Update index.js

index.js

@@ -1,7 +1,6 @@
 // index.js
-import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
-
-// Define available models (mirror of constants.py AVAILABLE_MODELS)
+// Note: The Python backend defines AVAILABLE_MODELS in constants.py for server-side use.
+// For the front-end, we replicate the list here so the UI can populate the dropdown.
 const AVAILABLE_MODELS = [
   { name: "Moonshot Kimi-K2", id: "moonshotai/Kimi-K2-Instruct" },
   { name: "DeepSeek V3", id: "deepseek-ai/DeepSeek-V3-0324" },
@@ -15,14 +14,15 @@ const AVAILABLE_MODELS = [
   { name: "Qwen3-Coder-480B-A35B", id: "Qwen/Qwen3-Coder-480B-A35B-Instruct" }
 ];
 
-const analyzeForm = document.getElementById('analyze-form');
+import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers';
+
 const modelSelect = document.getElementById('modelSelect');
 const inputText = document.getElementById('inputText');
 const resultSection = document.getElementById('result');
 const analyzeButton = document.getElementById('analyzeButton');
 let sentimentPipeline;
 
-// Populate model dropdown
+// Populate the dropdown
 AVAILABLE_MODELS.forEach(model => {
   const opt = document.createElement('option');
   opt.value = model.id;
@@ -38,16 +38,17 @@ async function initPipeline(modelId) {
   analyzeButton.disabled = false;
 }
 
-// Initialize with first model
+// Initialize on load with first model
 initPipeline(modelSelect.value);
 
-// Re-init pipeline on model change
+// Re-initialize when user selects a different model
 modelSelect.addEventListener('change', () => {
   initPipeline(modelSelect.value);
   resultSection.textContent = '';
 });
 
-analyzeForm.addEventListener('submit', async event => {
+// Handle form submit
+document.getElementById('analyze-form').addEventListener('submit', async event => {
   event.preventDefault();
   const text = inputText.value.trim();
   if (!text) return;
@@ -59,7 +60,7 @@ analyzeForm.addEventListener('submit', async event => {
   try {
     const output = await sentimentPipeline(text);
     const { label, score } = output[0];
-    resultSection.textContent = `Sentiment: ${label} (Confidence: ${(score * 100).toFixed(2)}%)`;
+    resultSection.textContent = `Sentiment: ${label} (Confidence: ${(score*100).toFixed(2)}%)`;
   } catch (err) {
     resultSection.textContent = 'Error analyzing sentiment.';
   }