Deploy IG test profile studio

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/default_persona.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -12,10 +12,12 @@ pinned: false
 
 This Space is dedicated to a single workflow:
 
-- upload a persona image
+- use preloaded persona image (or upload your own)
 - stream webcam input
 - get live full-body replacement output
 
 If segmentation is uncertain for a frame, the app falls back to a body-box estimate to keep the stream responsive.
 
+Default persona source: https://www.pexels.com/photo/elegant-woman-in-all-white-outfit-with-hijab-31215273/
+
 Set `HF_TOKEN` as a Space secret to avoid entering it in the UI.

app.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import time
+from pathlib import Path
 from typing import Any
 
 import cv2
@@ -19,6 +20,9 @@ FACE_CASCADE = cv2.CascadeClassifier(
 UPPER_BODY_CASCADE = cv2.CascadeClassifier(
     cv2.data.haarcascades + "haarcascade_upperbody.xml"
 )
+APP_DIR = Path(__file__).resolve().parent
+DEFAULT_AVATAR_PATH = APP_DIR / "assets" / "default_persona.jpg"
+DEFAULT_AVATAR_VALUE = str(DEFAULT_AVATAR_PATH) if DEFAULT_AVATAR_PATH.exists() else None
 
 
 def _init_selfie_segmenter() -> tuple[Any | None, str]:
@@ -547,8 +551,9 @@ Only one workflow is enabled:
         mirror = gr.Checkbox(label="Mirror output", value=True)
 
     avatar_upload = gr.Image(
-        label="Persona image (required, best: full-body portrait)",
+        label="Persona image (preloaded; replace if you want another look)",
         type="numpy",
+        value=DEFAULT_AVATAR_VALUE,
     )
 
     with gr.Row():