Update app.py

app.py

@@ -27,13 +27,13 @@ st.sidebar.markdown(
 
 
 default_image_url_compare = "images/HistRotate.png"
-st.sidebar.image(default_image_url_compare, caption='A 360 rotation augmentation for training models on histopathology images. Unlike training on natural images where the rotation may change the context of the visual data, rotating a histopathology patch does not change the context and it improves the learning process for better reliable embedding learning.', width=350)
+st.sidebar.image(default_image_url_compare, caption='A 360 rotation augmentation for training models on histopathology images. Unlike training on natural images where the rotation may change the context of the visual data, rotating a histopathology patch does not change the context and it improves the learning process for better reliable embedding learning.', width=300)
 
 default_image_url_compare = "images/FigPathDino_parameters_FLOPs_compare.png"
-st.sidebar.image(default_image_url_compare, caption='PathDino Vs its counterparts. Number of Parameters (Millions) vs the patch-level retrieval with macro avg F-score of majority vote (MV@5) on CAMELYON16 dataset. The bubble size represents the FLOPs.', width=350)
+st.sidebar.image(default_image_url_compare, caption='PathDino Vs its counterparts. Number of Parameters (Millions) vs the patch-level retrieval with macro avg F-score of majority vote (MV@5) on CAMELYON16 dataset. The bubble size represents the FLOPs.', width=300)
 
 default_image_url_compare = "images/ActivationMap.png"
-st.sidebar.image(default_image_url_compare, caption='Attention Visualization. When visualizing attention patterns, our PathDino transformer outperforms HIPT-small and DinoSSLPath, despite being trained on a smaller dataset of 6 million TCGA patches. In contrast, DinoSSLPath and HIPT were trained on much larger datasets, with 19 million and 104 million TCGA patches, respectively.', width=350)
+st.sidebar.image(default_image_url_compare, caption='Attention Visualization. When visualizing attention patterns, our PathDino transformer outperforms HIPT-small and DinoSSLPath, despite being trained on a smaller dataset of 6 million TCGA patches. In contrast, DinoSSLPath and HIPT were trained on much larger datasets, with 19 million and 104 million TCGA patches, respectively.', width=300)
 
 
 st.sidebar.markdown("### Citation")