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| import gradio as gr | |
| import torch | |
| from ultralytics import YOLO | |
| torch.hub.download_url_to_file( | |
| 'http://www.imesclub.org/images/stories/arabidentity.jpeg', 'one.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://lp-cms-production.imgix.net/2023-08/GettyImages-1224548888-16.9.jpg', 'two.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://s.wsj.net/public/resources/images/OB-EQ974_diwali_G_20091014112400.jpg', 'three.jpg') | |
| def handle_classify(image=None): | |
| """This function performs YOLOv8 object detection on the given image. | |
| Args: | |
| image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None. | |
| """ | |
| if not image: | |
| return "No image found" | |
| model_path = "racist2.0.pt" | |
| model = YOLO(model_path) | |
| results = model(image) | |
| result = results[0] | |
| top5 = [[result.names[class_index], result.probs.top5conf.tolist()[rank]] | |
| for class_index, rank in zip(result.probs.top5, range(5))] | |
| print(top5) | |
| return "\n".join(["\t".join(row) for row in top5]) | |
| inputs = [ | |
| gr.Image(type="filepath", label="Input Image"), | |
| ] | |
| outputs = gr.Textbox() | |
| title = "Racist model v2" | |
| examples = [['one.jpg'], | |
| ['two.jpg'], | |
| ['three.jpg']] | |
| yolo_app = gr.Interface( | |
| fn=handle_classify, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| examples=examples, | |
| cache_examples=True, | |
| ) | |
| # Launch the Gradio interface in debug mode with queue enabled | |
| yolo_app.launch(debug=True, enable_queue=True) |