Update app.py

app.py

@@ -24,16 +24,12 @@ def _generate_center_coordinates(l_x):
 
 def build_projection_operator(l_x, n_dir):
     """Compute the tomography design matrix.
-
     Parameters
     ----------
-
     l_x : int
         linear size of image array
-
     n_dir : int
         number of angles at which projections are acquired.
-
     Returns
     -------
     p : sparse matrix of shape (n_dir l_x, l_x**2)
@@ -110,15 +106,16 @@ 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.
-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."""
+des="""This example illustrates the utilization of the Lasso algorithm to reconstruct an image from a collection of parallel projections taken at various angles. 
+It also highlights the capability of the Lasso algorithm to accurately reconstruct images with no errors, even when noise is present in the projections.
 
-with gr.Blocks(title=title) as demo:
-    gr.Markdown(f"#{title}")
+The example demonstrates the effectiveness of the Lasso algorithm in reconstructing images using only a limited number of projections. 
+In contrast, alternative reconstruction methods like the Ridge algorithm tend to introduce numerous labeling errors in the pixel reconstruction process"""
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Compressive sensing: Tomography reconstruction with L1 prior (Lasso)")
+    gr.Markdown("This demo is based on [Compressive sensing: Tomography reconstruction with L1 prior (Lasso)](https://scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py).")
     gr.Markdown(f"{des}")
-    gr.Markdown("This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py).")
     
     with gr.Row():
         l=gr.Slider(minimum=100, maximum=500, step=1,  value = 128, label = "Linear size")
@@ -126,8 +123,9 @@ with gr.Blocks(title=title) as demo:
         alpha_l1=gr.Slider(minimum=0, maximum=1, step=0.001,  value = 0.001, label = "alpha l1")
 
 
+
     output = gr.Image()
     btn = gr.Button(value="Submit")
-    btn.click(fn=Generate_synthetic_images_and_projections, inputs = [l,alpha_l2,alpha_l1], outputs=output)
-
-demo.launch()
+    btn.click(Generate_synthetic_images_and_projections,[l,alpha_l2,alpha_l1],output)
+    
+demo.launch()