Update app.py

app.py

@@ -0,0 +1,51 @@
+from fastai.basics import *
+from fastai.vision import models
+from fastai.vision.all import *
+from fastai.metrics import *
+from fastai.data.all import *
+from fastai.callback import *
+from pathlib import Path
+import random
+import torchvision.transforms as transforms
+import gradio as gr
+import PIL
+
+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
+    return my_transforms(image_aux).unsqueeze(0).to(device)
+
+def predict(img):
+    img = PILImage.create(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==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=predict, inputs=["image"], outputs=["image"],
+             examples=['color_154.jpg','color_186.jpg']).launch(share=True)