Visualize Images when Hovering

app.py

@@ -558,7 +558,7 @@ def run_model(model_name):
         return
 
     # Nuevo selector para incluir o excluir el dataset pretrained
-    include_pretrained = st.checkbox("Incluir dataset pretrained", value=True, key=f"legend_{model_name}_pretrained")
+    include_pretrained = st.checkbox("Incluir dataset pretrained", value=False, key=f"legend_{model_name}_pretrained")
     if not include_pretrained:
         # Removemos la entrada pretrained del diccionario, si existe.
         embeddings.pop("pretrained", None)
@@ -890,7 +890,7 @@ def run_model(model_name):
                         legend_label=f"Real: {label}",
                         source=source)
 
-        show_real_only = st.checkbox("Show only real samples", value=False, key=f"show_real_only_{model_name}")
+        show_real_only = st.checkbox("Show only real samples", value=True, key=f"show_real_only_{model_name}")
 
         if not show_real_only:
 
@@ -1109,7 +1109,7 @@ def run_model(model_name):
                         df_heatmap['x'], df_heatmap['y'], df_heatmap[selected_feature],
                         statistic='mean', bins=[x_bins, y_bins]
                     )
-                except:
+                except TypeError:
                     cat = df_heatmap[selected_feature].astype('category')
                     cat_mapping = list(cat.cat.categories)
                     df_heatmap[selected_feature] = cat.cat.codes
@@ -1122,14 +1122,13 @@ def run_model(model_name):
                 heatmap_data = heat_stat.T
                 
                 # Crear el mapa de color
-                color_mapper = LinearColorMapper(palette="Viridis256", low=np.nanmin(heatmap_data), high=np.nanmax(heatmap_data))
+                color_mapper = LinearColorMapper(palette="Viridis256", low=np.nanmin(heatmap_data), high=np.nanmax(heatmap_data), nan_color = 'rgba(0, 0, 0, 0)')
                 
                 # Crear la figura para el heatmap con fondo blanco
                 heatmap_fig = figure(title=f"Heatmap de '{selected_feature}'",
                                     x_range=(x_min, x_max), y_range=(y_min, y_max),
                                     width=600, height=600,
-                                    tools="pan,wheel_zoom,reset,save", active_scroll="wheel_zoom")
-                heatmap_fig.background_fill_color = "white"
+                                    tools="pan,wheel_zoom,reset,save", active_scroll="wheel_zoom", tooltips=TOOLTIPS)
                 
                 # Dibujar el heatmap usando la imagen
                 heatmap_fig.image(image=[heatmap_data], x=x_min, y=y_min,
@@ -1152,6 +1151,21 @@ def run_model(model_name):
                         }}
                     """)
                 heatmap_fig.add_layout(color_bar, 'right')
+
+
+
+                # Agregar renderer de puntos invisibles para tooltips
+                source_points = ColumnDataSource(data={
+                    'x': df_heatmap['x'],
+                    'y': df_heatmap['y'],
+                    'img': df_heatmap['img'],
+                    'label': df_heatmap['name']  # Asegúrate de que esta columna exista; si no, usa otra
+                })
+                # Dibujar círculos con transparencia total (no se verán)
+                invisible_renderer = heatmap_fig.circle('x', 'y', size=10, source=source_points, fill_alpha=0, line_alpha=0.5)
+
+                hover_tool_points = HoverTool(renderers=[invisible_renderer], tooltips=TOOLTIPS)
+                heatmap_fig.add_tools(hover_tool_points)
                 
                 st.bokeh_chart(heatmap_fig)