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28 lines
795 B
Python
28 lines
795 B
Python
import requests
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import torch
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from PIL import Image
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from torchvision import transforms # type: ignore
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import gradio as gr
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model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
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# Download human-readable labels for ImageNet.
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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 = Image.fromarray(inp.astype("uint8"), "RGB")
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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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return {labels[i]: float(prediction[i]) for i in range(1000)}
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inputs = gr.Image()
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outputs = gr.Label(num_top_classes=3)
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demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)
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if __name__ == "__main__":
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demo.launch()
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