Update app.py

app.py

@@ -254,9 +254,9 @@ with gr.Blocks() as demo:
     gr.Markdown("""
    # 🔥 FLAMeS: FLAIR Lesion Segmentation for Multiple Sclerosis
 
-    Upload a skull-stripped FLAIR brain MRI in NIfTI (.nii.gz) format to generate a binary segmentation of multiple sclerosis lesions.  
+    Upload a FLAIR brain MRI in NIfTI format (.nii.gz) to generate a binary segmentation of multiple sclerosis lesions.  
     FLAMeS is based on the nnUNet framework<sup>2</sup> and was trained on 668 MRI scans acquired using Siemens, GE, and Philips 1.5T and 3T scanners<sup>1</sup>.
-    For skull-stripping, we suggest using [SynthStrip](https://surfer.nmr.mgh.harvard.edu/docs/synthstrip/) with the `--no-csf` flag for optimal results.
+    We suggest skull-stripping the image in advance using [SynthStrip](https://surfer.nmr.mgh.harvard.edu/docs/synthstrip/) with the `--no-csf` flag for optimal results. If that's not feasible, you can still upload your image as-is and enable the "Apply skull-stripping" option below.
 
     Inference takes approximately 1 minute per MRI, with processing limited to one scan at a time due to Hugging Face's zero-GPU usage constraints. To process multiple cases simultaneously, download [FLAMeS's model](https://huggingface.co/FrancescoLR/FLAMeS-model) and run it locally using your own GPU or CPU setup.