Update app.py

app.py

@@ -179,19 +179,7 @@ with tab1:
     
     # Option to toggle PCA
     use_pca = st.checkbox('Use PCA for Visualization', value=True)
-    
-    if 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!
-        """)
-        # Apply PCA for dimensionality reduction
-        pca = PCA(n_components=2)
-        X_transformed = pca.fit_transform(X)
-        user_features_transformed = pca.transform([user_features])[0]
-    else:
-        X_transformed = X[:, :2]  # Just use the first two features for visualization
-        user_features_transformed = user_features[:2]
-    
+        
     # Create a DataFrame for easier plotting with plotly
     df_transformed = pd.DataFrame(X_transformed, columns=['Feature1', 'Feature2'])
     
@@ -242,13 +230,17 @@ with tab1:
     ##### Here are the groups from our tidying method. Each color has a number at its center, representing its group.
     """)
     st.plotly_chart(fig)
-    
-    # Predict Cluster for User Input
-    dist_to_group1 = distance.euclidean(user_features_transformed, kmeans.cluster_centers_[0])
-    dist_to_group2 = distance.euclidean(user_features_transformed, kmeans.cluster_centers_[1])    
-    st.write(f"Distance to Group 1 centroid: {dist_to_group1}")
-    st.write(f"Distance to Group 2 centroid: {dist_to_group2}")
-    
+    if 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!
+        """)
+        # Apply PCA for dimensionality reduction
+        pca = PCA(n_components=2)
+        X_transformed = pca.fit_transform(X)
+        user_features_transformed = pca.transform([user_features])[0]
+    else:
+        X_transformed = X[:, :2]  # Just use the first two features for visualization
+        user_features_transformed = user_features[:2]
     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]}**")