deploying to spaces

app.py

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+import torch
+import torch.nn as nn
+import numpy as np
+from torchvision import models, transforms
+import time
+import os
+import copy
+import pickle
+from PIL import Image
+import datetime
+import gdown
+import urllib.request
+import gradio as gr
+import markdown
+
+url = 'https://drive.google.com/file/d/1qKiyp4r8SqUtz2ZWk3E6oZhyhl6t8lyG/view?usp=sharing'
+path_class_names = "./class_names_restnet_leeds_butterfly.pkl"
+gdown.download(url, path_class_names, quiet=False)
+
+url = 'https://drive.google.com/file/d/1Ep2YWU4M-yVkF7AFP3aD1sVhuriIDzFe/view?usp=sharing'
+path_model = "./model_state_restnet_leeds_butterfly.pth"
+gdown.download(url, path_model, quiet=False)
+
+url = "https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/Red_postman_butterfly_%28Heliconius_erato%29.jpg/1599px-Red_postman_butterfly_%28Heliconius_erato%29.jpg"
+path_input = "./h_erato.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
+
+url = "https://www.ukbutterflies.co.uk/photo_album/source/664a285ca7b4379147d598ea5127228f.jpg"
+path_input = "./d_plexippus.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
+
+# normalisation
+data_transforms_test = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+class_names = pickle.load(open(path_class_names, "rb"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+model_ft = models.resnet18(pretrained=True)
+num_ftrs = model_ft.fc.in_features
+model_ft.fc = nn.Linear(num_ftrs, len(class_names))
+model_ft = model_ft.to(device)
+model_ft.load_state_dict(copy.deepcopy(torch.load(path_model,device)))
+
+# Proper labeling
+id_to_name = {
+    '001_Danaus Plexippus': 'Danaus plexippus - Monarch',
+    '002_Heliconius Charitonius': 'Heliconius charitonius - Zebra Longwing',
+    '003_Heliconius Erato': 'Heliconius erato - Red Postman',
+    '004_Junonia Coenia': 'Junonia coenia - Common Buckeye',
+    '005_Lycaena Phlaeas': 'Lycaena phlaeas - Small Copper',
+    '006_Nymphalis Antiopa': 'Nymphalis antiopa - Mourning Cloak',
+    '007_Papilio Cresphontes': 'Papilio cresphontes - Giant Swallowtail',
+    '008_Pieris Rapae': 'Pieris rapae - Cabbage White',
+    '009_Vanessa Atalanta': 'Vanessa atalanta - Red Admiral',
+    '010_Vanessa Cardui': 'Vanessa cardui - Painted Lady',
+}
+
+def do_inference(img):
+  img_t = data_transforms_test(img)
+  batch_t = torch.unsqueeze(img_t, 0)
+  model_ft.eval()
+  # We don't need gradients for test, so wrap in 
+  # no_grad to save memory
+  with torch.no_grad():
+      batch_t = batch_t.to(device)
+      # forward propagation
+      output = model_ft( batch_t)
+      # get prediction
+      probs = torch.nn.functional.softmax(output, dim=1)
+      output = torch.argsort(probs, dim=1, descending=True).cpu().numpy()[0].astype(int)
+      probs = probs.cpu().numpy()[0]
+      probs = probs[output]
+      labels = np.array(class_names)[output]
+      return {id_to_name[labels[i]]: round(float(probs[i]),2) for i in range(len(labels))}
+
+im = gr.inputs.Image(shape=(512, 512), image_mode='RGB', 
+                         invert_colors=False, source="upload",
+                         type="pil")
+title = "Butterfly Classification Demo"
+description = "A pretrained ResNet18 CNN trained on the Leeds Butterfly Dataset. Libraries: PyTorch, Gradio."
+examples = [['./h_erato.jpg'],['d_plexippus.jpg']]
+article_text = markdown.markdown('''
+
+<h1 style="color:white">PyTorch image classification - A pretrained ResNet18 CNN trained on the <a href="http://www.josiahwang.com/dataset/leedsbutterfly/">Leeds Butterfly Dataset</a></h1>
+<br>
+<p>The Leeds Butterfly Dataset consists of 832 images in 10 classes:</p>
+<ul>
+  <li>Danaus plexippus - Monarch</li>
+  <li>Heliconius charitonius - Zebra Longwing</li>
+  <li>Heliconius erato - Red Postman</li>
+  <li>Lycaena phlaeas - Small Copper</li>
+  <li>Junonia coenia - Common Buckeye</li>
+  <li>Nymphalis antiopa - Mourning Cloak</li>
+  <li>Papilio cresphontes - Giant Swallowtail</li>
+  <li>Pieris rapae - Cabbage White</li>
+  <li>Vanessa atalanta - Red Admiral</li>
+  <li>Vanessa cardui - Painted Lady</li>
+</ul>
+<br>
+<p>Part of a dissertation project. Author: <a href="https://github.com/ttheland">ttheland</a></p>
+''')
+
+# enable queue
+enable_queue = True
+
+iface = gr.Interface(
+    do_inference,
+    im,
+    gr.outputs.Label(num_top_classes=2),
+    live=False,
+    interpretation=None,
+    title=title,
+    description=description,
+    article= article_text,
+    examples=examples,
+    enable_queue=enable_queue
+)
+
+iface.test.launch()
+
+iface.launch(share=True)

requirements.txt

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+torchvision
+gdown
+markdown