Update app.py

app.py

@@ -110,9 +110,9 @@ def Generate_synthetic_images_and_projections(l,alpha_l2,alpha_l1):
 
 title = "Compressive sensing: Tomography reconstruction with L1 prior (Lasso)"
 
-des="This example shows how to reconstruct an image from a set of parallel projections, acquired along different angles, using the Lasso algorithm."
-"The Lasso algorithm is a type of compressed sensing algorithm that uses prior information about the sparsity of the image to reconstruct it from a small number of projections."
-"The example shows that the Lasso algorithm can successfully reconstruct images with zero error, even if noise was added to the projections. In comparison, other methods of reconstruction, such as the Ridge algorithm, produce a large number of labeling errors for the pixels."
+des="This example shows how to reconstruct an image from a set of parallel projections, acquired along different angles, using the Lasso algorithm. The Lasso algorithm is a type of compressed sensing algorithm that uses prior information about the sparsity of the image to reconstruct it from a small number of projections. The example shows that the Lasso algorithm can successfully reconstruct images with zero error, even if noise was added to the projections. In comparison, other methods of reconstruction, such as the Ridge algorithm, produce a large number of labeling errors for the pixels.
+
+The example demonstrates the effectiveness of the Lasso algorithm for image reconstruction from a small number of projections. This is a promising technique for applications where it is not possible or practical to acquire a large number of projections, such as in medical imaging or in astronomy."
 
 The example demonstrates the effectiveness of the Lasso algorithm for image reconstruction from a small number of projections. This is a promising technique for applications where it is not possible or practical to acquire a large number of projections, such as in medical imaging or in astronomy."
 with gr.Blocks(title=title) as demo: