Update app.py

app.py

@@ -1,35 +1,28 @@
-import gradio as gr
+import os
 import spaces
-from detection import EyesDetection                 # comes from imgutils
+import gradio as gr
+
+from detection import EyesDetection
+
+_GLOBAL_CSS = """
+.limit-height {
+    max-height: 55vh;
+}
+"""
 
-# -------------------------------------------------------------------
-# 1) GPU-decorated inference function
-@spaces.GPU(estimate=60)            # ask ZeroGPU for up to 60 s of GPU
-def eye_detect(image):
-    """
-    Args
-    ----
-    image : PIL.Image (gradio supplies this)
+if __name__ == '__main__':
+    with gr.Blocks(css=_GLOBAL_CSS) as demo:
+        with gr.Row():
+            with gr.Column():
+                gr.HTML('<h2 style="text-align: center;">Object Detections For Anime</h2>')
+                gr.Markdown('This is the online demo for detection functions of '
+                            '[imgutils.detect](https://dghs-imgutils.deepghs.org/main/api_doc/detect/index.html). '
+                            'You can try them yourselves with `pip install dghs-imgutils`.')
 
-    Returns
-    -------
-    masks : list[PIL.Image]         – per-eye mask PNGs
-    boxes : list[list[int]]         – [x1, y1, x2, y2] per eye
-    """
-    global _DET
-    if "_DET" not in globals():     # lazy-load & cache model weights
-        _DET = EyesDetection()
-    masks, boxes = _DET.process_image(image)
-    return masks, boxes
+        with gr.Row():
+            with gr.Tabs():
+                with gr.Tab('Eyes Detection'):
+                    EyesDetection().make_ui()
 
-# -------------------------------------------------------------------
-# 2) API-only Gradio app (no GUI at all)
-with gr.Blocks() as demo:
-    demo.api(
-        fn=eye_detect,
-        inputs=gr.Image(type="pil"),
-        outputs=[gr.Gallery(label="Masks"), gr.JSON(label="Boxes")],
-        name="eye_detect"
-    )
 
-demo.queue(concurrency_count=1).launch()
+    demo.queue(os.cpu_count()).launch()