Change hardcoded test image to no-memory image to demonstrate dynamic messaging

✅ Changed Test Image:
- Updated HARDCODED_IMAGE_PATH from 'training/memory/out1.png' to 'training/no_memory/out1.png'
- Now uses an image without memory modules for testing
- Demonstrates the dynamic '❌ No memory modules' message

✅ Dynamic Behavior:
- Run All Tests will now show '❌ No memory modules' instead of '✅ Found 2 memory modules'
- Same JavaScript logic, different result based on actual detection
- No frontend changes needed - purely backend image swap

✅ Testing Both Scenarios:
- Hardcoded test: Shows '❌ No memory modules' (no memory image)
- Custom upload: Shows '✅ Found X memory modules' or '❌ No memory modules' based on uploaded image
- Perfect demonstration of dynamic messaging system

This change allows you to see the '❌ No memory modules' message in Run All Tests results.

main.py

@@ -37,7 +37,7 @@ MODEL_PATH = 'runs/detect/memory_module_detection/weights/best.pt'
 detector = MemoryModuleDetector(MODEL_PATH)
 
 # Hardcoded test image path
-HARDCODED_IMAGE_PATH = 'training/memory/out1.png'
+HARDCODED_IMAGE_PATH = 'training/no_memory/out1.png'
 
 def allowed_file(filename):
     """Check if file extension is allowed."""
@@ -199,10 +199,10 @@ def detect_memory_modules():
 def detect_hardcoded_image():
     """
     Process hardcoded test image for memory module detection.
-    
+
     Optional query parameters:
-    - confidence: confidence threshold (default: 0.5)
+    - confidence: confidence threshold (default: 0.8)
-    
+
     Returns:
     - JSON with detections and annotated image (base64)
     """
@@ -213,26 +213,26 @@ def detect_hardcoded_image():
                 'error': 'Model not loaded. Please train the model first.',
                 'success': False
             }), 500
-        
+
         # Check if hardcoded image exists
         if not os.path.exists(HARDCODED_IMAGE_PATH):
             return jsonify({
                 'error': f'Hardcoded test image not found at {HARDCODED_IMAGE_PATH}',
                 'success': False
             }), 404
-        
+
         # Get confidence threshold from query parameters (default 80%)
         conf_threshold = float(request.args.get('confidence', 0.8))
-        
+
         # Run detection
         detections, annotated_image = detector.detect(
-            HARDCODED_IMAGE_PATH, 
+            HARDCODED_IMAGE_PATH,
             conf_threshold=conf_threshold
         )
-        
+
         # Convert annotated image to base64
         annotated_base64 = image_to_base64(annotated_image)
-        
+
         # Prepare response
         response_data = {
             'success': True,
@@ -242,9 +242,9 @@ def detect_hardcoded_image():
             'confidence_threshold': conf_threshold,
             'test_image_path': HARDCODED_IMAGE_PATH
         }
-        
+
         logger.info(f"Processed hardcoded image: found {len(detections)} memory modules")
-        
+
         return jsonify(response_data)
     
     except Exception as e: