add show copy btn

app.py

@@ -7,10 +7,14 @@ from PIL import Image
 from model import Model
 from torchvision import transforms
 import warnings
+
 warnings.filterwarnings("ignore")
 
 
-def download_model(url="https://huggingface.co/MuGeminorum/SVHN-Recognition/resolve/main/model-122000.pth", local_path="model-122000.pth"):
+def download_model(
+    url="https://huggingface.co/MuGeminorum/SVHN-Recognition/resolve/main/model-122000.pth",
+    local_path="model-122000.pth",
+):
     # Check if the file exists
     if not os.path.exists(local_path):
         print(f"Downloading file from {url}...")
@@ -18,13 +22,13 @@ def download_model(url="https://huggingface.co/MuGeminorum/SVHN-Recognition/reso
         response = requests.get(url, stream=True)
 
         # Get the total file size in bytes
-        total_size = int(response.headers.get('content-length', 0))
+        total_size = int(response.headers.get("content-length", 0))
 
         # Initialize the tqdm progress bar
-        progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
+        progress_bar = tqdm(total=total_size, unit="B", unit_scale=True)
 
         # Open a local file with write-binary mode
-        with open(local_path, 'wb') as file:
+        with open(local_path, "wb") as file:
             for data in response.iter_content(chunk_size=1024):
                 # Update the progress bar
                 progress_bar.update(len(data))
@@ -42,22 +46,31 @@ def _infer(path_to_checkpoint_file, path_to_input_image):
     model = Model()
     model.restore(path_to_checkpoint_file)
     # model.cuda()
-    outstr = ''
+    outstr = ""
 
     with torch.no_grad():
-        transform = transforms.Compose([
-            transforms.Resize([64, 64]),
-            transforms.CenterCrop([54, 54]),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
-        ])
+        transform = transforms.Compose(
+            [
+                transforms.Resize([64, 64]),
+                transforms.CenterCrop([54, 54]),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+            ]
+        )
 
         image = Image.open(path_to_input_image)
-        image = image.convert('RGB')
+        image = image.convert("RGB")
         image = transform(image)
         images = image.unsqueeze(dim=0)  # .cuda()
 
-        length_logits, digit1_logits, digit2_logits, digit3_logits, digit4_logits, digit5_logits = model.eval()(images)
+        (
+            length_logits,
+            digit1_logits,
+            digit2_logits,
+            digit3_logits,
+            digit4_logits,
+            digit5_logits,
+        ) = model.eval()(images)
 
         length_prediction = length_logits.max(1)[1]
         digit1_prediction = digit1_logits.max(1)[1]
@@ -71,7 +84,7 @@ def _infer(path_to_checkpoint_file, path_to_input_image):
             digit2_prediction.item(),
             digit3_prediction.item(),
             digit4_prediction.item(),
-            digit5_prediction.item()
+            digit5_prediction.item(),
         ]
 
         for i in range(length_prediction.item()):
@@ -89,23 +102,19 @@ def inference(image_path, weight_path="model-122000.pth"):
         )
 
     if not image_path:
-        image_path = './examples/03.png'
+        image_path = "./examples/03.png"
 
     return _infer(weight_path, image_path)
 
 
-if __name__ == '__main__':
-    example_images = [
-        './examples/03.png',
-        './examples/457.png',
-        './examples/2003.png'
-    ]
+if __name__ == "__main__":
+    example_images = ["./examples/03.png", "./examples/457.png", "./examples/2003.png"]
 
     iface = gr.Interface(
         fn=inference,
-        inputs=gr.Image(type='filepath', label='Upload photo'),
-        outputs=gr.Textbox(label='Recognition result'),
-        examples=example_images
+        inputs=gr.Image(type="filepath", label="Upload photo"),
+        outputs=gr.Textbox(label="Recognition result", show_copy_button=True),
+        examples=example_images,
     )
 
     iface.launch()