Prepare Hugging Face Space deployment

README-CN.md

@@ -1,8 +1,26 @@
-# Nail Try-On Segmentation（YOLOv8s-seg + ONNX Runtime CPU）
+---
+title: Nail Segmentation Demo
+emoji: "💅"
+colorFrom: pink
+colorTo: blue
+sdk: gradio
+python_version: "3.10"
+app_file: app.py
+fullWidth: true
+---
+
+# Fingernail instance segmentation and virtual try-on demo built with YOLOv8, ONNX Runtime, and Gradio.
 
 [English](./README.md) | [中文](./README-CN.md)
 
-这是一个用于指甲实例分割与轻量试妆流程的基线项目。
+这是一个轻量的指甲实例分割与虚拟试妆 Demo，结合了基于 YOLOv8 的分割结果、ONNX Runtime CPU 推理，以及简洁的 Gradio 交互界面。
+
+## 快速入口
+
+- Demo 效果：[静态示例](#demo-效果)
+- 交互 Demo：[Gradio Demo](#gradio-demo)
+- Hugging Face Space：创建一个 Gradio Space 后，直接上传当前 `github_release/` 发布目录
+- Topics：`nail-segmentation`、`virtual-try-on`、`computer-vision`、`instance-segmentation`
 
 ## 功能特性
 

README.md

@@ -1,8 +1,26 @@
-# Nail Try-On Segmentation (YOLOv8s-seg + ONNX Runtime CPU)
+---
+title: Nail Segmentation Demo
+emoji: "💅"
+colorFrom: pink
+colorTo: blue
+sdk: gradio
+python_version: "3.10"
+app_file: app.py
+fullWidth: true
+---
+
+# Fingernail instance segmentation and virtual try-on demo built with YOLOv8, ONNX Runtime, and Gradio.
 
 [English](./README.md) | [中文](./README-CN.md)
 
-A baseline project for nail instance segmentation and lightweight try-on workflows.
+A lightweight demo for fingernail instance segmentation and virtual try-on workflows, with YOLOv8-based masks, ONNX Runtime CPU inference, and a simple Gradio interface.
+
+## Quick Links
+
+- Demo results: [Static examples](#demo-results)
+- Interactive demo: [Gradio demo](#gradio-demo)
+- Hugging Face Space: create a Gradio Space and upload this `github_release/` bundle
+- Topics: `nail-segmentation`, `virtual-try-on`, `computer-vision`, `instance-segmentation`
 
 ## Features
 

app.py

@@ -0,0 +1,8 @@
+from scripts.demo_gradio import build_demo
+
+
+demo = build_demo()
+
+
+if __name__ == "__main__":
+    demo.launch()

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "nail-seg-onnx-demo"
 version = "0.1.0"
-description = "Nail instance segmentation demo with ONNX Runtime CPU and Gradio"
+description = "Fingernail instance segmentation and virtual try-on demo built with YOLOv8, ONNX Runtime, and Gradio."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [

requirements.txt

@@ -0,0 +1,5 @@
+numpy>=1.24.0
+opencv-python-headless>=4.8.0
+pillow>=10.0.0
+onnxruntime>=1.17.0,<1.24.0
+gradio>=6.14.0,<7.0.0

scripts/demo_gradio.py

@@ -3,7 +3,6 @@ from pathlib import Path
 
 import cv2
 import gradio as gr
-import numpy as np
 
 from onnx_seg_utils import YoloV8SegONNX, render_instances
 
@@ -20,24 +19,20 @@ def load_examples(example_dir: Path):
     return [[str(p)] for p in files[:12]]
 
 
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model", type=str, default="models/nail-seg.onnx")
-    parser.add_argument("--example_dir", type=str, default="examples/input")
-    parser.add_argument("--host", type=str, default="127.0.0.1")
-    parser.add_argument("--port", type=int, default=7860)
-    parser.add_argument("--conf", type=float, default=0.55)
-    parser.add_argument("--iou", type=float, default=0.45)
-    args = parser.parse_args()
-
-    inferencer = YoloV8SegONNX(args.model, conf_thres=args.conf, iou_thres=args.iou)
+def build_demo(
+    model_path="models/nail-seg.onnx",
+    example_dir="examples/input",
+    default_conf=0.55,
+    default_iou=0.45,
+):
+    inferencer = YoloV8SegONNX(model_path, conf_thres=default_conf, iou_thres=default_iou)
 
     def predict(image_np, conf_thr=None, iou_thr=None, mask_thr=None, min_area_ratio=None):
         if image_np is None:
             return None, None, "Please upload an image first."
 
-        conf_thr = float(args.conf if conf_thr is None else conf_thr)
-        iou_thr = float(args.iou if iou_thr is None else iou_thr)
+        conf_thr = float(default_conf if conf_thr is None else conf_thr)
+        iou_thr = float(default_iou if iou_thr is None else iou_thr)
         mask_thr = float(0.5 if mask_thr is None else mask_thr)
         min_area_ratio = float(0.01 if min_area_ratio is None else min_area_ratio)
 
@@ -59,7 +54,7 @@ def main():
         mask_rgb = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB)
         return to_rgb(overlay), mask_rgb, msg
 
-    example_data = load_examples(Path(args.example_dir))
+    example_data = load_examples(Path(example_dir))
 
     with gr.Blocks(title="Nail Segmentation Demo (ONNX Runtime CPU)") as demo:
         gr.Markdown("## Nail Segmentation Demo (YOLOv8s-seg ONNX Runtime CPU)")
@@ -68,8 +63,8 @@ def main():
         with gr.Row():
             in_image = gr.Image(type="numpy", label="Input Image")
         with gr.Row():
-            conf_slider = gr.Slider(0.1, 0.95, value=args.conf, step=0.01, label="Confidence")
-            iou_slider = gr.Slider(0.1, 0.9, value=args.iou, step=0.01, label="NMS IoU")
+            conf_slider = gr.Slider(0.1, 0.95, value=default_conf, step=0.01, label="Confidence")
+            iou_slider = gr.Slider(0.1, 0.9, value=default_iou, step=0.01, label="NMS IoU")
         with gr.Row():
             mask_thr_slider = gr.Slider(0.2, 0.8, value=0.5, step=0.01, label="Mask Threshold")
             min_area_slider = gr.Slider(0.001, 0.05, value=0.01, step=0.001, label="Min Area Ratio")
@@ -80,11 +75,34 @@ def main():
             out_mask = gr.Image(type="numpy", label="Mask (Binary)")
         out_text = gr.Textbox(label="Result")
 
-        btn.click(fn=predict, inputs=[in_image, conf_slider, iou_slider, mask_thr_slider, min_area_slider], outputs=[out_overlay, out_mask, out_text])
+        btn.click(
+            fn=predict,
+            inputs=[in_image, conf_slider, iou_slider, mask_thr_slider, min_area_slider],
+            outputs=[out_overlay, out_mask, out_text],
+        )
 
         if example_data:
             gr.Examples(examples=example_data, inputs=[in_image], label="Examples")
 
+    return demo
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", type=str, default="models/nail-seg.onnx")
+    parser.add_argument("--example_dir", type=str, default="examples/input")
+    parser.add_argument("--host", type=str, default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=7860)
+    parser.add_argument("--conf", type=float, default=0.55)
+    parser.add_argument("--iou", type=float, default=0.45)
+    args = parser.parse_args()
+
+    demo = build_demo(
+        model_path=args.model,
+        example_dir=args.example_dir,
+        default_conf=args.conf,
+        default_iou=args.iou,
+    )
     demo.launch(server_name=args.host, server_port=args.port)