Change appearance

app.py

@@ -217,7 +217,7 @@ def run_job(files_or_zip, use_demo=False, progress=gr.Progress()):
 
             r = np.clip(mask_ar.astype(np.float32) * RED_SCALE, 0, 255).astype(np.uint8)
             g = np.clip(mask_cortex.astype(np.float32) * GREEN_SCALE, 0, 255).astype(np.uint8)
-            b = raw  # on garde le raw inchangé en B (tu peux mettre raw_boost si tu préfères)
+            b = raw_boost  # on garde le raw inchangé en B (tu peux mettre raw_boost si tu préfères)
 
             rgb = np.stack([r, g, b], axis=-1)
 
@@ -269,7 +269,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="MicroDeepAerenchyma — Segmentati
           <div>
             <h2 style="margin:0">Deep Aerenchyma</h2>
             <p style="margin:4px 0 0;opacity:.8">
-              Segmentation (CPU) — Upload images or a ZIP (subfolders supported). Outputs masks, panels, and a CSV.
+              Segmentation (CPU) — Upload microscopy images or a ZIP (nested subfolders supported). Outputs: masks, panels, and a CSV.
             </p>
           </div>
         </div>
@@ -277,7 +277,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="MicroDeepAerenchyma — Segmentati
     )
     gr.Markdown(
         """
-        # Deep Aerenchyma — Segmentation (CPU) - Authors: Hani Atef, Romain Fernandez (CIRAD, France)
+        # Public gradio space - Deep Aerenchyma segmentation tool (CPU) - Authors: Hani Atef, Romain Fernandez (CIRAD, France)
         Upload **multiple images** *or* **one ZIP** (supports nested folders).  
         Output: masks (`pred_AR`, `pred_Cortex`, `pred_Endoderm`), panels in `pred_global`, and `pred_metrics.csv`.
         """