Update index.js

index.js

@@ -3,77 +3,44 @@ import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers
 // 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) {
+// Configuração do modelo de NER
+const nerStatus = document.getElementById('status');
+const textInput = document.getElementById('text-input');
+const analyzeTextButton = document.getElementById('analyze-text');
+const textOutput = document.getElementById('text-output');
+
+nerStatus.textContent = 'Carregando modelo de NER...';
+const nerModel = await pipeline('ner', 'Xenova/distilbert-base-multilingual-cased-ner-hrl');
+nerStatus.textContent = 'Modelo de NER pronto!';
+
+// Função para análise de texto
+analyzeTextButton.addEventListener('click', async () => {
+    const inputText = textInput.value.trim();
+    if (!inputText) {
+        textOutput.textContent = 'Por favor, insira um texto para análise.';
         return;
     }
 
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => detect(e2.target.result);
+    textOutput.textContent = 'Analisando...';
+    const nerOutput = await nerModel(inputText);
 
-    reader.readAsDataURL(file);
+    // Renderizando as entidades detectadas
+    renderEntities(nerOutput);
 });
 
-
-// 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,
+// Função para exibir os resultados das entidades detectadas
+function renderEntities(entities) {
+    textOutput.innerHTML = '';
+    entities.forEach(entity => {
+        const { word, entity_group, score } = entity;
+
+        const entityElement = document.createElement('div');
+        entityElement.className = 'entity';
+        entityElement.innerHTML = `
+            <strong>Palavra:</strong> ${word} <br>
+            <strong>Entidade:</strong> ${entity_group} <br>
+            <strong>Confiança:</strong> ${(score * 100).toFixed(2)}%
+        `;
+        textOutput.appendChild(entityElement);
     });
-    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);
 }