Improve texts

app.py

@@ -7,14 +7,9 @@ import requests
 import SimpleITK as sitk  # noqa: N813
 import spaces
 import torch
-from huggingface_hub import hf_hub_download
-from monai.transforms import Compose, ScaleIntensityd, SpatialPadd
-from tqdm import tqdm
-
 from cinema import CineMA, ConvUNetR, ConvViT, heatmap_soft_argmax
 from cinema.examples.cine_cmr import plot_cmr_views
 from cinema.examples.inference.landmark_heatmap import (
-    plot_heatmaps,
     plot_landmarks,
     plot_lv,
 )
@@ -34,6 +29,9 @@ from cinema.examples.inference.segmentation_sax import (
 from cinema.examples.inference.segmentation_sax import (
     plot_volume_changes as plot_volume_changes_sax,
 )
+from huggingface_hub import hf_hub_download
+from monai.transforms import Compose, ScaleIntensityd, SpatialPadd
+from tqdm import tqdm
 
 # cache directories
 cache_dir = Path("/tmp/.cinema")
@@ -231,6 +229,8 @@ def mae_tab():
         gr.Markdown(
             """
             This page demonstrates the masking and reconstruction process of the masked autoencoder. The model was trained with a mask ratio of 0.75 over 74,000 studies.
+
+            Visualisation may take a few seconds as we download the model weights, process the data, and render the plots.
             """
         )
         with gr.Row():
@@ -253,7 +253,7 @@ def mae_tab():
                     label="Mask ratio",
                     value=0.75,
                 )
-                run_button = gr.Button("Run Masked Autoencoder", variant="primary")
+                run_button = gr.Button("Run masked autoencoder", variant="primary")
         run_button.click(
             fn=mae,
             inputs=[image_id, mask_ratio],
@@ -345,6 +345,8 @@ def segmentation_sax_tab():
         gr.Markdown(
             """
             This page demonstrates the segmentation of cardiac structures in the short-axis (SAX) view.
+
+            Visualisation may take dozens of seconds to update as we download model checkpoints, process multiple time frames sequentially, and generate the final plots.
             """
         )
 
@@ -487,6 +489,8 @@ def segmentation_lax_tab():
         gr.Markdown(
             """
             This page demonstrates the segmentation of cardiac structures in the long-axis (LAX) four-chamber (4C) view.
+
+            Visualisation may take a few seconds to update as we download model checkpoints, process multiple time frames, and generate the final plots.
             """
         )
 
@@ -659,7 +663,9 @@ def landmark_tab():
     with gr.Blocks() as landmark_interface:
         gr.Markdown(
             """
-            This page demonstrates landmark localisation in the long-axis (LAX) two-chamber (2C) and four-chamber (4C) views
+            This page demonstrates landmark localisation in the long-axis (LAX) two-chamber (2C) and four-chamber (4C) views.
+
+            Visualisation may take a few seconds to update as we download model checkpoints, process multiple time frames, and generate the final plots.
             """
         )
 

requirements.txt

@@ -17,6 +17,6 @@ scikit-learn==1.6.1
 scipy==1.15.2
 spaces==0.36.0
 timm==1.0.15
-git+https://github.com/mathpluscode/CineMA@460cf087e2a42f44ac80d6b0a722be0dcfbb755a#egg=cinema
+git+https://github.com/mathpluscode/CineMA@af1958f51e475d3d6658132c6a680d4fc4a10cac#egg=cinema
 --extra-index-url https://download.pytorch.org/whl/cu113
 torch==2.5.1