Create app.py

app.py

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+from huggingface_hub import from_pretrained_fastai
+import gradio as gr
+from fastai.vision.all import *
+
+from pathlib import Path
+import PIL
+import torchvision.transforms as transforms
+
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
+model = torch.jit.load("unet.pth")
+model = model.cpu()
+model.eval()
+
+def transform_image(image):
+    my_transforms = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(
+                                            [0.485, 0.456, 0.406],
+                                            [0.229, 0.224, 0.225])])
+    image_aux = image
+    
+image = transforms.Resize((480,640))(Image.fromarray(image))
+tensor = my_transforms(image_aux).unsqueeze(0).to(device)
+
+
+model.to(device)
+with torch.no_grad():
+    outputs = model(tensor)
+
+outputs = torch.argmax(outputs,1)
+
+
+mask = np.array(outputs.cpu())
+mask[mask==0]=255
+mask[mask==1]=150
+mask[mask==2]=76
+mask[mask==3]=25
+mask[mask==4]=0
+
+mask=np.reshape(mask,(480,640))
+return Image.fromarray(mask.astype('uint8'))
+
+
+    
+# Creamos la interfaz y la lanzamos. 
+gr.Interface(fn=transform_image, inputs=gr.inputs.Image(shape=(640, 480)), outputs=gr.outputs.Image(),examples=['color_188.jpg','color_189.jpg']).launch(share=False)
+ 
+