Update app.py

app.py

@@ -17,14 +17,13 @@ theme = gr.themes.Monochrome(
 
 description = f"""
 ## Description
-This demo can be used to evaluate the ability of k-means initializations strategies to make the algorithm convergence robust as measured by the 
-relative standard deviation of the inertia of the clustering (i.e. the sum of squared distances to the nearest cluster center).
+This demo can be used to evaluate the ability of k-means initializations strategies to make the algorithm convergence robust as measured by the relative standard deviation of the inertia of the clustering (i.e. the sum of squared distances to the nearest cluster center).
 
 The dataset used for evaluation is a 2D grid of isotropic Gaussian clusters widely spaced.
 
 The Inertia plot shows the best inertia reached for each combination of the model (KMeans or MiniBatchKMeans), and either random initialization or k-means++ initialization.
 
-The Cluster Allocation plot demonstrates one single run of the MiniBatchKMeans estimator using a single random initialization. 
+The Cluster Allocation plot demonstrates one single run of the MiniBatchKMeans estimator using random initialization. 
 
 The demo is based on the [scikit-learn docs](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py)
 """