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()