Update app.py

app.py

@@ -293,7 +293,7 @@ with gr.Blocks() as demo:
     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>.
     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.
+    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, install the [nnUNet v2](https://github.com/MIC-DKFZ/nnUNet), download [FLAMeS's model](https://huggingface.co/FrancescoLR/FLAMeS-model) and run it locally using your own GPU or CPU setup.
     
     **Disclaimer:** Uploaded data is stored temporarily, no one has access to it, and it is deleted when the app is closed. For details, see [Gradio's file access guide](https://www.gradio.app/main/guides/file-access). Human subjects data should only be uploaded for processing if permitted by your institution's human subjects protection office.
     This is a research tool and is not intended for clinical use. Clinical decisions should not be based on the outputs of this tool.