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| import gradio as gr | |
| from ultralytics import YOLO | |
| from PIL import Image | |
| # Load the trained YOLOv8 model | |
| model = YOLO(r"C:\Users\yahya\Downloads\best.pt") | |
| # Define the prediction function | |
| def predict(image): | |
| results = model(image) # Run YOLOv8 model on the uploaded image | |
| results_img = results[0].plot() # Get image with bounding boxes | |
| return Image.fromarray(results_img) | |
| # Create Gradio interface | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Image(type="pil"), | |
| title="Helmet Detection with YOLOv8", | |
| description="Upload an image to detect helmets." | |
| ) | |
| # Launch Gradio app | |
| interface.launch(share=True) | |