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dhivehi-character-ai
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// import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// // Since we will download the model from the Hugging Face Hub, we can skip the local model check
// env.allowLocalModels = false;

// // Reference the elements that we will need
// const status = document.getElementById('status');
// const fileUpload = document.getElementById('upload');
// const imageContainer = document.getElementById('container');
// const example = document.getElementById('example');

// const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';

// // Create a new object detection pipeline
// status.textContent = 'Loading model...';
// const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
// status.textContent = 'Ready';

// example.addEventListener('click', (e) => {
//     e.preventDefault();
//     detect(EXAMPLE_URL);
// });

// fileUpload.addEventListener('change', function (e) {
//     const file = e.target.files[0];
//     if (!file) {
//         return;
//     }

//     const reader = new FileReader();

//     // Set up a callback when the file is loaded
//     reader.onload = e2 => detect(e2.target.result);

//     reader.readAsDataURL(file);
// });


// // Detect objects in the image
// async function detect(img) {
//     imageContainer.innerHTML = '';
//     imageContainer.style.backgroundImage = `url(${img})`;

//     status.textContent = 'Analysing...';
//     const output = await detector(img, {
//         threshold: 0.5,
//         percentage: true,
//     });
//     status.textContent = '';
//     output.forEach(renderBox);
// }

// // Render a bounding box and label on the image
// function renderBox({ box, label }) {
//     const { xmax, xmin, ymax, ymin } = box;

//     // Generate a random color for the box
//     const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);

//     // Draw the box
//     const boxElement = document.createElement('div');
//     boxElement.className = 'bounding-box';
//     Object.assign(boxElement.style, {
//         borderColor: color,
//         left: 100 * xmin + '%',
//         top: 100 * ymin + '%',
//         width: 100 * (xmax - xmin) + '%',
//         height: 100 * (ymax - ymin) + '%',
//     })

//     // Draw label
//     const labelElement = document.createElement('span');
//     labelElement.textContent = label;
//     labelElement.className = 'bounding-box-label';
//     labelElement.style.backgroundColor = color;

//     boxElement.appendChild(labelElement);
//     imageContainer.appendChild(boxElement);
// }


import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';

// Since we will download the model from the Hugging Face Hub, we can skip the local model check
env.allowLocalModels = false;

// Reference the elements that we will need
const status = document.getElementById('status');
const userInput = document.getElementById('user-input');
const outputContainer = document.getElementById('output');
const submitButton = document.getElementById('submit-button');

// Load the text-generation pipeline
status.textContent = 'Loading model...';
const generator = await pipeline('text-generation', 'meta-llama/Llama-2-7b-hf');
status.textContent = 'Model loaded. Ready to chat!';

// Add event listener to the submit button
submitButton.addEventListener('click', async () => {
    const inputText = userInput.value.trim();

    if (!inputText) {
        outputContainer.innerText = 'Please enter a prompt.';
        return;
    }

    // Update status to show the user we're processing
    status.textContent = 'Generating response...';

    try {
        // Generate text from the user input
        const response = await generator(inputText, {
            max_new_tokens: 100,
            temperature: 0.7, // Controls randomness; lower = more deterministic
            top_p: 0.95,     // Nucleus sampling
        });

        // Display the generated response
        outputContainer.innerText = response[0].generated_text;
    } catch (error) {
        console.error(error);
        outputContainer.innerText = 'Error generating response. Please try again.';
    }

    // Reset the status
    status.textContent = 'Model loaded. Ready to chat!';
});