Update explanation

app.py

@@ -79,10 +79,18 @@ def plot_data(dataset: str, perplexity: int, n_samples: int, tsne: bool):
 
 
 title = "t-SNE: The effect of various perplexity values on the shape"
-description = (
-    "An illustration of t-SNE on the two concentric circles and the"
-    "S-curve datasets for different perplexity values."
-)
+description = """
+t-Stochastic Neighborhood Embedding ([t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)) is a powerful technique dimensionality reduction and visualization of high dimensional datasets.
+
+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.
+
+By comparing the resulting visualizations, we demonstrate how changing the perplexity value affects the shape of the visualization.
+
+Created by [@Hnabil](https://huggingface.co/Hnabil) based on [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html)
+"""
+
 
 
 with gr.Blocks(title=title) as demo: