enhance demo UI with procedure info and links

README.md

@@ -7,17 +7,49 @@ sdk: gradio
 sdk_version: 5.12.0
 python_version: 3.11
 app_file: app.py
-pinned: false
+pinned: true
 license: mit
-short_description: Facial surgery outcome prediction from clinical photography
+short_description: Facial surgery outcome prediction with 6 procedures
+tags:
+  - medical-imaging
+  - face
+  - landmarks
+  - thin-plate-spline
+  - surgery-simulation
 ---
 
 # LandmarkDiff
 
 Anatomically-conditioned facial surgery outcome prediction from standard clinical photography.
 
-Upload a face photo, select a surgical procedure, adjust intensity, and see the predicted outcome in real time using thin-plate spline warping.
+Upload a face photo, select a surgical procedure, adjust intensity, and see the predicted outcome
+in real time using thin-plate spline warping on CPU.
 
-**Supported Procedures:** Rhinoplasty, Blepharoplasty, Rhytidectomy, Orthognathic, Brow Lift, Mentoplasty
+## Supported Procedures
 
-[GitHub](https://github.com/dreamlessx/LandmarkDiff-public) | [Paper (MICCAI 2026)](https://github.com/dreamlessx/LandmarkDiff-public/tree/main/paper)
+| Procedure | Description |
+|-----------|-------------|
+| **Rhinoplasty** | Nose reshaping (bridge, tip, alar width) |
+| **Blepharoplasty** | Eyelid surgery (lid position, canthal tilt) |
+| **Rhytidectomy** | Facelift (midface and jawline tightening) |
+| **Orthognathic** | Jaw surgery (maxilla/mandible repositioning) |
+| **Brow Lift** | Brow elevation and forehead ptosis reduction |
+| **Mentoplasty** | Chin surgery (projection and vertical height) |
+
+## How It Works
+
+1. **MediaPipe landmarks** -- 478-point facial mesh extraction
+2. **Anatomical displacement** -- procedure-specific landmark shifts (intensity 0-100)
+3. **TPS deformation** -- thin-plate spline warps the image smoothly
+4. **Masked compositing** -- blends the surgical region back into the original
+
+GPU modes (ControlNet, img2img) with photorealistic rendering are available in the full package.
+
+## Links
+
+- [GitHub](https://github.com/dreamlessx/LandmarkDiff-public)
+- [Documentation](https://github.com/dreamlessx/LandmarkDiff-public/tree/main/docs)
+- [Wiki](https://github.com/dreamlessx/LandmarkDiff-public/wiki)
+- [Discussions](https://github.com/dreamlessx/LandmarkDiff-public/discussions)
+
+**Version:** v0.2.0

app.py

@@ -11,6 +11,22 @@ from landmarkdiff.conditioning import render_wireframe
 from landmarkdiff.manipulation import apply_procedure_preset, PROCEDURE_LANDMARKS
 from landmarkdiff.masking import generate_surgical_mask
 
+VERSION = "v0.2.0"
+
+GITHUB_URL = "https://github.com/dreamlessx/LandmarkDiff-public"
+DOCS_URL = f"{GITHUB_URL}/tree/main/docs"
+WIKI_URL = f"{GITHUB_URL}/wiki"
+DISCUSSIONS_URL = f"{GITHUB_URL}/discussions"
+
+PROCEDURE_DESCRIPTIONS = {
+    "rhinoplasty": "Nose reshaping -- adjusts nasal bridge, tip projection, and alar width",
+    "blepharoplasty": "Eyelid surgery -- modifies upper/lower lid position and canthal tilt",
+    "rhytidectomy": "Facelift -- tightens midface and jawline contours",
+    "orthognathic": "Jaw surgery -- repositions maxilla and mandible for skeletal alignment",
+    "brow_lift": "Brow lift -- elevates brow position and reduces forehead ptosis",
+    "mentoplasty": "Chin surgery -- adjusts chin projection and vertical height",
+}
+
 
 def warp_image_tps(image, src_pts, dst_pts):
     """Thin-plate spline warp (CPU only)."""
@@ -129,19 +145,69 @@ def intensity_sweep(image_rgb, procedure):
     return results
 
 
+# -- Build the procedure table for the description --
+_proc_rows = "\n".join(
+    f"| **{name.replace('_', ' ').title()}** | {desc} |"
+    for name, desc in PROCEDURE_DESCRIPTIONS.items()
+)
+
+HEADER_MD = f"""
+# LandmarkDiff
+
+**Anatomically-conditioned facial surgery outcome prediction from standard clinical photography**
+
+Upload a face photo, select a procedure, and adjust intensity to see a predicted surgical outcome in real time.
+This demo runs TPS (thin-plate spline) warping on CPU. The full package also supports
+GPU-accelerated ControlNet and img2img inference modes.
+
+---
+
+### Supported Procedures
+
+| Procedure | Description |
+|-----------|-------------|
+{_proc_rows}
+
+---
+
+### How It Works
+
+1. **Landmark detection** -- MediaPipe extracts a 478-point facial mesh from the input photo.
+2. **Anatomical displacement** -- Procedure-specific presets shift landmark subsets by calibrated
+   vectors (intensity 0-100 controls magnitude).
+3. **TPS deformation** -- A thin-plate spline maps source landmarks to displaced targets, warping
+   the image smoothly while preserving non-surgical regions.
+4. **Masked compositing** -- A procedure-aware mask blends the warped region back into the
+   original, keeping hair, background, and uninvolved anatomy intact.
+
+In GPU modes the deformed wireframe is passed to a ControlNet-conditioned Stable Diffusion
+pipeline for photorealistic rendering, followed by CodeFormer + Real-ESRGAN post-processing.
+
+---
+
+[GitHub]({GITHUB_URL}) | \
+[Documentation]({DOCS_URL}) | \
+[Wiki]({WIKI_URL}) | \
+[Discussions]({DISCUSSIONS_URL})
+"""
+
+FOOTER_MD = f"""
+---
+<p style="text-align:center; color:#888; font-size:0.85em;">
+LandmarkDiff {VERSION} &middot;
+<a href="{GITHUB_URL}">GitHub</a> &middot;
+<a href="{WIKI_URL}">Wiki</a> &middot;
+<a href="{DISCUSSIONS_URL}">Discussions</a> &middot;
+MIT License
+</p>
+"""
+
+
 with gr.Blocks(
     title="LandmarkDiff - Surgical Outcome Prediction",
     theme=gr.themes.Soft(),
 ) as demo:
-    gr.Markdown(
-        "# LandmarkDiff\n"
-        "**Anatomically-conditioned facial surgery outcome prediction**\n\n"
-        "Upload a face photo, select a procedure, and adjust intensity. "
-        "This demo uses TPS warping (CPU) for real-time preview. "
-        "GPU-accelerated ControlNet/img2img modes are available in the full package.\n\n"
-        "[GitHub](https://github.com/dreamlessx/LandmarkDiff-public) | "
-        "[Paper](https://github.com/dreamlessx/LandmarkDiff-public/tree/main/paper)"
-    )
+    gr.Markdown(HEADER_MD)
 
     with gr.Tab("Single Procedure"):
         with gr.Row():
@@ -181,7 +247,7 @@ with gr.Blocks(
             )
 
     with gr.Tab("Compare Procedures"):
-        gr.Markdown("Compare all procedures side by side at the same intensity.")
+        gr.Markdown("Compare all six procedures side by side at the same intensity.")
         with gr.Row():
             with gr.Column(scale=1):
                 cmp_image = gr.Image(label="Upload Face Photo", type="numpy", height=300)
@@ -206,7 +272,9 @@ with gr.Blocks(
         )
 
     with gr.Tab("Intensity Sweep"):
-        gr.Markdown("See how a procedure looks across intensity levels.")
+        gr.Markdown(
+            "See how a procedure looks across intensity levels (0% through 100% in 20% steps)."
+        )
         with gr.Row():
             with gr.Column(scale=1):
                 sweep_image = gr.Image(label="Upload Face Photo", type="numpy", height=300)
@@ -225,5 +293,7 @@ with gr.Blocks(
             outputs=[sweep_gallery],
         )
 
+    gr.Markdown(FOOTER_MD)
+
 if __name__ == "__main__":
     demo.launch()