Update app.py

app.py

@@ -291,11 +291,11 @@ with gradio.Blocks() as demo:
             with gradio.Column():
                 gradio.Markdown("# A Data-Driven Approach for Multi-Lattice Transitions")
                 gradio.HTML("Martha Baldwin, Carnegie Mellon University<br/>Nicholas A. Meisel, Penn State<br/>Christopher McComb, Carnegie Mellon University")
-                gradio.Markdown("Additive manufacturing is advantageous for producing lightweight components while maintaining function and form. This ability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be necessary to use multiple lattice cell types, also known as multi-lattice structures. In such structures, abrupt transitions between geometries may cause stress concentrations, making the boundary a primary failure point; thus, transition regions should be created between each lattice cell type. Although computational approaches have been proposed, smooth transition regions are still difficult to intuit and design, especially between lattices of drastically different geometries. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells. In particular, the work focuses on identifying the relationships that exist within the latent space produced by the variational autoencoder. Through computational experimentation, it was found that the smoothness of transition regions was higher when the endpoints were located closer together in the latent space.")
+                gradio.Markdown("_Abstract_: Additive manufacturing is advantageous for producing lightweight components while maintaining function and form. This ability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be necessary to use multiple lattice cell types, also known as multi-lattice structures. In such structures, abrupt transitions between geometries may cause stress concentrations, making the boundary a primary failure point; thus, transition regions should be created between each lattice cell type. Although computational approaches have been proposed, smooth transition regions are still difficult to intuit and design, especially between lattices of drastically different geometries. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells. In particular, the work focuses on identifying the relationships that exist within the latent space produced by the variational autoencoder. Through computational experimentation, it was found that the smoothness of transition regions was higher when the endpoints were located closer together in the latent space.")
             with gradio.Column():
                 download = gradio.HTML("<a href=\"https://huggingface.co/spaces/cmudrc/lattice-interpolation/resolve/main/M169970.pdf\" style=\"width: 60%; display: block; margin: auto;\"><img src=\"https://huggingface.co/spaces/cmudrc/lattice-interpolation/resolve/main/coverpage.png\"></a>")
     
-        
+    gradio.Markdown("Lattices are used in 3D-printing to reduce weight, and its usually good to use more than one type of lattice. This demo can create smooth transitions between different lattices to improve the strength of the part. To use the demo, set the characteristics of the lattice unit cells you want to use as the endpoints, select the length of the transitions, and then hit `Interpolate!`")     
 
     with gradio.Row():
         with gradio.Column(min_width=250):
@@ -318,7 +318,7 @@ with gradio.Blocks() as demo:
 
     steps = gradio.Dropdown(interpolation_options, label="Interpolation Length",
                             value=random.choice(interpolation_options))
-    btn = gradio.Button("Run")
+    btn = gradio.Button("Interpolate!")
     img = gradio.Image(label="Transition")
     btn.click(fn=interpolate, inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img])
     examples = gradio.Examples(examples=[["hamburger_box", "hot_dog_box", "1.00", "1.00", "2", "2", "20"],