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import torch
import requests
import gradio as gr
from PIL import Image
from torchvision import transforms
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 get_predictions(inp):
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
predictions = torch.nn.functional.softmax(model(inp)[0], dim = 0)
conf = {labels[i]: float(predictions[i]) for i in range(1000)}
return conf
iclass = gr.Interface(fn = get_predictions,
inputs = gr.Image(type = "pil"),
outputs = gr.Label(num_top_classes = 2)
)
iclass.launch()
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