add model back into loop

sketch.js

@@ -14,7 +14,7 @@ const inference = new HfInference();
 
 
 
-let PROMPT, promptResult, submitButton, addButton, promptInput, inputValues, modelDisplay, modelResult
+let PROMPT, PREPROMPT, promptResult, submitButton, addButton, promptInput, inputValues, modelDisplay, modelResult
 
 // const detector = await pipeline('text-generation', 'meta-llama/Meta-Llama-3-8B', 'Xenova/LaMini-Flan-T5-783M');
 
@@ -23,10 +23,6 @@ let blankArray = []
 let MODELNAME = 'Xenova/gpt-3.5-turbo'
 // 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')
 
-
-var 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 ${inputValues}. Replace any [FILL] with an appropriate word of your choice.` 
-
-
 ///// p5 STUFF
 
 
@@ -125,7 +121,7 @@ new p5(function(p5){
         }
     }
 
-    function getInputs(){
+    async function getInputs(){
         // Map the list of blanks text values to a new list
         let inputValues = blankArray.map(i => i.value())
         console.log(inputValues)
@@ -133,8 +129,11 @@ new p5(function(p5){
         // Do model stuff in this function instead of in general
         PROMPT = promptInput.value() // updated check of the prompt field
         
+        BLANKS = inputValues // get ready to feed array list into model
+
+        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 ${inputValues}. Replace any [FILL] with an appropriate word of your choice.` 
+
         await runModel()
-        // BLANKS = inputValues // get ready to feed array list into model
     }
 
     // var modelResult = submitButton.mousePressed(runModel) = function(){
@@ -166,7 +165,7 @@ new p5(function(p5){
 // var blankArray = [`${blankAResult}`, `${blankBResult}`, `${blankCResult}`]
 
 
-async function runModel(prompt, blanks){
+async function runModel(){
     // Chat completion API
     const out = await inference.chatCompletion({
         model: MODELNAME,
@@ -175,24 +174,24 @@ async function runModel(prompt, blanks){
         max_tokens: 100
     });
 
-//     // 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
-//     // });
-//     console.log(out)
-
-//     var modelResult = await out.choices[0].message.content
-//     // var modelResult = await out[0].generated_text
-//     console.log(modelResult);
-
-//     return modelResult
-// }
+    // 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
+    // });
+    console.log(out)
+
+    var modelResult = await out.choices[0].message.content
+    // var modelResult = await out[0].generated_text
+    console.log(modelResult);
+
+    return modelResult
+}
 
 
 // Reference the elements that we will need