Update app.py

app.py

@@ -13,17 +13,48 @@ import gradio as gr
 
 
 # Cargamos el learner
-learn = load_learner('export.pkl')
+#learn = load_learner('export.pkl')
+model = torch.jit.load("unet.pth")
 
 # Definimos las etiquetas de nuestro modelo
-labels = learn.dls.vocab
+#labels = learn.dls.vocab
+
+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):
     img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    
+    image = transforms.Resize((480,640))(img)
+    tensor = transform_image(image=image)
+    
+    with torch.no_grad():
+        outputs = model(tensor)
+
+    outputs = torch.argmax(outputs,1)
+    
+    mask = np.array(outputs.cpu())
+    mask[mask==0]=255 #grape
+    mask[mask==1]=150 #leaves
+    mask[mask==2]=76 #pole
+    mask[mask==2]=74 #pole
+    mask[mask==3]=29 #wood
+    mask[mask==3]=25 #wood
+
+    mask=np.reshape(mask,(480,640))
+    
+    return Image.fromarray(mask.astype('uint8'))
+    #pred,pred_idx,probs = learn.predict(img)
+    #return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    
     
 # Creamos la interfaz y la lanzamos. 
 gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['color_154.jpg','color_155.jpg']).launch(share=False)