Update

README.md

@@ -4,35 +4,7 @@ emoji: 📊
 colorFrom: gray
 colorTo: red
 sdk: gradio
-sdk_version: 3.19.1
+sdk_version: 3.34.0
 app_file: app.py
 pinned: false
 ---
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

app.py

@@ -21,13 +21,9 @@ from models.yolo import Model
 from utils.datasets import letterbox
 from utils.general import non_max_suppression, scale_coords
 
-TITLE = 'zymk9/yolov5_anime'
-DESCRIPTION = 'This is an unofficial demo for https://github.com/zymk9/yolov5_anime.'
+DESCRIPTION = '# [zymk9/yolov5_anime](https://github.com/zymk9/yolov5_anime)'
 
-HF_TOKEN = os.getenv('HF_TOKEN')
-MODEL_REPO = 'hysts/yolov5_anime'
-MODEL_FILENAME = 'yolov5x_anime.pth'
-CONFIG_FILENAME = 'yolov5x.yaml'
+MODEL_REPO = 'public-data/yolov5_anime'
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
@@ -36,8 +32,7 @@ def load_sample_image_paths() -> list[pathlib.Path]:
         dataset_repo = 'hysts/sample-images-TADNE'
         path = huggingface_hub.hf_hub_download(dataset_repo,
                                                'images.tar.gz',
-                                               repo_type='dataset',
-                                               use_auth_token=HF_TOKEN)
+                                               repo_type='dataset')
         with tarfile.open(path) as f:
             f.extractall()
     return sorted(image_dir.glob('*'))
@@ -46,11 +41,8 @@ def load_sample_image_paths() -> list[pathlib.Path]:
 def load_model(device: torch.device) -> torch.nn.Module:
     torch.set_grad_enabled(False)
     model_path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                                 MODEL_FILENAME,
-                                                 use_auth_token=HF_TOKEN)
-    config_path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                                  CONFIG_FILENAME,
-                                                  use_auth_token=HF_TOKEN)
+                                                 'yolov5x_anime.pth')
+    config_path = huggingface_hub.hf_hub_download(MODEL_REPO, 'yolov5x.yaml')
     state_dict = torch.load(model_path)
     model = Model(cfg=config_path)
     model.load_state_dict(state_dict)
@@ -98,25 +90,32 @@ examples = [[path.as_posix(), 0.4, 0.5] for path in image_paths]
 
 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 model = load_model(device)
-func = functools.partial(predict, device=device, model=model)
-
-gr.Interface(
-    fn=func,
-    inputs=[
-        gr.Image(label='Input', type='pil'),
-        gr.Slider(label='Score Threshold',
-                  minimum=0,
-                  maximum=1,
-                  step=0.05,
-                  value=0.4),
-        gr.Slider(label='IoU Threshold',
-                  minimum=0,
-                  maximum=1,
-                  step=0.05,
-                  value=0.5),
-    ],
-    outputs=gr.Image(label='Output'),
-    examples=examples,
-    title=TITLE,
-    description=DESCRIPTION,
-).queue().launch(show_api=False)
+fn = functools.partial(predict, device=device, model=model)
+
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label='Input', type='pil')
+            score_threshold = gr.Slider(label='Score Threshold',
+                                        minimum=0,
+                                        maximum=1,
+                                        step=0.05,
+                                        value=0.4)
+            iou_threshold = gr.Slider(label='IoU Threshold',
+                                      minimum=0,
+                                      maximum=1,
+                                      step=0.05,
+                                      value=0.5)
+            run_button = gr.Button('Run')
+        with gr.Column():
+            result = gr.Image(label='Output')
+
+    inputs = [image, score_threshold, iou_threshold]
+    gr.Examples(examples=examples,
+                inputs=inputs,
+                outputs=result,
+                fn=fn,
+                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
+    run_button.click(fn=fn, inputs=inputs, outputs=result, api_name='predict')
+demo.queue(max_size=15).launch()

requirements.txt

@@ -1,4 +1,4 @@
-opencv-python-headless==4.5.5.62
-scipy>=1.7.3
-torch>=1.10.1
-torchvision>=0.11.2
+opencv-python-headless==4.7.0.72
+scipy==1.10.1
+torch==2.0.1
+torchvision==0.15.2

style.css

@@ -0,0 +1,3 @@
+h1 {
+  text-align: center;
+}