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\n $\"The$ \n

`","pinned":true,"hidden":false,"mode":"ojs"},{"id":148,"value":"img_5933 = FileAttachment(\"IMG_5933.jpeg\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":151,"value":"img_5928 = FileAttachment(\"IMG_5928.jpeg\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":152,"value":"img_5929 = FileAttachment(\"IMG_5929.jpeg\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":153,"value":"img_5930 = FileAttachment(\"IMG_5930.jpeg\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":149,"value":"img_5934 = FileAttachment(\"IMG_5934.jpeg\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":150,"value":"img_5935 = FileAttachment(\"IMG_5935.jpeg\").image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":143,"value":"capture","pinned":true,"hidden":false,"mode":"ojs"},{"id":156,"value":"photos = [img_5928, img_5929, img_5930, img_5933, img_5934, img_5935]","pinned":true,"hidden":false,"mode":"ojs"},{"id":159,"value":"photo_embeddings = {\n var embeddings = [];\n for (var photo of photos) {\n let embedding = await vision_model(\n await processor(await transformers.RawImage.fromBlob(photo))\n );\n embeddings.push(embedding.image_embeds.data);\n }\n return embeddings;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":119,"value":"navigator.mediaDevices\n .getUserMedia({ video: true, audio: false })\n .then((stream) => {\n document.querySelector(\"#video\").srcObject = stream;\n document.querySelector(\"#video\").play();\n })\n .catch((err) => {\n console.error(`An error occurred: ${err}`);\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":33,"value":"viewof browse_image = Inputs.file()","pinned":true,"hidden":false,"mode":"ojs"},{"id":81,"value":"viewof text = Inputs.text({\n label: \"Compare to text\",\n placeholder: \"descriptive phrase\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":94,"value":"md`Similarity score: ${(score * 100).toLocaleString()}%`","pinned":true,"hidden":false,"mode":"ojs"},{"id":71,"value":"browse_image.image()","pinned":true,"hidden":false,"mode":"ojs"},{"id":68,"value":"score = cosineSimilarity(\n textEmbeddings.text_embeds.data,\n embeddings.image_embeds.data\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":105,"value":"html``","pinned":true,"hidden":false,"mode":"ojs"},{"id":4,"value":"transformers = import(\"https://cdn.jsdelivr.net/npm/@xenova/transformers\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":11,"value":"processor = await transformers.AutoProcessor.from_pretrained(\n \"Xenova/clip-vit-base-patch16\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":13,"value":"vision_model = await transformers.CLIPVisionModelWithProjection.from_pretrained(\n \"Xenova/clip-vit-base-patch16\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":15,"value":"default_image = await transformers.RawImage.read(\n \"https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":36,"value":"image = browse_image\n ? await transformers.RawImage.fromBlob(await browse_image.blob())\n : await transformers.RawImage.read(\n \"https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg\"\n )","pinned":true,"hidden":false,"mode":"ojs"},{"id":30,"value":"image","pinned":true,"hidden":false,"mode":"ojs"},{"id":17,"value":"image_inputs = await processor(image)","pinned":true,"hidden":false,"mode":"ojs"},{"id":19,"value":"embeddings = await vision_model(image_inputs)","pinned":true,"hidden":false,"mode":"ojs"},{"id":26,"value":"embeddings.image_embeds.data","pinned":true,"hidden":false,"mode":"ojs"},{"id":49,"value":"tokenizer = await transformers.AutoTokenizer.from_pretrained(\n \"Xenova/clip-vit-base-patch16\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":52,"value":"text_model = await transformers.CLIPTextModelWithProjection.from_pretrained(\n \"Xenova/clip-vit-base-patch16\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":57,"value":"text_inputs = tokenizer(texts, { padding: true, truncation: true })","pinned":true,"hidden":false,"mode":"ojs"},{"id":59,"value":"textEmbeddings = await text_model(text_inputs)","pinned":true,"hidden":false,"mode":"ojs"},{"id":62,"value":"function dotProduct(a, b) {\n let sum = 0;\n for (let i = 0; i < a.length; i++) {\n sum += a[i] * b[i];\n }\n return sum;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":64,"value":"function magnitude(a) {\n return Math.sqrt(dotProduct(a, a));\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":66,"value":"function cosineSimilarity(a, b) {\n if (a.length !== b.length) {\n throw new Error(\"Vectors must have the same dimensions\");\n }\n\n const magA = magnitude(a);\n const magB = magnitude(b);\n\n if (magA === 0 || magB === 0) {\n throw new Error(\n \"Magnitude of one of the vectors is zero, cannot calculate cosine similarity\"\n );\n }\n\n return dotProduct(a, b) / (magA * magB);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":54,"value":"texts = [text]","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W2f","stdlib":"1","titling":"auto"}