Update

.pre-commit-config.yaml

@@ -1,35 +1,60 @@
 repos:
-- repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.2.0
-  hooks:
-  - id: check-executables-have-shebangs
-  - id: check-json
-  - id: check-merge-conflict
-  - id: check-shebang-scripts-are-executable
-  - id: check-toml
-  - id: check-yaml
-  - id: double-quote-string-fixer
-  - id: end-of-file-fixer
-  - id: mixed-line-ending
-    args: ['--fix=lf']
-  - id: requirements-txt-fixer
-  - id: trailing-whitespace
-- repo: https://github.com/myint/docformatter
-  rev: v1.4
-  hooks:
-  - id: docformatter
-    args: ['--in-place']
-- repo: https://github.com/pycqa/isort
-  rev: 5.12.0
-  hooks:
-    - id: isort
-- repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.991
-  hooks:
-    - id: mypy
-      args: ['--ignore-missing-imports']
-- repo: https://github.com/google/yapf
-  rev: v0.32.0
-  hooks:
-  - id: yapf
-    args: ['--parallel', '--in-place']
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-executables-have-shebangs
+      - id: check-json
+      - id: check-merge-conflict
+      - id: check-shebang-scripts-are-executable
+      - id: check-toml
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+      - id: requirements-txt-fixer
+      - id: trailing-whitespace
+  - repo: https://github.com/myint/docformatter
+    rev: v1.7.5
+    hooks:
+      - id: docformatter
+        args: ["--in-place"]
+  - repo: https://github.com/pycqa/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+        args: ["--profile", "black"]
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.8.0
+    hooks:
+      - id: mypy
+        args: ["--ignore-missing-imports"]
+        additional_dependencies:
+          [
+            "types-python-slugify",
+            "types-requests",
+            "types-PyYAML",
+            "types-pytz",
+          ]
+  - repo: https://github.com/psf/black
+    rev: 24.2.0
+    hooks:
+      - id: black
+        language_version: python3.10
+        args: ["--line-length", "119"]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout
+        args:
+          [
+            "--extra-keys",
+            "metadata.interpreter metadata.kernelspec cell.metadata.pycharm",
+          ]
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.1
+    hooks:
+      - id: nbqa-black
+      - id: nbqa-pyupgrade
+        args: ["--py37-plus"]
+      - id: nbqa-isort
+        args: ["--float-to-top"]

.style.yapf

@@ -1,5 +0,0 @@
-[style]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = false
-spaces_before_comment = 2
-split_before_logical_operator = true

.vscode/settings.json

@@ -0,0 +1,30 @@
+{
+    "editor.formatOnSave": true,
+    "files.insertFinalNewline": false,
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.formatOnType": true,
+        "editor.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+    },
+    "[jupyter]": {
+        "files.insertFinalNewline": false
+    },
+    "black-formatter.args": [
+        "--line-length=119"
+    ],
+    "isort.args": ["--profile", "black"],
+    "flake8.args": [
+        "--max-line-length=119"
+    ],
+    "ruff.lint.args": [
+        "--line-length=119"
+    ],
+    "notebook.output.scrolling": true,
+    "notebook.formatOnCellExecution": true,
+    "notebook.formatOnSave.enabled": true,
+        "notebook.codeActionsOnSave": {
+            "source.organizeImports": "explicit"
+        }
+}

README.md

@@ -4,7 +4,7 @@ emoji: 🦀
 colorFrom: blue
 colorTo: pink
 sdk: gradio
-sdk_version: 3.36.1
+sdk_version: 4.19.2
 app_file: app.py
 pinned: false
 ---

app.py

@@ -14,12 +14,10 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-DESCRIPTION = '# [Age Estimation](https://github.com/yu4u/age-estimation-pytorch)'
+DESCRIPTION = "# [Age Estimation](https://github.com/yu4u/age-estimation-pytorch)"
 
 
-def get_model(model_name='se_resnext50_32x4d',
-              num_classes=101,
-              pretrained='imagenet'):
+def get_model(model_name="se_resnext50_32x4d", num_classes=101, pretrained="imagenet"):
     model = pretrainedmodels.__dict__[model_name](pretrained=pretrained)
     dim_feats = model.last_linear.in_features
     model.last_linear = nn.Linear(dim_feats, num_classes)
@@ -28,9 +26,8 @@ def get_model(model_name='se_resnext50_32x4d',
 
 
 def load_model(device):
-    model = get_model(model_name='se_resnext50_32x4d', pretrained=None)
-    path = huggingface_hub.hf_hub_download(
-        'public-data/yu4u-age-estimation-pytorch', 'pretrained.pth')
+    model = get_model(model_name="se_resnext50_32x4d", pretrained=None)
+    path = huggingface_hub.hf_hub_download("public-data/yu4u-age-estimation-pytorch", "pretrained.pth")
     model.load_state_dict(torch.load(path))
     model = model.to(device)
     model.eval()
@@ -47,23 +44,11 @@ def load_image(path):
     return image
 
 
-def draw_label(image,
-               point,
-               label,
-               font=cv2.FONT_HERSHEY_SIMPLEX,
-               font_scale=0.8,
-               thickness=1):
+def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX, font_scale=0.8, thickness=1):
     size = cv2.getTextSize(label, font, font_scale, thickness)[0]
     x, y = point
-    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0),
-                  cv2.FILLED)
-    cv2.putText(image,
-                label,
-                point,
-                font,
-                font_scale, (255, 255, 255),
-                thickness,
-                lineType=cv2.LINE_AA)
+    cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)
+    cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness, lineType=cv2.LINE_AA)
 
 
 @torch.inference_mode()
@@ -77,55 +62,58 @@ def predict(image, model, face_detector, device, margin=0.4, input_size=224):
 
     if len(detected) > 0:
         for i, d in enumerate(detected):
-            x1, y1, x2, y2, w, h = d.left(), d.top(
-            ), d.right() + 1, d.bottom() + 1, d.width(), d.height()
+            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
             xw1 = max(int(x1 - margin * w), 0)
             yw1 = max(int(y1 - margin * h), 0)
             xw2 = min(int(x2 + margin * w), image_w - 1)
             yw2 = min(int(y2 + margin * h), image_h - 1)
-            faces[i] = cv2.resize(image[yw1:yw2 + 1, xw1:xw2 + 1],
-                                  (input_size, input_size))
+            faces[i] = cv2.resize(image[yw1 : yw2 + 1, xw1 : xw2 + 1], (input_size, input_size))
 
             cv2.rectangle(image, (x1, y1), (x2, y2), (255, 255, 255), 2)
             cv2.rectangle(image, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
 
         # predict ages
-        inputs = torch.from_numpy(
-            np.transpose(faces.astype(np.float32), (0, 3, 1, 2))).to(device)
+        inputs = torch.from_numpy(np.transpose(faces.astype(np.float32), (0, 3, 1, 2))).to(device)
         outputs = F.softmax(model(inputs), dim=-1).cpu().numpy()
         ages = np.arange(0, 101)
         predicted_ages = (outputs * ages).sum(axis=-1)
 
         # draw results
         for age, d in zip(predicted_ages, detected):
-            draw_label(image, (d.left(), d.top()), f'{int(age)}')
+            draw_label(image, (d.left(), d.top()), f"{int(age)}")
     return image
 
 
-device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 model = load_model(device)
 face_detector = dlib.get_frontal_face_detector()
-fn = functools.partial(predict,
-                       model=model,
-                       face_detector=face_detector,
-                       device=device)
+fn = functools.partial(predict, model=model, face_detector=face_detector, device=device)
 
-image_dir = pathlib.Path('sample_images')
-examples = [path.as_posix() for path in sorted(image_dir.glob('*.jpg'))]
+image_dir = pathlib.Path("sample_images")
+examples = [path.as_posix() for path in sorted(image_dir.glob("*.jpg"))]
 
-with gr.Blocks(css='style.css') as demo:
+with gr.Blocks(css="style.css") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
         with gr.Column():
-            image = gr.Image(label='Input', type='filepath')
-            run_button = gr.Button('Run')
+            image = gr.Image(label="Input", type="filepath")
+            run_button = gr.Button("Run")
         with gr.Column():
-            result = gr.Image(label='Result')
-
-    gr.Examples(examples=examples,
-                inputs=image,
-                outputs=result,
-                fn=fn,
-                cache_examples=os.getenv('CACHE_EXAMPLES') == '1')
-    run_button.click(fn=fn, inputs=image, outputs=result, api_name='predict')
-demo.queue(max_size=15).launch()
+            result = gr.Image(label="Result")
+
+    gr.Examples(
+        examples=examples,
+        inputs=image,
+        outputs=result,
+        fn=fn,
+        cache_examples=os.getenv("CACHE_EXAMPLES") == "1",
+    )
+    run_button.click(
+        fn=fn,
+        inputs=image,
+        outputs=result,
+        api_name="predict",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=15).launch()

requirements.txt

@@ -1,6 +1,6 @@
-dlib>=19.23
-numpy>=1.22.2
-opencv-python-headless>=4.5.5.62
-pretrainedmodels>=0.7.4
-torch>=1.10.2
-torchvision>=0.11.3
+dlib==19.24.2
+numpy==1.26.4
+opencv-python-headless==4.9.0.80
+pretrainedmodels==0.7.4
+torch==2.0.1
+torchvision==0.15.2

style.css

@@ -1,3 +1,11 @@
 h1 {
   text-align: center;
+  display: block;
+}
+
+#duplicate-button {
+  margin: auto;
+  color: #fff;
+  background: #1565c0;
+  border-radius: 100vh;
 }