Update app.js

app.js

@@ -1,11 +1,11 @@
-// app.js (ES module version using transformers.js for local emotion classification)
+// app.js (ES module version using transformers.js for local sentiment classification)
 
 import { pipeline } from "https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.7.6/dist/transformers.min.js";
 
 // Global variables
 let reviews = [];
 let apiToken = ""; // kept for UI compatibility, but not used with local inference
-let sentimentPipeline = null; // transformers.js text-classification pipeline (emotion)
+let sentimentPipeline = null; // transformers.js text-classification pipeline
 
 // DOM elements
 const analyzeBtn = document.getElementById("analyze-btn");
@@ -32,32 +32,31 @@ document.addEventListener("DOMContentLoaded", function () {
     apiToken = savedToken;
   }
 
-  // Initialize transformers.js emotion model
+  // Initialize transformers.js sentiment model
   initSentimentModel();
 });
 
-// Initialize transformers.js text-classification pipeline with boltuix/bert-emotion
+// Initialize transformers.js text-classification pipeline with a supported model
 async function initSentimentModel() {
   try {
-    // Inform the user that we are downloading/loading the model
     if (statusElement) {
-      statusElement.textContent = "Loading emotion model...";
+      statusElement.textContent = "Loading sentiment model...";
     }
 
-    // Allocate a pipeline for text-classification with the specified model
-    // The model will be downloaded and cached in the browser
+    // Use a transformers.js-supported text-classification model.
+    // Xenova/distilbert-base-uncased-finetuned-sst-2-english is a common choice.
     sentimentPipeline = await pipeline(
       "text-classification",
-      "boltuix/bert-emotion"
+      "Xenova/distilbert-base-uncased-finetuned-sst-2-english"
     );
 
     if (statusElement) {
-      statusElement.textContent = "Emotion model ready";
+      statusElement.textContent = "Sentiment model ready";
     }
   } catch (error) {
-    console.error("Failed to load emotion model:", error);
+    console.error("Failed to load sentiment model:", error);
     showError(
-      "Failed to load emotion model. Please check your network connection and try again."
+      "Failed to load sentiment model. Please check your network connection and try again."
     );
     if (statusElement) {
       statusElement.textContent = "Model load failed";
@@ -116,7 +115,7 @@ function analyzeRandomReview() {
   }
 
   if (!sentimentPipeline) {
-    showError("Emotion model is not ready yet. Please wait a moment.");
+    showError("Sentiment model is not ready yet. Please wait a moment.");
     return;
   }
 
@@ -132,7 +131,7 @@ function analyzeRandomReview() {
   sentimentResult.innerHTML = ""; // Reset previous result
   sentimentResult.className = "sentiment-result"; // Reset classes
 
-  // Call local emotion model (transformers.js)
+  // Call local sentiment model (transformers.js)
   analyzeSentiment(selectedReview)
     .then((result) => displaySentiment(result))
     .catch((error) => {
@@ -145,33 +144,32 @@ function analyzeRandomReview() {
     });
 }
 
-// Call local transformers.js pipeline for emotion classification
+// Call local transformers.js pipeline for sentiment classification
 async function analyzeSentiment(text) {
   if (!sentimentPipeline) {
-    throw new Error("Emotion model is not initialized.");
+    throw new Error("Sentiment model is not initialized.");
   }
 
-  // transformers.js text-classification pipeline returns
-  // an array of objects: [{ label: 'joy', score: 0.92 }, ...]
+  // transformers.js text-classification pipeline returns:
+  // [{ label: 'POSITIVE', score: 0.99 }, ...]
   const output = await sentimentPipeline(text);
 
-  // For compatibility with the existing displaySentiment logic,
-  // we convert it to the expected format: [[{ label, score }]]
   if (!Array.isArray(output) || output.length === 0) {
-    throw new Error("Invalid emotion output from local model.");
+    throw new Error("Invalid sentiment output from local model.");
   }
 
+  // Wrap to match [[{ label, score }]] shape expected by displaySentiment
   return [output];
 }
 
-// Display sentiment (emotion) result
+// Display sentiment result
 function displaySentiment(result) {
-  // Default to neutral-like state if we can't parse the result
+  // Default to neutral if we can't parse the result
   let sentiment = "neutral";
   let score = 0.5;
   let label = "NEUTRAL";
 
-  // Parse the (normalized) response format: [[{label: 'JOY', score: 0.99}]]
+  // Expected format: [[{label: 'POSITIVE', score: 0.99}]]
   if (
     Array.isArray(result) &&
     result.length > 0 &&
@@ -190,17 +188,10 @@ function displaySentiment(result) {
           ? sentimentData.score
           : 0.5;
 
-      // Map emotion labels to generic sentiment buckets (optional)
-      // You can customize this mapping based on the model's label set.
-      if (
-        ["JOY", "LOVE", "OPTIMISM", "EXCITED", "HAPPY"].includes(label)
-      ) {
+      // Determine sentiment bucket
+      if (label === "POSITIVE" && score > 0.5) {
         sentiment = "positive";
-      } else if (
-        ["ANGER", "SADNESS", "FEAR", "DISGUST", "PESSIMISM"].includes(
-          label
-        )
-      ) {
+      } else if (label === "NEGATIVE" && score > 0.5) {
         sentiment = "negative";
       } else {
         sentiment = "neutral";