Update app.py

app.py

@@ -295,10 +295,7 @@ def main():
                     "in almost 30 design styles. After fetching all these images, we started adding metadata such as "
                     "captions (from the BLIP captioning model) and segmentation maps (from the HuggingFace UperNetForSemanticSegmentation model). "
                     )
-            st.write("For the gathering and inference of the metadata we used the Fondant framework (https://github.com/ml6team/fondant) provided by ML6 (https://www.ml6.eu/), which is an open source "
-                     "data centric framework for data preparation. The pipeline used for training this controlnet will soon be available as an "
-                     "example pipeline within Fondant and can be easily adapted for building your own dataset."
-                     )
+          
             st.write("### About the model")
             st.write(
                 "These were then used to train the controlnet model to generate quality interior design images by using "
@@ -311,10 +308,7 @@ def main():
             )
             
             st.write("### Trivia")
-            st.write("The first time someone uses the demo after startup, the models still need to be loaded into memory. "
-                    "After this initial load, the model is cached as a resource and can be used for all the users. "
-                    "To avoid simultaneous requests, we have implemented a queueing mechanism that ensures that only one "
-                    "user accesses the model at a time (similar to the Gradio framework).\n"
+            
                     )
             st.write("To enable the features in the demo, we calculate the underlying segmentation maps and categories that "
                     "are present in the image. This allows us to hide some of the manual work for the user, and "