Upload app.py with huggingface_hub

app.py

@@ -6,6 +6,8 @@ Age/gender     : FaceAge-DINOv3 ONNX (CPU, ~1.2 GB)
 """
 import os
 import numpy as np
+
+_HF_TOKEN = os.environ.get("HF_TOKEN") or None
 import gradio as gr
 from PIL import Image, ImageDraw, ImageFont
 
@@ -58,8 +60,9 @@ def _load_age_model():
 
     print("[AgeModel] Downloading ONNX from HuggingFace Hub …")
     onnx_path = hf_hub_download(
-        repo_id  = "trungthanhtran/faceage-dino",
+        repo_id  = "TrungTran/faceage-dino",
         filename = "faceage_dino_fp32.onnx",
+        token    = _HF_TOKEN,
     )
 
     opts = ort.SessionOptions()
@@ -273,10 +276,10 @@ Upload a photo. **YuNet** auto-detects faces, then **FaceAge-DINOv3** predicts a
 | ⚡ Speed | ~100 ms / face on CPU (ONNX FP32) |
 | 🔍 Detector | YuNet (OpenCV, ~350 KB) |
 
-[📄 Model Card](https://huggingface.co/trungthanhtran/faceage-dino)
+[📄 Model Card](https://huggingface.co/TrungTran/faceage-dino)
 """
 
-with gr.Blocks(title="FaceAge-DINOv3", theme=gr.themes.Soft()) as demo:
+with gr.Blocks(title="FaceAge-DINOv3") as demo:
     gr.Markdown(_DESC)
 
     with gr.Row():
@@ -302,9 +305,9 @@ with gr.Blocks(title="FaceAge-DINOv3", theme=gr.themes.Soft()) as demo:
 
     gr.Markdown("""
 ---
-*Trained on LAGENDA · IMDB-Clean · UTKFace · AgeDB · FairFace · Open Images.*
+*Our Collection: LAGENDA · IMDB-Clean · UTKFace · AgeDB · FairFace · Open Images.*
 *DINOv3-ViT-L pretrained by Meta AI on LVD-1.68B images.*
 """)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(theme=gr.themes.Soft())