Spaces:
Running
Running
| import gradio as gr | |
| import os | |
| import torch | |
| def update_value(val): | |
| return f'Value is set to {val}' | |
| def yolov7_inference( | |
| image: gr.Image = None, | |
| conf_threshold: gr.Slider = 0.20, | |
| ): | |
| 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] | |
| demo = gr.Blocks() | |
| with demo: | |
| dd = gr.Interface( | |
| yolov7_inference, | |
| gr.Image(type="pil"), | |
| "image", | |
| title="The detection of jar lid defects using Yolov7", | |
| examples=[ | |
| os.path.join(os.path.dirname(__file__), "example1.JPG"), | |
| os.path.join(os.path.dirname(__file__), "example2.JPG"), | |
| os.path.join(os.path.dirname(__file__), "example3.JPG"), | |
| ], | |
| ) | |
| md = gr.Markdown("Confidence Threshold") | |
| conf_threshold = gr.Slider(minimum=0, maximum=1, step=0.1, label='Value') | |
| #inp = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.05, label="Value"), | |
| #inp.change(fn=yolov7_inference, inputs=inp, outputs=md) | |
| conf_threshold.change(fn=update_value, inputs=conf_threshold, outputs=md) | |
| demo.launch() | |