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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width">
<title>OpenAI CLIP Image Search in JavaScript (Using ONNX Web Runtime)</title>
<script src="enable-threads.js"></script>
<script src="./vips/vips.js"></script>
</head>
<body>
<style>
body * {
font-family: monospace;
}
</style>
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.12.0/dist/ort.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@3.17.0/dist/tf.min.js"></script> <!-- NOTE: tfjs is currently only used for image preprocessing stuff. -->
<div>
<h1 style="font-size:1rem;">Sort/search images using OpenAI's CLIP in your browser</h1>
<p>This web app sorts/searches through images in a directory on your computer using OpenAI's CLIP model, and the new File System Access API. <a href="https://github.com/josephrocca/clip-image-sorter">Here's the Github repo</a> for this web app, and <a href="https://github.com/josephrocca/openai-clip-js">here's the Github repo</a> for the web-ported CLIP models. Feel free to open an issue or <a href="https://twitter.com/rocca27" target="_blank">DM me on Twitter</a> if you have any questions about this demo.</p>
<p>All processing happens in your browser, on your device - i.e. your images are <b>not</b> uploaded to a server for processing.</p>
<p id="browserCompatibilityWarning" style="padding:0.25rem; background:rgb(255, 227, 160); display:none;"><b>Note</b>: This page uses new browser features (File System Access API, and credentialless COEP) that are currently only available in some browsers. As of writing, it works in Chrome, Edge and Brave. Other browsers like Firefox and Safari are often a bit slower in implementing cutting-edge features.</p>
<script>
if(Date.now() < 1648725949710+1000*60*60*24*365) { // display until start of April 2023
browserCompatibilityWarning.style.display = "";
}
</script>
<hr>
<div id="modelNameSelectCtn" style="padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 1:</b> Choose model:
<select onchange="window.MODEL_NAME=this.value;">
<option value="clip_vit_32">CLIP ViT-B/32 (recommended)</option>
<option value="clip_vit_32_uint8">CLIP ViT-B/32 (quantized - inaccurate embeddings)</option>
<option value="lit_b16b">LiT B16B</option>
</select>
</div>
<div id="initCtnEl" style="padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 2:</b> Download and initialize the models.
<br>
Download image model: <progress id="imageModelLoadingProgressBarEl" value="0"></progress> <span id="imageModelLoadingMbEl"></span>
<br>
Download text model: <progress id="textModelLoadingProgressBarEl" value="0"></progress> <span id="textModelLoadingMbEl"></span>
<br>
Initialize workers: <progress id="workerInitProgressBarEl" value="0"></progress>
<div style="display:none;"> <!-- more workers (dividing threads between them) doesn't seem to make things faster -->
Number of image embedding workers/threads: <input id="numThreadsEl" type="range" min="1" max="4" value="1" oninput="numThreadsDisplayEl.textContent=this.value"> <span id="numThreadsDisplayEl"></span> <script>numThreadsEl.max = navigator.hardwareConcurrency; numThreadsDisplayEl.textContent=numThreadsEl.value;</script>
</div>
<br>
<button id="initWorkersBtn" onclick="modelNameSelectCtn.style.pointerEvents='none'; modelNameSelectCtn.style.opacity=0.5; initializeWorkers()">initialize workers</button>
</div>
<div id="pickDirCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 3:</b> Pick a directory of images (images in subdirectories will be included).
<br>
<button id="pickDirectoryBtn" onclick="pickDirectory({source:'local'})">pick directory</button> &nbsp;&nbsp;&nbsp;&nbsp;or&nbsp;&nbsp;&nbsp;&nbsp; <button id="useRedditImagesBtn" onclick="pickDirectory({source:'reddit'})">use ~200k reddit images</button> (remove nsfw:<input id="removeRedditNsfwEl" type="checkbox" checked>)
<br>
<div id="redditLoadProgressCtn" style="display:none;">Download progress: <progress id="redditProgressBarEl" value="0"></progress> <span id="redditProgressMbEl"></span></div>
<div id="existingEmbeddingsProgressCtn" style="display:none;">Loading existing embeddings: <span id="existingEmbeddingsLoadedEl">none</span></div>
</div>
<div id="computeEmbeddingsCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 4:</b> Compute image embeddings. <span style="opacity:0.5;">(they will be saved as &lt;ModelName&gt;_embeddings.tsv in the selected directory)</span>
<br>
<button id="computeEmbeddingsBtn" onclick="computeImageEmbeddings(); this.disabled=true;">compute image embeddings</button>
<br>
<span id="computeEmbeddingsProgressEl">0</span> images embedded (<span id="computeEmbeddingsSpeedEl">?</span> ms per image) <span id="preexistingEmbeddingsEl"></span>
</div>
<div id="existingEmbeddingsFoundCtnEl" style="display:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 5:</b> <b>Existing embeddings found.</b>
<br>
Only needed if you've added or changed images: <button onclick="existingEmbeddingsFoundCtnEl.style.display='none'; computeEmbeddingsCtnEl.style.display=''; disableCtn(searchCtnEl); computeEmbeddingsBtn.click()">(re)compute image embeddings</button>
<input id="onlyEmbedNewImagesCheckbox" type="checkbox" checked> Only new images?
</div>
<div id="searchCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 6:</b> Enter a search term.
<br>
<input id="searchTextEl" style="width:300px;" value="" placeholder="Enter search text here..." onkeyup="if(event.which==13) searchSort()">
<button id="searchBtn" onclick="searchSort()">search</button>
</div>
</div>
<hr>
<b>Results</b> <span style="opacity:0.5;">(hover for cosine similarities)</span>
<div id="resultsEl" style="margin-top:1rem; min-height:100vh;"><span style="opacity:0.5;">Click the search button to compute the results.</span></div>
<script>
/////////////
// STEP 1 //
/////////////
window.MODEL_NAME = "clip_vit_32";
window.modelData = {
clip_vit_32: {
image: {
modelUrl: (quantized) => `https://huggingface.co/rocca/openai-clip-js/resolve/main/clip-image-vit-32-${quantized ? "uint8" : "float32"}.onnx`,
embed: async function(blob, session) {
let rgbData = await getRgbData(blob);
const feeds = {input: new ort.Tensor('float32', rgbData, [1,3,224,224])};
const results = await session.run(feeds);
const embedVec = results["output"].data; // Float32Array
return embedVec;
}
},
text: {
modelUrl: (quantized) => `https://huggingface.co/rocca/openai-clip-js/resolve/main/clip-text-vit-32-${quantized ? "uint8" : "float32-int32"}.onnx`,
embed: async function(text, session) {
if(!window.textTokenizerClip) {
let Tokenizer = (await import("https://deno.land/x/clip_bpe@v0.0.6/mod.js")).default;
window.textTokenizerClip = new Tokenizer();
}
let textTokens = window.textTokenizerClip.encodeForCLIP(text);
textTokens = Int32Array.from(textTokens);
const feeds = {input: new ort.Tensor('int32', textTokens, [1, 77])};
const results = await session.run(feeds);
return [...results["output"].data];
},
}
},
lit_b16b: {
image: {
modelUrl: () => 'https://huggingface.co/rocca/lit-web/resolve/main/embed_images.onnx',
embed: async function(blob, session) {
// TODO: Maybe remove tf from this code so you can remove the whole tfjs dependency
blob = await bicubicResizeAndCenterCrop(blob);
let inputImg = new Image();
await new Promise(r => inputImg.onload=r, inputImg.src=URL.createObjectURL(blob));
let img = tf.browser.fromPixels(inputImg);
img = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);
let float32RgbData = img.dataSync();
const feeds = {'images': new ort.Tensor('float32', float32RgbData, [1,224,224,3])};
const results = await session.run(feeds);
return results["Identity_1:0"].data;
},
},
text: {
modelUrl: () => 'https://huggingface.co/rocca/lit-web/resolve/main/embed_text_tokens.onnx',
embed: async function(text, session) {
if(!window.bertTextTokenizerLit) {
window.bertTextTokenizerLit = await import("./bert-text-tokenizer.js").then(m => new m.BertTokenizer());
await window.bertTextTokenizerLit.load();
}
let textTokens = window.bertTextTokenizerLit.tokenize(text);
textTokens.unshift(101); // manually put CLS token at the start
textTokens.length = 16;
textTokens = [...textTokens.slice(0, 16)].map(e => e == undefined ? 0 : e); // pad with zeros to length of 16
textTokens = Int32Array.from(textTokens);
const feeds = {'text_tokens': new ort.Tensor('int32', textTokens, [1,16])};
const results = await session.run(feeds);
return [...results["Identity_1:0"].data];
}
}
},
};
let imageWorkers = [];
let onnxImageSessions = [];
let onnxTextSession;
let textTokenizer;
async function initializeWorkers() {
initWorkersBtn.disabled = true;
numThreadsEl.disabled = true;
let useQuantizedModel = false;
if(MODEL_NAME.endsWith("_uint8")) {
MODEL_NAME = MODEL_NAME.replace(/_uint8$/g, "");
useQuantizedModel = true;
}
let imageOnnxBlobPromise = downloadBlobWithProgress(window.modelData[MODEL_NAME].image.modelUrl(useQuantizedModel), function(e) {
let ratio = e.loaded / e.total;
imageModelLoadingProgressBarEl.value = ratio;
imageModelLoadingMbEl.innerHTML = Math.round(ratio*e.total/1e6)+" MB";
});
let textOnnxBlobPromise = downloadBlobWithProgress(window.modelData[MODEL_NAME].text.modelUrl(useQuantizedModel), function(e) {
let ratio = e.loaded / e.total;
textModelLoadingProgressBarEl.value = ratio;
textModelLoadingMbEl.innerHTML = Math.round(ratio*e.total/1e6)+" MB";
});
let [imageOnnxBlob, textOnnxBlob] = await Promise.all([imageOnnxBlobPromise, textOnnxBlobPromise])
let imageModelUrl = window.URL.createObjectURL(imageOnnxBlob);
let textModelUrl = window.URL.createObjectURL(textOnnxBlob);
let numImageWorkers = Number(numThreadsEl.value);
// Inference latency is about 5x faster with wasm threads, but this requires these headers: https://web.dev/coop-coep/ I'm using this as a hack (in enable-threads.js) since Github pages doesn't allow setting headers: https://github.com/gzuidhof/coi-serviceworker
if(self.crossOriginIsolated) {
ort.env.wasm.numThreads = Math.ceil(navigator.hardwareConcurrency / numImageWorkers) / 2; // divide by two to utilise only half the CPU's threads because trying to use all the cpu's threads actually makes it slower
}
workerInitProgressBarEl.max = numImageWorkers + 2; // +2 because of text model and bpe library
let imageModelExecutionProviders = ["wasm"]; // webgl is not compatible with this model (need to tweak conversion data/op types)
for(let i = 0; i < numImageWorkers; i++) {
let session = await ort.InferenceSession.create(imageModelUrl, { executionProviders: imageModelExecutionProviders });
onnxImageSessions.push(session);
imageWorkers.push({
session,
busy: false,
});
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
}
console.log("Image model loaded.");
onnxTextSession = await ort.InferenceSession.create(textModelUrl, { executionProviders: ["wasm"] }); // webgl is not compatible with this model (need to tweak conversion data/op types)
console.log("Text model loaded.");
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
window.URL.revokeObjectURL(imageModelUrl);
window.URL.revokeObjectURL(textModelUrl);
window.vips = await Vips(); // for bicubicly resizing images (since that's what CLIP expects)
window.vips.EMBIND_AUTOMATIC_DELETELATER = false;
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
disableCtn(initCtnEl);
enableCtn(pickDirCtnEl);
}
/////////////
// STEP 2 //
/////////////
let directoryHandle;
let embeddingsFileHandle;
let embeddings;
let dataSource;
async function pickDirectory(opts={}) {
dataSource = opts.source;
if(dataSource === "local") {
if(!window.showDirectoryPicker) return alert("Your browser does not support some modern features (specifically, File System Access API) required to use this web app. Please try updating your browser, or switching to Chrome, Edge, or Brave.");
directoryHandle = await window.showDirectoryPicker();
embeddingsFileHandle = await directoryHandle.getFileHandle(`${window.MODEL_NAME}_embeddings.tsv`, {create:true});
pickDirectoryBtn.disabled = true;
useRedditImagesBtn.disabled = true;
pickDirectoryBtn.textContent = "Loading...";
}
let redditEmbeddingsBlob;
if(dataSource === "reddit") {
if(window.MODEL_NAME !== "clip_vit_32") return alert("Sorry, there are only pre-computed Reddit image embeddings for the CLIP ViT-B/32 model at the moment.");
if(!removeRedditNsfwEl.checked && !confirm("Are you sure you'd like to see NSFW Reddit images?")) return;
if(removeRedditNsfwEl.checked) alert("Note that NSFW images are filtered from Reddit using CLIP, and CLIP can make mistakes, so some NSFW images may still be shown.");
pickDirectoryBtn.disabled = true;
useRedditImagesBtn.disabled = true;
useRedditImagesBtn.textContent = "Loading...";
redditLoadProgressCtn.style.display = "";
redditEmbeddingsBlob = await downloadBlobWithProgress("https://huggingface.co/datasets/rocca/top-reddit-posts/resolve/main/clip_embeddings_top_50_images_per_subreddit.tsv.gz", function(e) {
let ratio = e.loaded / e.total;
redditProgressBarEl.value = ratio;
redditProgressMbEl.innerHTML = Math.round(ratio*213)+" MB";
});
}
try {
existingEmbeddingsProgressCtn.style.display = "";
embeddings = {};
let file, opts;
if(dataSource === "local") {
file = await embeddingsFileHandle.getFile();
opts = {};
}
if(dataSource === "reddit") {
file = redditEmbeddingsBlob;
opts = {decompress:"gzip"};
}
let i = 0;
for await (let line of makeTextFileLineIterator(file, opts)) {
if(!line || !line.trim()) continue; // <-- to skip final new line (not sure if this is needed)
let [filePath, embeddingVec] = line.split("\t");
embeddings[filePath] = JSON.parse(embeddingVec);
i++;
if(i % 1000 === 0) {
existingEmbeddingsLoadedEl.innerHTML = i;
await sleep(10);
}
}
} catch(e) {
embeddings = undefined;
console.log("No existing embedding found, or the embeddings file was corrupted:", e);
existingEmbeddingsProgressCtn.style.display = "none";
}
pickDirectoryBtn.textContent = "Done.";
useRedditImagesBtn.textContent = "Done.";
disableCtn(pickDirCtnEl);
enableCtn(computeEmbeddingsCtnEl);
enableCtn(searchCtnEl);
if(embeddings && Object.keys(embeddings).length > 0) {
computeEmbeddingsCtnEl.style.display = "none";
existingEmbeddingsFoundCtnEl.style.display = "";
}
if(dataSource === "reddit") {
disableCtn(existingEmbeddingsFoundCtnEl);
}
}
/////////////
// STEP 3 //
/////////////
let totalEmbeddingsCount = 0;
let imagesEmbedded;
let recentEmbeddingTimes = []; // how long each embed took in ms, newest at end
let recomputeAllEmbeddings;
let imagesBeingProcessedNow = 0;
let needToSaveEmbeddings = false;
async function computeImageEmbeddings() {
imagesEmbedded = 0;
totalEmbeddingsCount = Object.keys(embeddings).length;
recomputeAllEmbeddings = !onlyEmbedNewImagesCheckbox.checked;
let gotSomeExistingEmbeddings = totalEmbeddingsCount > 0;
if(onlyEmbedNewImagesCheckbox.checked && gotSomeExistingEmbeddings) {
preexistingEmbeddingsEl.innerHTML = `(loaded ${Object.keys(embeddings).length} existing embeddings)`;
}
if(recomputeAllEmbeddings || !gotSomeExistingEmbeddings) {
embeddings = {}; // <-- maps file path (relative to top/selected directory) to embedding
}
try {
await recursivelyProcessImagesInDir(directoryHandle);
await saveEmbeddings();
} catch(e) {
console.error(e);
alert(e.message);
}
disableCtn(computeEmbeddingsCtnEl);
enableCtn(searchCtnEl);
}
async function recursivelyProcessImagesInDir(dirHandle, currentPath="") {
for await (let [name, handle] of dirHandle) {
const {kind} = handle;
let path = `${currentPath}/${name}`;
if (handle.kind === 'directory') {
await recursivelyProcessImagesInDir(handle, path);
} else {
let isImage = /\.(png|jpg|jpeg|webp)$/.test(path);
if(!isImage) continue;
let alreadyGotEmbedding = !!embeddings[path];
if(alreadyGotEmbedding && !recomputeAllEmbeddings) continue;
if(needToSaveEmbeddings) {
await saveEmbeddings();
needToSaveEmbeddings = false;
}
while(imageWorkers.filter(w => !w.busy).length === 0) await sleep(1);
let worker = imageWorkers.filter(w => !w.busy)[0];
worker.busy = true;
imagesBeingProcessedNow++;
(async function() {
let startTime = Date.now();
let blob = await handle.getFile();
const embedVec = await modelData[MODEL_NAME].image.embed(blob, worker.session);
embeddings[path] = [...embedVec];
worker.busy = false;
imagesEmbedded++;
totalEmbeddingsCount++;
computeEmbeddingsProgressEl.innerHTML = imagesEmbedded;
let saveInterval = totalEmbeddingsCount > 50_000 ? 10_000 : 1000; // since saves take longer if there are lots of embeddings
if(imagesEmbedded % saveInterval === 0) {
needToSaveEmbeddings = true;
}
recentEmbeddingTimes.push(Date.now()-startTime);
if(recentEmbeddingTimes.length > 100) recentEmbeddingTimes = recentEmbeddingTimes.slice(-50);
if(recentEmbeddingTimes.length > 10) computeEmbeddingsSpeedEl.innerHTML = Math.round(recentEmbeddingTimes.slice(-20).reduce((a,v) => a+v, 0)/20);
imagesBeingProcessedNow--;
})();
}
}
while(imagesBeingProcessedNow > 0) await sleep(10);
}
/////////////
// STEP 4 //
/////////////
async function searchSort() {
searchBtn.disabled = true;
if(dataSource === "local") {
for(let imgEl of [...document.querySelectorAll("img")]) {
URL.revokeObjectURL(imgEl.src);
}
}
resultsEl.innerHTML = "Loading...";
await sleep(50);
let searchTextEmbedding = await modelData[MODEL_NAME].text.embed(searchTextEl.value, onnxTextSession);
let similarities = {};
for(let [path, embedding] of Object.entries(embeddings)) {
similarities[path] = cosineSimilarity(searchTextEmbedding, embedding);
}
let similarityEntries = Object.entries(similarities).sort((a,b) => b[1]-a[1]).slice(0, 5000);
if(dataSource === "reddit" && removeRedditNsfwEl.checked) {
let nsfwTextEmbedding = await modelData[MODEL_NAME].text.embed(atob('cG9ybiBuYWtlZCBwZW5pcyB2YWdpbmEgbnVkZSBzZXggZGljayBwdXNzeSBzZXh1YWwgcG9ybm9ncmFwaGljIGFzcyBib29icw=='), onnxTextSession); // nsfw words (hidden with `btoa`)
let nsfwSimilarities = {};
for(let [path, similarity] of similarityEntries) {
let embedding = embeddings[path];
nsfwSimilarities[path] = cosineSimilarity(nsfwTextEmbedding, embedding);
}
similarityEntries = similarityEntries.filter(e => nsfwSimilarities[e[0]] < 0.2093);
}
let resultHtml = "";
let numResults = 0;
for(let [path, score] of similarityEntries.slice(0, 500)) {
if(dataSource === "local") {
let handle = await getFileHandleByPath(path);
let url = URL.createObjectURL(await handle.getFile());
resultHtml += `<img src="${url}" style="max-height:400px;" title="${path}: ${score}" loading="lazy"/>`;
}
if(dataSource === "reddit") {
let imageUrl = `https://i.redd.it/${path.split("__")[1]}`;
let postUrl = `https://reddit.com/comments/${path.split("__")[0].split("/")[1]}`;
resultHtml += `<a href="${postUrl}" target="_blank"><img src="${imageUrl}" onload="this.style.height='';this.style.width='';this.style.border='';" style="max-height:400px; height:300px; width:300px; border:1px solid black;" title="${path}: ${score}" loading="lazy"/></a>`;
}
numResults++;
}
if(!resultHtml) {
resultsEl.innerHTML = "No results found after filtering NSFW.";
} else {
resultsEl.innerHTML = resultHtml;
}
searchBtn.disabled = false;
}
/////////////////////////////
// FUNCTIONS / UTILITIES //
/////////////////////////////
async function getFileHandleByPath(path) {
let handle = directoryHandle;
let chunks = path.split("/").slice(1);
for(let i = 0; i < chunks.length; i++) {
let chunk = chunks[i];
if(i === chunks.length-1) {
handle = await handle.getFileHandle(chunk);
} else {
handle = await handle.getDirectoryHandle(chunk);
}
}
return handle;
}
async function getRgbData(blob) {
// let blob = await fetch(imgUrl, {referrer:""}).then(r => r.blob());
let resizedBlob = await bicubicResizeAndCenterCrop(blob);
let img = await createImageBitmap(resizedBlob);
let canvas = new OffscreenCanvas(224, 224);
let ctx = canvas.getContext("2d");
ctx.drawImage(img, 0, 0);
let imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
let rgbData = [[], [], []]; // [r, g, b]
// remove alpha and put into correct shape:
let d = imageData.data;
for(let i = 0; i < d.length; i += 4) {
let x = (i/4) % canvas.width;
let y = Math.floor((i/4) / canvas.width)
if(!rgbData[0][y]) rgbData[0][y] = [];
if(!rgbData[1][y]) rgbData[1][y] = [];
if(!rgbData[2][y]) rgbData[2][y] = [];
rgbData[0][y][x] = d[i+0]/255;
rgbData[1][y][x] = d[i+1]/255;
rgbData[2][y][x] = d[i+2]/255;
// From CLIP repo: Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
rgbData[0][y][x] = (rgbData[0][y][x] - 0.48145466) / 0.26862954;
rgbData[1][y][x] = (rgbData[1][y][x] - 0.4578275) / 0.26130258;
rgbData[2][y][x] = (rgbData[2][y][x] - 0.40821073) / 0.27577711;
}
rgbData = Float32Array.from(rgbData.flat().flat());
return rgbData;
}
async function bicubicResizeAndCenterCrop(blob) {
let im1 = vips.Image.newFromBuffer(await blob.arrayBuffer());
// Resize so smallest side is 224px:
const scale = 224 / Math.min(im1.height, im1.width);
let im2 = im1.resize(scale, { kernel: vips.Kernel.cubic });
// crop to 224x224:
let left = (im2.width - 224) / 2;
let top = (im2.height - 224) / 2;
let im3 = im2.crop(left, top, 224, 224)
let outBuffer = new Uint8Array(im3.writeToBuffer('.png'));
im1.delete(), im2.delete(), im3.delete();
return new Blob([outBuffer], { type: 'image/png' });
}
function downloadBlobWithProgress(url, onProgress) {
return new Promise((res, rej) => {
var blob;
var xhr = new XMLHttpRequest();
xhr.open('GET', url, true);
xhr.responseType = 'arraybuffer';
xhr.onload = function(e) {
blob = new Blob([this.response]);
};
xhr.onprogress = onProgress;
xhr.onloadend = function(e){
res(blob);
}
xhr.send();
});
}
async function saveEmbeddings(opts={}) {
let writable = await embeddingsFileHandle.createWritable();
let textBatch = "";
let i = 0;
for(let [filePath, embeddingVec] of Object.entries(embeddings)) {
let vecString = opts.compress ? JSON.stringify(embeddingVec.map(n => n.toFixed(3))).replace(/"/g, "") : JSON.stringify(embeddingVec);
textBatch += `${filePath}\t${vecString}\n`;
i++;
if(i % 1000 === 0) {
await writable.write(textBatch);
textBatch = "";
}
}
await writable.write(textBatch);
await writable.close();
}
// Tweaked version of example from here: https://developer.mozilla.org/en-US/docs/Web/API/ReadableStreamDefaultReader/read
async function* makeTextFileLineIterator(blob, opts={}) {
const utf8Decoder = new TextDecoder("utf-8");
let stream = await blob.stream();
if(opts.decompress === "gzip") stream = stream.pipeThrough(new DecompressionStream("gzip"));
let reader = stream.getReader();
let {value: chunk, done: readerDone} = await reader.read();
chunk = chunk ? utf8Decoder.decode(chunk, {stream: true}) : "";
let re = /\r\n|\n|\r/gm;
let startIndex = 0;
while (true) {
let result = re.exec(chunk);
if (!result) {
if (readerDone) {
break;
}
let remainder = chunk.substr(startIndex);
({value: chunk, done: readerDone} = await reader.read());
chunk = remainder + (chunk ? utf8Decoder.decode(chunk, {stream: true}) : "");
startIndex = re.lastIndex = 0;
continue;
}
yield chunk.substring(startIndex, result.index);
startIndex = re.lastIndex;
}
if (startIndex < chunk.length) {
// last line didn't end in a newline char
yield chunk.substr(startIndex);
}
}
function cosineSimilarity(A, B) {
if(A.length !== B.length) throw new Error("A.length !== B.length");
let dotProduct = 0, mA = 0, mB = 0;
for(let i = 0; i < A.length; i++){
dotProduct += A[i] * B[i];
mA += A[i] * A[i];
mB += B[i] * B[i];
}
mA = Math.sqrt(mA);
mB = Math.sqrt(mB);
let similarity = dotProduct / (mA * mB);
return similarity;
}
function sleep(ms) {
return new Promise(r => setTimeout(r, ms));
}
function enableCtn(el) {
el.style.opacity = 1;
el.style.pointerEvents = "";
}
function disableCtn(el) {
el.style.opacity = 0.5;
el.style.pointerEvents = "none";
}
// From the PyTorch model running on CUDA:
// Text: "a portrait of an astronaut with the American flag"
// Embedding: [-1.6626e-01, 5.2277e-02, -1.5332e-01, 4.4946e-01, 2.0667e-01, -2.9565e-01, 4.0588e-02, -4.1016e-01, -1.5027e-01, 3.1934e-01, -6.9702e-02, -2.5488e-01, 1.2335e-01, -9.5337e-02, 2.4109e-01, -4.8950e-02, 2.6074e-01, 5.3835e-04, 2.1033e-01, 3.7012e-01, 4.5679e-01, 3.9795e-01, 3.1641e-01, 3.9551e-01, 1.3931e-02, -4.3060e-02, 4.8798e-02, 3.7158e-01, 1.1731e-01, -3.7256e-01, -2.7295e-01, 3.3130e-01, 5.4980e-01, -2.9816e-02, -2.5806e-01, -1.0016e-01, 8.0750e-02, -6.7139e-02, -2.4072e-01, 2.4353e-01, -3.2202e-01, -1.0327e-01, 1.1566e-01, 6.2646e-01, 1.8262e-01, 2.7539e-01, -1.1816e-01, 4.9512e-01, 8.9539e-02, 5.6299e-01, 2.1313e-01, -1.5625e-01, 1.9958e-01, -5.0049e-01, -2.5854e-01, -4.0430e-01, -1.1298e-01, -6.6338e-03, 2.5391e-01, -5.0629e-02, 2.2253e-01, -2.7295e-01, -5.8289e-03, -4.8804e-01, -7.7820e-02, -3.5187e-02, -3.7537e-02, 4.3213e-01, 3.8300e-02, 2.1045e-01, -3.0347e-01, -9.8999e-02, -1.7407e-01, 2.8882e-01, 1.1322e-01, -1.0883e-01, 1.7065e-01, -2.1191e-01, 1.7920e-01, 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</script>
</body>
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