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import torch
import requests
from PIL import Image
from torchvision import transforms
import gradio as gr
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
# Download human-readable labels for ImageNet.
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=["lion.jpg", "cheetah.jpg"])
demo.launch(
# show_api=True, share=True
)
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