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| <html> | |
| <head> | |
| <meta charset="UTF-8" /> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0" /> | |
| <script src="https://cdn.tailwindcss.com"></script> | |
| <!-- polyfill for firefox + import maps --> | |
| <script src="https://unpkg.com/[email protected]/dist/es-module-shims.js"></script> | |
| <script type="importmap"> | |
| { | |
| "imports": { | |
| "@huggingface/inference": "https://cdn.jsdelivr.net/npm/@huggingface/[email protected]/+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" | |
| > | |
| Image to text 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 you want to run. Check out over 100 models for image to text | |
| <a | |
| href="https://huggingface.co/tasks/image-to-text" | |
| class="underline text-blue-500" | |
| target="_blank" | |
| > | |
| here</a | |
| >. The default model is for image captioning, but you can do text extraction, ... | |
| </p> | |
| <!-- Default model: https://huggingface.co/nlpconnect/vit-gpt2-image-captioning --> | |
| <input | |
| type="text" | |
| id="model" | |
| class="rounded border-2 border-blue-500 shadow-md px-3 py-2 w-96 mt-6" | |
| value="nlpconnect/vit-gpt2-image-captioning" | |
| required | |
| /> | |
| <p class="mt-8">Finally 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="prompt" | |
| /> | |
| <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"; | |
| 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 prompt = document.getElementById("prompt").files[0]; | |
| document.getElementById("logs").textContent = ""; | |
| const {generated_text} = await hf.imageToText({model, data: prompt}); | |
| document.getElementById("logs").textContent = generated_text; | |
| } catch (err) { | |
| alert("Error: " + err.message); | |
| } finally { | |
| running = false; | |
| } | |
| } | |
| window.launch = launch; | |
| </script> | |
| </body> | |
| </html> |