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image-recognition-demo / app.py

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	import os
	import torch
	import gradio as gr

	from PIL import Image
	from torchvision import transforms


	"""
	Built following:
	https://huggingface.co/spaces/pytorch/ResNet/tree/main
	https://www.gradio.app/image_classification_in_pytorch/
	"""

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

	# Load PyTorch model
	model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', 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")

	# Inference!
	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

	# Define ins outs placeholders
	inputs = gr.inputs.Image(type='pil')
	outputs = gr.outputs.Label(type="confidences",num_top_classes=5)

	# Define style
	title = "Image Recognition Demo"
	description = "This is a prototype application which demonstrates how artifical intelligence based systems can recognize what object(s) is present in an image. This fundamental task in computer vision known as `Image Classification` has applications stretching from autonomous vehicles to medical imaging. To use it, simply upload your image, or click one of the examples images to load them, which I took at <a href='https://espacepourlavie.ca/en/biodome' target='_blank'>Montréal Biodôme</a>! Read more at the links below."
	article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1512.03385' target='_blank'>Deep Residual Learning for Image Recognition</a> \| <a href='https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py' target='_blank'>Github Repo</a></p>"

	# Run inference
	gr.Interface(inference,
	inputs,
	outputs,
	examples=["example1.jpg", "example2.jpg"],
	title=title,
	description=description,
	article=article,
	analytics_enabled=False).launch()