Update app.py

app.py

@@ -1,41 +1,46 @@
 import gradio as gr
 from fastai.vision.all import *
+
+from pathlib import Path
+import PIL
 import torchvision.transforms as transforms
-import torch
-from PIL import Image
-import numpy as np
+
+
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
-model = torch.jit.load("unet.pth").to(device)
+model = torch.jit.load("unet.pth")
+model = model.cpu()
 model.eval()
 
 def transform_image(image):
-    # Definimos las transformaciones necesarias para la imagen
-    resize_transform = transforms.Resize((480, 640))
-    tensor_transforms = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ])
+    my_transforms = transforms.Compose([transforms.ToTensor(),
+                                        transforms.Normalize(
+                                            [0.485, 0.456, 0.406],
+                                            [0.229, 0.224, 0.225])])
+    image_aux = image
     
-    # Aplicamos las transformaciones
-    image = resize_transform(Image.fromarray(image))
-    tensor = tensor_transforms(image).unsqueeze(0).to(device)
+    image = transforms.Resize((480,640))(Image.fromarray(image))
+    tensor = my_transforms(image_aux).unsqueeze(0).to(device)
 
-    # Realizamos la inferencia
+
+    model.to(device)
     with torch.no_grad():
         outputs = model(tensor)
-        outputs = torch.argmax(outputs, 1)
-
-    # Convertimos el tensor de salida a una imagen
-    mask = np.array(outputs.cpu().squeeze(0))
-    mask = np.where(mask == 0, 255, mask)
-    mask = np.where(mask == 1, 150, mask)
-    mask = np.where(mask == 2, 76, mask)
-    mask = np.where(mask == 3, 25, mask)
-    mask = np.where(mask == 4, 0, mask)
-    
-    mask = mask.reshape((480, 640))
+
+    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_154.jpg', 'color_189.jpg']).launch(share=False)
+
+    
+# 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)