Added interactive plots

app.py

@@ -163,15 +163,44 @@ def main():
         
         st.write(f'Silhouette Score of K-Means: {silhouette_score(preprocessed_df, km_preds):2f}%')
         
-        fig, ax = plt.subplots(figsize=(10,7))
-        ax.set_title('KMeans Algorithm')
-        ax.scatter(km_centroids[:,0], km_centroids[:,1], s=200, c='black', alpha=0.5, label='Centroids')
-        scatter = ax.scatter(preprocessed_df[:,0], preprocessed_df[:,1], c=km_preds, s=20)
-        plt.scatter(input_data[0], input_data[1], s=150, c='red', marker='X', label='Input Data')
-        plt.legend()
-        plt.colorbar(scatter, ax=ax, label='Cluster Labels')
-        ax.grid(True)
-        st.pyplot(fig)
+        tab1, tab2 = st.tabs(['Matplotlib Plot', 'Interactive Plot'])
+        
+        with tab1:
+            fig, ax = plt.subplots(figsize=(10,7))
+            ax.set_title('KMeans Algorithm')
+            ax.scatter(km_centroids[:,0], km_centroids[:,1], s=200, c='black', alpha=0.5, label='Centroids')
+            scatter = ax.scatter(preprocessed_df[:,0], preprocessed_df[:,1], c=km_preds, s=20)
+            plt.scatter(input_data[0], input_data[1], s=150, c='red', marker='X', label='Input Data')
+            plt.legend()
+            plt.colorbar(scatter, ax=ax, label='Cluster Labels')
+            ax.grid(True)
+            st.pyplot(fig)
+            
+        with tab2:
+            data_df = pd.DataFrame({
+                'x': preprocessed_df[:, 0],
+                'y': preprocessed_df[:, 1],
+                'Type': ['Data Point'] * len(preprocessed_df),
+                'Cluster': km_preds
+                })
+
+            centroids_df = pd.DataFrame({
+                'x': km_centroids[:, 0],
+                'y': km_centroids[:, 1],
+                'Type': ['Centroid'] * len(km_centroids),
+                'Cluster': ['Centroid'] * len(km_centroids)
+                })
+
+            input_df = pd.DataFrame({
+                'x': [input_data[0]],
+                'y': [input_data[1]],
+                'Type': ['Input Data'],
+                'Cluster': ['Input Data']
+                })
+            
+            plot_df = pd.concat([data_df, centroids_df, input_df])
+            
+            st.scatter_chart(plot_df, x='x', y='y', color='Cluster', size='Type')
         
         url = 'https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html'
         st.caption('For more details of the hyperparameters, check out the the documentation of KMeans [source](%s) of the dataset.' % url)
@@ -201,12 +230,24 @@ def main():
         else:
             st.error('Cannot calculate Silhouette Score with unscaled data.')
         
-        fig, ax = plt.subplots(figsize=(10,7))
-        ax.set_title('DBSCAN Model')
-        scatter = ax.scatter(preprocessed_df[:, 0], preprocessed_df[:, 1], s=20, c=dbs_labels, cmap='viridis')
-        ax.grid(True)
-        plt.colorbar(scatter, ax=ax, label='Cluster Labels')
-        st.pyplot(fig)
+        tab1, tab2 = st.tabs(['Matplotlib Plot', 'Interactive Plot'])
+        
+        with tab1: 
+            fig, ax = plt.subplots(figsize=(10,7))
+            ax.set_title('DBSCAN Model')
+            scatter = ax.scatter(preprocessed_df[:, 0], preprocessed_df[:, 1], s=20, c=dbs_labels, cmap='viridis')
+            ax.grid(True)
+            plt.colorbar(scatter, ax=ax, label='Cluster Labels')
+            st.pyplot(fig)
+            
+        with tab2:
+            scatter_df = pd.DataFrame({
+                'x': preprocessed_df[:, 0],
+                'y': preprocessed_df[:, 1],
+                'Cluster': dbs_labels
+            })
+            
+            st.scatter_chart(scatter_df, x='x', y='y', color='Cluster')
         
         url = 'https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html'
         st.caption('For more details of the hyperparameters, check out the the documentation of DBSCAN [source](%s) of the dataset.' % url)
@@ -244,13 +285,25 @@ def main():
             st.write(f'Silhouette Score of Spectral Clustering: {silhouette_score(preprocessed_df, km_preds):2f}%')
         else:
             st.error('Cannot calculate Silhouette Score with unscaled data.')
+            
+        tab1, tab2 = st.tabs(['Matplotlib Plot', 'Interactive Plot'])
         
-        fig, ax = plt.subplots(figsize=(10,7))
-        scatter = ax.scatter(preprocessed_df[:, 0], preprocessed_df[:, 1], c=sc_labels, s=20)
-        ax.set_title('Spectral Clustering')
-        plt.colorbar(scatter, ax=ax, label='Cluster Labels')
-        plt.grid(True)
-        st.pyplot(fig)
+        with tab1:
+            fig, ax = plt.subplots(figsize=(10,7))
+            scatter = ax.scatter(preprocessed_df[:, 0], preprocessed_df[:, 1], c=sc_labels, s=20)
+            ax.set_title('Spectral Clustering')
+            plt.colorbar(scatter, ax=ax, label='Cluster Labels')
+            plt.grid(True)
+            st.pyplot(fig)
+            
+        with tab2:
+            scatter_df = pd.DataFrame({
+                'x': preprocessed_df[:, 0],
+                'y': preprocessed_df[:, 1],
+                'Cluster': sc_labels
+            })
+            
+            st.scatter_chart(scatter_df, x='x', y='y', color='Cluster')
         
         url = 'https://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html'
         st.caption('For more details of the hyperparameters, check out the the documentation of Spectral Clustering [source](%s) of the dataset.' % url)