Update app.py

app.py

@@ -17,47 +17,7 @@ examples=["filibe-1-1.jpg",
           "ohrid-3-1.jpg",
           "varna-1-1.jpg"]
 
-#@title DataLoader
-path = "train_val_cropped"
-dblock = DataBlock(blocks = (ImageBlock, CategoryBlock),
-                   get_items = get_image_files,
-                   splitter = GrandparentSplitter(train_name="train",
-                                                  valid_name="valid"),
-                   get_y=parent_label,
-                   item_tfms=RandomResizedCrop(128, min_scale=0.7),
-                   batch_tfms=[*aug_transforms(),
-                   Normalize.from_stats(*imagenet_stats)])
-dls_augmented = dblock.dataloaders(path, shuffle=True)
 
-def gradcam(img):
-    x,= first(dls_augmented.test_dl([img]))
-    hook_output = Hook()
-    hook = learn.model[0].register_forward_hook(hook_output.hook_func)
-    with torch.no_grad(): output = learn.model.eval()(x)
-    act = hook_output.stored[0]
-    hook.remove()
-
-    input_size=act.shape[0]
-    out_size=learn.model[1][-1].in_features
-    kernel_size=act.shape[1]
-    new_act = tensor(np.zeros((out_size, kernel_size, kernel_size)))
-    sum = tensor(np.zeros((1, kernel_size, kernel_size)))
-    for i in range(0,input_size,4):
-        sum=tensor(np.zeros((1, kernel_size, kernel_size)))
-    for j in range(i, i+4):
-        sum=sum+act[j, :, :]
-    new_act[int(i/4), :, :]=sum/4
-    cam_map = torch.einsum('ck,kij->cij', learn.model[1][-1].weight, new_act)
-    gcam=cam_map[1].detach().cpu()
-    x_dec = TensorImage(dls_augmented.train.decode((x,))[0][0])
-    _,ax = plt.subplots()
-    x_dec.show(ctx=ax)
-    ax.imshow(cam_map[1].detach().cpu(), alpha=0.6, extent=(0,128,128,0), interpolation='bilinear', cmap='magma');
-    return gcam
-
-
-
-demo = gr.Interface(fn=gradcam, inputs=image, outputs="image", examples=examples)
-#demo = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)
+demo = gr.Interface(fn=classify_img, inputs=image, outputs=label, examples=examples)
 
 demo.launch(inline=False)