Create app.py

app.py

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+from huggingface_hub import from_pretrained_fastai
+import gradio as gr
+from fastai.all import *
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
+model = torch.jit.load("hrnet.pth")
+model = model.cpu()
+
+import torchvision.transforms as transforms
+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
+    return my_transforms(image_aux).unsqueeze(0).to(device)
+
+# Definimos una función que se encarga de llevar a cabo las predicciones
+def predict(img):
+    image = transforms.Resize((480,640))(img)
+    tensor = transform_image(image=image)
+    model.to(device)
+    with torch.no_grad():
+        outputs = model(tensor)
+    
+    outputs = torch.argmax(outputs,1)
+    
+    mask = np.array(outputs.cpu())
+    mask[mask==1] = 255
+    mask[mask==2] = 150
+    mask[mask==3] = 76
+    mask[mask==4] = 29
+    mask=np.reshape(mask,(480,640))
+    return Image.fromarray(mask.astype('uint8'))
+    
+# Creamos la interfaz y la lanzamos. 
+gr.Interface(fn=predict, inputs=gr.inputs.Image(type='filepath'), outputs=gr.outputs.Image(type='pil'), examples=['color_154.jpg','color_155.jpg']).launch(share=False)
+