#!/usr/bin/env python3

import sys

from ultralytics import YOLO

image_path = sys.argv[1]

# Load the small pretrained YOLOv8 object-detection model.
model = YOLO("yolov8n.pt")

# Run object detection on a test image.
results = model(image_path)

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")