Update app.py

app.py

@@ -63,7 +63,7 @@ def compare_lasso_sparse():
   # compare the regression coefficients
   coeff_diff = linalg.norm(sparse_lasso.coef_ - dense_lasso.coef_)
   print(f"Distance between coefficients : {coeff_diff:.2e}")
-  return return f"Matrix density : {(Xs_sp.nnz / float(X.size) * 100):.3f}%\t\n"+ f"Sparse Lasso done in {(time() - t0):.3f}s\t\n" + f"Dense Lasso done in  {(time() - t0):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n"
+  return f"Matrix density : {(Xs_sp.nnz / float(X.size) * 100):.3f}%\t\n"+ f"Sparse Lasso done in {(time() - t0):.3f}s\t\n" + f"Dense Lasso done in  {(time() - t0):.3f}s\t\n" + f"Distance between coefficients : {coeff_diff:.2e}\t\n" 
 
 
 X,X_sp,y = load_dataset()
@@ -87,9 +87,8 @@ and run the same comparisons as above. Because the data is now sparse,
 we expect the implementation that uses the sparse data format to be faster.
 '''
 
-conclusion = '''**We show that linear_model.Lasso provides 
-the same results for dense and sparse data 
-and that in the case of sparse data the speed is improved**.
+conclusion = '''**Conclusion**
+We show that linear_model.Lasso provides the same results for dense and sparse data and that in the case of sparse data the speed is improved**.
 '''
 with gr.Blocks() as demo:
     gr.Markdown(f"# {title}")