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Create visialqa.html
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visialqa.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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<script src="https://cdn.tailwindcss.com"></script>
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<!-- polyfill for firefox + import maps -->
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<script src="https://unpkg.com/es-module-shims@1.7.0/dist/es-module-shims.js"></script>
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<script type="importmap">
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{
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"imports": {
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"@huggingface/inference": "https://cdn.jsdelivr.net/npm/@huggingface/inference@2.1.1/+esm"
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}
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}
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</script>
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</head>
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<body>
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<form class="w-[90%] mx-auto pt-8" onsubmit="launch(); return false;">
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<h1 class="text-3xl font-bold">
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<span
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class="bg-clip-text text-transparent bg-gradient-to-r from-pink-500 to-violet-500"
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>
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Document & visual question answering demo with
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<a href="https://github.com/huggingface/huggingface.js">
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<kbd>@huggingface/inference</kbd>
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</a>
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</span>
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</h1>
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<p class="mt-8">
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First, input your token if you have one! Otherwise, you may encounter
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rate limiting. You can create a token for free at
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<a
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target="_blank"
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href="https://huggingface.co/settings/tokens"
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class="underline text-blue-500"
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>hf.co/settings/tokens</a
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>
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</p>
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<input
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type="text"
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id="token"
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class="rounded border-2 border-blue-500 shadow-md px-3 py-2 w-96 mt-6"
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placeholder="token (optional)"
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/>
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<p class="mt-8">
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Pick the model type and the model you want to run. Check out models for
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<a
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href="https://huggingface.co/tasks/document-question-answering"
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class="underline text-blue-500"
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target="_blank"
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>
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document</a
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> and
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<a
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href="https://huggingface.co/tasks/visual-question-answering"
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class="underline text-blue-500"
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target="_blank"
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>image</a> question answering.
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</p>
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<div class="space-x-2 flex text-sm mt-8">
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<label>
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<input class="sr-only peer" name="type" type="radio" value="document" onclick="update_model(this.value)" checked />
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<div class="px-3 py-3 rounded-lg shadow-md flex items-center justify-center text-slate-700 bg-gradient-to-r peer-checked:font-semibold peer-checked:from-pink-500 peer-checked:to-violet-500 peer-checked:text-white">
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Document
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</div>
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</label>
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<label>
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<input class="sr-only peer" name="type" type="radio" value="image" onclick="update_model(this.value)" />
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<div class="px-3 py-3 rounded-lg shadow-md flex items-center justify-center text-slate-700 bg-gradient-to-r peer-checked:font-semibold peer-checked:from-pink-500 peer-checked:to-violet-500 peer-checked:text-white">
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Image
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</div>
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</label>
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</div>
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<input
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id="model"
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class="rounded border-2 border-blue-500 shadow-md px-3 py-2 w-96 mt-6"
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value="impira/layoutlm-document-qa"
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required
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/>
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<p class="mt-8">The input image</p>
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<input type="file" required accept="image/*"
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class="rounded border-blue-500 shadow-md px-3 py-2 w-96 mt-6 block"
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rows="5"
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id="image"
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/>
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<p class="mt-8">The question</p>
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<input
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type="text"
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id="question"
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class="rounded border-2 border-blue-500 shadow-md px-3 py-2 w-96 mt-6"
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required
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/>
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<button
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id="submit"
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class="my-8 bg-green-500 rounded py-3 px-5 text-white shadow-md disabled:bg-slate-300"
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>
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Run
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</button>
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<p class="text-gray-400 text-sm">Output logs</p>
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<div id="logs" class="bg-gray-100 rounded p-3 mb-8 text-sm">
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Output will be here
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</div>
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<p>Check out the <a class="underline text-blue-500"
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href="https://huggingface.co/spaces/huggingfacejs/doc-vis-qa/blob/main/index.html"
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target="_blank">source code</a></p>
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</form>
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<script type="module">
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import {HfInference} from "@huggingface/inference";
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const default_models = {
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"document": "impira/layoutlm-document-qa",
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"image": "dandelin/vilt-b32-finetuned-vqa",
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};
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let running = false;
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async function launch() {
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if (running) {
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return;
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}
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running = true;
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try {
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const hf = new HfInference(
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document.getElementById("token").value.trim() || undefined
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);
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const model = document.getElementById("model").value.trim();
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const model_type = document.querySelector("[name=type]:checked").value;
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const image = document.getElementById("image").files[0];
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const question = document.getElementById("question").value.trim();
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document.getElementById("logs").textContent = "";
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const method = model_type === "document" ? hf.documentQuestionAnswering : hf.visualQuestionAnswering;
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const {answer, score} = await method({model, inputs: {
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image, question
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}});
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document.getElementById("logs").textContent = answer + ": " + score;
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} catch (err) {
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alert("Error: " + err.message);
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} finally {
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running = false;
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}
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}
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window.launch = launch;
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window.update_model = (model_type) => {
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const model_input = document.getElementById("model");
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const cur_model = model_input.value.trim();
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let new_model = "";
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if (
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model_type === "document" && cur_model === default_models["image"]
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|| model_type === "image" && cur_model === default_models["document"]
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|| cur_model === ""
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) {
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new_model = default_models[model_type];
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}
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model_input.value = new_model;
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};
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</script>
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</body>
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</html>
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