mirror of
https://github.com/huggingface/candle.git
synced 2025-06-15 18:28:24 +00:00
7ad82b87e4
* implement wasm module * add example to workspace * add UI explore semantic similiarity * change status messages * formatting * minor changes
369 lines
13 KiB
HTML
369 lines
13 KiB
HTML
<html>
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<head>
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<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
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<title>Candle Bert</title>
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</head>
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<body></body>
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</html>
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<style>
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@import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
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html,
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body {
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font-family: "Source Sans 3", sans-serif;
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}
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</style>
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<script src="https://cdn.tailwindcss.com"></script>
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<script type="module" src="./code.js"></script>
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<script type="module">
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import { hcl } from "https://cdn.skypack.dev/d3-color@3";
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import { interpolateReds } from "https://cdn.skypack.dev/d3-scale-chromatic@3";
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import { scaleLinear } from "https://cdn.skypack.dev/d3-scale@4";
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import {
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getModelInfo,
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getEmbeddings,
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getWikiText,
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cosineSimilarity,
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} from "./utils.js";
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const bertWorker = new Worker("./bertWorker.js", {
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type: "module",
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});
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const inputContainerEL = document.querySelector("#input-container");
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const textAreaEl = document.querySelector("#input-area");
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const outputAreaEl = document.querySelector("#output-area");
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const formEl = document.querySelector("#form");
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const searchInputEl = document.querySelector("#search-input");
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const formWikiEl = document.querySelector("#form-wiki");
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const searchWikiEl = document.querySelector("#search-wiki");
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const outputStatusEl = document.querySelector("#output-status");
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const modelSelectEl = document.querySelector("#model");
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const sentencesRegex =
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/(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<![A-Z]\.)(?<=\.|\?)\s/gm;
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let sentenceEmbeddings = [];
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let currInputText = "";
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let isCalculating = false;
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function toggleTextArea(state) {
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if (state) {
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textAreaEl.hidden = false;
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textAreaEl.focus();
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} else {
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textAreaEl.hidden = true;
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}
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}
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inputContainerEL.addEventListener("focus", (e) => {
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toggleTextArea(true);
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});
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textAreaEl.addEventListener("blur", (e) => {
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toggleTextArea(false);
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});
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textAreaEl.addEventListener("focusout", (e) => {
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toggleTextArea(false);
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if (currInputText === textAreaEl.value || isCalculating) return;
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populateOutputArea(textAreaEl.value);
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calculateEmbeddings(textAreaEl.value);
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});
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modelSelectEl.addEventListener("change", (e) => {
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if (currInputText === "" || isCalculating) return;
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populateOutputArea(textAreaEl.value);
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calculateEmbeddings(textAreaEl.value);
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});
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function populateOutputArea(text) {
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currInputText = text;
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const sentences = text.split(sentencesRegex);
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outputAreaEl.innerHTML = "";
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for (const [id, sentence] of sentences.entries()) {
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const sentenceEl = document.createElement("span");
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sentenceEl.id = `sentence-${id}`;
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sentenceEl.innerText = sentence + " ";
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outputAreaEl.appendChild(sentenceEl);
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}
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}
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formEl.addEventListener("submit", async (e) => {
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e.preventDefault();
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if (isCalculating || currInputText === "") return;
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toggleInputs(true);
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const modelID = modelSelectEl.value;
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const { modelURL, tokenizerURL, configURL, search_prefix } =
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getModelInfo(modelID);
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const text = searchInputEl.value;
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const query = search_prefix + searchInputEl.value;
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outputStatusEl.classList.remove("invisible");
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outputStatusEl.innerText = "Calculating embeddings for query...";
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isCalculating = true;
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const out = await getEmbeddings(
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bertWorker,
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modelURL,
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tokenizerURL,
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configURL,
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modelID,
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[query]
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);
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outputStatusEl.classList.add("invisible");
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const queryEmbeddings = out.output[0];
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// calculate cosine similarity with all sentences given the query
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const distances = sentenceEmbeddings
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.map((embedding, id) => ({
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id,
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similarity: cosineSimilarity(queryEmbeddings, embedding),
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}))
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.sort((a, b) => b.similarity - a.similarity)
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// getting top 10 most similar sentences
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.slice(0, 10);
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const colorScale = scaleLinear()
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.domain([
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distances[distances.length - 1].similarity,
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distances[0].similarity,
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])
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.range([0, 1])
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.interpolate(() => interpolateReds);
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outputAreaEl.querySelectorAll("span").forEach((el) => {
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el.style.color = "unset";
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el.style.backgroundColor = "unset";
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});
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distances.forEach((d) => {
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const el = outputAreaEl.querySelector(`#sentence-${d.id}`);
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const color = colorScale(d.similarity);
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const fontColor = hcl(color).l < 70 ? "white" : "black";
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el.style.color = fontColor;
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el.style.backgroundColor = color;
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});
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outputAreaEl
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.querySelector(`#sentence-${distances[0].id}`)
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.scrollIntoView({
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behavior: "smooth",
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block: "center",
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inline: "nearest",
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});
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isCalculating = false;
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toggleInputs(false);
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});
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async function calculateEmbeddings(text) {
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isCalculating = true;
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toggleInputs(true);
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const modelID = modelSelectEl.value;
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const { modelURL, tokenizerURL, configURL, document_prefix } =
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getModelInfo(modelID);
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const sentences = text.split(sentencesRegex);
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const allEmbeddings = [];
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outputStatusEl.classList.remove("invisible");
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for (const [id, sentence] of sentences.entries()) {
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const query = document_prefix + sentence;
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outputStatusEl.innerText = `Calculating embeddings: sentence ${
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id + 1
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} of ${sentences.length}`;
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const embeddings = await getEmbeddings(
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bertWorker,
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modelURL,
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tokenizerURL,
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configURL,
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modelID,
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[query],
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updateStatus
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);
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allEmbeddings.push(embeddings);
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}
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outputStatusEl.classList.add("invisible");
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sentenceEmbeddings = allEmbeddings.map((e) => e.output[0]);
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isCalculating = false;
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toggleInputs(false);
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}
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function updateStatus(data) {
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if ("status" in data) {
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if (data.status === "loading") {
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outputStatusEl.innerText = data.message;
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outputStatusEl.classList.remove("invisible");
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}
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}
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}
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function toggleInputs(state) {
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const interactive = document.querySelectorAll(".interactive");
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interactive.forEach((el) => {
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if (state) {
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el.disabled = true;
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} else {
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el.disabled = false;
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}
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});
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}
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searchWikiEl.addEventListener("input", () => {
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searchWikiEl.setCustomValidity("");
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});
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formWikiEl.addEventListener("submit", async (e) => {
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e.preventDefault();
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if ("example" in e.submitter.dataset) {
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searchWikiEl.value = e.submitter.innerText;
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}
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const text = searchWikiEl.value;
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if (isCalculating || text === "") return;
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try {
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const wikiText = await getWikiText(text);
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searchWikiEl.setCustomValidity("");
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textAreaEl.innerHTML = wikiText;
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populateOutputArea(wikiText);
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calculateEmbeddings(wikiText);
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searchWikiEl.value = "";
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} catch {
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searchWikiEl.setCustomValidity("Invalid Wikipedia article name");
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searchWikiEl.reportValidity();
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}
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});
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</script>
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</head>
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<body class="container max-w-4xl mx-auto p-4">
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<main class="grid grid-cols-1 gap-5 relative">
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<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
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<div>
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<h1 class="text-5xl font-bold">Candle BERT</h1>
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<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
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<p class="max-w-lg">
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Running sentence embeddings and similarity search in the browser using
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the Bert Model written with
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<a
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href="https://github.com/huggingface/candle/"
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target="_blank"
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class="underline hover:text-blue-500 hover:no-underline"
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>Candle
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</a>
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and compiled to Wasm. Embeddings models from are from
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<a
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href="https://huggingface.co/sentence-transformers/"
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target="_blank"
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class="underline hover:text-blue-500 hover:no-underline"
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>
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Sentence Transformers
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</a>
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and
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<a
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href="https://huggingface.co/intfloat/"
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target="_blank"
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class="underline hover:text-blue-500 hover:no-underline"
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>
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Liang Wang - e5 Models
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</a>
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</p>
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</div>
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<div>
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<label for="model" class="font-medium block">Models Options: </label>
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<select
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id="model"
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class="border-2 border-gray-500 rounded-md font-light interactive disabled:cursor-not-allowed w-full max-w-max"
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>
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<option value="intfloat_e5_small_v2" selected>
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intfloat/e5-small-v2 (133 MB)
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</option>
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<option value="intfloat_e5_base_v2">
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intfloat/e5-base-v2 (438 MB)
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</option>
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<option value="intfloat_multilingual_e5_small">
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intfloat/multilingual-e5-small (471 MB)
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</option>
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<option value="sentence_transformers_all_MiniLM_L6_v2">
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sentence-transformers/all-MiniLM-L6-v2 (90.9 MB)
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</option>
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<option value="sentence_transformers_all_MiniLM_L12_v2">
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sentence-transformers/all-MiniLM-L12-v2 (133 MB)
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</option>
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</select>
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</div>
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<div>
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<h3 class="font-medium">Examples:</h3>
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<form
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id="form-wiki"
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class="flex text-xs rounded-md justify-between w-min gap-3"
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>
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<input type="submit" hidden />
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<button data-example class="disabled:cursor-not-allowed interactive">
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Pizza
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</button>
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<button data-example class="disabled:cursor-not-allowed interactive">
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Paris
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</button>
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<button data-example class="disabled:cursor-not-allowed interactive">
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Physics
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</button>
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<input
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type="text"
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id="search-wiki"
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title="Search Wikipedia article by title"
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class="font-light py-0 mx-1 resize-none outline-none w-32 disabled:cursor-not-allowed interactive"
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placeholder="Load Wikipedia article..."
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/>
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<button
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title="Search Wikipedia article and load into input"
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class="bg-gray-700 hover:bg-gray-800 text-white font-normal px-2 py-1 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"
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>
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Load
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</button>
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</form>
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</div>
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<form
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id="form"
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class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center"
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>
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<input type="submit" hidden />
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<input
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type="text"
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id="search-input"
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class="font-light w-full px-3 py-2 mx-1 resize-none outline-none interactive disabled:cursor-not-allowed"
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placeholder="Search query here..."
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/>
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<button
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class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"
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>
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Search
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</button>
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</form>
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<div>
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<h3 class="font-medium">Input text:</h3>
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<div class="flex justify-between items-center">
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<div class="rounded-md inline text-xs">
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<span id="output-status" class="m-auto font-light invisible"
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>C</span
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>
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</div>
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</div>
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<div
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id="input-container"
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tabindex="0"
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class="min-h-[250px] bg-slate-100 text-gray-500 rounded-md p-4 flex flex-col gap-2 relative"
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>
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<textarea
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id="input-area"
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hidden
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value=""
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placeholder="Input text to perform semantic similarity search..."
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class="flex-1 resize-none outline-none left-0 right-0 top-0 bottom-0 m-4 absolute interactive disabled:invisible"
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></textarea>
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<p id="output-area" class="grid-rows-2">
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Input text to perform semantic similarity search...
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</p>
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</div>
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</div>
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</main>
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</body>
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</html>
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