Update app.py

app.py

@@ -42,9 +42,9 @@ def get_uniform_grid(n_samples):
 
 
 DATA_MAPPING = {
-    'circles': get_circles,
-    's-curve': get_s_curve,
-    'uniform grid': get_uniform_grid,
+    'Circles': get_circles,
+    'S-curve': get_s_curve,
+    'Uniform Grid': get_uniform_grid,
 }
 
 
@@ -84,7 +84,7 @@ t-Stochastic Neighborhood Embedding ([t-SNE](https://scikit-learn.org/stable/mod
 
 One of the key parameters in t-SNE is perplexity, which controls the number of nearest neighbors used to represent each data point in the low-dimensional space.
 
-In this illustration, we explore the impact of various perplexity values on t-SNE visualizations using three commonly used datasets: concentric circles, S-curve and Uniform Grid.
+In this illustration, we explore the impact of various perplexity values on t-SNE visualizations using three commonly used datasets: Concentric Circles, S-curve and Uniform Grid.
 
 By comparing the resulting visualizations, we demonstrate how changing the perplexity value affects the shape of the visualization.