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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Emotion Analyzer - Hugging Face</title>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4/dist/css/bootstrap.min.css">
<style>
body {
font-family: Arial, sans-serif;
background-color: #f8f9fa;
}
.hero-section {
background: #007bff;
color: white;
padding: 60px 20px;
text-align: center;
}
.loader {
display: none;
margin: 10px auto;
border: 5px solid #f3f3f3;
border-top: 5px solid #3498db;
border-radius: 50%;
width: 40px;
height: 40px;
animation: spin 1s linear infinite;
}
@keyframes spin {
0% { transform: rotate(0deg); }
100% { transform: rotate(360deg); }
}
.fade-in {
animation: fadeIn 0.5s ease-in-out;
}
@keyframes fadeIn {
from { opacity: 0; }
to { opacity: 1; }
}
</style>
</head>
<body>
<div class="hero-section">
<h1>AI-Powered Text Emotion Analyzer </h1>
<p>Analyze and detect emotions in text with AI-powered precision using the RoBERTa-based GoEmotions model.</p>
</div>
<div class="container text-center">
<h1 class="mb-4">RoBERTa-base GoEmotions Model</h1>
<p class="lead text-justify">
The <strong>RoBERTa-base GoEmotions</strong> model was fine-tuned on the <strong>GoEmotions dataset</strong>, which consists of Reddit comments labeled with 28 different emotions. Since GoEmotions is a <strong>multi-label dataset</strong>, the model was trained to predict one or more emotions for each input text. The base model, <strong>RoBERTa-base</strong>, is a pre-trained transformer-based language model known for its ability to capture contextual meaning from text efficiently. The training process involved <strong>multi-label classification</strong>, where the model learned to assign probabilities to multiple emotions per input, with a typical classification threshold set at <strong>0.5</strong>.
</p>
<p class="lead text-justify">
To enhance inference speed and efficiency, an <strong>ONNX version</strong> of the model was created, including an <strong>INT8 quantized variant</strong>. This conversion allows for <strong>faster predictions</strong>, <strong>smaller dependencies</strong>, and <strong>cross-platform compatibility</strong>. The quantized model further reduces the file size by <strong>75%</strong> while maintaining near-original accuracy, making it ideal for applications where efficiency and portability are crucial.
</p>
</div>
<div class="container text-center">
<h1 class="mb-3">🔍 Emotion Analyzer</h1>
<h2 class="mb-3">Analyze emotions in text using AI 🤖</h2>
<p class="mb-4">Enter a sentence to detect emotions like joy, sadness, or anger.</p>
<input type="text" id="userInput" class="form-control w-75 mx-auto mb-3" placeholder="Type your sentence here...">
<button class="btn btn-primary" onclick="fetchEmotion()">Analyze Emotion</button>
<div class="loader" id="loader"></div>
<table class="table table-striped table-bordered mt-4 w-75 mx-auto" id="emotionTable">
<thead class="thead-dark">
<tr>
<th>Emotion</th>
<th>Score</th>
</tr>
</thead>
<tbody></tbody>
</table>
</div>
<script>
import fetch from "node-fetch";
async function query(data) {
document.getElementById("loader").style.display = "block";
const response = await fetch(
"https://api-inference.huggingface.co/models/SamLowe/roberta-base-go_emotions",
{
headers: {
Authorization: "Bearer {HF_TOKEN}",
"Content-Type": "application/json",
},
method: "POST",
body: JSON.stringify(data),
}
);
document.getElementById("loader").style.display = "none";
return await response.json();
}
async function fetchEmotion() {
const inputText = document.getElementById("userInput").value;
if (!inputText) {
alert("⚠️ Please enter some text!");
return;
}
document.getElementById("emotionTable").querySelector("tbody").innerHTML = "";
const response = await query({ inputs: inputText });
displayEmotionTable(response[0]);
}
function displayEmotionTable(emotions) {
const tableBody = document.getElementById("emotionTable").querySelector("tbody");
emotions.sort((a, b) => b.score - a.score);
emotions.forEach(emotion => {
if (emotion.score > 0.01) {
const row = `<tr class="fade-in">
<td>${emotion.label}</td>
<td>${(emotion.score * 100).toFixed(2)}%</td>
</tr>`;
tableBody.innerHTML += row;
}
});
}
</script>
</body>
</html> |