changed df name, n_digits info

app.py

@@ -160,10 +160,14 @@ with gr.Blocks(title=title, theme=theme) as demo:
     gr.Markdown("As the ground truth is known here, we also apply different cluster quality metrics to judge the goodness of fit of the cluster labels to the ground truth.")
     gr.Markdown("Cluster quality metrics evaluated (see [Clustering performance evaluation](https://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation) \
                 for definitions and discussions of the metrics):")
+    gr.Markdown("---")
+    gr.Markdown(" We will be utilizing [digits](https://huggingface.co/datasets/sklearn-docs/digits) dataset. This dataset contains handwritten digits from 0 to 9. \
+        In the context of clustering, one would like to group images such that the handwritten digits on the image are the same.")
+
 
     with gr.Row():
         with gr.Column(scale=0.5):
-            kmeans_n_digit = gr.Slider(minimum=2, maximum=10, label="KMeans n_digits",  step=1, value=10)
+            kmeans_n_digit = gr.Slider(minimum=2, maximum=10, label="KMeans n_digits", info="n_digits is number of handwritten digits" , step=1, value=10)
             random_n_digit = gr.Slider(minimum=2, maximum=10, label="Random n_digits",  step=1, value=10)
             pca_n_digit = gr.Slider(minimum=2, maximum=10, label="PCA n_digits",step=1, value=10)
             
@@ -172,10 +176,8 @@ with gr.Blocks(title=title, theme=theme) as demo:
         with gr.Column(scale=0.5):
             sample_df = pd.DataFrame(np.zeros((9,4)),columns=['metrics', 'k-means++', 'random', 'PCA-based'])
         
-            output = gr.Dataframe(sample_df, label="Output Table")
-            
+            output = gr.Dataframe(sample_df, label="Clustering Metrics")
             
-    
     with gr.Row():
             sub_btn = gr.Button("Submit")
             sub_btn.click(fn=do_submit, inputs=[kmeans_n_digit,random_n_digit, pca_n_digit], outputs=[plt_out, output])