app.py

import os
os.system("pip install gradio==2.7.5.2")
from torchvision import transforms
import torch
import urllib
from PIL import Image
import gradio as gr
import torch

# Images
torch.hub.download_url_to_file('https://static.scientificamerican.com/sciam/cache/file/7A715AD8-449D-4B5A-ABA2C5D92D9B5A21_source.png', 'bird.png')

model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True,
                       **{'topN': 6, 'device':'cpu', 'num_classes': 200})

transform_test = transforms.Compose([
    transforms.Resize((600, 600), Image.BILINEAR),
    transforms.CenterCrop((448, 448)),
    # transforms.RandomHorizontalFlip(),  # only if train
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])


model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})

def birds(img):
  scaled_img = transform_test(img)
  torch_images = scaled_img.unsqueeze(0)

  with torch.no_grad():
      top_n_coordinates, concat_out, raw_logits, concat_logits, part_logits, top_n_index, top_n_prob = model(torch_images)

      _, predict = torch.max(concat_logits, 1)
      pred_id = predict.item()
      return model.bird_classes[pred_id].split('.')[1]

inputs = gr.inputs.Image(type='pil', label="Original Image")
outputs = gr.outputs.Textbox(label="bird class")

title = "ntsnet"
description = "demo for ntsnet to classify birds. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='http://artelab.dista.uninsubria.it/res/research/papers/2019/2019-IVCNZ-Nawaz-Birds.pdf'>Are These Birds Similar: Learning Branched Networks for Fine-grained Representations</a> | <a href='https://github.com/nicolalandro/ntsnet-cub200'>Github Repo</a></p>"

examples = [
            ['bird.png']
]
gr.Interface(birds, inputs, outputs, title=title, description=description, 
             article=article, examples=examples, analytics_enabled=False).launch(cache_examples=True,enable_queue=True)