app.py

import torch

model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval() # evaluation

import requests # 用於發送 HTTP 請求
from PIL import Image
from torchvision import transforms

# 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) # Resnet18輸入格式:(batch_size=1, C, H, W)，batch_size=1表示一次可以處理單張圖片
  with torch.no_grad(): # no_grad:不需要計算gradient
    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
  return confidences

import gradio as gr

gr.Interface(fn=predict,
             inputs=gr.Image(type="pil"),
             outputs=gr.Label(num_top_classes=3),
             examples=["./images/lion.jpg", "./images/cheetah.jpg"]).launch(auth=("admin", "pass1234"))