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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 = "<p style='text-align: center'>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. <a href='https://github.com/ultralytics/yolov5'>Source code</a> |<a href='https://apps.apple.com/app/id1452689527'>iOS App</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
examples = [['zidane.jpg'], ['bus.jpg']]
gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(
debug=True)
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