Update app.py

app.py

@@ -4,11 +4,11 @@ import gradio as gr
 class Hook():
   def hook_func(self, m, i, o): self.stored = o.detach().clone()
 
-learn_inf = load_learner("resnet152_fit_one_cycle_freeze_91acc.pkl", cpu=True)
+learn = load_learner("resnet152_fit_one_cycle_freeze_91acc.pkl", cpu=True)
 
 categories = ('arbanasi', 'filibe', 'gjirokoster', 'iskodra', 'kula', 'kuzguncuk', 'larissa_ampelakia', 'mardin', 'ohrid', 'pristina', 'safranbolu', 'selanik', 'sozopol_suzebolu', 'tiran', 'varna')
 def classify_img(img):
-  pred,idx,probs=learn_inf.predict(img)
+  pred,idx,probs=learn.predict(img)
   return dict(zip(categories, map(float, probs)))
 
 image=gr.inputs.Image(shape=(128,128))
@@ -16,7 +16,20 @@ label=gr.outputs.Label()
 examples=["filibe-1-1.jpg",
           "ohrid-3-1.jpg",
           "varna-1-1.jpg"]
-def gradcam(img_create, learn):
+
+#@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)
@@ -27,14 +40,13 @@ def gradcam(img_create, learn):
     input_size=act.shape[0]
     out_size=learn.model[1][-1].in_features
     kernel_size=act.shape[1]
-    a=act
-    new_act=tensor(np.zeros((out_size,kernel_size,kernel_size)))
-    sum=tensor(np.zeros((1,kernel_size,kernel_size)))
+    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
+        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])
@@ -42,8 +54,10 @@ def gradcam(img_create, learn):
     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
+
 gcam=gradcam(learn_inf,learn)
+
 demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image")
+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)