| <!DOCTYPE html> |
| <html> |
| <head> |
| <meta charset="UTF-8"/> |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"/> |
| <script src="https://cdn.tailwindcss.com"></script> |
| |
| <script src="https://unpkg.com/es-module-shims@1.7.0/dist/es-module-shims.js"></script> |
| <script type="importmap"> |
| { |
| "imports": { |
| "@huggingface/inference": "https://cdn.jsdelivr.net/npm/@huggingface/inference@2.1.1/+esm" |
| } |
| } |
| </script> |
| </head> |
| <body> |
| <form class="w-[90%] mx-auto pt-8" onsubmit="launch(); return false;"> |
| <h1 class="text-3xl font-bold"> |
| <span |
| class="bg-clip-text text-transparent bg-gradient-to-r from-pink-500 to-violet-500" |
| > |
| Document & visual question answering demo with |
| <a href="https://github.com/huggingface/huggingface.js"> |
| <kbd>@huggingface/inference</kbd> |
| </a> |
| </span> |
| </h1> |
|
|
| <p class="mt-8"> |
| First, input your token if you have one! Otherwise, you may encounter |
| rate limiting. You can create a token for free at |
| <a |
| target="_blank" |
| href="https://huggingface.co/settings/tokens" |
| class="underline text-blue-500" |
| >hf.co/settings/tokens</a |
| > |
| </p> |
|
|
| <input |
| type="text" |
| id="token" |
| class="rounded border-2 border-blue-500 shadow-md px-3 py-2 w-96 mt-6" |
| placeholder="token (optional)" |
| /> |
|
|
| <p class="mt-8"> |
| Pick the model type and the model you want to run. Check out models for |
| <a |
| href="https://huggingface.co/tasks/document-question-answering" |
| class="underline text-blue-500" |
| target="_blank" |
| > |
| document</a |
| > and |
| <a |
| href="https://huggingface.co/tasks/visual-question-answering" |
| class="underline text-blue-500" |
| target="_blank" |
| >image</a> question answering. |
| </p> |
|
|
| <div class="space-x-2 flex text-sm mt-8"> |
| <label> |
| <input class="sr-only peer" name="type" type="radio" value="document" onclick="update_model(this.value)" checked /> |
| <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"> |
| Document |
| </div> |
| </label> |
| <label> |
| <input class="sr-only peer" name="type" type="radio" value="image" onclick="update_model(this.value)" /> |
| <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"> |
| Image |
| </div> |
| </label> |
| </div> |
| |
| <input |
| id="model" |
| class="rounded border-2 border-blue-500 shadow-md px-3 py-2 w-96 mt-6" |
| value="impira/layoutlm-document-qa" |
| required |
| /> |
|
|
| <p class="mt-8">The input image</p> |
|
|
| <input type="file" required accept="image/*" |
| class="rounded border-blue-500 shadow-md px-3 py-2 w-96 mt-6 block" |
| rows="5" |
| id="image" |
| /> |
|
|
| <p class="mt-8">The question</p> |
|
|
| <input |
| type="text" |
| id="question" |
| class="rounded border-2 border-blue-500 shadow-md px-3 py-2 w-96 mt-6" |
| required |
| /> |
|
|
| <button |
| id="submit" |
| class="my-8 bg-green-500 rounded py-3 px-5 text-white shadow-md disabled:bg-slate-300" |
| > |
| Run |
| </button> |
|
|
| <p class="text-gray-400 text-sm">Output logs</p> |
| <div id="logs" class="bg-gray-100 rounded p-3 mb-8 text-sm"> |
| Output will be here |
| </div> |
|
|
| </form> |
|
|
| <script type="module"> |
| import {HfInference} from "@huggingface/inference"; |
| const default_models = { |
| "document": "impira/layoutlm-document-qa", |
| "image": "dandelin/vilt-b32-finetuned-vqa", |
| }; |
| let running = false; |
| async function launch() { |
| if (running) { |
| return; |
| } |
| running = true; |
| try { |
| const hf = new HfInference( |
| document.getElementById("token").value.trim() || undefined |
| ); |
| const model = document.getElementById("model").value.trim(); |
| const model_type = document.querySelector("[name=type]:checked").value; |
| const image = document.getElementById("image").files[0]; |
| const question = document.getElementById("question").value.trim(); |
| document.getElementById("logs").textContent = ""; |
| const method = model_type === "document" ? hf.documentQuestionAnswering : hf.visualQuestionAnswering; |
| const result = await method({model, inputs: { |
| }}); |
| document.getElementById("logs").textContent = JSON.stringify(result, null, 2); |
| } catch (err) { |
| alert("Error: " + err.message); |
| } finally { |
| running = false; |
| } |
| } |
| window.launch = launch; |
| window.update_model = (model_type) => { |
| const model_input = document.getElementById("model"); |
| const cur_model = model_input.value.trim(); |
| let new_model = ""; |
| if ( |
| model_type === "document" && cur_model === default_models["image"] |
| || model_type === "image" && cur_model === default_models["document"] |
| || cur_model === "" |
| ) { |
| new_model = default_models[model_type]; |
| } |
| model_input.value = new_model; |
| }; |
| </script> |
| </body> |
| </html> |
