initial commit

background.js

@@ -0,0 +1,124 @@
+import { pipeline, env } from './transformers/transformers.min.js';
+
+env.allowLocalModels = true;
+
+env.backends.onnx.wasm.wasmPaths = {
+  'default': './transformers/',
+  'threaded': './transformers/',
+  'simd': './transformers/',
+  'simd-threaded': './transformers/',
+};
+
+env.allowRemoteModels = false;
+
+let classifier = null;
+let llmInitializationPromise = null;
+
+async function initializeLLM() {
+  if (classifier) {
+    return;
+  }
+
+  if (llmInitializationPromise) {
+    return llmInitializationPromise;
+  }
+
+  llmInitializationPromise = (async () => {
+    try {
+      classifier = await pipeline(
+        'zero-shot-classification',
+        'nli-deberta-v3-small',
+        {
+          local_files_only: true,
+          model_file_name: "model.onnx",
+        }
+      );
+      llmInitializationPromise = null;
+    } catch (error) {
+      classifier = null;
+      llmInitializationPromise = null;
+      throw error;
+    }
+  })();
+  return llmInitializationPromise;
+}
+
+initializeLLM();
+
+chrome.runtime.onMessage.addListener((message, sender, sendResponse) => {
+  (async () => {
+    if (!classifier) {
+      try {
+        await initializeLLM();
+      } catch (error) {
+        sendResponse({ type: "LLM_ERROR", error: "LLM initialization failed: " + error.message });
+        return;
+      }
+    }
+
+    try {
+      switch (message.type) {
+        case 'ANALYZE_SENTIMENT':
+          const headlineForSentiment = message.payload.headline;
+          const sentimentLabels = ['negative', 'positive', 'neutral', 'depressing', 'worrying'];
+          const sentimentOutput = await classifier(headlineForSentiment, sentimentLabels);
+
+          let detectedSentiment = 'neutral';
+          if (sentimentOutput && sentimentOutput.scores && sentimentOutput.labels) {
+            const sortedSentimentResults = sentimentOutput.scores.map((score, index) => ({
+              label: sentimentOutput.labels[index],
+              score: score
+            })).sort((a, b) => b.score - a.score);
+
+            if (sortedSentimentResults.length > 0 && sortedSentimentResults[0].score > 0.7) {
+              detectedSentiment = sortedSentimentResults[0].label;
+            } else if (sortedSentimentResults.length > 0) {
+                const topLabel = sortedSentimentResults[0].label;
+                if (['negative', 'depressing', 'worrying'].includes(topLabel)) {
+                    detectedSentiment = topLabel;
+                } else {
+                }
+            }
+          }
+          sendResponse({ type: "ANALYSIS_RESULT", result: { sentiment: detectedSentiment } });
+          break;
+
+        case 'ANALYZE_HEADLINE_CATEGORY':
+          const headline = message.payload.headline;
+          const selectedIssues = message.payload.selectedIssues || [];
+          const candidateLabels = ['gun-control', 'gun-rights', 'privacy', 'immigration', 'climate-change', 'healthcare', 'racial-justice', 'lgbtq-rights', 'economic-inequality', 'reproductive-rights', 'education-reform', 'criminal-justice', 'voting-rights', 'campaign-finance', 'foreign-policy', 'national-security', 'free-speech', 'net-neutrality', 'labor-rights', 'affordable-housing', 'environmental-protection', 'space-exploration', 'drug-policy-reform', 'veterans-affairs', 'senior-citizen-rights'];
+          const analysisOutput = await classifier(headline, candidateLabels);
+
+          let categories = [];
+          if (analysisOutput && analysisOutput.scores && analysisOutput.labels) {
+            const sortedResults = analysisOutput.scores.map((score, index) => ({
+              label: analysisOutput.labels[index],
+              score: score
+            })).sort((a, b) => b.score - a.score);
+
+            categories = sortedResults
+              .filter(item => item.score > 0.5 && selectedIssues.includes(item.label))
+              .map(item => item.label);
+
+            if (categories.length === 0 && sortedResults.length > 0) {
+              const topLabel = sortedResults[0].label;
+              if (selectedIssues.includes(topLabel)) {
+                  categories.push(topLabel);
+              } else {
+              }
+            }
+          }
+          sendResponse({ type: "ANALYSIS_RESULT", result: { categories: categories } });
+          break;
+
+        default:
+          sendResponse({ type: "UNKNOWN_MESSAGE" });
+          break;
+      }
+    } catch (error) {
+      sendResponse({ type: "LLM_ERROR", error: "Processing error: " + error.message });
+    }
+  })();
+
+  return true;
+});