start

src/main/java/com/auction/scraper/TroostwijkScraper.java

@@ -354,6 +354,8 @@ public class TroostwijkScraper {
      * discovers Dutch auctions, scrapes lots, and begins monitoring.
      */
     public static void main(String[] args) throws Exception {
+        System.out.println("=== Troostwijk Auction Scraper ===\n");
+
         // Configuration parameters (replace with your own values)
         String databaseFile = "troostwijk.db";
 
@@ -366,27 +368,34 @@ public class TroostwijkScraper {
         // Example: "smtp:your.email@gmail.com:abcd1234efgh5678:recipient@example.com"
         // Get app password: Google Account > Security > 2-Step Verification > App passwords
 
-        String yoloCfg = "models/yolov4.cfg";      // path to YOLO config file
-        String yoloWeights = "models/yolov4.weights"; // path to YOLO weights file
-        String yoloClasses = "models/coco.names";   // list of class names
+        // YOLO model paths (optional - scraper works without object detection)
+        String yoloCfg = "models/yolov4.cfg";
+        String yoloWeights = "models/yolov4.weights";
+        String yoloClasses = "models/coco.names";
 
         // Load native OpenCV library
         System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
 
+        System.out.println("Initializing scraper...");
         TroostwijkScraper scraper = new TroostwijkScraper(databaseFile, notificationConfig, "",
                 yoloCfg, yoloWeights, yoloClasses);
 
         // Step 1: Discover auctions in NL
+        System.out.println("\n[1/3] Discovering Dutch auctions...");
         List<Integer> auctions = scraper.discoverDutchAuctions();
-        System.out.println("Found auctions: " + auctions);
+        System.out.println("✓ Found " + auctions.size() + " auctions: " + auctions);
 
         // Step 2: Fetch lots for each auction
+        System.out.println("\n[2/3] Fetching lot details...");
         for (int saleId : auctions) {
+            System.out.println("  Processing sale " + saleId + "...");
             scraper.fetchLotsForSale(saleId);
         }
 
         // Step 3: Start monitoring bids and closures
+        System.out.println("\n[3/3] Starting monitoring service...");
         scraper.scheduleMonitoring();
+        System.out.println("✓ Monitoring active. Press Ctrl+C to stop.\n");
     }
 
     // ----------------------------------------------------------------------
@@ -710,23 +719,53 @@ public class TroostwijkScraper {
     }
 
     /**
-     * Service for performing object detection on images using OpenCV’s DNN
+     * Service for performing object detection on images using OpenCV's DNN
      * module.  The DNN module can load pre‑trained models from several
      * frameworks (Darknet, TensorFlow, ONNX, etc.)【784097309529506†L209-L233】.  Here
      * we load a YOLO model (Darknet) by specifying the configuration and
      * weights files.  For each image we run a forward pass and return a
      * list of detected class labels.
+     *
+     * If model files are not found, the service operates in disabled mode
+     * and returns empty lists.
      */
     static class ObjectDetectionService {
         private final Net net;
         private final List<String> classNames;
+        private final boolean enabled;
+
         ObjectDetectionService(String cfgPath, String weightsPath, String classNamesPath) throws IOException {
-            // Load network
-            this.net = Dnn.readNetFromDarknet(cfgPath, weightsPath);
-            this.net.setPreferableBackend(DNN_BACKEND_OPENCV);
-            this.net.setPreferableTarget(DNN_TARGET_CPU);
-            // Load class names (one per line)
-            this.classNames = Files.readAllLines(Paths.get(classNamesPath));
+            // Check if model files exist
+            Path cfgFile = Paths.get(cfgPath);
+            Path weightsFile = Paths.get(weightsPath);
+            Path classNamesFile = Paths.get(classNamesPath);
+
+            if (!Files.exists(cfgFile) || !Files.exists(weightsFile) || !Files.exists(classNamesFile)) {
+                System.out.println("⚠️  Object detection disabled: YOLO model files not found");
+                System.out.println("   Expected files:");
+                System.out.println("   - " + cfgPath);
+                System.out.println("   - " + weightsPath);
+                System.out.println("   - " + classNamesPath);
+                System.out.println("   Scraper will continue without image analysis.");
+                this.enabled = false;
+                this.net = null;
+                this.classNames = new ArrayList<>();
+                return;
+            }
+
+            try {
+                // Load network
+                this.net = Dnn.readNetFromDarknet(cfgPath, weightsPath);
+                this.net.setPreferableBackend(DNN_BACKEND_OPENCV);
+                this.net.setPreferableTarget(DNN_TARGET_CPU);
+                // Load class names (one per line)
+                this.classNames = Files.readAllLines(classNamesFile);
+                this.enabled = true;
+                System.out.println("✓ Object detection enabled with YOLO");
+            } catch (Exception e) {
+                System.err.println("⚠️  Object detection disabled: " + e.getMessage());
+                throw new IOException("Failed to initialize object detection", e);
+            }
         }
         /**
          * Detects objects in the given image file and returns a list of
@@ -736,9 +775,13 @@ public class TroostwijkScraper {
          * post‑processing【784097309529506†L324-L344】.
          *
          * @param imagePath absolute path to the image
-         * @return list of detected class names
+         * @return list of detected class names (empty if detection disabled)
          */
         List<String> detectObjects(String imagePath) {
+            if (!enabled) {
+                return new ArrayList<>();
+            }
+
             List<String> labels = new ArrayList<>();
             Mat image = Imgcodecs.imread(imagePath);
             if (image.empty()) return labels;