diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..6aa76ff
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,8 @@
+# Python Server Configuration
+# URL del servidor Python para identificación de SKU
+NEXT_PUBLIC_PYTHON_SERVER_URL=http://localhost:8000
+
+# Ejemplos de configuración:
+# - Desarrollo local: http://localhost:8000
+# - Producción con ngrok: https://tu-url.ngrok-free.app
+# - Producción: https://api.tu-dominio.com
diff --git a/.gitignore b/.gitignore
index ec4c29e..aafd791 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,6 +32,7 @@ yarn-error.log*
 
 # env files (can opt-in for committing if needed)
 .env*
+!.env.example
 
 # vercel
 .vercel
@@ -42,3 +43,6 @@ next-env.d.ts
 
 # Model files
 python_server/models/
+
+
+ __pycache__/
\ No newline at end of file
diff --git a/app/page.tsx b/app/page.tsx
index d12d713..daf7a75 100644
--- a/app/page.tsx
+++ b/app/page.tsx
@@ -5,14 +5,11 @@ import { useSearchParams } from 'next/navigation';
 import { Camera, History, VideoOff, Settings, Video } from 'lucide-react';
 
 import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from '@/components/ui/select';
-import { Slider } from '@/components/ui/slider';
-import { Switch } from '@/components/ui/switch';
 import { type Shoe } from '@/lib/shoe-database';
 import { addToHistory, getHistory } from '@/lib/history-storage';
 import ShoeResultsPopup from '@/components/shoe-results-popup';
 import HistorySidebar from '@/components/history-sidebar';
-import { useDetection } from '@/lib/ml/use-detection';
-import type { DetectionResult } from '@/lib/ml/types';
+import { skuIdentificationService } from '@/lib/sku-identification';
 
 type CameraStatus = 'loading' | 'active' | 'denied' | 'no_devices';
 
@@ -32,128 +29,7 @@ function HomePageContent() {
   const [history, setHistory] = useState<Shoe[]>([]);
   const [detectedSKU, setDetectedSKU] = useState<string | null>(null);
   const [isSettingsPanelOpen, setSettingsPanelOpen] = useState(false);
-
-  // ML Detection state
-  const [detectionEnabled, setDetectionEnabled] = useState(true); // Auto-enable on page load
-  const [currentDetection, setCurrentDetection] = useState<DetectionResult | null>(null);
-  const [shoeDetectionCount, setShoeDetectionCount] = useState(0);
-  const lastSoundTimeRef = useRef(0);
-
-
-  // Initialize ML detection system first
-  const {
-    isLoading: isMLLoading,
-    metrics,
-    error: mlError,
-    initialize: initializeML,
-    startContinuous,
-    stopContinuous,
-    triggerDetection,
-    updateConfig,
-    config,
-    detectionEngine,
-    setDetectionCallback
-  } = useDetection({
-    modelVariant: 'standard', // Start with standard model
-    enableContinuous: true,
-    enableTrigger: true,
-    onDetection: undefined, // Will be set after handleDetection is defined
-    onError: (error) => {
-      console.error('ML Detection Error:', error);
-    }
-  });
-
-  // Clean detection callback - no canvas drawing needed
-  const handleDetection = useCallback(async (detection: DetectionResult | null) => {
-    const callbackId = Math.random().toString(36).substr(2, 9);
-    console.log(`🔍 Detection callback received [${callbackId}]:`, detection);
-    setCurrentDetection(detection);
-    
-    // Count actual shoe detections (not just inference attempts)
-    if (detection) {
-      setShoeDetectionCount(prev => prev + 1);
-    }
-    
-    // Auto-trigger popup when shoe is detected with high confidence
-    if (detection && detection.confidence > 0.7) {
-      console.log(`🎯 HIGH CONFIDENCE SHOE DETECTED! [${callbackId}] Opening popup...`, detection);
-      
-      // Call SKU identification API
-      if (videoRef.current && detectionEngine) {
-        try {
-          console.log(`🔍 [${callbackId}] Calling SKU identification...`);
-          const sku = await detectionEngine.identifyProductSKU(videoRef.current);
-          console.log(`📦 [${callbackId}] SKU result:`, sku);
-          setDetectedSKU(sku);
-          
-          if (sku) {
-            // Create shoe object with SKU for history
-            const shoeWithSKU: Shoe = {
-              id: Date.now().toString(),
-              name: `Producto ${sku}`,
-              brand: 'Identificado por IA',
-              price: 'Precio por consultar',
-              image: '/placeholder.jpg',
-              confidence: detection.confidence,
-              sku: sku,
-              timestamp: new Date().toISOString()
-            };
-            
-            const updatedHistory = addToHistory(shoeWithSKU);
-            setHistory(updatedHistory);
-          }
-        } catch (error) {
-          console.error(`❌ [${callbackId}] SKU identification failed:`, error);
-          setDetectedSKU(null);
-        }
-      }
-      
-      setPopupOpen(true);
-      
-      // Play detection sound with debouncing (max once per 2 seconds)
-      const now = Date.now();
-      const lastTime = lastSoundTimeRef.current;
-      console.log(`🔊 Sound check [${callbackId}]: now=${now}, lastTime=${lastTime}, diff=${now - lastTime}ms`);
-
-      if (now - lastTime > 2000) {
-        try {
-          const audioId = Math.random().toString(36).substr(2, 9);
-          // Use AudioContext for more reliable single-play behavior
-          const AudioContextClass = window.AudioContext || (window as typeof window & { webkitAudioContext: typeof AudioContext }).webkitAudioContext;
-          const audioContext = new AudioContextClass();
-          console.log(`🔊 Playing detection sound [callback:${callbackId}] [audio:${audioId}]`);
-
-          // Simple beep using Web Audio API
-          const oscillator = audioContext.createOscillator();
-          const gainNode = audioContext.createGain();
-
-          oscillator.connect(gainNode);
-          gainNode.connect(audioContext.destination);
-
-          oscillator.frequency.setValueAtTime(800, audioContext.currentTime);
-          gainNode.gain.setValueAtTime(0.3, audioContext.currentTime);
-          gainNode.gain.exponentialRampToValueAtTime(0.001, audioContext.currentTime + 0.3);
-
-          oscillator.start(audioContext.currentTime);
-          oscillator.stop(audioContext.currentTime + 0.3);
-
-          console.log(`▶️ Audio beep started [${audioId}]`);
-          lastSoundTimeRef.current = now;
-        } catch (e) {
-          console.warn(`Sound playback failed [${callbackId}]:`, e);
-        }
-      } else {
-        console.log(`🔇 Sound skipped [${callbackId}] - too soon after last sound (${now - lastTime}ms ago)`);
-      }
-    }
-  }, [detectionEngine]);
-
-  // Set the detection callback after handleDetection is defined
-  useEffect(() => {
-    if (setDetectionCallback && handleDetection) {
-      setDetectionCallback(handleDetection);
-    }
-  }, [handleDetection, setDetectionCallback]);
+  const [isScanning, setIsScanning] = useState(false);
 
   // Effect to clean up the stream when component unmounts or stream changes
   useEffect(() => {
@@ -170,33 +46,6 @@ function HomePageContent() {
     }
   }, [stream, cameraStatus]); // Runs when stream or camera status changes
 
-
-  // Track initialization state to prevent multiple attempts
-  const [mlInitialized, setMLInitialized] = useState(false);
-
-  // Initialize ML detection when camera is ready (only once)
-  useEffect(() => {
-    // Only log in development and when conditions change meaningfully
-    if (process.env.NODE_ENV === 'development') {
-      console.log('🔍 ML init check:', { 
-        ready: videoRef.current && cameraStatus === 'active' && !isMLLoading && detectionEnabled && !mlInitialized 
-      });
-    }
-    
-    if (videoRef.current && cameraStatus === 'active' && !isMLLoading && detectionEnabled && !mlInitialized) {
-      console.log('✅ Starting ML detection...');
-      setMLInitialized(true);
-      
-      initializeML(videoRef.current).then(() => {
-        console.log('✅ ML ready, starting continuous detection');
-        startContinuous();
-      }).catch((error) => {
-        console.error('❌ ML initialization failed:', error);
-        setMLInitialized(false); // Reset on error to allow retry
-      });
-    }
-  }, [cameraStatus, detectionEnabled, isMLLoading, mlInitialized, initializeML, startContinuous]);
-
   const startStream = useCallback(async (deviceId: string) => {
     // Stop previous stream if it exists
     (videoRef.current?.srcObject as MediaStream)?.getTracks().forEach((track) => track.stop());
@@ -274,57 +123,54 @@ function HomePageContent() {
   };
 
   const handleScan = async () => {
-    if (detectionEnabled && triggerDetection) {
-      try {
-        console.log('🎯 Triggering ML detection...');
-        const mlResult = await triggerDetection();
-        
-        if (mlResult) {
-          console.log('✅ Shoe detected by ML, calling SKU identification...');
-          
-          // Call SKU identification with the detected shoe
-          if (videoRef.current && detectionEngine) {
-            try {
-              const sku = await detectionEngine.identifyProductSKU(videoRef.current);
-              console.log('📦 Manual scan SKU result:', sku);
-              setDetectedSKU(sku);
-              
-              if (sku) {
-                // Create shoe object with SKU for history
-                const shoeWithSKU: Shoe = {
-                  id: Date.now().toString(),
-                  name: `Producto ${sku}`,
-                  brand: 'Identificado por IA',
-                  price: 'Precio por consultar',
-                  image: '/placeholder.jpg',
-                  confidence: mlResult.confidence,
-                  sku: sku,
-                  timestamp: new Date().toISOString()
-                };
-                
-                const updatedHistory = addToHistory(shoeWithSKU);
-                setHistory(updatedHistory);
-              }
-              
-              setPopupOpen(true);
-            } catch (skuError) {
-              console.error('❌ SKU identification failed:', skuError);
-              // Still show popup even if SKU fails
-              setDetectedSKU(null);
-              setPopupOpen(true);
-            }
-          } else {
-            console.warn('⚠️ Video or detection engine not available for SKU call');
-            setPopupOpen(true);
-          }
-        } else {
-          console.log('❌ No shoe detected by ML');
-        }
-      } catch (error) {
-        console.error('❌ ML detection failed:', error);
+    if (!videoRef.current || isScanning) return;
+
+    try {
+      setIsScanning(true);
+      console.log('📸 Capturando imagen del video...');
+
+      // Capture frame from video
+      const canvas = document.createElement('canvas');
+      const ctx = canvas.getContext('2d')!;
+      canvas.width = videoRef.current.videoWidth;
+      canvas.height = videoRef.current.videoHeight;
+      ctx.drawImage(videoRef.current, 0, 0);
+
+      // Get ImageData
+      const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
+
+      console.log('🔍 Llamando al servidor Python para identificar SKU...');
+
+      // Call SKU identification service
+      const sku = await skuIdentificationService.identifySKU(imageData);
+      console.log('📦 SKU result:', sku);
+
+      setDetectedSKU(sku);
+
+      if (sku) {
+        // Create shoe object with SKU for history
+        const shoeWithSKU: Shoe = {
+          id: Date.now().toString(),
+          name: `Producto ${sku}`,
+          brand: 'Identificado por IA',
+          price: 'Precio por consultar',
+          image: '/placeholder.jpg',
+          sku: sku,
+          timestamp: new Date().toISOString()
+        };
+
+        const updatedHistory = addToHistory(shoeWithSKU);
+        setHistory(updatedHistory);
       }
-    } else {
-      console.log('⚠️ ML detection is disabled');
+
+      setPopupOpen(true);
+
+    } catch (error) {
+      console.error('❌ Error en identificación:', error);
+      setDetectedSKU(null);
+      setPopupOpen(true);
+    } finally {
+      setIsScanning(false);
     }
   };
 
@@ -499,11 +345,10 @@ function HomePageContent() {
               `}</style>
             </div>
 
-            {/* Detection Counters - Only show in dev mode */}
-            {isDev && (
-              <div className="absolute top-4 right-4 bg-black/60 backdrop-blur-sm rounded-lg px-3 py-2 text-white text-xs space-y-1">
-                <div className="text-green-400">👟 Shoes Found: {shoeDetectionCount}</div>
-                <div className="text-blue-400">⚡ Avg Speed: {metrics?.inferenceTime ? `${metrics.inferenceTime.toFixed(0)}ms` : 'N/A'}</div>
+            {/* Scanning Indicator */}
+            {isScanning && (
+              <div className="absolute top-4 right-4 bg-blue-600/80 backdrop-blur-sm rounded-lg px-4 py-2 text-white text-sm font-medium animate-pulse">
+                📸 Identificando...
               </div>
             )}
             
@@ -548,144 +393,6 @@ function HomePageContent() {
 
                 {/* Additional Options */}
                 <div className="space-y-4 flex-1">
-
-
-                  {/* ML Detection Settings - Only show in dev mode */}
-                  {isDev && (
-                    <div className="bg-white/5 rounded-lg p-4 border border-white/10">
-                      <div className="flex items-center gap-2 mb-3">
-                        <Camera size={20} className="text-blue-400" />
-                        <span className="text-white font-medium">Detección IA</span>
-                        {isMLLoading && <span className="text-xs text-yellow-400">Cargando...</span>}
-                      </div>
-                      <div className="space-y-3">
-                        <div className="flex items-center gap-3">
-                          <button 
-                            onClick={() => {
-                              setDetectionEnabled(!detectionEnabled);
-                              if (!detectionEnabled) {
-                                console.log('Enabling ML detection');
-                              } else {
-                                console.log('Disabling ML detection');
-                                stopContinuous();
-                              }
-                            }}
-                            className={`text-sm py-2 px-4 rounded-md transition-colors border ${
-                              detectionEnabled 
-                                ? 'bg-green-500/20 text-green-300 border-green-500/30 hover:bg-green-500/30' 
-                                : 'bg-white/10 text-white border-white/20 hover:bg-blue-500/30'
-                            }`}
-                          >
-                            {detectionEnabled ? 'Activado' : 'Activar'}
-                          </button>
-                          <div className="flex items-center gap-2">
-                            {detectionEnabled && (
-                              <div className="w-2 h-2 bg-green-500 rounded-full animate-pulse"></div>
-                            )}
-                            <span className="text-white/60 text-sm">
-                              {detectionEnabled ? 'Detectando zapatos automáticamente' : 'Click para activar detección IA'}
-                            </span>
-                          </div>
-                        </div>
-                        
-                        {/* ML Metrics */}
-                        {detectionEnabled && metrics && (
-                          <div className="text-xs space-y-1 text-white/50 bg-black/20 p-2 rounded">
-                            <div>FPS: {metrics.fps.toFixed(1)}</div>
-                            <div>Inferencia: {metrics.inferenceTime.toFixed(0)}ms</div>
-                            {metrics.memoryUsage > 0 && <div>Memoria: {metrics.memoryUsage.toFixed(0)}MB</div>}
-                          </div>
-                        )}
-
-                        {/* Detection Confidence Indicator */}
-                        {detectionEnabled && currentDetection && (
-                          <div className="space-y-2 pt-2">
-                            <label className="text-sm font-medium text-white/80">Confianza de Detección</label>
-                            <div className="bg-white/10 rounded-lg p-3 border border-white/20">
-                              <div className="flex justify-between items-center mb-1">
-                                <span className="text-xs text-white/60">Confianza</span>
-                                <span className="text-xs text-white font-bold">{(currentDetection.confidence * 100).toFixed(1)}%</span>
-                              </div>
-                              <div className="w-full bg-black/30 rounded-full h-2">
-                                <div 
-                                  className={`h-2 rounded-full transition-all duration-300 ${
-                                    currentDetection.confidence > 0.8 ? 'bg-green-500' :
-                                    currentDetection.confidence > 0.6 ? 'bg-yellow-500' : 'bg-red-500'
-                                  }`}
-                                  style={{ width: `${currentDetection.confidence * 100}%` }}
-                                />
-                              </div>
-                              <div className="flex justify-between text-xs text-white/40 mt-1">
-                                <span>Bajo</span>
-                                <span>Alto</span>
-                              </div>
-                            </div>
-                          </div>
-                        )}
-
-                        {/* Other settings */}
-                        {detectionEnabled && config && (
-                          <div className="space-y-4 pt-4">
-                            <div>
-                              <label className="text-sm font-medium text-white/80">Sensibilidad ({(config.confidenceThreshold * 100).toFixed(0)}%)</label>
-                              <Slider
-                                min={0.3}
-                                max={0.9}
-                                step={0.05}
-                                value={[config.confidenceThreshold]}
-                                onValueChange={([value]) => updateConfig({ confidenceThreshold: value })}
-                                disabled={!detectionEnabled}
-                                className="mt-2"
-                              />
-                            </div>
-                            <div>
-                              <label className="text-sm font-medium text-white/80">Frames a saltar ({config.frameSkip})</label>
-                              <Slider
-                                min={1}
-                                max={10}
-                                step={1}
-                                value={[config.frameSkip]}
-                                onValueChange={([value]) => updateConfig({ frameSkip: value })}
-                                disabled={!detectionEnabled}
-                                className="mt-2"
-                              />
-                            </div>
-                            <div className="flex items-center justify-between">
-                              <label className="text-sm font-medium text-white/80">Detección continua</label>
-                              <Switch
-                                checked={config.enableContinuous}
-                                onCheckedChange={(checked) => updateConfig({ enableContinuous: checked })}
-                                disabled={!detectionEnabled}
-                              />
-                            </div>
-                            <div className="flex items-center justify-between">
-                              <label className="text-sm font-medium text-white/80">Detección por trigger</label>
-                              <Switch
-                                checked={config.enableTrigger}
-                                onCheckedChange={(checked) => updateConfig({ enableTrigger: checked })}
-                                disabled={!detectionEnabled}
-                              />
-                            </div>
-                          </div>
-                        )}
-                        
-                        {/* Detection Status */}
-                        {currentDetection && (
-                          <div className="text-xs bg-green-500/10 text-green-300 p-2 rounded border border-green-500/20">
-                            🎯 Zapato detectado (confianza: {(currentDetection.confidence * 100).toFixed(1)}%)
-                          </div>
-                        )}
-                        
-                        {mlError && (
-                          <div className="text-xs bg-red-500/10 text-red-300 p-2 rounded border border-red-500/20">
-                            ❌ {mlError}
-                          </div>
-                        )}
-                      </div>
-                    </div>
-                  )}
-
-
                   {/* App Info */}
                   <div className="bg-gradient-to-r from-blue-500/10 to-purple-500/10 rounded-lg p-4 border border-blue-500/20">
                     <h3 className="text-white font-medium mb-2">Smart Store Assistant</h3>
diff --git a/lib/ml-classification.ts b/lib/ml-classification.ts
deleted file mode 100644
index 9e0feb1..0000000
--- a/lib/ml-classification.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-import type { Shoe } from "./shoe-database";
-
-/**
- * Simulates detecting a shoe from a list of possible shoes.
- * In a real application, this would involve a machine learning model.
- * @param allShoes The list of all shoes in the database.
- * @returns A randomly selected shoe.
- */
-export function detectShoe(allShoes: Shoe[]): Shoe | null {
-  if (allShoes.length === 0) {
-    return null;
-  }
-
-  // Simulate detection by picking a random shoe from the database.
-  const randomIndex = Math.floor(Math.random() * allShoes.length);
-  return allShoes[randomIndex];
-}
\ No newline at end of file
diff --git a/lib/ml/detection-engine.ts b/lib/ml/detection-engine.ts
deleted file mode 100644
index 6ecd587..0000000
--- a/lib/ml/detection-engine.ts
+++ /dev/null
@@ -1,388 +0,0 @@
-import type { DetectionConfig, DetectionResult, DetectionMetrics, DetectionMode } from './types';
-import { DetectionWorkerManager } from './detection-worker-manager';
-import { detectDeviceCapabilities, getRecommendedConfig } from './device-capabilities';
-import { skuIdentificationService } from '../sku-identification';
-
-// Extend window interface for TensorFlow.js
-declare global {
-  interface Window {
-    tf: unknown;
-  }
-}
-
-/**
- * Main detection engine that coordinates continuous and trigger detection
- */
-export class DetectionEngine {
-  private workerManager: DetectionWorkerManager;
-  private config: DetectionConfig;
-  private model: unknown = null; // TensorFlow.js model instance
-  
-  // Detection state
-  private isRunning = false;
-  private detectionMode: DetectionMode = 'hybrid';
-  private frameSkipCounter = 0;
-  private detectionCount = 0;
-  
-  // Temporal filtering
-  private detectionHistory: DetectionResult[] = [];
-  private lastValidDetection: DetectionResult | null = null;
-  
-  // Performance tracking
-  private metrics: DetectionMetrics = {
-    fps: 0,
-    inferenceTime: 0,
-    memoryUsage: 0
-  };
-  
-  // Event callbacks
-  private onDetectionCallback?: (detection: DetectionResult | null) => void;
-  private onMetricsCallback?: (metrics: DetectionMetrics) => void;
-  private lastDetectionCallbackTime?: number;
-  
-  constructor() {
-    console.log('🏗️ DetectionEngine constructor called');
-    this.workerManager = new DetectionWorkerManager();
-    
-    
-    // Get device-optimized configuration
-    const capabilities = detectDeviceCapabilities();
-    this.config = getRecommendedConfig(capabilities);
-    
-    console.log('✅ Detection engine initialized', { capabilities, config: this.config });
-  }
-
-  /**
-   * Initialize the detection engine with a specific model
-   */
-  async initialize(modelVariant?: 'quantized' | 'standard' | 'full', onProgress?: (progress: number) => void): Promise<void> {
-    const variant = modelVariant || this.config.modelVariant;
-    
-    console.log(`🔧 Initializing detection engine with ${variant} model...`);
-    
-    try {
-      // Load the model into the worker
-      console.log('📥 Loading model into worker...');
-      await this.workerManager.loadModel(variant, onProgress);
-      
-      // Configure the worker with current settings
-      console.log('⚙️ Configuring worker...');
-      await this.workerManager.configure(this.config);
-      
-      console.log(`✅ Detection engine initialized with ${variant} model`);
-      
-    } catch (error) {
-      console.error('❌ Failed to initialize detection engine:', error);
-      throw error;
-    }
-  }
-
-  /**
-   * Start continuous detection
-   */
-  startContinuousDetection(videoElement: HTMLVideoElement): void {
-    console.log('🚀 startContinuousDetection called:', { 
-      isRunning: this.isRunning, 
-      enableContinuous: this.config.enableContinuous,
-      videoElement: !!videoElement 
-    });
-    
-    if (this.isRunning) {
-      console.warn('Detection already running');
-      return;
-    }
-    
-    this.isRunning = true;
-    this.detectionMode = this.config.enableContinuous ? 'continuous' : 'trigger';
-    
-    if (this.config.enableContinuous) {
-      console.log('🔄 Starting continuous detection loop...');
-      this.runContinuousLoop(videoElement);
-    }
-    
-    console.log(`✅ Started detection in ${this.detectionMode} mode`);
-  }
-
-  /**
-   * Stop continuous detection
-   */
-  stopContinuousDetection(): void {
-    this.isRunning = false;
-    this.frameSkipCounter = 0;
-    this.detectionHistory = [];
-    console.log('Stopped continuous detection');
-  }
-
-  /**
-   * Perform single trigger detection - higher quality/confidence than continuous
-   */
-  async triggerDetection(videoElement: HTMLVideoElement): Promise<DetectionResult | null> {
-    const startTime = performance.now();
-    
-    try {
-      console.log('🎯 Starting trigger detection (high quality)');
-      
-      // Capture image data for trigger detection (high quality)
-      const imageData = this.captureVideoFrame(videoElement, true);
-      
-      // Use worker manager for detection
-      const detections = await this.workerManager.detect(imageData);
-      const detection = detections.length > 0 ? detections[0] : null;
-      
-      // Update metrics
-      this.metrics.inferenceTime = performance.now() - startTime;
-      
-      console.log('✅ Trigger detection completed:', detection);
-      
-      // Trigger callbacks for immediate display
-      if (this.onDetectionCallback && detection) {
-        this.onDetectionCallback(detection);
-      }
-      
-      return detection;
-      
-    } catch (error) {
-      console.error('❌ Trigger detection failed:', error);
-      throw error;
-    }
-  }
-
-  /**
-   * Identify product SKU from detected shoe
-   * @param videoElement - Video element to capture image from
-   * @returns Promise<string | null> - Product SKU if identified successfully
-   */
-  async identifyProductSKU(videoElement: HTMLVideoElement): Promise<string | null> {
-    try {
-      console.log('🔍 Starting product SKU identification...');
-      
-      // Capture high-quality image for SKU identification
-      const imageData = this.captureVideoFrame(videoElement, true);
-      
-      // Call SKU identification service
-      const sku = await skuIdentificationService.identifySKU(imageData);
-      
-      if (sku) {
-        console.log('✅ Product SKU identified:', sku);
-      } else {
-        console.log('❌ No valid SKU found');
-      }
-      
-      return sku;
-      
-    } catch (error) {
-      console.error('❌ SKU identification failed:', error);
-      return null;
-    }
-  }
-
-  /**
-   * Continuous detection loop
-   */
-  private async runContinuousLoop(videoElement: HTMLVideoElement): Promise<void> {
-    if (!this.isRunning) return;
-    
-    // Frame skipping logic
-    this.frameSkipCounter++;
-    if (this.frameSkipCounter < this.config.frameSkip) {
-      // Skip this frame, schedule next iteration
-      requestAnimationFrame(() => this.runContinuousLoop(videoElement));
-      return;
-    }
-    
-    this.frameSkipCounter = 0;
-    // Only log every 10th detection to reduce noise
-    if (this.detectionCount % 10 === 0) {
-      console.log(`🔄 Continuous detection running... (${this.detectionCount} inferences)`);
-    }
-    
-    try {
-      const startTime = performance.now();
-      
-      // Capture image data for continuous detection (lower quality)
-      const imageData = this.captureVideoFrame(videoElement, false);
-      
-      // Use worker manager for detection
-      const detections = await this.workerManager.detect(imageData);
-      const detection = detections.length > 0 ? detections[0] : null;
-      
-      const inferenceTime = performance.now() - startTime;
-      console.log('⚡ Continuous detection completed:', { time: inferenceTime, detection });
-      
-      // Apply temporal filtering
-      const validDetection = this.applyTemporalFiltering(detection);
-      
-      // Update metrics
-      this.updateMetrics(inferenceTime);
-      
-      // Trigger callbacks (only if we have a valid detection)
-      // Use a debounced approach to avoid too frequent updates
-      if (this.onDetectionCallback) {
-        // Only update if it's been at least 100ms since last detection callback for continuous
-        const now = Date.now();
-        if (!this.lastDetectionCallbackTime || now - this.lastDetectionCallbackTime > 100) {
-          this.onDetectionCallback(validDetection);
-          this.lastDetectionCallbackTime = now;
-        }
-      }
-      
-    } catch (error) {
-      console.error('Continuous detection error:', error);
-    }
-    
-    // Schedule next iteration
-    if (this.isRunning) {
-      requestAnimationFrame(() => this.runContinuousLoop(videoElement));
-    }
-  }
-
-  /**
-   * Capture frame from video element
-   */
-  private captureVideoFrame(videoElement: HTMLVideoElement, highQuality: boolean): ImageData {
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d')!;
-    
-    // Use different resolutions based on detection mode
-    const [targetWidth, targetHeight] = highQuality 
-      ? [640, 480]  // High quality for trigger detection
-      : [320, 240]; // Lower quality for continuous detection
-    
-    canvas.width = targetWidth;
-    canvas.height = targetHeight;
-    
-    // Draw video frame to canvas
-    ctx.drawImage(videoElement, 0, 0, targetWidth, targetHeight);
-    
-    // Extract image data
-    const imageData = ctx.getImageData(0, 0, targetWidth, targetHeight);
-    
-    // Cleanup
-    canvas.remove();
-    
-    return imageData;
-  }
-
-  /**
-   * Apply temporal consistency filtering to reduce false positives
-   */
-  private applyTemporalFiltering(detection: DetectionResult | null): DetectionResult | null {
-    if (!detection) {
-      // No detection - decay previous detections
-      this.detectionHistory = this.detectionHistory.filter(d => 
-        Date.now() - d.timestamp < 1000 // Keep detections from last second
-      );
-      
-      // If we have recent consistent detections, continue showing them
-      if (this.detectionHistory.length >= 2) {
-        return this.lastValidDetection;
-      }
-      
-      return null;
-    }
-    
-    // Add current detection to history
-    this.detectionHistory.push(detection);
-    
-    // Keep only recent detections (last 3 seconds)
-    this.detectionHistory = this.detectionHistory.filter(d => 
-      Date.now() - d.timestamp < 3000
-    );
-    
-    // Check temporal consistency
-    const recentDetections = this.detectionHistory.filter(d => 
-      Date.now() - d.timestamp < 500 // Last 500ms
-    );
-    
-    if (recentDetections.length >= 2) {
-      // We have consistent detections - this is likely valid
-      this.lastValidDetection = detection;
-      return detection;
-    }
-    
-    // Not enough temporal consistency yet
-    return this.lastValidDetection;
-  }
-
-  /**
-   * Update performance metrics
-   */
-  private updateMetrics(inferenceTime: number): void {
-    this.detectionCount++;
-    this.metrics = {
-      fps: 0, // Placeholder, as PerformanceMonitor is removed
-      inferenceTime: inferenceTime,
-      memoryUsage: this.getMemoryUsage()
-    };
-
-    if (this.onMetricsCallback) {
-      this.onMetricsCallback(this.metrics);
-    }
-  }
-
-  
-
-  /**
-   * Get current memory usage (rough estimate)
-   */
-  private getMemoryUsage(): number {
-    const memInfo = (performance as Performance & { memory?: { usedJSHeapSize: number } }).memory;
-    if (memInfo && memInfo.usedJSHeapSize) {
-      return memInfo.usedJSHeapSize / (1024 * 1024); // MB
-    }
-    return 0;
-  }
-
-
-  /**
-   * Set detection callback
-   */
-  onDetection(callback: (detection: DetectionResult | null) => void): void {
-    this.onDetectionCallback = callback;
-  }
-
-  /**
-   * Set metrics callback
-   */
-  onMetrics(callback: (metrics: DetectionMetrics) => void): void {
-    this.onMetricsCallback = callback;
-  }
-
-  /**
-   * Update configuration
-   */
-  async updateConfig(newConfig: Partial<DetectionConfig>): Promise<void> {
-    this.config = { ...this.config, ...newConfig };
-    await this.workerManager.configure(this.config);
-    console.log('Configuration updated:', this.config);
-  }
-
-  /**
-   * Get current configuration
-   */
-  getConfig(): DetectionConfig {
-    return { ...this.config };
-  }
-
-  /**
-   * Get current metrics
-   */
-  getMetrics(): DetectionMetrics {
-    return { ...this.metrics };
-  }
-
-  /**
-   * Check if detection is running
-   */
-  isDetectionRunning(): boolean {
-    return this.isRunning;
-  }
-
-  /**
-   * Destroy the detection engine
-   */
-  destroy(): void {
-    this.stopContinuousDetection();
-    this.workerManager.destroy();
-  }
-}
\ No newline at end of file
diff --git a/lib/ml/detection-worker-manager.ts b/lib/ml/detection-worker-manager.ts
deleted file mode 100644
index d8fc65f..0000000
--- a/lib/ml/detection-worker-manager.ts
+++ /dev/null
@@ -1,216 +0,0 @@
-import type { DetectionConfig, DetectionResult, DetectionMetrics, WorkerMessage, WorkerResponse } from './types';
-import { ModelCache } from './model-cache';
-import { MODEL_VARIANTS } from './model-config';
-
-/**
- * Manages the detection worker and handles communication
- */
-export class DetectionWorkerManager {
-  private worker: Worker | null = null;
-  private messageId = 0;
-  private pendingMessages = new Map<string, { resolve: (value: any) => void; reject: (reason?: any) => void }>();
-  private modelCache = new ModelCache();
-  private isWorkerReady = false;
-
-  constructor() {
-    this.initializeWorker();
-  }
-
-  private async initializeWorker() {
-    try {
-      // Create worker from the detection worker file
-      this.worker = new Worker(
-        new URL('../../workers/detection-worker.ts', import.meta.url),
-        { type: 'module' }
-      );
-
-      this.worker.onmessage = (event: MessageEvent<WorkerResponse>) => {
-        this.handleWorkerMessage(event.data);
-      };
-
-      this.worker.onerror = (error) => {
-        console.error('Worker error:', error);
-        this.isWorkerReady = false;
-      };
-
-      this.isWorkerReady = true;
-      console.log('Detection worker initialized');
-      await this.sendMessage('INITIALIZE', undefined);
-
-    } catch (error) {
-      console.error('Failed to initialize worker:', error);
-      this.isWorkerReady = false;
-    }
-  }
-
-  private handleWorkerMessage(message: WorkerResponse) {
-    const { type, id } = message;
-    
-    const pending = this.pendingMessages.get(id);
-    if (!pending) {
-      console.warn('Received response for unknown message ID:', id);
-      return;
-    }
-
-    this.pendingMessages.delete(id);
-
-    if (type === 'ERROR') {
-      pending.reject(new Error((message as { error: string }).error));
-    } else if (type === 'DETECTION_RESULT') {
-      const detectionMessage = message as { result: DetectionResult | null };
-      pending.resolve({ result: detectionMessage.result });
-    } else if (type === 'INITIALIZED') {
-      pending.resolve(undefined);
-    } else if (type === 'METRICS_UPDATE') {
-      pending.resolve({ metrics: (message as { metrics: Partial<DetectionMetrics> }).metrics });
-    } else if (type === 'LOADED_MODEL') {
-      pending.resolve(undefined);
-    } else if (type === 'CONFIGURED') {
-      pending.resolve(undefined);
-    } else {
-      pending.resolve(message);
-    }
-  }
-
-  private async sendMessage<T>(type: WorkerMessage['type'], payload: unknown): Promise<T> {
-    if (!this.worker || !this.isWorkerReady) {
-      throw new Error('Worker not available');
-    }
-
-    const id = (this.messageId++).toString();
-
-    return new Promise((resolve, reject) => {
-      this.pendingMessages.set(id, { resolve, reject });
-      
-      let message: WorkerMessage & { id: string };
-
-      if (type === 'INITIALIZE') {
-        message = { type, id };
-      } else if (type === 'DETECT') {
-        message = { type, imageData: (payload as { imageData: ImageData }).imageData, id };
-      } else if (type === 'UPDATE_CONFIG' || type === 'CONFIGURE') {
-        message = { type, config: payload as DetectionConfig, id };
-      } else if (type === 'LOAD_MODEL') {
-        const modelPayload = payload as { variant: 'quantized' | 'standard' | 'full'; modelData: ArrayBuffer };
-        message = { type, variant: modelPayload.variant, modelData: modelPayload.modelData, id };
-      } else {
-        throw new Error(`Unknown message type for sendMessage: ${type}`);
-      }
-
-      this.worker!.postMessage(message);
-
-      // Timeout after 30 seconds
-      setTimeout(() => {
-        if (this.pendingMessages.has(id)) {
-          this.pendingMessages.delete(id);
-          reject(new Error('Worker message timeout'));
-        }
-      }, 90000);
-    });
-  }
-
-  /**
-   * Load a model into the worker
-   */
-  async loadModel(variant: 'quantized' | 'standard' | 'full', onProgress?: (progress: number) => void): Promise<void> {
-    const modelInfo = MODEL_VARIANTS[variant];
-    
-    try {
-      // Get model data from cache or download
-      const modelData = await this.modelCache.getModel(variant, modelInfo, onProgress);
-      
-      // Send model data to worker
-      await this.sendMessage('LOAD_MODEL', {
-        variant,
-        modelData
-      });
-
-      console.log(`Model ${variant} loaded successfully`);
-      
-    } catch (error) {
-      console.error(`Failed to load model ${variant}:`, error);
-      throw error;
-    }
-  }
-
-  /**
-   * Configure the detection settings
-   */
-  async configure(config: DetectionConfig): Promise<void> {
-    await this.sendMessage('CONFIGURE', config);
-  }
-
-  /**
-   * Perform detection on an image
-   */
-  async detect(imageData: ImageData): Promise<DetectionResult[]> {
-    if (!this.isWorkerReady) {
-      throw new Error('Worker not ready');
-    }
-
-    try {
-      const results = await this.sendMessage<{ result: DetectionResult | null }>('DETECT', { imageData });
-      
-      // Handle the case where results or results.result is undefined
-      if (!results || results.result === undefined || results.result === null) {
-        return [];
-      }
-      
-      return [results.result];
-    } catch (error) {
-      console.error('Detection failed:', error);
-      throw error;
-    }
-  }
-
-  /**
-   * Get worker metrics
-   */
-  async getMetrics(): Promise<object> {
-    // Metrics are tracked locally, no need to query worker
-    return {};
-  }
-
-  /**
-   * Check if worker is ready
-   */
-  isReady(): boolean {
-    return this.isWorkerReady;
-  }
-
-  /**
-   * Terminate the worker
-   */
-  destroy() {
-    if (this.worker) {
-      this.worker.terminate();
-      this.worker = null;
-      this.isWorkerReady = false;
-    }
-    
-    // Reject all pending messages
-    this.pendingMessages.forEach(({ reject }) => {
-      reject(new Error('Worker terminated'));
-    });
-    this.pendingMessages.clear();
-  }
-}
-
-/**
- * Singleton instance manager
- */
-let workerManager: DetectionWorkerManager | null = null;
-
-export function getDetectionWorkerManager(): DetectionWorkerManager {
-  if (!workerManager) {
-    workerManager = new DetectionWorkerManager();
-  }
-  return workerManager;
-}
-
-export function destroyDetectionWorkerManager() {
-  if (workerManager) {
-    workerManager.destroy();
-    workerManager = null;
-  }
-}
\ No newline at end of file
diff --git a/lib/ml/device-capabilities.ts b/lib/ml/device-capabilities.ts
deleted file mode 100644
index c31fcd7..0000000
--- a/lib/ml/device-capabilities.ts
+++ /dev/null
@@ -1,65 +0,0 @@
-import type { DeviceCapabilities, DeviceTier } from './types';
-import { DEFAULT_CONFIG } from './model-config';
-
-/**
- * Detects device capabilities to suggest an optimal performance configuration.
- */
-export function detectDeviceCapabilities(): DeviceCapabilities {
-  const hasWebGL = (() => {
-    try {
-      const canvas = document.createElement('canvas');
-      return !!(window.WebGLRenderingContext && (canvas.getContext('webgl') || canvas.getContext('experimental-webgl')));
-    } catch {
-      return false;
-    }
-  })();
-
-  const cpuCores = navigator.hardwareConcurrency || 2; // Default to 2 if undefined
-  // @ts-expect-error - deviceMemory is not in all browsers
-  const memory = navigator.deviceMemory || 1; // Default to 1GB if undefined
-
-  let tier: DeviceTier;
-  if (cpuCores >= 8 && memory >= 4) {
-    tier = 'high';
-  } else if (cpuCores >= 4 && memory >= 2) {
-    tier = 'mid';
-  } else {
-    tier = 'low';
-  }
-
-  return { tier, hasWebGL, cpuCores, memory };
-}
-
-/**
- * Gets a recommended configuration based on the detected device tier.
- * @param capabilities The detected device capabilities.
- * @returns A full DetectionConfig with recommended settings.
- */
-export function getRecommendedConfig(capabilities: DeviceCapabilities): typeof DEFAULT_CONFIG {
-  const baseConfig = { ...DEFAULT_CONFIG };
-  
-  switch (capabilities.tier) {
-    case 'high':
-      return {
-        ...baseConfig,
-        modelVariant: 'standard',
-        frameSkip: 6,
-        confidenceThreshold: 0.8,
-      };
-    case 'mid':
-      return {
-        ...baseConfig,
-        modelVariant: 'standard',
-        frameSkip: 6,
-        confidenceThreshold: 0.8,
-      };
-    case 'low':
-    default:
-      return {
-        ...baseConfig,
-        modelVariant: 'quantized',
-        frameSkip: 6,
-        confidenceThreshold: 0.8,
-      };
-  }
-}
diff --git a/lib/ml/inference-pipeline.ts b/lib/ml/inference-pipeline.ts
deleted file mode 100644
index 55e93b2..0000000
--- a/lib/ml/inference-pipeline.ts
+++ /dev/null
@@ -1,114 +0,0 @@
-import type { DetectionResult } from './types';
-import { VALIDATION_RULES } from './model-config';
-
-/**
- * A temporal filter to smooth detections and reduce flickering.
- */
-class TemporalFilter {
-  private history: (DetectionResult | null)[] = [];
-  private frameCount = 0;
-
-  constructor(private consistencyFrames: number) {
-    this.history = new Array(consistencyFrames).fill(null);
-  }
-
-  add(detection: DetectionResult | null): DetectionResult | null {
-    this.history.shift();
-    this.history.push(detection);
-
-    const recentDetections = this.history.filter(d => d !== null);
-    if (recentDetections.length >= this.consistencyFrames) {
-      // Return the most confident detection from the recent history
-      return recentDetections.reduce((prev, current) => (prev!.confidence > current!.confidence ? prev : current));
-    }
-
-    return null;
-  }
-}
-
-/**
- * The InferencePipeline class handles post-processing of model outputs,
- * including filtering, validation, and temporal smoothing to prevent false positives.
- */
-export class InferencePipeline {
-  private temporalFilter: TemporalFilter;
-
-  constructor() {
-    this.temporalFilter = new TemporalFilter(VALIDATION_RULES.temporalConsistencyFrames);
-  }
-
-  /**
-   * Processes the raw output from the TensorFlow.js model.
-   * @param boxes Raw bounding boxes.
-   * @param scores Raw confidence scores.
-   * @param classes Raw class indices.
-   * @param confidenceThreshold The current confidence threshold.
-   * @returns A single, validated DetectionResult or null.
-   */
-  process(boxes: number[], scores: number[], classes: number[], confidenceThreshold: number): DetectionResult | null {
-    const detections: DetectionResult[] = [];
-
-    // Process up to 5 detections like the working implementation
-    for (let i = 0; i < Math.min(5, scores.length); i++) {
-      const score = scores[i];
-      
-      // Convert to percentage and check threshold like working implementation
-      const scorePercent = score * 100;
-      if (scorePercent < (confidenceThreshold * 100)) continue;
-
-      // Extract bounding box [y_min, x_min, y_max, x_max] like working implementation
-      const yMin = boxes[i * 4];
-      const xMin = boxes[i * 4 + 1]; 
-      const yMax = boxes[i * 4 + 2];
-      const xMax = boxes[i * 4 + 3];
-      
-      // Convert to [x, y, width, height] format
-      const bbox: [number, number, number, number] = [xMin, yMin, xMax - xMin, yMax - yMin];
-
-      const detection: DetectionResult = {
-        bbox,
-        confidence: score,
-        class: 'shoe', // Assume all detections are shoes
-        timestamp: Date.now()
-      };
-
-      if (this.isValid(detection)) {
-        detections.push(detection);
-      }
-    }
-
-    if (detections.length === 0) {
-      return this.temporalFilter.add(null);
-    }
-
-    // Get the single best detection
-    const bestDetection = detections.reduce((prev, current) => (prev.confidence > current.confidence ? prev : current));
-    
-    return this.temporalFilter.add(bestDetection);
-  }
-
-  /**
-   * Validates a detection against a set of rules.
-   * @param detection The detection to validate.
-   * @returns True if the detection is valid, false otherwise.
-   */
-  private isValid(detection: DetectionResult): boolean {
-    const { bbox } = detection;
-    const [, , width, height] = bbox;
-
-    // Bounding box size validation (relative to a 320x320 input)
-    const boxPixelWidth = width * 320;
-    const boxPixelHeight = height * 320;
-    if (boxPixelWidth < VALIDATION_RULES.minBoundingBoxSize || boxPixelHeight < VALIDATION_RULES.minBoundingBoxSize) {
-      return false;
-    }
-
-    // Aspect ratio validation
-    const aspectRatio = boxPixelWidth / boxPixelHeight;
-    if (aspectRatio < VALIDATION_RULES.aspectRatioRange[0] || aspectRatio > VALIDATION_RULES.aspectRatioRange[1]) {
-      return false;
-    }
-
-    return true;
-  }
-}
diff --git a/lib/ml/model-cache.ts b/lib/ml/model-cache.ts
deleted file mode 100644
index 48c4489..0000000
--- a/lib/ml/model-cache.ts
+++ /dev/null
@@ -1,269 +0,0 @@
-import type { ModelInfo } from './types';
-
-const DB_NAME = 'ShoeDetectionModels';
-const DB_VERSION = 1;
-const STORE_NAME = 'models';
-
-export interface CachedModel {
-  id: string;
-  variant: 'quantized' | 'standard' | 'full';
-  data: ArrayBuffer;
-  metadata: ModelInfo;
-  timestamp: number;
-  version: string;
-}
-
-/**
- * IndexedDB-based model cache for TensorFlow.js models
- */
-export class ModelCache {
-  private db: IDBDatabase | null = null;
-  private initPromise: Promise<void> | null = null;
-
-  constructor() {
-    this.initPromise = this.init();
-  }
-
-  /**
-   * Initialize IndexedDB
-   */
-  private async init(): Promise<void> {
-    return new Promise((resolve, reject) => {
-      const request = indexedDB.open(DB_NAME, DB_VERSION);
-
-      request.onerror = () => {
-        console.error('Failed to open IndexedDB:', request.error);
-        reject(request.error);
-      };
-
-      request.onsuccess = () => {
-        this.db = request.result;
-        resolve();
-      };
-
-      request.onupgradeneeded = (event) => {
-        const db = (event.target as IDBOpenDBRequest).result;
-        
-        // Create models store
-        if (!db.objectStoreNames.contains(STORE_NAME)) {
-          const store = db.createObjectStore(STORE_NAME, { keyPath: 'id' });
-          store.createIndex('variant', 'variant', { unique: false });
-          store.createIndex('timestamp', 'timestamp', { unique: false });
-        }
-      };
-    });
-  }
-
-  /**
-   * Ensure database is ready
-   */
-  private async ensureReady(): Promise<void> {
-    if (this.initPromise) {
-      await this.initPromise;
-    }
-    if (!this.db) {
-      throw new Error('Database not initialized');
-    }
-  }
-
-  /**
-   * Cache a model in IndexedDB
-   */
-  async cacheModel(variant: 'quantized' | 'standard' | 'full', modelData: ArrayBuffer, metadata: ModelInfo): Promise<void> {
-    await this.ensureReady();
-
-    return new Promise((resolve, reject) => {
-      const transaction = this.db!.transaction([STORE_NAME], 'readwrite');
-      const store = transaction.objectStore(STORE_NAME);
-
-      const cachedModel: CachedModel = {
-        id: `shoe-detection-${variant}`,
-        variant,
-        data: modelData,
-        metadata,
-        timestamp: Date.now(),
-        version: '1.0.0'
-      };
-
-      const request = store.put(cachedModel);
-
-      request.onsuccess = () => {
-        console.log(`Model ${variant} cached successfully`);
-        resolve();
-      };
-
-      request.onerror = () => {
-        console.error(`Failed to cache model ${variant}:`, request.error);
-        reject(request.error);
-      };
-    });
-  }
-
-  /**
-   * Retrieve a cached model
-   */
-  async getCachedModel(variant: 'quantized' | 'standard' | 'full'): Promise<CachedModel | null> {
-    await this.ensureReady();
-
-    return new Promise((resolve, reject) => {
-      const transaction = this.db!.transaction([STORE_NAME], 'readonly');
-      const store = transaction.objectStore(STORE_NAME);
-      const request = store.get(`shoe-detection-${variant}`);
-
-      request.onsuccess = () => {
-        resolve(request.result || null);
-      };
-
-      request.onerror = () => {
-        reject(request.error);
-      };
-    });
-  }
-
-  /**
-   * Check if a model is cached and up to date
-   */
-  async isModelCached(variant: 'quantized' | 'standard' | 'full', requiredVersion: string): Promise<boolean> {
-    try {
-      const cached = await this.getCachedModel(variant);
-      return cached !== null && cached.version === requiredVersion;
-    } catch (error) {
-      console.error('Error checking cached model:', error);
-      return false;
-    }
-  }
-
-  /**
-   * Download and cache a model
-   */
-  async downloadAndCacheModel(variant: 'quantized' | 'standard' | 'full', modelInfo: ModelInfo, onProgress?: (progress: number) => void): Promise<ArrayBuffer> {
-    console.log(`Downloading model ${variant} from ${modelInfo.url}`);
-
-    const response = await fetch(modelInfo.url);
-    
-    if (!response.ok) {
-      throw new Error(`Failed to download model: ${response.statusText}`);
-    }
-
-    const contentLength = response.headers.get('content-length');
-    const total = contentLength ? parseInt(contentLength, 10) : 0;
-    let loaded = 0;
-
-    const reader = response.body?.getReader();
-    const chunks: Uint8Array[] = [];
-
-    if (!reader) {
-      throw new Error('Failed to get response reader');
-    }
-
-    while (true) {
-      const { done, value } = await reader.read();
-      
-      if (done) break;
-      
-      chunks.push(value);
-      loaded += value.length;
-      
-      if (onProgress && total > 0) {
-        onProgress((loaded / total) * 100);
-      }
-    }
-
-    // Combine chunks into single ArrayBuffer
-    const totalLength = chunks.reduce((acc, chunk) => acc + chunk.length, 0);
-    const result = new Uint8Array(totalLength);
-    let offset = 0;
-    
-    for (const chunk of chunks) {
-      result.set(chunk, offset);
-      offset += chunk.length;
-    }
-
-    const modelData = result.buffer;
-
-    // Cache the model
-    await this.cacheModel(variant, modelData, modelInfo);
-
-    return modelData;
-  }
-
-  /**
-   * Get or download a model
-   */
-  async getModel(variant: 'quantized' | 'standard' | 'full', modelInfo: ModelInfo, onProgress?: (progress: number) => void): Promise<ArrayBuffer> {
-    // Check if model is already cached
-    const isCache = await this.isModelCached(variant, '1.0.0');
-    
-    if (isCache) {
-      console.log(`Using cached model ${variant}`);
-      const cached = await this.getCachedModel(variant);
-      return cached!.data;
-    }
-
-    // Download and cache the model
-    return await this.downloadAndCacheModel(variant, modelInfo, onProgress);
-  }
-
-  /**
-   * Clear old cached models
-   */
-  async clearOldModels(maxAge: number = 7 * 24 * 60 * 60 * 1000): Promise<void> {
-    await this.ensureReady();
-
-    const cutoffTime = Date.now() - maxAge;
-
-    return new Promise((resolve, reject) => {
-      const transaction = this.db!.transaction([STORE_NAME], 'readwrite');
-      const store = transaction.objectStore(STORE_NAME);
-      const index = store.index('timestamp');
-      const range = IDBKeyRange.upperBound(cutoffTime);
-      
-      const request = index.openCursor(range);
-      
-      request.onsuccess = (event) => {
-        const cursor = (event.target as IDBRequest).result;
-        
-        if (cursor) {
-          cursor.delete();
-          cursor.continue();
-        } else {
-          console.log('Old models cleared');
-          resolve();
-        }
-      };
-
-      request.onerror = () => {
-        reject(request.error);
-      };
-    });
-  }
-
-  /**
-   * Get cache storage usage
-   */
-  async getCacheStats(): Promise<{ totalSize: number; modelCount: number; models: string[] }> {
-    await this.ensureReady();
-
-    return new Promise((resolve, reject) => {
-      const transaction = this.db!.transaction([STORE_NAME], 'readonly');
-      const store = transaction.objectStore(STORE_NAME);
-      const request = store.getAll();
-
-      request.onsuccess = () => {
-        const models = request.result as CachedModel[];
-        const totalSize = models.reduce((sum, model) => sum + model.data.byteLength, 0);
-        const modelNames = models.map(m => m.variant);
-
-        resolve({
-          totalSize,
-          modelCount: models.length,
-          models: modelNames
-        });
-      };
-
-      request.onerror = () => {
-        reject(request.error);
-      };
-    });
-  }
-}
\ No newline at end of file
diff --git a/lib/ml/model-config.ts b/lib/ml/model-config.ts
deleted file mode 100644
index d3c08fb..0000000
--- a/lib/ml/model-config.ts
+++ /dev/null
@@ -1,57 +0,0 @@
-import type { DetectionConfig, ModelInfo } from './types';
-
-/**
- * Configuration for different model variants.
- * I've used the models from the original repo and another one from TensorFlow Hub.
- */
-export const MODEL_VARIANTS: Record<'quantized' | 'standard' | 'full', ModelInfo> = {
-  quantized: {
-    variant: 'quantized',
-    url: '/models/model.json',
-    size: 2 * 1024 * 1024, // ~2MB
-    name: 'SSD-MobileNetV2 Quantized',
-    description: 'Fastest, for continuous detection.'
-  },
-  standard: {
-    variant: 'standard',
-    url: '/models/model.json',
-    size: 2 * 1024 * 1024, // Same model, different configs
-    name: 'SSD-MobileNetV2 Standard',
-    description: 'Balanced speed and accuracy.'
-  },
-  full: {
-    variant: 'full',
-    url: '/models/model.json',
-    size: 2 * 1024 * 1024, // Same model, different configs
-    name: 'SSD-MobileNetV2 Full',
-    description: 'Most accurate, for on-demand scan.'
-  }
-};
-
-/**
- * Default detection configuration.
- */
-export const DEFAULT_CONFIG: DetectionConfig = {
-  frameSkip: 6,
-  confidenceThreshold: 0.8, // Default to 80% confidence
-  modelVariant: 'standard',
-  maxDetections: 5, // Match the working implementation (process up to 5 detections)
-  inputSize: [300, 300], // Match the working implementation
-  enableContinuous: true,
-  enableTrigger: true,
-};
-
-/**
- * Class labels for the models.
- * IMPORTANT: This must match the order of the model's output classes.
- */
-export const CLASS_LABELS = ['shoe'];
-
-/**
- * Rules to validate detections and reduce false positives.
- */
-export const VALIDATION_RULES = {
-  minBoundingBoxSize: 30, // Minimum pixel width/height of a bounding box
-  aspectRatioRange: [0.5, 2.0], // Plausible aspect ratio (width / height) for a shoe
-  temporalConsistencyFrames: 3, // Must be detected in N consecutive frames
-};
\ No newline at end of file
diff --git a/lib/ml/types.ts b/lib/ml/types.ts
deleted file mode 100644
index 1bbf078..0000000
--- a/lib/ml/types.ts
+++ /dev/null
@@ -1,84 +0,0 @@
-/**
- * This file contains all the TypeScript interfaces for the ML detection system.
- */
-
-/**
- * Configuration for the detection engine.
- */
-export interface DetectionConfig {
-  frameSkip: number;
-  confidenceThreshold: number;
-  modelVariant: 'quantized' | 'standard' | 'full';
-  maxDetections: number;
-  inputSize: [number, number];
-  enableContinuous: boolean;
-  enableTrigger: boolean;
-}
-
-/**
- * Information about a specific model variant.
- */
-export interface ModelInfo {
-  variant: 'quantized' | 'standard' | 'full';
-  url: string;
-  size: number; // in bytes
-  name: string;
-  description: string;
-}
-
-/**
- * Represents a single detected object.
- */
-export interface DetectionResult {
-  bbox: [number, number, number, number]; // [x, y, width, height]
-  confidence: number;
-  class: string;
-  timestamp: number;
-}
-
-/**
- * Defines the performance tier of the user's device.
- */
-export type DeviceTier = 'low' | 'mid' | 'high';
-
-/**
- * Holds information about the device's capabilities.
- */
-export interface DeviceCapabilities {
-  tier: DeviceTier;
-  hasWebGL: boolean;
-  cpuCores: number;
-  memory: number; // in GB
-}
-
-/**
- * Performance metrics for the detection engine.
- */
-export interface DetectionMetrics {
-  fps: number;
-  inferenceTime: number;
-  memoryUsage: number; // in MB
-}
-
-/**
- * Detection mode type.
- */
-export type DetectionMode = 'continuous' | 'trigger' | 'hybrid';
-
-/**
- * Types for messages sent to and from the detection worker.
- */
-export type WorkerMessage =
-  | { type: 'INITIALIZE' }
-  | { type: 'DETECT'; imageData: ImageData }
-  | { type: 'UPDATE_CONFIG'; config: DetectionConfig }
-  | { type: 'LOAD_MODEL'; variant: 'quantized' | 'standard' | 'full'; modelData: ArrayBuffer }
-  | { type: 'CONFIGURE'; config: DetectionConfig };
-
-export type WorkerResponse =
-  | { type: 'INITIALIZED'; id: string }
-  | { type: 'DETECTION_RESULT'; result: DetectionResult | null; id: string }
-  | { type: 'METRICS_UPDATE'; metrics: Partial<DetectionMetrics>; id: string }
-  | { type: 'ERROR'; error: string; id: string }
-  | { type: 'LOADED_MODEL'; id: string }
-  | { type: 'CONFIGURED'; id: string };
diff --git a/lib/ml/use-detection.ts b/lib/ml/use-detection.ts
deleted file mode 100644
index b59cc03..0000000
--- a/lib/ml/use-detection.ts
+++ /dev/null
@@ -1,307 +0,0 @@
-import { useEffect, useRef, useState, useCallback } from 'react';
-import type { DetectionConfig, DetectionResult, DetectionMetrics } from './types';
-import { DetectionEngine } from './detection-engine';
-
-interface UseDetectionOptions {
-  modelVariant?: 'quantized' | 'standard' | 'full';
-  enableContinuous?: boolean;
-  enableTrigger?: boolean;
-  onDetection?: (detection: DetectionResult | null) => void;
-  onError?: (error: Error) => void;
-}
-
-interface UseDetectionReturn {
-  // State
-  isLoading: boolean;
-  isDetecting: boolean;
-  currentDetection: DetectionResult | null;
-  metrics: DetectionMetrics | null;
-  error: string | null;
-  
-  // Actions
-  initialize: (videoElement: HTMLVideoElement) => Promise<DetectionEngine>;
-  startContinuous: () => void;
-  stopContinuous: () => void;
-  triggerDetection: () => Promise<DetectionResult | null>;
-  updateConfig: (config: Partial<DetectionConfig>) => Promise<void>;
-  setDetectionCallback: (callback: (detection: DetectionResult | null) => void) => void;
-  
-  // Config
-  config: DetectionConfig | null;
-  
-  // Engine reference
-  detectionEngine: DetectionEngine | null;
-}
-
-/**
- * React hook for shoe detection functionality
- */
-export function useDetection(options: UseDetectionOptions = {}): UseDetectionReturn {
-  const {
-    modelVariant = 'standard',
-    enableContinuous = true,
-    enableTrigger = true,
-    onDetection,
-    onError
-  } = options;
-
-  // Store the callback in a ref so it can be updated
-  const detectionCallbackRef = useRef<((detection: DetectionResult | null) => void) | undefined>(onDetection);
-
-  // State
-  const [isLoading, setIsLoading] = useState(false);
-  const [isDetecting, setIsDetecting] = useState(false);
-  const [currentDetection, setCurrentDetection] = useState<DetectionResult | null>(null);
-  const [metrics, setMetrics] = useState<DetectionMetrics | null>(null);
-  const [error, setError] = useState<string | null>(null);
-  const [config, setConfig] = useState<DetectionConfig | null>(null);
-
-  // Refs
-  const detectionEngineRef = useRef<DetectionEngine | null>(null);
-  const videoElementRef = useRef<HTMLVideoElement | null>(null);
-  const initializationPromiseRef = useRef<Promise<void> | null>(null);
-
-  // Initialize detection engine
-  const initialize = useCallback(async (videoElement: HTMLVideoElement): Promise<DetectionEngine> => {
-    console.log('🚀 useDetection.initialize called:', { videoElement: !!videoElement });
-    
-    // Prevent multiple initializations
-    if (initializationPromiseRef.current) {
-      console.log('⚠️ Initialization already in progress, returning existing promise');
-      return initializationPromiseRef.current;
-    }
-
-    setIsLoading(true);
-    setError(null);
-
-    const initPromise = (async () => {
-      try {
-        console.log('🏗️ Creating detection engine...');
-        // Create detection engine
-        const engine = new DetectionEngine();
-        detectionEngineRef.current = engine;
-        videoElementRef.current = videoElement;
-
-        // Set up event listeners
-        engine.onDetection((detection) => {
-          setCurrentDetection(detection);
-          detectionCallbackRef.current?.(detection);
-        });
-
-        engine.onMetrics((newMetrics) => {
-          setMetrics(newMetrics);
-        });
-
-        // Initialize with progress tracking
-        await engine.initialize(modelVariant, (progress) => {
-          // You could add progress state here if needed
-          console.log(`Model loading: ${progress.toFixed(1)}%`);
-        });
-
-        // Get initial configuration
-        const initialConfig = engine.getConfig();
-        setConfig(initialConfig);
-
-        console.log('Detection hook initialized successfully');
-        return engine; // Return engine instance
-
-      } catch (err) {
-        const error = err instanceof Error ? err : new Error('Unknown initialization error');
-        console.error('Detection initialization failed:', error);
-        setError(error.message);
-        onError?.(error);
-        throw error;
-      } finally {
-        setIsLoading(false);
-      }
-    })();
-
-    initializationPromiseRef.current = initPromise;
-    return initPromise;
-  }, [modelVariant, onDetection, onError]);
-
-  // Start continuous detection
-  const startContinuous = useCallback(() => {
-    console.log('🔄 useDetection.startContinuous called:', {
-      hasEngine: !!detectionEngineRef.current,
-      hasVideo: !!videoElementRef.current,
-      enableContinuous
-    });
-    
-    if (!detectionEngineRef.current || !videoElementRef.current) {
-      console.warn('Detection engine or video element not available');
-      return;
-    }
-
-    if (!enableContinuous) {
-      console.warn('Continuous detection is disabled');
-      return;
-    }
-
-    try {
-      console.log('🚀 Starting continuous detection...');
-      detectionEngineRef.current.startContinuousDetection(videoElementRef.current);
-      setIsDetecting(true);
-      setError(null);
-      console.log('✅ Continuous detection started successfully');
-    } catch (err) {
-      const error = err instanceof Error ? err : new Error('Failed to start continuous detection');
-      console.error('❌ Start continuous detection failed:', error);
-      setError(error.message);
-      onError?.(error);
-    }
-  }, [enableContinuous, onError]);
-
-  // Stop continuous detection
-  const stopContinuous = useCallback(() => {
-    if (!detectionEngineRef.current) {
-      return;
-    }
-
-    try {
-      detectionEngineRef.current.stopContinuousDetection();
-      setIsDetecting(false);
-      setCurrentDetection(null);
-    } catch (err) {
-      console.error('Stop continuous detection failed:', err);
-    }
-  }, []);
-
-  // Trigger single detection
-  const triggerDetection = useCallback(async (): Promise<DetectionResult | null> => {
-    if (!detectionEngineRef.current || !videoElementRef.current) {
-      throw new Error('Detection engine or video element not available');
-    }
-
-    if (!enableTrigger) {
-      throw new Error('Trigger detection is disabled');
-    }
-
-    try {
-      setError(null);
-      const detection = await detectionEngineRef.current.triggerDetection(videoElementRef.current);
-      
-      // Update current detection state
-      setCurrentDetection(detection);
-      detectionCallbackRef.current?.(detection);
-      
-      return detection;
-    } catch (err) {
-      const error = err instanceof Error ? err : new Error('Trigger detection failed');
-      console.error('Trigger detection failed:', error);
-      setError(error.message);
-      onError?.(error);
-      throw error;
-    }
-  }, [enableTrigger, onError]);
-
-  // Update configuration
-  const updateConfig = useCallback(async (newConfig: Partial<DetectionConfig>): Promise<void> => {
-    if (!detectionEngineRef.current) {
-      throw new Error('Detection engine not available');
-    }
-
-    try {
-      await detectionEngineRef.current.updateConfig(newConfig);
-      const updatedConfig = detectionEngineRef.current.getConfig();
-      setConfig(updatedConfig);
-      setError(null);
-    } catch (err) {
-      const error = err instanceof Error ? err : new Error('Failed to update configuration');
-      console.error('Update config failed:', error);
-      setError(error.message);
-      onError?.(error);
-      throw error;
-    }
-  }, [onError]);
-
-  // Cleanup on unmount
-  useEffect(() => {
-    return () => {
-      if (detectionEngineRef.current) {
-        detectionEngineRef.current.destroy();
-        detectionEngineRef.current = null;
-      }
-      initializationPromiseRef.current = null;
-      videoElementRef.current = null;
-    };
-  }, []);
-
-  return {
-    // State
-    isLoading,
-    isDetecting,
-    currentDetection,
-    metrics,
-    error,
-    
-    // Actions
-    initialize,
-    startContinuous,
-    stopContinuous,
-    triggerDetection,
-    updateConfig,
-    setDetectionCallback: (callback: (detection: DetectionResult | null) => void) => {
-      detectionCallbackRef.current = callback;
-    },
-    
-    // Config
-    config,
-    
-    // Engine reference
-    detectionEngine: detectionEngineRef.current
-  };
-}
-
-/**
- * Hook for detection metrics monitoring
- */
-export function useDetectionMetrics(detectionEngine: DetectionEngine | null) {
-  const [metrics, setMetrics] = useState<DetectionMetrics | null>(null);
-
-  useEffect(() => {
-    if (!detectionEngine) return;
-
-    detectionEngine.onMetrics(setMetrics);
-
-    // Get initial metrics
-    const initialMetrics = detectionEngine.getMetrics();
-    setMetrics(initialMetrics);
-  }, [detectionEngine]);
-
-  return metrics;
-}
-
-/**
- * Hook for performance monitoring and adjustments
- */
-export function usePerformanceOptimization(detectionEngine: DetectionEngine | null) {
-  const [recommendations, setRecommendations] = useState<string[]>([]);
-
-  useEffect(() => {
-    if (!detectionEngine) return;
-
-    const interval = setInterval(() => {
-      const metrics = detectionEngine.getMetrics();
-      const newRecommendations: string[] = [];
-
-      if (metrics.fps < 15) {
-        newRecommendations.push('Consider increasing frame skip or switching to a lighter model');
-      }
-
-      if (metrics.inferenceTime > 100) {
-        newRecommendations.push('Inference time is high, consider switching to quantized model');
-      }
-
-      if (metrics.memoryUsage > 100) {
-        newRecommendations.push('High memory usage detected');
-      }
-
-      setRecommendations(newRecommendations);
-    }, 5000); // Check every 5 seconds
-
-    return () => clearInterval(interval);
-  }, [detectionEngine]);
-
-  return recommendations;
-}
\ No newline at end of file
diff --git a/lib/sku-identification.ts b/lib/sku-identification.ts
index f5e78b0..5aafb1b 100644
--- a/lib/sku-identification.ts
+++ b/lib/sku-identification.ts
@@ -13,7 +13,7 @@ interface CacheEntry {
  */
 export class SKUIdentificationService {
   private cache = new Map<string, CacheEntry>();
-  private readonly API_ENDPOINT = 'https://pegasus-working-bison.ngrok-free.app/predictfile';
+  private readonly API_ENDPOINT = `${process.env.NEXT_PUBLIC_PYTHON_SERVER_URL}/predictfile`;
   private readonly CACHE_TTL = 60 * 60 * 1000; // 1 hour cache
   private readonly MAX_CACHE_SIZE = 100; // Prevent memory leaks
 
diff --git a/package-lock.json b/package-lock.json
index 9c04773..b3e988e 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -12,9 +12,9 @@
         "@radix-ui/react-dialog": "^1.1.15",
         "@radix-ui/react-select": "^2.2.6",
         "@radix-ui/react-separator": "^1.1.7",
+        "@radix-ui/react-slider": "^1.3.6",
         "@radix-ui/react-slot": "^1.2.3",
-        "@tensorflow/tfjs": "^4.22.0",
-        "@tensorflow/tfjs-backend-webgl": "^4.22.0",
+        "@radix-ui/react-switch": "^1.2.6",
         "class-variance-authority": "^0.7.1",
         "clsx": "^2.1.1",
         "embla-carousel-react": "^8.6.0",
@@ -1415,6 +1415,39 @@
         }
       }
     },
+    "node_modules/@radix-ui/react-slider": {
+      "version": "1.3.6",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-slider/-/react-slider-1.3.6.tgz",
+      "integrity": "sha512-JPYb1GuM1bxfjMRlNLE+BcmBC8onfCi60Blk7OBqi2MLTFdS+8401U4uFjnwkOr49BLmXxLC6JHkvAsx5OJvHw==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/number": "1.1.1",
+        "@radix-ui/primitive": "1.1.3",
+        "@radix-ui/react-collection": "1.1.7",
+        "@radix-ui/react-compose-refs": "1.1.2",
+        "@radix-ui/react-context": "1.1.2",
+        "@radix-ui/react-direction": "1.1.1",
+        "@radix-ui/react-primitive": "2.1.3",
+        "@radix-ui/react-use-controllable-state": "1.2.2",
+        "@radix-ui/react-use-layout-effect": "1.1.1",
+        "@radix-ui/react-use-previous": "1.1.1",
+        "@radix-ui/react-use-size": "1.1.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+        "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/@radix-ui/react-slot": {
       "version": "1.2.3",
       "resolved": "https://registry.npmjs.org/@radix-ui/react-slot/-/react-slot-1.2.3.tgz",
@@ -1433,6 +1466,35 @@
         }
       }
     },
+    "node_modules/@radix-ui/react-switch": {
+      "version": "1.2.6",
+      "resolved": "https://registry.npmjs.org/@radix-ui/react-switch/-/react-switch-1.2.6.tgz",
+      "integrity": "sha512-bByzr1+ep1zk4VubeEVViV592vu2lHE2BZY5OnzehZqOOgogN80+mNtCqPkhn2gklJqOpxWgPoYTSnhBCqpOXQ==",
+      "license": "MIT",
+      "dependencies": {
+        "@radix-ui/primitive": "1.1.3",
+        "@radix-ui/react-compose-refs": "1.1.2",
+        "@radix-ui/react-context": "1.1.2",
+        "@radix-ui/react-primitive": "2.1.3",
+        "@radix-ui/react-use-controllable-state": "1.2.2",
+        "@radix-ui/react-use-previous": "1.1.1",
+        "@radix-ui/react-use-size": "1.1.1"
+      },
+      "peerDependencies": {
+        "@types/react": "*",
+        "@types/react-dom": "*",
+        "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc",
+        "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "@types/react-dom": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/@radix-ui/react-use-callback-ref": {
       "version": "1.1.1",
       "resolved": "https://registry.npmjs.org/@radix-ui/react-use-callback-ref/-/react-use-callback-ref-1.1.1.tgz",
@@ -1903,128 +1965,6 @@
         "tailwindcss": "4.1.12"
       }
     },
-    "node_modules/@tensorflow/tfjs": {
-      "version": "4.22.0",
-      "resolved": "https://registry.npmjs.org/@tensorflow/tfjs/-/tfjs-4.22.0.tgz",
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diff --git a/package.json b/package.json
index fa0ef8f..36a94f7 100644
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diff --git a/public/models/group1-shard1of1.bin b/public/models/group1-shard1of1.bin
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diff --git a/public/models/model.json b/public/models/model.json
deleted file mode 100644
index 5bf4ffc..0000000
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"float32", "quantization": {"min": -1.2574968782125735, "scale": 0.013236809244342878, "dtype": "uint8"}}, {"name": "BoxPredictor_4/ClassPredictor_depthwise/depthwise_weights", "shape": [3, 3, 256, 1], "dtype": "float32", "quantization": {"min": -1.8091270993737614, "scale": 0.017229781898797727, "dtype": "uint8"}}, {"name": "BoxPredictor_4/ClassPredictor_depthwise/depthwise_bn_offset", "shape": [256], "dtype": "float32", "quantization": {"min": -1.2952956358591716, "scale": 0.015061577161153158, "dtype": "uint8"}}, {"name": "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/Conv2D_weights", "shape": [1, 1, 256, 64], "dtype": "float32", "quantization": {"min": -0.4122393015552969, "scale": 0.00288279231856851, "dtype": "uint8"}}, {"name": "FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/Conv2D_bn_offset", "shape": [64], "dtype": "float32", "quantization": {"min": -0.0837679733248318, "scale": 0.005584531554988786, "dtype": "uint8"}}, {"name": "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/depthwise_weights", "shape": [3, 3, 64, 1], "dtype": "float32", "quantization": {"min": -4.725577842488009, "scale": 0.036632386375876035, "dtype": "uint8"}}, {"name": "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/depthwise_bn_offset", "shape": [64], "dtype": "float32", "quantization": {"min": -6.295907862046186, "scale": 0.0374756420359892, "dtype": "uint8"}}, {"name": "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Conv2D_weights", "shape": [1, 1, 64, 128], "dtype": "float32", "quantization": {"min": -2.5655372058644015, "scale": 0.017334710850435146, "dtype": "uint8"}}, {"name": "FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/Conv2D_bn_offset", "shape": [128], "dtype": "float32", "quantization": {"min": -3.7671572544995473, "scale": 0.03883667272679946, "dtype": "uint8"}}, {"name": "BoxPredictor_5/BoxEncodingPredictor_depthwise/depthwise_weights", "shape": [3, 3, 128, 1], "dtype": "float32", "quantization": {"min": -2.0120678415485456, "scale": 0.014475308212579465, "dtype": "uint8"}}, {"name": "BoxPredictor_5/BoxEncodingPredictor_depthwise/depthwise_bn_offset", "shape": [128], "dtype": "float32", "quantization": {"min": -1.9828704226250742, "scale": 0.015613152934055703, "dtype": "uint8"}}, {"name": "BoxPredictor_5/ClassPredictor_depthwise/depthwise_weights", "shape": [3, 3, 128, 1], "dtype": "float32", "quantization": {"min": -2.3186090637655816, "scale": 0.018401659236234776, "dtype": "uint8"}}, {"name": "BoxPredictor_5/ClassPredictor_depthwise/depthwise_bn_offset", "shape": [128], "dtype": "float32", "quantization": {"min": -2.489729236153995, "scale": 0.02127973706114526, "dtype": "uint8"}}, {"name": "Preprocessor/map/while/ResizeImage/stack_1", "shape": [3], "dtype": "int32"}, {"name": "Preprocessor/map/while/ResizeImage/resize/ExpandDims/dim", "shape": [], "dtype": "int32"}, {"name": "Preprocessor/map/while/ResizeImage/stack", "shape": [2], "dtype": "int32"}, {"name": "Preprocessor/map/while/add/y", "shape": [], "dtype": "int32"}, {"name": "ConstantFolding/Postprocessor/BatchMultiClassNonMaxSuppression/ones/packed_const_axis", "shape": [], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_3/x", "shape": [], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Reshape_4/shape", "shape": [2], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_17/x", "shape": [], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/mul_1/x", "shape": [], "dtype": "float32", "quantization": {"min": -1.0, "scale": 1.0, "dtype": "uint8"}}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Slice_4/begin", "shape": [2], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Reshape_1/shape", "shape": [2], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/sub_2/x", "shape": [], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Slice/begin", "shape": [3], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Select/e", "shape": [], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Reshape/shape", "shape": [3], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/non_max_suppression_with_scores/iou_threshold", "shape": [], "dtype": "float32", "quantization": {"min": 0.6000000238418579, "scale": 1.0, "dtype": "uint8"}}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/non_max_suppression_with_scores/score_threshold", "shape": [], "dtype": "float32", "quantization": {"min": 9.99999993922529e-09, "scale": 1.0, "dtype": "uint8"}}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/add_1/y", "shape": [], "dtype": "float32", "quantization": {"min": 0.0, "scale": 1.0, "dtype": "uint8"}}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ones_1/Reshape/shape", "shape": [1], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/zeros_1/Const", "shape": [], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/truediv/x", "shape": [], "dtype": "float32", "quantization": {"min": 1.0, "scale": 1.0, "dtype": "uint8"}}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/strided_slice/stack_1", "shape": [1], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ChangeCoordinateFrame/strided_slice_2/stack_1", "shape": [1], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/zeros_1/shape_as_tensor", "shape": [1], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/strided_slice_2/stack", "shape": [1], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/strided_slice_2/stack_1", "shape": [1], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/range_1/delta", "shape": [], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/zeros_1", "shape": [2], "dtype": "int32"}, {"name": "Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/zeros_11", "shape": [1], "dtype": "int32"}, {"name": "ConstantFolding/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Slice_5/size_const_axis", "shape": [], "dtype": "int32"}, {"name": "ConstantFolding/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/Slice_1/size_const_axis", "shape": [], "dtype": "int32"}, {"name": "ConstantFolding/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/stack_5/values_1_const_axis", "shape": [], "dtype": "int32"}, {"name": "ConstantFolding/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/stack_1/values_1_const_axis", "shape": [], "dtype": "int32"}]}]}
\ No newline at end of file
diff --git a/workers/detection-worker.ts b/workers/detection-worker.ts
deleted file mode 100644
index 7bf39fa..0000000
--- a/workers/detection-worker.ts
+++ /dev/null
@@ -1,195 +0,0 @@
-import type { DetectionConfig, DetectionResult, WorkerMessage, WorkerResponse } from '../lib/ml/types';
-import { InferencePipeline } from '../lib/ml/inference-pipeline';
-
-declare const self: DedicatedWorkerGlobalScope;
-
-let tfGlobal: any = null;
-let model: any = null;
-let config: DetectionConfig | null = null;
-let pipeline: InferencePipeline | null = null;
-
-async function initialize(id: string) {
-  console.log('Initializing worker...');
-
-  tfGlobal = await import('@tensorflow/tfjs');
-  await import('@tensorflow/tfjs-backend-webgl');
-
-  await tfGlobal.setBackend('webgl');
-  await tfGlobal.ready();
-  console.log('TensorFlow.js backend set to:', tfGlobal.getBackend());
-  
-  pipeline = new InferencePipeline();
-  
-  self.postMessage({ type: 'INITIALIZED', id });
-}
-
-async function loadModelWorker(variant: 'quantized' | 'standard' | 'full', modelData: ArrayBuffer, id: string) {
-  console.log(`Worker: Loading model ${variant}...`);
-  try {
-    if (!tfGlobal) {
-      throw new Error('TensorFlow.js not initialized');
-    }
-    
-    // Use local model files from public folder with full URL for worker context
-    const baseUrl = self.location.origin;
-    const modelUrls = {
-      'quantized': `${baseUrl}/models/model.json`,
-      'standard': `${baseUrl}/models/model.json`, 
-      'full': `${baseUrl}/models/model.json`
-    };
-    
-    console.log(`Worker: Loading REAL model from ${modelUrls[variant]}`);
-    
-    // Load the real model like in the working GitHub implementation
-    model = await tfGlobal.loadGraphModel(modelUrls[variant]);
-    console.log('Worker: Real model loaded successfully', model);
-    
-    // Warm up the model like the working implementation
-    if (model && config) {
-      console.log('Worker: Warming up model with input size:', config.inputSize);
-      const dummyFloat = tfGlobal.zeros([1, ...config.inputSize, 3]);
-      const dummyInput = tfGlobal.cast(dummyFloat, 'int32');
-      dummyFloat.dispose();
-      
-      const result = await model.executeAsync(
-        { image_tensor: dummyInput },
-        ['detection_boxes', 'num_detections', 'detection_classes', 'detection_scores']
-      );
-      console.log('Worker: Warmup result:', result);
-      dummyInput.dispose();
-      if (Array.isArray(result)) {
-        result.forEach(t => t.dispose());
-      } else if (result) {
-        result.dispose();
-      }
-      console.log('Worker: Model warmed up successfully.');
-    }
-    self.postMessage({ type: 'LOADED_MODEL', id });
-  } catch (error) {
-    console.error(`Worker: Failed to load model ${variant}:`, error);
-    self.postMessage({ type: 'ERROR', error: error instanceof Error ? error.message : 'Unknown error during model loading', id });
-  }
-}
-
-async function configureWorker(newConfig: DetectionConfig, id: string) {
-  console.log('Worker: Configuring...');
-  config = newConfig;
-  self.postMessage({ type: 'CONFIGURED', id });
-}
-
-async function detect(imageData: ImageData, id: string) {
-  console.log('Worker: detect function called.');
-  if (!model || !config || !pipeline) {
-    self.postMessage({ type: 'ERROR', error: 'Worker not initialized or configured.', id });
-    return;
-  }
-
-  const tensor = tfGlobal.tidy(() => {
-    // Convert ImageData to tensor in Web Worker context
-    const { data, width, height } = imageData;
-    
-    // In Web Worker, we need to create tensor manually from the pixel data
-    // Convert RGBA to RGB by dropping every 4th value (alpha channel)
-    const rgbData = new Uint8Array(width * height * 3);
-    for (let i = 0; i < width * height; i++) {
-      rgbData[i * 3] = data[i * 4];     // R
-      rgbData[i * 3 + 1] = data[i * 4 + 1]; // G  
-      rgbData[i * 3 + 2] = data[i * 4 + 2]; // B
-      // Skip alpha channel (data[i * 4 + 3])
-    }
-    
-    // Create tensor from RGB data
-    const img = tfGlobal.tensor3d(rgbData, [height, width, 3]);
-    
-    // Resize to model input size (300x300) - this returns float32
-    const resized = tfGlobal.image.resizeBilinear(img, config!.inputSize);
-    
-    // Cast to int32 as required by the model
-    const int32Tensor = tfGlobal.cast(resized, 'int32');
-    
-    return int32Tensor.expandDims(0); // Now properly int32
-  });
-
-  try {
-    console.log('Worker: About to execute model with tensor shape:', tensor.shape, 'dtype:', tensor.dtype);
-    // Use the same input format as the working implementation
-    const result = await model.executeAsync(
-      { image_tensor: tensor }, 
-      ['detection_boxes', 'num_detections', 'detection_classes', 'detection_scores']
-    );
-    tensor.dispose();
-
-    // Reduced logging for performance
-    if (process.env.NODE_ENV === 'development') {
-      console.log('Worker: Model execution completed, processing results...');
-    }
-
-    if (!result || !Array.isArray(result) || result.length < 4) {
-      console.error('Worker: Invalid model output:', result);
-      self.postMessage({ type: 'DETECTION_RESULT', result: null, id });
-      return;
-    }
-
-    // Match the working implementation: [boxes, num_detections, classes, scores]
-    const [boxes, numDetections, classes, scores] = result;
-    console.log('Worker: Extracting data from tensors...');
-    
-    const boxesData = await boxes.data();
-    const scoresData = await scores.data();
-    const classesData = await classes.data();
-    
-    // Only log detailed outputs when debugging specific issues
-    const maxScore = Math.max(...Array.from(scoresData.slice(0, 10)));
-    const scoresAbove30 = Array.from(scoresData.slice(0, 10)).filter(s => s > 0.3).length;
-    
-    if (process.env.NODE_ENV === 'development' && (maxScore > 0.3 || scoresAbove30 > 0)) {
-      console.log('Worker: Potential detection found:', { maxScore, scoresAbove30 });
-    }
-
-    result.forEach(t => t.dispose());
-
-    const detectionResult = pipeline.process(
-      boxesData as number[],
-      scoresData as number[],
-      classesData as number[],
-      config.confidenceThreshold
-    );
-
-    console.log('Worker detectionResult:', detectionResult);
-
-    self.postMessage({ type: 'DETECTION_RESULT', result: detectionResult, id });
-  } catch (error) {
-    tensor.dispose();
-    console.error('Worker: Detection execution failed:', error);
-    console.error('Worker: Error stack:', error.stack);
-    self.postMessage({ type: 'ERROR', error: error instanceof Error ? error.message : 'Detection execution failed', id });
-  }
-}
-
-(async () => {
-  self.onmessage = async (event: MessageEvent<WorkerMessage>) => {
-    try {
-      switch (event.data.type) {
-        case 'INITIALIZE':
-          await initialize(event.data.id);
-          break;
-        case 'LOAD_MODEL':
-          await loadModelWorker(event.data.variant, event.data.modelData, event.data.id);
-          break;
-        case 'CONFIGURE':
-          await configureWorker(event.data.config, event.data.id);
-          break;
-        case 'DETECT':
-          await detect(event.data.imageData, event.data.id);
-          break;
-        case 'UPDATE_CONFIG':
-          await configureWorker(event.data.config, event.data.id);
-          break;
-        default:
-          throw new Error(`Unknown message type: ${(event.data as any).type}`);
-      }
-    } catch (error) {
-      self.postMessage({ type: 'ERROR', error: error instanceof Error ? error.message : 'Unknown error in worker', id: event.data.id });
-    }
-  };
-})();
\ No newline at end of file