app.py

import torch
import gradio as gr
import requests
from torchvision import transforms

# Load model
model = torch.hub.load('pytorch/vision:v0.23.0', 'resnet34', 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=["lion.jpg", "cheetah.jpg", "cat.avif", "hot-dog.avif", "llama.jpg", "medieval_knight.jpg"],
    css=".footer{display:none !important}"
).launch()