Update app.py

app.py

@@ -79,7 +79,6 @@ with tab1:
     ##### Clustering with K-Means is a machine learning concept like tidying a messy room by grouping similar items, but for data instead of physical objects.
     """)
     
-
     # Check if 'use_pca' is already in the session state
     if 'use_pca' not in st.session_state:
         st.session_state.use_pca = True
@@ -92,7 +91,6 @@ with tab1:
     else:
         X_transformed = X[:, :2]  # Just use the first two features for visualization
         user_features_transformed = user_features[:2]
-
     
     st.write("""
     ### Visualizing Groups
@@ -123,7 +121,7 @@ with tab1:
             fig.add_trace(go.Scatter(x=x_data[hull.vertices], y=y_data[hull.vertices], fill='toself', fillcolor=px.colors.qualitative.Set1[cluster], opacity=0.5, line=dict(width=0), showlegend=False))
     
     # Add scatter plot based on PCA toggle
-    if use_pca:
+    if st.session_state.use_pca:
         fig.add_trace(go.Scatter(x=df_transformed['Feature1'], y=df_transformed['Feature2'], mode='markers', marker=dict(color=y_kmeans, colorscale=px.colors.qualitative.Set1), showlegend=False))
     else:
         fig.add_trace(go.Scatter(x=df_transformed['Feature1'], y=df_transformed['Feature2'], mode='markers', marker=dict(color=y_kmeans, colorscale=px.colors.qualitative.Set1, symbol='square'), showlegend=False))
@@ -144,13 +142,17 @@ with tab1:
     # Update layout
     fig.update_layout(width=1200, height=500)
     st.plotly_chart(fig)
-
+    
     # Checkbox after the plot
     if st.checkbox('Use PCA for Visualization', value=st.session_state.use_pca):
         st.session_state.use_pca = True
     else:
         st.session_state.use_pca = False
-
+    
+    if st.session_state.use_pca:
+        st.write("""
+        ##### 🧠 PCA (Principal Component Analysis) is like looking at a messy room from the best angle to see the most mess. It helps us see our data more clearly!
+        """)
     
     st.write(f"##### Overlapping clusters mean some flowers are very similar and hard to tell apart just by looking at these features.")
     st.write(f"# Based on your flower data (⭐), it likely belongs to **Group {dmojis[predicted_cluster[0]+1]}**")
@@ -162,6 +164,7 @@ with tab1:
     """)
 
 
+
 with tab2:
     st.write("""
     ## Advanced Overview of Clustering