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Merged
snibo13 merged 3 commits into from
Apr 6, 2025
Merged

Undo weird merge #1

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942eb79
Added analytics tag
snibo13 Feb 17, 2025
cb1a27d
New resume
snibo13 Apr 6, 2025
3eefc3f
Merge
snibo13 Apr 6, 2025
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Binary file removed Resume.pdf
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14 changes: 7 additions & 7 deletions index.html
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
<div id="masthead-content" style="max-width: 650px;">
<svg viewBox="0 0 60 60" xmlns="http://www.w3.org/2000/svg"
style="height: 70px; align-self: flex-start">
<circle cx="30" cy="30" r="30" fill="var(--zomp)" />
<circle cx="30" cy="30" r="30" fill="var(--accent-1)" />
</svg>

<h1>Hi! I'm Sidney Nimako</h1>
Expand All @@ -32,7 +32,7 @@ <h1>Hi! I'm Sidney Nimako</h1>
<!-- My research interests are in assistive robotics, human-robot interaction, and robotic manipulation. -->
I am interested in building human-centered robotic systems. I am particularly interested in
researching
search and rescue systems, assistive robotics, safe pHRI, and robotic manipulation.
search and rescue system, assistive robotics, safe pHRI, and robotic manipulation.
<br>
<br>
Currently:
Expand Down Expand Up @@ -89,7 +89,7 @@ <h1>00 Work</h1>
<p class="sub-section-title">Hardware Engineering Intern</p>
<p class="sub-section-text">DoorDash Labs</p>
<p class="sub-section-text">2022 + 2023</p>
<!-- <p class="sub-section-text">San Francisco, CA</p> -->
<p class="sub-section-text">San Francisco, CA</p>
</div>
<p class="large-description sub-section-text">I worked with DoorDash Labs to develop
software
Expand All @@ -109,9 +109,9 @@ <h1>00 Work</h1>
<p class="sub-section-title">Undergraduate Research Assistant</p>
<p class="sub-section-text">CMU Robomechanics Lab</p>
<p class="sub-section-text">2022 - 2023</p>
<!-- <p class="sub-section-text">Pittsburgh, PA</p> -->
<p class="sub-section-text">Pittsburgh, PA</p>
</div>
<p class="large-description sub-section-text">I worked with a couple of masters students and
<p class="large-description sub-section-text">I worked with
Dr.
Aaron Johnson on exploring the costs and conrols of a spined quadruped robot. I focused
on
Expand All @@ -123,7 +123,7 @@ <h1>00 Work</h1>
<p class="sub-section-title">Robotics Research Intern</p>
<p class="sub-section-text">Meta AI Research</p>
<p class="sub-section-text">2021</p>
<!-- <p class="sub-section-text">Pittsburgh, PA</p> -->
<p class="sub-section-text">Pittsburgh, PA</p>
</div>
<p class="large-description sub-section-text">Worked on the development of a three digit
dextrous
Expand All @@ -138,7 +138,7 @@ <h1>00 Work</h1>
<p class="sub-section-title">VR Development Intern</p>
<p class="sub-section-text">Biomotivate</p>
<p class="sub-section-text">2020</p>
<!-- <p class="sub-section-text">Pittsburgh, PA</p> -->
<p class="sub-section-text">Pittsburgh, PA</p>
</div>
<p class="large-description sub-section-text">I worked with a Pittsburgh based start up to
develop
Expand Down
257 changes: 171 additions & 86 deletions shrtn.html
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@@ -1,43 +1,62 @@
<!DOCTYPE html>
<html lang="en">

<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Sentence Embedding Calculator</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/4.15.0/tf.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow-text/4.15.0/tf-text.min.js"></script>

<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/universal-sentence-encoder"></script>
<script src="https://cdn.jsdelivr.net/npm/@mlc-ai/web-llm@0.2.77/lib/index.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/universal-sentence-encoder"></script>
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Knewave&display=swap" rel="stylesheet">
<link href="https://fonts.googleapis.com/css2?family=Sriracha&display=swap" rel="stylesheet">
<style>
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
max-width: 800px;
margin: 2rem auto;
padding: 0 1rem;
line-height: 1.5;
font-family: "Sriracha", serif;
background-color: #50c8e6;

}

.container {
background-color: #f8f9fa;
padding: 2rem;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}

textarea {
width: 100%;
padding: 0.5rem;
margin: 1rem 0;
border: 1px solid #ced4da;
border-radius: 4px;
min-height: 100px;
font-size: 1rem;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
justify-items: center;
}

#title {
font-family: 'Knewave';
font-size: 10vh;
}

form {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
justify-items: center;
}

input[type="text"] {
width: 60vw;
font-size: 24px;
border: 3px solid #000000;
border-radius: 5px;
min-height: 40px;
text-align: center;
}

button {
background-color: #007bff;
background-color: black;
color: white;
border: none;
padding: 0.5rem 1rem;
border-radius: 4px;
padding: 30px 30px;
border-radius: 20px;
cursor: pointer;
font-size: 1rem;
}
Expand All @@ -52,9 +71,9 @@
}

#result {
margin-top: 1rem;
margin-top: 36px;
white-space: pre-wrap;
font-family: monospace;
font-size: 24px;
}

.error {
Expand All @@ -72,85 +91,151 @@
}
</style>
</head>

<body>
<div class="container">
<h1>Sentence Embedding Calculator</h1>
<p>Enter a sentence below to compute its embedding using TensorFlow Universal Sentence Encoder.</p>

<textarea id="input" placeholder="Enter your sentence here..."></textarea>

<button id="calculate">Calculate Embedding</button>

<h1 id="title">SHRTN</h1>
<h1 id="initial-sentence"> The quick brown fox jumps over the lazy dog</h1>

<form autocomplete="off" autocapitalize="none" autocorrect="on">
<input type="text" id="input" placeholder="Enter your sentence here...">
<!-- <p>Character count:
<p id="character-count">0</p> -->
</p>
<br>
<span style="display: flex; flex-direction: row; width: 50%; justify-content: space-between;">

<button id="calculate">Calculate Embedding</button>
<button id="next">Next Sentence</button>
</span>
</form>
<!-- <textarea id="input" placeholder="Enter your sentence here..."></textarea>

<button id="calculate">Calculate Embedding</button> -->

<div id="loading" class="loading">
Loading model and computing embedding... This might take a few moments on first run.
Computing embedding...
</div>

<div id="result"></div>
</div>

<script>
<script lang="ts">
const input = document.getElementById('input');
const calculateBtn = document.getElementById('calculate');
const loading = document.getElementById('loading');
const calculate = document.getElementById('calculate');
const result = document.getElementById('result');
const loading = document.getElementById('loading');
const initial_sentence = document.getElementById('initial-sentence');
const form = document.querySelector('form');
const characterCountText = document.getElementById('character-count');

let model;
let indx = Math.floor(Math.random() * 50);
let list;
let initialEmbedding;
let initialSentence;
let characterCount;
let initialCharacterCount;

// Create a type with a character difference, embedding difference, and score.
// const Score = {
// characterDiff: number,
// embeddingDiff: number,
// score: number
// };

const scores = [];


loadModel();
fetch("../shrtn.json")
.then(response => response.json())
.then(data => {
list = data;
initialSentence = list.sentences[indx];
console.log(initialSentence);
// const initialSentence = list.sentences[indx];
initialCharacterCount = initialSentence.length
characterCount = initialCharacterCount;
initial_sentence.textContent = initialSentence;
input.value = initialSentence;
});

function computeScore(characterDiff, embeddingDiff) {
// First 5 character are 5 points
// Next 5 characters are 8 points
// Next 2 characters are 10 points
// All other characters are 15 points
let characterSubScore = 5 * Math.min(characterDiff, 5) +
8 * Math.min(Math.max(characterDiff - 5, 0), 5) +
10 * Math.min(Math.max(characterDiff - 10, 0), 2) +
15 * Math.max(characterDiff - 12, 0);

let embeddingSubScore = 10 * (1 - embeddingDiff);

return Math.floor(embeddingSubScore * characterSubScore);

let model = null;
}

async function loadModel() {
try {
model = await tf.loadGraphModel(
'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder-lite/1/default/1',
{ fromTFHub: true }
);
return true;
} catch (error) {
console.error('Error loading model:', error);
return false;
}
}
model = await use.load();
console.log('Model loaded');
loading.classList.remove('visible');

async function calculateEmbedding(text) {
if (!model) {
const loaded = await loadModel();
if (!loaded) {
throw new Error('Failed to load model');
}
}
// Embed the initial sentence.
initialEmbedding = await model.embed(initialSentence);
characterCountText.textContent = characterCount;
}

const embeddings = await model.predict(tf.tensor([text]));
return Array.from(await embeddings.data());
function calculateCharacterCountDifference(sentence) {
return initialCharacterCount - sentence.length;
}

calculateBtn.addEventListener('click', async () => {
const text = input.value.trim();

if (!text) {
result.innerHTML = '<div class="error">Please enter a sentence.</div>';
return;
}
function calculateEmbeddingDifference(embedding) {
return tf.losses.cosineDistance(embedding, initialEmbedding).arraySync();
}

calculateBtn.disabled = true;
calculate.addEventListener('click', async () => {
loading.classList.add('visible');
result.innerHTML = '';

try {
const embedding = await calculateEmbedding(text);

const formattedResult = `Embedding vector (${embedding.length} dimensions):

[${embedding.slice(0, 8).map(n => n.toFixed(6)).join(', ')},
...,
${embedding.slice(-8).map(n => n.toFixed(6)).join(', ')}]`;

result.textContent = formattedResult;
} catch (error) {
result.innerHTML = '<div class="error">Error computing embedding. Please try again.</div>';
console.error('Error:', error);
} finally {
calculateBtn.disabled = false;
loading.classList.remove('visible');
result.textContent = '';

if (!model) {
await loadModel();
}
// Embed the input sentence.
const embeddings = await model.embed(input.value);
const characterDiff = Math.round(calculateCharacterCountDifference(input.value), 2);
const embeddingDiff = Math.max(0, calculateEmbeddingDifference(embeddings)).toFixed(2);

// Print the embeddings.
scores.push({
characterDiff,
embeddingDiff,
score: computeScore(characterDiff, embeddingDiff)
});
result.textContent = `Character count difference: ${characterDiff}\n` +
`Embedding difference: ${embeddingDiff}` + '\n' + `Score: ${computeScore(characterDiff, embeddingDiff)}`;
loading.classList.remove('visible');
});

next.addEventListener('click', () => {
// Refresh the page
window.location.reload();
});


form.addEventListener('submit', (e) => {
e.preventDefault();
calculate.click();
});

input.addEventListener('keydown', () => {
characterCount = input.value.length;
characterCountText.textContent = characterCount;
});



</script>
</body>

</html>
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