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23 lines
732 B
Python
23 lines
732 B
Python
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import gradio as gr
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import torch
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import requests
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from torchvision import transforms
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model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
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response = requests.get("https://git.io/JJkYN")
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labels = response.text.split("\n")
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def predict(inp):
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inp = transforms.ToTensor()(inp).unsqueeze(0)
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with torch.no_grad():
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prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
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confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
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return confidences
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demo = gr.Interface(fn=predict,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Label(num_top_classes=3),
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examples=[["cheetah.jpg"]],
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)
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demo.launch()
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