Update app.py

app.py

@@ -297,7 +297,7 @@ def register_rigid_affine(prev_stripped: Path, new_stripped: Path, reg_dir: Path
     return registered_path, affine_tx, fixed
 
 
-@spaces.GPU(duration=500)
+@spaces.GPU(duration=300)
 def run_flames_single(input_nii: Path, out_mask_path: Path, device: str = "cuda") -> Path:
     """Run FLAMeS (nnUNetv2) on a single input NIfTI and write a mask. Uses shared MODEL_ROOT."""
     with (Path(input_nii).open("rb")):
@@ -508,7 +508,7 @@ def _redact_paths(s: str) -> str:
     return s
 
 
-@spaces.GPU(duration=500)
+@spaces.GPU(duration=300)
 def run_pipeline(file1, file2, dilate_prev_radius_vox=1, min_lesion_vol_ml=0.01):
     """
     file1: previous (baseline) FLAIR (.nii/.nii.gz or DICOM .zip)
@@ -800,7 +800,7 @@ with gr.Blocks(
   rigid/affine co-registration of the two scans with SimpleITK, 
   and lesion segmentation using <em>FLAMeS</em> deep learning model.</p>
 
-  <p>New lesions are then calculated and highlighted on the structural scan.</p>
+  <p>Lesion difference masks between the two scans are then calculated and made available for download.</p>
 
   <p><strong>Note: This application is a <em>research preview</em>. 
   For clinical reporting, all results should be reviewed and validated by a qualified radiologist.</strong></p>