Add treemap

page_personas.py

@@ -4,6 +4,7 @@ import numpy as np
 import textwrap
 import matplotlib.pyplot as plt
 import seaborn as sns
+import squarify
 from adjustText import adjust_text
 from wordcloud import WordCloud
 from sklearn.decomposition import PCA
@@ -63,6 +64,9 @@ def show(df):
                 f"<h2 style='text-align: center;'>Mean Answer Scores</h2>", unsafe_allow_html=True)
     get_kmeans_table(df)
     show_clustering_heatmap(df, chinese_font)
+    st.markdown(
+            f"<h2 style='text-align: center;'>Agreement between personas</h2>", unsafe_allow_html=True)
+    create_treemap()
 
 def plot_loadings_for_cluster(cluster_id, df_cluster, cluster_names, chinese_font, num_components=2, num_top_features=30):
 
@@ -372,4 +376,39 @@ def add_border(img_array, color, width):
     border_img_array[:, :width, :] = color
     border_img_array[:, -width:, :] = color
     border_img_array[width:-width, width:-width, :] = img_array
-    return border_img_array
+    return border_img_array
+
+def create_treemap():
+    categories = {
+        'Ethical Consumption and Labor Concerns': 3.2,
+        'Environmental Awareness and Action': 3.8,
+        'Economic Views and Sustainability': 2.9,
+        'Personal Finance and Investment': 3.5,
+        'Technology and AI Engagement': 3.1,
+        'Health and Safety Concerns': 4.2,
+        'Climate and Pollution Concerns': 4.0
+    }
+    
+    # Create sizes for the rectangles based on the average agreement levels
+    sizes = [value for value in categories.values()]
+    
+    # Create labels for the rectangles with the category name and the size
+    labels = [f'{key}\n({value:.2f})' for key, value in categories.items()]
+    
+    # Choose colors for each category
+    colors = [plt.cm.Spectral(i/float(len(labels))) for i in range(len(labels))]
+
+    # Create a figure and a set of subplots
+    fig, ax = plt.subplots(figsize=(12, 8))
+    
+    # Create the treemap on the created axes ax
+    squarify.plot(sizes=sizes, label=labels, color=colors, alpha=0.8, text_kwargs={'fontsize':9}, ax=ax)
+    
+    # Remove the axis for a cleaner look
+    ax.axis('off')
+    
+    # Add a title to the plot
+    plt.title('Average Agreement Level by Question Category', fontsize=15)
+
+    # Use the figure object (fig) in st.pyplot() to display the plot
+    st.pyplot(fig)