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import gradio as gr
import torch
from torchvision import transforms
from PIL import Image
import requests
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
gr.Interface(fn=predict,
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
examples=["imgs/lion.jpg",
"imgs/car.jpg",
"imgs/cheetah.jpg",
"imgs/banana.jpg",
"imgs/bus.jpg",
"imgs/parfum.jpg",
"imgs/alligator.jpg",
"imgs/arc.jpg"]).launch()
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