Update app.py

app.py

@@ -2667,26 +2667,12 @@ def create_interface():
     )
     
     with gr.Blocks(theme=theme, title="ProFound: Vision Foundation Models for Prostate Multiparametric MR Images") as demo:
-        # Header with logo
-        with gr.Row():
-            with gr.Column(scale=4):
-                gr.Markdown("""
-                # ProFound: Vision Foundation Models for Prostate Multiparametric MR Images 🏥🔬
-                
-                ProFound is a suite of vision foundation models, pre-trained on multiparametric 3D magnetic resonance (MR) images from large collections of prostate cancer patients.
-                        
-                For more details, check out our [GitHub repository](https://github.com/pipiwang/ProFound).
-                """)
-            with gr.Column(scale=1):
-                # Display UCL Hawkes Institute logo
-                gr.Image(
-                    value="UCL-Hawkes-Institute-logo.jpg",
-                    show_label=False,
-                    show_download_button=False,
-                    container=False,
-                    height=120,
-                    interactive=False
-                )
+        # Header
+        gr.Markdown("""
+        # ProFound: Vision Foundation Models for Prostate Multiparametric MR Images 🏥🔬
+        
+        ProFound is a suite of vision foundation models, pre-trained on multiparametric 3D magnetic resonance (MR) images from large collections of prostate cancer patients.
+        """)
         
         # Create State components to manage data 
         pretrain_state = gr.State(value=None)
@@ -2756,15 +2742,7 @@ def create_interface():
                     outputs=[pretrain_plot]
                 )
 
-                gr.Markdown("""
-                **Acknowledgement**
-                            
-                This work is supported by the International Alliance for Cancer Early Detection, an alliance between Cancer Research UK, Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester.
 
-                This work is also supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre.
-                            
-                The authors acknowledge the use of resources provided by the Isambard-AI National AI Research Resource (AIRR). Isambard-AI is operated by the University of Bristol and is funded by the UK Government's Department for Science, Innovation and Technology (DSIT) via UK Research and Innovation; and the Science and Technology Facilities Council [ST/AIRR/I-A-I/1023].
-                """)
 
             # Classification tab
             with gr.TabItem("🎯 Assessment of Prostate Cancer Patients", id="classification"):
@@ -2804,15 +2782,7 @@ def create_interface():
                     outputs=[cls_plot, cls_result]
                 )
 
-                gr.Markdown("""
-                **Acknowledgement**
-                            
-                This work is supported by the International Alliance for Cancer Early Detection, an alliance between Cancer Research UK, Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester.
 
-                This work is also supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre.
-                            
-                The authors acknowledge the use of resources provided by the Isambard-AI National AI Research Resource (AIRR). Isambard-AI is operated by the University of Bristol and is funded by the UK Government's Department for Science, Innovation and Technology (DSIT) via UK Research and Innovation; and the Science and Technology Facilities Council [ST/AIRR/I-A-I/1023].
-                """)
             
             # Segmentation tab
             with gr.TabItem("✂️ Segmentation of Prostate Cancer Lesions", id="segmentation"):
@@ -2885,15 +2855,7 @@ def create_interface():
                     outputs=[slice_browser_plot]
                 )
 
-                gr.Markdown("""
-                **Acknowledgement**
-                            
-                This work is supported by the International Alliance for Cancer Early Detection, an alliance between Cancer Research UK, Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester.
 
-                This work is also supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre.
-
-                The authors acknowledge the use of resources provided by the Isambard-AI National AI Research Resource (AIRR). Isambard-AI is operated by the University of Bristol and is funded by the UK Government's Department for Science, Innovation and Technology (DSIT) via UK Research and Innovation; and the Science and Technology Facilities Council [ST/AIRR/I-A-I/1023].
-                """)
 
             # Anatomy Segmentation tab - NEW
             with gr.TabItem("🫀 Segmentation of Prostate Anatomy", id="anatomy_segmentation"):
@@ -2992,15 +2954,7 @@ def create_interface():
                     outputs=[anat_volume_3d_plot]
                 )
 
-                gr.Markdown("""
-                **Acknowledgement**
-                            
-                This work is supported by the International Alliance for Cancer Early Detection, an alliance between Cancer Research UK, Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester.
 
-                This work is also supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre.
-
-                The authors acknowledge the use of resources provided by the Isambard-AI National AI Research Resource (AIRR). Isambard-AI is operated by the University of Bristol and is funded by the UK Government's Department for Science, Innovation and Technology (DSIT) via UK Research and Innovation; and the Science and Technology Facilities Council [ST/AIRR/I-A-I/1023].
-                """)
     
     return demo