Update

README.md

@@ -4,35 +4,7 @@ emoji: 🦀
 colorFrom: green
 colorTo: gray
 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

@@ -6,17 +6,14 @@ import functools
 import os
 import pathlib
 import tarfile
-import urllib
+import urllib.request
 
 import cv2
 import gradio as gr
 import huggingface_hub
 import numpy as np
 
-TITLE = 'nagadomi/lbpcascade_animeface'
-DESCRIPTION = 'This is an unofficial demo for https://github.com/nagadomi/lbpcascade_animeface.'
-
-HF_TOKEN = os.getenv('HF_TOKEN')
+DESCRIPTION = '# [nagadomi/lbpcascade_animeface](https://github.com/nagadomi/lbpcascade_animeface)'
 
 
 def load_sample_image_paths() -> list[pathlib.Path]:
@@ -25,8 +22,7 @@ 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('*'))
@@ -41,14 +37,14 @@ def load_model() -> cv2.CascadeClassifier:
 
 
 def detect(image_path: str, detector: cv2.CascadeClassifier) -> np.ndarray:
-    image_path = cv2.imread(image_path)
-    gray = cv2.cvtColor(image_path, cv2.COLOR_BGR2GRAY)
+    image = cv2.imread(image_path)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     preds = detector.detectMultiScale(gray,
                                       scaleFactor=1.1,
                                       minNeighbors=5,
                                       minSize=(24, 24))
 
-    res = image_path.copy()
+    res = image.copy()
     for x, y, w, h in preds:
         cv2.rectangle(res, (x, y), (x + w, y + h), (0, 255, 0), 2)
     return res[:, :, ::-1]
@@ -58,13 +54,21 @@ image_paths = load_sample_image_paths()
 examples = [[path.as_posix()] for path in image_paths]
 
 detector = load_model()
-func = functools.partial(detect, detector=detector)
-
-gr.Interface(
-    fn=func,
-    inputs=gr.Image(label='Input', type='filepath'),
-    outputs=gr.Image(label='Output', type='numpy'),
-    examples=examples,
-    title=TITLE,
-    description=DESCRIPTION,
-).queue().launch(show_api=False)
+fn = functools.partial(detect, detector=detector)
+
+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')
+        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()

requirements.txt

@@ -1 +1 @@
-opencv-python-headless>=4.5.5.62
+opencv-python-headless>=4.7.0.72

style.css

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