Update app.py

app.py

@@ -109,16 +109,12 @@ def classifyCar(im):
     
     # if a car was detected, extract the car and perform modernity score classification
     if len(boxes) != 0:
-      #max_idx = torch.tensor([(x[2] - x[0])*(x[3] - x[1]) for x in boxes]).argmax().item()
-      
-      #im2 = Image.fromarray(np.uint8(im)).convert('RGB').crop(boxes[max_idx].to(torch.int64).numpy())
-      im2 = cropImage(outputs, im, boxes, car_class_true)
-      #carResize = transforms.Compose([transforms.Resize((224, 224))])
-      #im2 = carResize(im2)
+        im2 = cropImage(outputs, im, boxes, car_class_true)
+
       
-      with torch.no_grad():
-        scores = torch.nn.functional.softmax(DesignModernityModel(carTransforms(im2).unsqueeze(0))[0])
-      label = {LABELS[i]: float(scores[i]) for i in range(n_labels)}
+        with torch.no_grad():
+            scores = torch.nn.functional.softmax(DesignModernityModel(carTransforms(im2).unsqueeze(0))[0])
+        label = {LABELS[i]: float(scores[i]) for i in range(n_labels)}
       
     # if no car was detected, show original image and print "No car detected"  
     else: