Improve Heatmap Legend

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 from bokeh.plotting import figure
-from bokeh.models import ColumnDataSource, DataTable, TableColumn, CustomJS, Select, Button, HoverTool, LinearColorMapper, ColorBar, FuncTickFormatter
+from bokeh.models import ColumnDataSource, DataTable, TableColumn, CustomJS, Select, Button, HoverTool, LinearColorMapper, ColorBar, FuncTickFormatter, FixedTicker
 from bokeh.layouts import column
 from bokeh.palettes import Reds9, Blues9, Oranges9, Purples9, Greys9, BuGn9, Greens9
 from sklearn.decomposition import PCA
@@ -1098,7 +1098,7 @@ def run_model(model_name):
                 # Si la variable seleccionada no es numérica, la convertimos a códigos numéricos
                 # y guardamos la correspondencia para la leyenda.
                 cat_mapping = None
-                if not pd.api.types.is_numeric_dtype(df_heatmap[selected_feature]):
+                if df_heatmap[selected_feature].dtype == bool or not pd.api.types.is_numeric_dtype(df_heatmap[selected_feature]):
                     cat = df_heatmap[selected_feature].astype('category')
                     cat_mapping = list(cat.cat.categories)
                     df_heatmap[selected_feature] = cat.cat.codes
@@ -1139,7 +1139,11 @@ def run_model(model_name):
                 color_bar = ColorBar(color_mapper=color_mapper, location=(0, 0))
                 # Si se usó conversión a categoría, formateamos la barra para mostrar las etiquetas originales
                 if cat_mapping is not None:
-
+                    
+                    # Creamos ticks fijos solo para cada categoría
+                    ticks = list(range(len(cat_mapping)))
+                    color_bar.ticker = FixedTicker(ticks=ticks)
+                    
                     categories_json = json.dumps(cat_mapping)
                     color_bar.formatter = FuncTickFormatter(code=f"""
                         var categories = {categories_json};