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 *
+import PIL
+import torchvision.transforms as transforms
+
+repo_id = "paascorb/practica3_Segmentation"
+
+learner = from_pretrained_fastai(repo_id)
+
+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)
+
+def predict(img):
+    img = PIL.Image.fromarray(img, "RGB")
+    image = transforms.Resize((480,640))(img)
+    tensor = transform_image(image=image) 
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    learner.to(device)
+    with torch.no_grad():
+        outputs = learner(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'))
+    
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=outputs=[gr.outputs.Image(type="pil", label="VFNet Inference")],
+                examples=['color_155.jpg','color_154.jpg']).launch(share=False)