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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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<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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</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 result = await method({model, inputs: { |
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}}); |
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document.getElementById("logs").textContent = JSON.stringify(result, null, 2); |
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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> |
