app.py

app.py

@@ -70,40 +70,40 @@ def dog_detector(img_path):
 # Set PIL to be tolerant of image files that are truncated.
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
-train_dir = 'dogImages/train'
-test_dir = 'dogImages/test'
-valid_dir = 'dogImages/valid'
+#train_dir = 'dogImages/train'
+#test_dir = 'dogImages/test'
+#valid_dir = 'dogImages/valid'
 
 # Transforms for the training, validation, and testing sets
-train_transforms = transforms.Compose([
-                                       transforms.RandomRotation(40),
-                                       transforms.RandomResizedCrop(224),
-                                       transforms.RandomHorizontalFlip(),
-                                       transforms.ToTensor(),
-                                       transforms.Normalize([0.485, 0.456, 0.406],
-                                                            [0.229, 0.224, 0.225])
-])
-
-valid_transforms = transforms.Compose([transforms.Resize(224),
-                                      transforms.CenterCrop(224),
-                                      transforms.ToTensor(),
-                                       transforms.Normalize([0.485, 0.456, 0.406],
-                                                            [0.229, 0.224, 0.225])])
-test_transforms = transforms.Compose([transforms.Resize(224),
-                                      transforms.CenterCrop(224),
-                                      transforms.ToTensor(),
-                                       transforms.Normalize([0.485, 0.456, 0.406],
-                                                            [0.229, 0.224, 0.225])])
+#train_transforms = transforms.Compose([
+#                                       transforms.RandomRotation(40),
+#                                       transforms.RandomResizedCrop(224),
+#                                       transforms.RandomHorizontalFlip(),
+#                                       transforms.ToTensor(),
+#                                       transforms.Normalize([0.485, 0.456, 0.406],
+#                                                            [0.229, 0.224, 0.225])
+#])
+
+#valid_transforms = transforms.Compose([transforms.Resize(224),
+#                                      transforms.CenterCrop(224),
+#                                      transforms.ToTensor(),
+#                                       transforms.Normalize([0.485, 0.456, 0.406],
+#                                                            [0.229, 0.224, 0.225])])
+#test_transforms = transforms.Compose([transforms.Resize(224),
+#                                      transforms.CenterCrop(224),
+#                                      transforms.ToTensor(),
+#                                       transforms.Normalize([0.485, 0.456, 0.406],
+#                                                            [0.229, 0.224, 0.225])])
 
 # Dataloaders
-train_folder = datasets.ImageFolder(train_dir, transform=train_transforms)
-valid_folder = datasets.ImageFolder(valid_dir, transform=valid_transforms)
-test_folder = datasets.ImageFolder(test_dir, transform=test_transforms)
+#train_folder = datasets.ImageFolder(train_dir, transform=train_transforms)
+#valid_folder = datasets.ImageFolder(valid_dir, transform=valid_transforms)
+#test_folder = datasets.ImageFolder(test_dir, transform=test_transforms)
 
 # DataLoaders
-train_dataloaders = torch.utils.data.DataLoader(train_folder, batch_size=65, shuffle=True)
-valid_dataloaders = torch.utils.data.DataLoader(valid_folder, batch_size=35, shuffle=True)
-test_dataloaders = torch.utils.data.DataLoader(test_folder, batch_size= 68, shuffle=True)
+#train_dataloaders = torch.utils.data.DataLoader(train_folder, batch_size=65, shuffle=True)
+#valid_dataloaders = torch.utils.data.DataLoader(valid_folder, batch_size=35, shuffle=True)
+#test_dataloaders = torch.utils.data.DataLoader(test_folder, batch_size= 68, shuffle=True)
 
 model = models.resnet152(pretrained=True)
 
@@ -184,6 +184,9 @@ def run_app(image_path):
   else:
     out_str = 'Error... No Dog or Human Face present!! Nothing Detected!!'
     return out_str
+    
+ def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
 
 demo = gr.Blocks()
 
@@ -198,15 +201,29 @@ with demo:
 
     Else If uploaded image is of Human: it will give its resembling breed of dog
     """)
+    
+    with gr.Row():
+                paths = sorted(pathlib.Path('images').glob('*.jpg'))
+                example_images = gr.Dataset(components=[input_image],
+                                            samples=[[path.as_posix()]
+                                                     for path in paths])
   
-    inp = gr.Image(type='pil')
-    out1 = gr.Textbox()
+    inp = gr.Image(label='input image of dog/human', type='pil')
+    #out1 = gr.Textbox()
     # out2 = gr.Image(type="pil")
     #out = gr.Gallery()
-    submit = gr.Button("Generate")
+    submit = gr.Button("Generate Breed Name")
 
     submit.click(fn=run_app,
                  inputs=inp,
-                 outputs=[out1])
+                 outputs=gr.Textbox(label='breed'))
+                 
+    example_images.click(fn=set_example_image,
+                             inputs=example_images,
+                             outputs=example_images.components)
+                             
+    gr.Markdown(
+            '<center><img src="https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.dog-breed-identification-app" alt="visitor badge"/></center>'
+        )
 
 demo.launch(enable_queue=True, debug=True)