Fix: parsing results

app.py

@@ -17,9 +17,9 @@ COLORS = np.random.uniform(0, 255, size=(80, 3))
 
 # Function to parse YOLO detections
 def parse_detections(results, yolo_version):
+    boxes, colors, names = [], [], []
     if yolo_version == "yolov5":
         detections = results.pandas().xyxy[0].to_dict()
-        boxes, colors, names = [], [], []
         for i in range(len(detections["xmin"])):
             confidence = detections["confidence"][i]
             if confidence < 0.2:
@@ -35,7 +35,6 @@ def parse_detections(results, yolo_version):
         confidences = results[0].boxes.conf  # Confidence scores
         class_ids = results[0].boxes.cls  # Class IDs
         names.append(results[0].names)  # Class names (from model)
-        colors = []
         # Append predefined color based on category (class ID)
         for class_id in class_ids:
             # Map class ID to a color from the COLORS list (make sure you have enough colors)