Update app.py

app.py

@@ -83,29 +83,55 @@ with tab1:
     df = pd.DataFrame(X, columns=iris.feature_names)
     df['cluster'] = y_kmeans
     
-    fig = px.scatter(df, x=df.columns[0], y=df.columns[1], color='cluster',
-                     title='Visualizing Groups with K-Means Clustering',
-                     labels={df.columns[0]: 'Feature 1', df.columns[1]: 'Feature 2'},
-                     color_continuous_scale=px.colors.qualitative.Set1)
+    # fig = px.scatter(df, x=df.columns[0], y=df.columns[1], color='cluster',
+    #                  title='Visualizing Groups with K-Means Clustering',
+    #                  labels={df.columns[0]: 'Feature 1', df.columns[1]: 'Feature 2'},
+    #                  color_continuous_scale=px.colors.qualitative.Set1)
+    
+    # # Remove the legend
+    # #fig.update_layout(showlegend=False)
+    # fig.update_layout(width=1200, height=500)
+    # # Add user input as a star marker
+    # fig.add_scatter(x=[user_features[0]], y=[user_features[1]], mode='markers', marker=dict(symbol='star', size=30, color='white'))
+    # fig.update_coloraxes(showscale=False)
+    # #fig.update_traces(visible='legendonly')
+    # # Add annotation for user input
+    # fig.add_annotation(
+    #     x=user_features[0], 
+    #     y=user_features[1],
+    #     xshift=10,
+    #     text="Your Flower ("+dmojis[(predicted_cluster[0]+1)]+")",
+    #     font=dict(color='white', size=30),
+    #     arrowhead=2,
+    #     ax=10,
+    #     ay=-40
+    # )
+    
+    # # Add centroids with group numbers
+    # for i, coord in enumerate(kmeans.cluster_centers_):
+    #     fig.add_annotation(
+    #         x=coord[0], 
+    #         y=coord[1],
+    #         text=dmojis[i+1],
+    #         showarrow=True,
+    #         font=dict(color='white', size=30)
+    #     )
+
+    # For tab1
+    fig = go.Figure()
+    
+    # Add shaded regions
+    for cluster in np.unique(y_kmeans):
+        cluster_data = df[df['cluster'] == cluster]
+        x_data = cluster_data[df.columns[0]]
+        y_data = cluster_data[df.columns[1]]
+        fig.add_trace(go.Histogram2dContour(x=x_data, y=y_data, colorscale=px.colors.qualitative.Set1, showscale=False, opacity=0.8))
+    
+    # Add scatter plot
+    fig.add_trace(go.Scatter(x=df[df.columns[0]], y=df[df.columns[1]], mode='markers', marker=dict(color=y_kmeans, colorscale=px.colors.qualitative.Set1), showlegend=False))
     
-    # Remove the legend
-    #fig.update_layout(showlegend=False)
-    fig.update_layout(width=1200, height=500)
     # Add user input as a star marker
-    fig.add_scatter(x=[user_features[0]], y=[user_features[1]], mode='markers', marker=dict(symbol='star', size=30, color='white'))
-    fig.update_coloraxes(showscale=False)
-    #fig.update_traces(visible='legendonly')
-    # Add annotation for user input
-    fig.add_annotation(
-        x=user_features[0], 
-        y=user_features[1],
-        xshift=10,
-        text="Your Flower ("+dmojis[(predicted_cluster[0]+1)]+")",
-        font=dict(color='white', size=30),
-        arrowhead=2,
-        ax=10,
-        ay=-40
-    )
+    fig.add_trace(go.Scatter(x=[user_features[0]], y=[user_features[1]], mode='markers', marker=dict(symbol='star', size=30, color='white')))
     
     # Add centroids with group numbers
     for i, coord in enumerate(kmeans.cluster_centers_):
@@ -116,6 +142,10 @@ with tab1:
             showarrow=True,
             font=dict(color='white', size=30)
         )
+    
+    # Update layout
+    fig.update_layout(width=1200, height=500)
+
 
 
     st.write("""