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	import torch
	from PIL import Image
	from torchvision import transforms
	import gradio as gr
	import os

	os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")

	# get list of models
	torch.hub.list('zhanghang1989/ResNeSt', force_reload=True)
	# load pretrained models, using ResNeSt-50 as an example
	model = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True)
	model.eval()
	# Download an example image from the pytorch website
	torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
	# sample execution (requires torchvision)



	def inference(input_image):
	preprocess = transforms.Compose([
	transforms.Resize(256),
	transforms.CenterCrop(224),
	transforms.ToTensor(),
	transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
	])
	input_tensor = preprocess(input_image)
	input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

	# move the input and model to GPU for speed if available
	if torch.cuda.is_available():
	input_batch = input_batch.to('cuda')
	model.to('cuda')

	with torch.no_grad():
	output = model(input_batch)
	# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
	probabilities = torch.nn.functional.softmax(output[0], dim=0)

	# Read the categories
	with open("imagenet_classes.txt", "r") as f:
	categories = [s.strip() for s in f.readlines()]
	# Show top categories per image
	top5_prob, top5_catid = torch.topk(probabilities, 5)
	result = {}
	for i in range(top5_prob.size(0)):
	result[categories[top5_catid[i]]] = top5_prob[i].item()
	return result

	inputs = gr.inputs.Image(type='pil')
	outputs = gr.outputs.Label(type="confidences",num_top_classes=5)

	title = "RESNEST"
	description = "Gradio demo for RESNEST, A new ResNet variant. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
	article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.08955'>ResNeSt: Split-Attention Networks</a> \| <a href='https://github.com/zhanghang1989/ResNeSt'>Github Repo</a></p>"

	examples = [
	['dog.jpg']
	]
	gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()