Create app.py

app.py

@@ -0,0 +1,37 @@
+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
+
+model = load_learner('export (2).pkl')
+
+def transform_image(image):
+   my_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalzie([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])
+   return my_transforms(image).unsqueeze(0).to(device)
+
+def predict(img):
+   img = PILImage.create(img)
+   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+   image = transforms.Resize((480,640))(img)
+   tensor = transform_image(image=image)
+   model.to(device)
+   with torch.no_grad():
+      outputs = model(tensor)
+   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))
+   Image.fromarray(mask.astype('uint8')) 
+   
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128,128)), outputs=gr.inputs.Image(shape=(128,128)), examples=['color_157.jpg','color_158.jpg']).launch(share=False)