Update app.py

app.py

@@ -8,7 +8,6 @@ class Hook():
 
 learn = load_learner("resnet152_fit_one_cycle_freeze_91acc.pkl", cpu=True)
 
-#@title DataLoader
 path = "train_val_cropped"
 dblock = DataBlock(blocks = (ImageBlock, CategoryBlock),
                    get_items = get_image_files,
@@ -19,7 +18,6 @@ dblock = DataBlock(blocks = (ImageBlock, CategoryBlock),
                    batch_tfms=[*aug_transforms(),
                    Normalize.from_stats(*imagenet_stats)])
 dls_augmented = dblock.dataloaders(path, shuffle=True)
-dls_augmented.train.show_batch(max_n=8, nrows=2, figsize=(28,10))
 
 class Hook():
   def hook_func(self, m, i, o): self.stored = o.detach().clone()
@@ -60,15 +58,15 @@ def gradcam(img_create):
   return im, dict(zip(categories, map(float, probs)))
 
 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.predict(img)
-  return dict(zip(categories, map(float, probs)))
+#def classify_img(img):
+#  pred,idx,probs=learn.predict(img)
+#  return dict(zip(categories, map(float, probs)))
 
 image=gr.inputs.Image(shape=(128,128))
 label=gr.outputs.Label()
-examples_=[]
-for i in glob.glob("valid/**/*.jpg", recursive=True):
-    examples_.append(i)
+#examples_=[]
+#for i in glob.glob("valid/**/*.jpg", recursive=True):
+#    examples_.append(i)
 
 examples=["filibe-1-1.jpg",
           "ohrid-3-1.jpg",