Create app.py

app.py

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+from huggingface_hub import hf_hub_url, hf_hub_download
+
+import gradio as gr
+import numpy as np
+import requests
+
+import torch
+from torchvision import transforms
+from torch.autograd import Variable
+
+from PIL import Image
+
+import warnings
+warnings.filterwarnings('ignore')
+
+path_to_model = hf_hub_download(repo_id="opetrova/face-frontalization", filename="generator_v0.pt")
+
+# Download network.py into the current directory
+network_url = hf_hub_url(repo_id="opetrova/face-frontalization", filename="network.py")
+r = requests.get(network_url, allow_redirects=True)
+open('network.py', 'wb').write(r.content)
+
+saved_model = torch.load(path_to_model, map_location=torch.device('cpu'))
+
+def frontalize(image):
+    
+    # Convert the test image to a [1, 3, 128, 128]-shaped torch tensor
+    # (as required by the frontalization model)
+    preprocess = transforms.Compose((transforms.ToPILImage(), 
+                                     transforms.Resize(size = (128, 128)), 
+                                     transforms.ToTensor()))
+    input_tensor = torch.unsqueeze(preprocess(image), 0)
+    
+    # Use the saved model to generate an output (whose values go between -1 and 1, 
+    # and this will need to get fixed before the output is displayed)
+    generated_image = saved_model(Variable(input_tensor.type('torch.FloatTensor')))
+    generated_image = generated_image.detach().squeeze().permute(1, 2, 0).numpy()
+    generated_image = (generated_image + 1.0) / 2.0
+    
+    return generated_image
+
+iface = gr.Interface(frontalize, gr.inputs.Image(type="numpy"), "image")
+iface.launch()