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| import gradio as gr | |
| import torch | |
| ############### | |
| def yolov7_inference( | |
| image: gr.inputs.Image = None, | |
| conf_threshold: gr.inputs.Slider = 0.50, | |
| ): | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| path = 'y7-prdef.pt' | |
| model = torch.hub.load("WongKinYiu/yolov7","custom",f"{path}") | |
| model.conf = conf_threshold | |
| results = model([image], size=640) | |
| return results.render()[0] | |
| inputs = [ | |
| gr.inputs.Image(type="pil", label="Input Image"), | |
| gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.50, step=0.05, label="Confidence Threshold"), | |
| ] | |
| demo_app = gr.Interface( | |
| fn=yolov7_inference, | |
| inputs=inputs, | |
| outputs=gr.outputs.Image(type="filepath", label="Output Image"), | |
| title="Detection of jar lid defects (Yolov7)", | |
| description = "App detecting jar lids that are damaged (deformation, hole, scratch) versus intact. | Ruthger Righart ", | |
| article = "<p style='text-align: center'><a href='https://www.rrighart.com' target='_blank'>Webpage</a></p> <p style='text-align: center'><a href='https://www.kaggle.com/code/rrighart/detection-of-product-defects-using-yolov7' target='_blank'>Kaggle</a></p>", | |
| examples=[['t1.JPG', 0.50]], | |
| cache_examples=True, | |
| ) | |
| demo_app.launch(debug=False, enable_queue=True) | |