New update

README.md

@@ -1,54 +1,14 @@
 ---
-title: Alpeccaai Pose
-emoji: 🦴
-colorFrom: blue
-colorTo: indigo
+title: Alpeccaai
+emoji: 💻
+colorFrom: red
+colorTo: blue
 sdk: gradio
-sdk_version: 5.9.1
+sdk_version: 6.18.0
+python_version: '3.12'
 app_file: app.py
 pinned: false
-license: mit
+short_description: 'Advanced ai prototype '
 ---
 
-# Alpeccaai Pose
-
-Whole-body 2D pose estimation (DWPose / RTMPose via `rtmlib`) for **RIGFORGE**.
-Returns keypoints in the exact JSON shape RIGFORGE reads under
-**AI keypoints → Import pose keypoints**, and exposes a `/detect` endpoint the
-in-tool **⚡ Detect via HF Space** button calls.
-
-## Deploy (replace the starter template)
-```bash
-git clone https://huggingface.co/spaces/CREATORJD/Alpeccaai
-cd Alpeccaai
-# copy app.py, requirements.txt, README.md into this folder (overwrite app.py)
-git add app.py requirements.txt README.md
-git commit -m "RIGFORGE pose endpoint (DWPose -> rigpose.json)"
-git push
-```
-Keep **Hardware = ZeroGPU** in Space settings. The model is built lazily inside
-the `@spaces.GPU` function so CUDA is visible during the call; it falls back to
-CPU automatically (fine for single images).
-
-## Make the one-click button work
-Set the Space **Visibility = Public** (Settings → Visibility). Then in RIGFORGE,
-type `CREATORJD/Alpeccaai` into the Space field and click **⚡ Detect via HF Space**.
-A private Space can't be called from the static HTML — use the Colab notebook or
-manual import instead.
-
-## Output format
-```json
-{
-  "format": "coco17",
-  "canvas_width": 1122, "canvas_height": 1402,
-  "keypoints": [[x, y, score], ...17],
-  "people": [{ "pose_keypoints_2d": [x,y,s, ...] }]
-}
-```
-`canvas_width/height` let RIGFORGE scale coordinates to its working image exactly.
-
-## Notes
-- Feed **one** figure. For a character sheet, crop the FRONT panel first.
-- For heavily stylized poses, swap in an anime-trained model (Shuhong Chen,
-  *Pose Estimation of Illustrated Characters*, WACV 2022) — output maps into the
-  same importer.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,106 +0,0 @@
-"""
-Alpeccaai Pose — Hugging Face Space (ZeroGPU)
-Runs DWPose / RTMPose whole-body 2D pose via rtmlib and returns keypoint JSON
-in the exact format RIGFORGE's "AI keypoints -> Import pose keypoints" reads.
-
-Endpoint: /detect  (the RIGFORGE "Detect via HF Space" button calls this).
-"""
-import os, json, tempfile
-import numpy as np
-import cv2
-import gradio as gr
-
-# ZeroGPU: CUDA is only visible INSIDE a @spaces.GPU function, so the model is
-# built lazily on first call (see get_model). Off ZeroGPU this is a no-op.
-try:
-    import spaces
-    gpu = spaces.GPU(duration=60)
-except Exception:
-    def gpu(fn):
-        return fn
-
-# COCO-17 body order == first 17 COCO-WholeBody points == RIGFORGE's order:
-#  0 nose 1 L_eye 2 R_eye 3 L_ear 4 R_ear 5 L_sho 6 R_sho 7 L_elb 8 R_elb
-#  9 L_wri 10 R_wri 11 L_hip 12 R_hip 13 L_knee 14 R_knee 15 L_ank 16 R_ank
-SKELETON = [(5,7),(7,9),(6,8),(8,10),(5,6),(11,12),(5,11),(6,12),
-            (11,13),(13,15),(12,14),(14,16),(0,5),(0,6)]
-
-_model = None
-def get_model():
-    global _model
-    if _model is None:
-        import onnxruntime as ort
-        from rtmlib import Wholebody
-        dev = "cuda" if "CUDAExecutionProvider" in ort.get_available_providers() else "cpu"
-        _model = Wholebody(mode="balanced", backend="onnxruntime", device=dev)
-    return _model
-
-def _openpose18(b):
-    """COCO-17 -> OpenPose-COCO18 flat list (neck = shoulder midpoint)."""
-    neck = [(b[5][0]+b[6][0])/2, (b[5][1]+b[6][1])/2, min(b[5][2], b[6][2])]
-    order = [b[0], neck, b[6], b[8], b[10], b[5], b[7], b[9],
-             b[12], b[14], b[16], b[11], b[13], b[15], b[2], b[1], b[4], b[3]]
-    flat = []
-    for x, y, s in order:
-        flat += [float(x), float(y), float(s)]
-    return flat
-
-@gpu
-def detect(image, conf=0.3):
-    if image is None:
-        raise gr.Error("Upload a single, isolated character figure (not a sheet).")
-    rgb = np.array(image.convert("RGB"))
-    bgr = rgb[:, :, ::-1].copy()
-    h, w = bgr.shape[:2]
-
-    kpts, scores = get_model()(bgr)
-    if kpts is None or len(kpts) == 0:
-        raise gr.Error("No person detected. Use a clean front T/A-pose crop.")
-
-    p = int(np.argmax(scores.mean(axis=1)))
-    kp, sc = kpts[p], scores[p]
-    body = [[float(kp[i][0]), float(kp[i][1]), float(sc[i])] for i in range(17)]
-
-    out = {
-        "format": "coco17",
-        "canvas_width": int(w), "canvas_height": int(h),
-        "keypoints": body,
-        "people": [{"pose_keypoints_2d": _openpose18(body)}],
-        "model": "rtmlib.Wholebody(balanced)",
-    }
-
-    prev = rgb.copy()
-    r = max(3, w // 200); lw = max(2, w // 300)
-    for x, y, s in body:
-        if s >= conf:
-            cv2.circle(prev, (int(x), int(y)), r, (61, 240, 224), -1)
-    for a, b in SKELETON:
-        if body[a][2] >= conf and body[b][2] >= conf:
-            cv2.line(prev, (int(body[a][0]), int(body[a][1])),
-                     (int(body[b][0]), int(body[b][1])), (255, 61, 127), lw)
-
-    path = os.path.join(tempfile.gettempdir(), "rigpose.json")
-    with open(path, "w") as f:
-        json.dump(out, f, indent=2)
-    return prev, path
-
-with gr.Blocks(title="Alpeccaai Pose") as demo:
-    gr.Markdown(
-        "# Alpeccaai Pose — DWPose keypoints\n"
-        "Upload **one isolated character figure** (front T/A-pose is best — *not* a "
-        "multi-pose reference sheet). Returns whole-body 2D keypoints as JSON for "
-        "RIGFORGE → **AI keypoints → Import pose keypoints**, or call `/detect` from "
-        "the in-tool **Detect via HF Space** button."
-    )
-    with gr.Row():
-        inp = gr.Image(type="pil", label="Single character figure")
-        with gr.Column():
-            conf = gr.Slider(0.0, 1.0, value=0.3, label="Keypoint confidence threshold")
-            btn = gr.Button("Detect", variant="primary")
-    with gr.Row():
-        out_img = gr.Image(label="Detected skeleton")
-        out_file = gr.File(label="rigpose.json")
-    btn.click(detect, inputs=[inp, conf], outputs=[out_img, out_file], api_name="detect")
-
-if __name__ == "__main__":
-    demo.launch()

requirements.txt

@@ -1,7 +0,0 @@
-gradio
-pydantic==2.10.6
-rtmlib
-onnxruntime-gpu
-opencv-python-headless
-numpy
-pillow