import gradio as gr import torch from PIL import Image # Images #torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg') torch.hub.download_url_to_file('https://github.com/josuehu/deteccion-somnolencia-distracciones/Distracciones/we.jpg', 'we.jpg') # Model model = torch.hub.load('Josuehu/Deteccion-somnolencia-distracciones', 'best') # force_reload=True to update def yolo(im, size=640): g = (size / max(im.size)) # gain im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize results = model(im) # inference results.render() # updates results.imgs with boxes and labels return Image.fromarray(results.imgs[0]) inputs = gr.inputs.Image(type='pil', label="Original Image") outputs = gr.outputs.Image(type="pil", label="Output Image") title = "YOLOv5" description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use." article = "

YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. Source code |iOS App | PyTorch Hub

" examples = [['zidane.jpg'], ['bus.jpg']] gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch( debug=True)