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test.py

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+from ultralytics import YOLO
+
+# Load the small pretrained YOLOv8 object-detection model.
+model = YOLO("yolov8n.pt")
+
+# Run object detection on a test image.
+results = model("bus4.jpg")
+
+has_bus = False
+max_confidence = 0.0
+
+for result in results:
+    class_names = result.names
+    detected_classes = result.boxes.cls.tolist()
+    confidences = result.boxes.conf.tolist()
+    
+    # Check each detected object and its matching confidence score
+    for cls_id, conf in zip(detected_classes, confidences):
+        if class_names[int(cls_id)] == "bus":
+            has_bus = True
+            # Keep track of the highest confidence if there are multiple buses
+            if conf > max_confidence:
+                max_confidence = conf
+
+# Output the Yes/No answer and the confidence percentage
+if has_bus:
+    print("Yes")
+    print(f"Confidence: {max_confidence * 100:.1f}%")
+else:
+    print("No")