > 7){
            console.log(blanks.length)
            addButton.style('visibility','hidden')
        }
    }

    async function getInputs(){
        // Map the list of blanks text values to a new list
        let BLANKSVALUES = blanksArray.map(i => i.value())
        console.log(BLANKSVALUES)
        
        // Do model stuff in this function instead of in general
        let PROMPT = promptInput.value() // updated check of the prompt field
        
        // BLANKS = inputValues // get ready to feed array list into model

        let PREPROMPT = `Please return an array of sentences. In each sentence, fill in the [BLANK] in the following sentence with each word I provide in the array ${BLANKSVALUES}. Replace any [FILL] with an appropriate word of your choice.` 

        // we pass PROMPT and PREPROMPT to the model function, don't need to pass BLANKSVALUES bc it's passed into the PREPROMPT already here

        let modelResult = await runModel(PREPROMPT, PROMPT)

        await displayModel(modelResult)
    }

    async function displayModel(m){
        modelDisplay = p5.createElement("p", "Results:");
        await modelDisplay.html(m)
    }

    // async function showResults(){
    //     modelDisplay = p5.createElement("p", "Results:");
    //     // modelDisplay.position(0, 380);
    //         setTimeout(() => {
    //             modelDisplay.html(modelResult)
    //     }, 2000);
    // }

    // var modelResult = submitButton.mousePressed(runModel) = function(){
    //     // listens for the button to be clicked
    //     // run the prompt through the model here
    //     // modelResult = runModel()
    //     // return modelResult
    //     runModel()
    // }

    // function makeblank(i){
    //     i = p5.createInput("");
    //     i.position(0, 300); //append to last blank and move buttons down
    //     i.size(200);
    //   }    
});


///// MODEL STUFF

async function runModel(PREPROMPT, PROMPT){
    // // Chat completion API

    // pipeline/transformers version TEST
    let pipe = await pipeline('text-generation', 'Xenova/distilgpt2');

    // 'meta-llama/Meta-Llama-3-70B-Instruct'
    // 'openai-community/gpt2'
    // 'Xenova/gpt-3.5-turbo'

    out = await pipe((PREPROMPT, PROMPT), {
        max_tokens: 250,
        return_full_text: false,
        repetition_penalty: 1.5,
        num_return_sequences: 2
    })

    // out = await pipe((PREPROMPT, PROMPT))
    
    console.log(out)

    var modelResult = await out.generated_text
    console.log(modelResult)

    return modelResult

}


    // inference API version, not working in spaces
    // const out = await inference.chatCompletion({    
    //     model: MODELNAME,
    //     messages: [{ role: "user", content: PREPROMPT + PROMPT }],
    //     max_tokens: 100
    // });

    // console.log(out)

    // // modelResult = await out.messages[0].content

    // var modelResult = await out.choices[0].message.content
    // // var modelResult = await out[0].generated_text
    // console.log(modelResult);

    // return modelResult



    //inference.fill_mask({
    // let out = await pipe(PREPROMPT + PROMPT)
    // let out = await pipe(PREPROMPT + PROMPT, {
    //     max_new_tokens: 250,
    //     temperature: 0.9,
    //     // return_full_text: False,
    //     repetition_penalty: 1.5,
    //     // no_repeat_ngram_size: 2,
    //     // num_beams: 2,
    //     num_return_sequences: 1
    // });



// var PROMPT = `The [BLANK] works as a [blank] but wishes for [blank].`
// /// this needs to run on button click, use string variables to blank in the form
// var PROMPT = promptInput.value()


// var blanksArray = ["mother", "father", "sister", "brother"]
// // for num of blanks put in list
// var blanksArray = [`${blankAResult}`, `${blankBResult}`, `${blankCResult}`]

//Error: Server Xenova/distilgpt2 does not seem to support chat completion. Error: HfApiJson(Deserialize(Error("unknown variant `transformers.js`, expected one of `text-generation-inference`, `transformers`, `allennlp`, `flair`, `espnet`, `asteroid`, `speechbrain`, `timm`, `sentence-transformers`, `spacy`, `sklearn`, `stanza`, `adapter-transformers`, `fasttext`, `fairseq`, `pyannote-audio`, `doctr`, `nemo`, `fastai`, `k2`, `diffusers`, `paddlenlp`, `mindspore`, `open_clip`, `span-marker`, `bertopic`, `peft`, `setfit`", line: 1, column: 397)))