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<!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>
    <!-- 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"
                >
					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>