fix: remove unused import

Author: ammar-agent

scripts/check_bundle_size.sh

@@ -5,8 +5,8 @@ set -euo pipefail
 cd "$(dirname "$0")/.."
 
 # Budgets (in bytes)
-MAX_INDEX_GZIP=409600      # 400KB gzipped
-MAX_WORKER_SIZE=10240      # 10KB uncompressed
+MAX_INDEX_GZIP=409600 # 400KB gzipped
+MAX_WORKER_SIZE=10240 # 10KB uncompressed
 
 echo "Checking bundle size budgets..."
 
@@ -30,18 +30,18 @@ INDEX_SIZE_KB=$((INDEX_SIZE / 1024))
 MAX_INDEX_KB=$((MAX_INDEX_GZIP / 1024))
 
 echo "Main bundle (gzipped): ${INDEX_SIZE_KB}KB (budget: ${MAX_INDEX_KB}KB)"
-if (( INDEX_SIZE > MAX_INDEX_GZIP )); then
+if ((INDEX_SIZE > MAX_INDEX_GZIP)); then
   echo "❌ Main bundle exceeds budget by $((INDEX_SIZE - MAX_INDEX_GZIP)) bytes" >&2
   exit 1
 fi
 
 # Check worker uncompressed size
-WORKER_SIZE=$(wc -c < "$WORKER_FILE" | tr -d ' ')
+WORKER_SIZE=$(wc -c <"$WORKER_FILE" | tr -d ' ')
 WORKER_SIZE_KB=$((WORKER_SIZE / 1024))
 MAX_WORKER_KB=$((MAX_WORKER_SIZE / 1024))
 
 echo "Worker bundle: ${WORKER_SIZE_KB}KB (budget: ${MAX_WORKER_KB}KB)"
-if (( WORKER_SIZE > MAX_WORKER_SIZE )); then
+if ((WORKER_SIZE > MAX_WORKER_SIZE)); then
   echo "❌ Worker bundle exceeds budget by $((WORKER_SIZE - MAX_WORKER_SIZE)) bytes" >&2
   echo "   This likely means models.json or ai-tokenizer leaked into the worker" >&2
   exit 1

scripts/check_eager_imports.sh

@@ -1,6 +1,6 @@
 #!/usr/bin/env bash
 # Detects eager imports of heavy packages in startup-critical and renderer/worker files
-# 
+#
 # Main process: AI SDK packages must be lazy-loaded to maintain fast startup (<4s)
 # Renderer/Worker: Large data files (models.json) and ai-tokenizer must never be imported
 
@@ -105,18 +105,18 @@ done
 # Check bundled worker if it exists
 if [ -f dist/tokenStats.worker-*.js ]; then
   WORKER_FILE=$(find dist -name 'tokenStats.worker-*.js' | head -1)
-  WORKER_SIZE=$(wc -c < "$WORKER_FILE" | tr -d ' ')
-  
+  WORKER_SIZE=$(wc -c <"$WORKER_FILE" | tr -d ' ')
+
   echo "==> Checking worker bundle for heavy imports..."
-  
+
   # If worker is suspiciously large (>50KB), likely has models.json or ai-tokenizer
-  if (( WORKER_SIZE > 51200 )); then
+  if ((WORKER_SIZE > 51200)); then
     echo "❌ WORKER TOO LARGE: $WORKER_FILE is ${WORKER_SIZE} bytes (>50KB)"
     echo "   This suggests models.json (701KB) or ai-tokenizer leaked in"
-    
+
     # Try to identify what's in there
-    if grep -q "models.json" "$WORKER_FILE" 2>/dev/null || \
-       strings "$WORKER_FILE" 2>/dev/null | grep -q "anthropic\|openai" | head -10; then
+    if grep -q "models.json" "$WORKER_FILE" 2>/dev/null \
+      || strings "$WORKER_FILE" 2>/dev/null | grep -q "anthropic\|openai" | head -10; then
       echo "   Found model names in bundle - likely models.json"
     fi
     failed=1

src/utils/main/StreamingTokenTracker.test.ts

@@ -8,30 +8,30 @@ import { StreamingTokenTracker } from "./StreamingTokenTracker";
 describe("StreamingTokenTracker", () => {
   it("should reinitialize tokenizer when model changes", () => {
     const tracker = new StreamingTokenTracker();
-    
+
     // Set first model
     tracker.setModel("openai:gpt-4");
     const count1 = tracker.countTokens("test");
-    
+
     // Switch to different model
     tracker.setModel("anthropic:claude-opus-4");
     const count2 = tracker.countTokens("test");
-    
+
     // Both should return valid counts
     expect(count1).toBeGreaterThan(0);
     expect(count2).toBeGreaterThan(0);
   });
-  
+
   it("should not reinitialize when model stays the same", () => {
     const tracker = new StreamingTokenTracker();
-    
+
     // Set model twice
     tracker.setModel("openai:gpt-4");
     const count1 = tracker.countTokens("test");
-    
-    tracker.setModel("openai:gpt-4");  // Same model
+
+    tracker.setModel("openai:gpt-4"); // Same model
     const count2 = tracker.countTokens("test");
-    
+
     // Should get same count (cached)
     expect(count1).toBe(count2);
   });

src/utils/main/StreamingTokenTracker.ts

@@ -17,7 +17,7 @@ export class StreamingTokenTracker {
   /**
    * Initialize tokenizer for the current model
    * Should be called when model changes or on first stream
-   * 
+   *
    * IMPORTANT: Reinitializes tokenizer when model changes to ensure correct encoding.
    * getTokenizerForModel() closes over the model string, so we must create a new
    * tokenizer instance when switching models.

src/utils/main/tokenizer.test.ts

@@ -2,51 +2,51 @@
  * Tests for tokenizer cache behavior
  */
 
-import { describe, it, expect, beforeEach } from "@jest/globals";
+import { describe, it, expect } from "@jest/globals";
 import { getTokenizerForModel } from "./tokenizer";
 
 describe("tokenizer cache", () => {
   const testText = "Hello, world!";
-  
+
   it("should use different cache keys for different models", () => {
     // Get tokenizers for different models
     const gpt4Tokenizer = getTokenizerForModel("openai:gpt-4");
     const claudeTokenizer = getTokenizerForModel("anthropic:claude-opus-4");
-    
+
     // Count tokens with first model
     const gpt4Count = gpt4Tokenizer.countTokens(testText);
-    
+
     // Count tokens with second model
     const claudeCount = claudeTokenizer.countTokens(testText);
-    
+
     // Counts may differ because different encodings
     // This test mainly ensures no crash and cache isolation
     expect(typeof gpt4Count).toBe("number");
     expect(typeof claudeCount).toBe("number");
     expect(gpt4Count).toBeGreaterThan(0);
     expect(claudeCount).toBeGreaterThan(0);
   });
-  
+
   it("should return same count for same (model, text) pair from cache", () => {
     const tokenizer = getTokenizerForModel("openai:gpt-4");
-    
+
     // First call
     const count1 = tokenizer.countTokens(testText);
-    
+
     // Second call should hit cache
     const count2 = tokenizer.countTokens(testText);
-    
+
     expect(count1).toBe(count2);
   });
-  
+
   it("should normalize model keys for cache consistency", () => {
     // These should map to the same cache key
     const tokenizer1 = getTokenizerForModel("anthropic:claude-opus-4");
     const tokenizer2 = getTokenizerForModel("anthropic/claude-opus-4");
-    
+
     const count1 = tokenizer1.countTokens(testText);
     const count2 = tokenizer2.countTokens(testText);
-    
+
     // Should get same count since they normalize to same model
     expect(count1).toBe(count2);
   });

src/utils/main/tokenizer.ts

@@ -68,10 +68,10 @@ export async function loadTokenizerModules(): Promise<void> {
 /**
  * LRU cache for token counts by (model, text) pairs
  * Avoids re-tokenizing identical strings with the same encoding
- * 
+ *
  * Key: CRC32 checksum of "model:text" to ensure counts are model-specific
  * Value: token count
- * 
+ *
  * IMPORTANT: Cache key includes model because different encodings produce different counts.
  * For async tokenization (approx → exact), the key stays stable so exact overwrites approx.
  */
@@ -89,7 +89,7 @@ const tokenCountCache = new LRUCache<number, number>({
  * Avoids re-tokenizing identical strings (system messages, tool definitions, etc.)
  *
  * Cache key includes model to prevent cross-model count reuse.
- * 
+ *
  * NOTE: For async tokenization, this returns an approximation immediately and caches
  * the accurate count in the background. Subsequent calls with the same (model, text) pair
  * will use the cached accurate count once ready.