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294240b f45de0c 294240b 7de7329 294240b 7de7329 57e0d85 294240b 483e2af 294240b 7de7329 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | import gradio as gr
import torch
import requests
from torchvision import transforms # type: ignore
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def predict(inp):
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
return confidences
demo = gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
examples=[["cheetah.jpg"]],
api_name="predict"
)
demo.launch()
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