Update app.py

app.py

@@ -205,7 +205,7 @@ class RSM_BoxBehnken:
 
         # Convertir la malla de variables naturales a codificadas
         x_grid_coded = self.natural_to_coded(x_grid_natural, varying_variables[0])
-        y_grid_coded = self.natural_to_coded(y_grid_natural, varying_variables[1])
+        y_grid_coded = self.natural_to_coded(y_range_natural, varying_variables[1])
 
         # Crear un DataFrame para la predicción con variables codificadas
         prediction_data = pd.DataFrame({
@@ -302,179 +302,31 @@ class RSM_BoxBehnken:
 
 # --- Funciones para la Interfaz de Gradio ---
 
-def load_data(data_str):
-    global rsm, data
-
-    x1_name = "Glucosa_g_L"
-    x2_name = "Proteina_Pescado_g_L"
-    x3_name = "Sulfato_Manganeso_g_L"
-    y_name = "Abs_600nm"
-    x1_levels = sorted(list(set(data[x1_name])))
-    x2_levels = sorted(list(set(data[x2_name])))
-    x3_levels = sorted(list(set(data[x3_name])))
-
-    rsm = RSM_BoxBehnken(data, x1_name, x2_name, x3_name, y_name, x1_levels, x2_levels, x3_levels)
-    return data.round(3), gr.update(visible=True)
-
-
-def fit_and_optimize_model():
-    if 'rsm' not in globals():
-        return [None]*11
-
-    model_completo, pareto_completo = rsm.fit_model()
-    model_simplificado, pareto_simplificado = rsm.fit_simplified_model()
-    # Personalized model fitting is now triggered separately by custom_model_button
-    optimization_table = rsm.optimize()
-    equation = rsm.get_simplified_equation()
-    prediction_table = rsm.generate_prediction_table()
-    contribution_table = rsm.calculate_contribution_percentage()
-    anova_table = rsm.calculate_detailed_anova()
-
-    rsm.generate_all_plots() # Generate all plots for all models after fitting
-
-    equation_formatted = equation.replace(" + ", "<br>+ ").replace(" ** ", "^").replace("*", " × ")
-    equation_formatted = f"### Ecuación del Modelo Simplificado:<br>{equation_formatted}"
-
-    excel_path = rsm.save_tables_to_excel()
-    zip_path = rsm.save_figures_to_zip()
-
-    return (
-        model_completo.summary().as_html(),
-        pareto_completo,
-        model_simplificado_output, # output_components are correctly referenced now
-        pareto_simplificado,
-        equation_formatted,
-        optimization_table,
-        prediction_table,
-        contribution_table,
-        anova_table,
-        zip_path,
-        excel_path
-    )
-
-def fit_custom_model(factor_checkboxes, interaction_checkboxes, model_personalized_output_component, pareto_personalized_output_component):
-    if 'rsm' not in globals():
-        return [None]*2 # adjust output number
-
-    formula_parts = [rsm.x1_name, rsm.x2_name, rsm.x3_name] if "factors" in factor_checkboxes else []
-    if "x1_sq" in factor_checkboxes: formula_parts.append(f'I({rsm.x1_name}**2)')
-    if "x2_sq" in factor_checkboxes: formula_parts.append(f'I({rsm.x2_name}**2)')
-    if "x3_sq" in factor_checkboxes: formula_parts.append(f'I({rsm.x3_name}**2)')
-    if "x1x2" in interaction_checkboxes: formula_parts.append(f'{rsm.x1_name}:{rsm.x2_name}')
-    if "x1x3" in interaction_checkboxes: formula_parts.append(f'{rsm.x1_name}:{rsm.x3_name}')
-    if "x2x3" in interaction_checkboxes: formula_parts.append(f'{rsm.x2_name}:{rsm.x3_name}')
-
-    if not formula_parts:
-        formula = f'{rsm.y_name} ~ 1' # Intercept-only model if nothing selected
-    else:
-        formula = f'{rsm.y_name} ~ ' + ' + '.join(formula_parts)
-
-    custom_model, pareto_custom = rsm.fit_personalized_model(formula) # Fit personalized model
-    rsm.generate_all_plots() # Regenerate plots to include personalized model plots
-
-    return  custom_model.summary().as_html(), pareto_custom # return values for outputs
-
-
-def show_plot(current_index, all_figures, model_type): # Modified to accept model_type
-    figure_list = []
-    if model_type == 'full':
-        figure_list = rsm.all_figures_full
-    elif model_type == 'simplified':
-        figure_list = rsm.all_figures_simplified
-    elif model_type == 'personalized':
-        figure_list = rsm.all_figures_personalized
-
-    if not figure_list:
-        return None, f"No hay gráficos disponibles para el modelo {model_type}.", current_index
-    selected_fig = figure_list[current_index]
-    plot_info_text = f"Gráfico {current_index + 1} de {len(figure_list)} (Modelo {model_type.capitalize()})" # Updated plot info
-    return selected_fig, plot_info_text, current_index
-
-def navigate_plot(direction, current_index, all_figures, model_type): # Modified to accept model_type
-    figure_list = []
-    if model_type == 'full':
-        figure_list = rsm.all_figures_full
-    elif model_type == 'simplified':
-        figure_list = rsm.all_figures_simplified
-    elif model_type == 'personalized':
-        figure_list = rsm.all_figures_personalized
-
-    if not figure_list:
-        return None, f"No hay gráficos disponibles para el modelo {model_type}.", current_index
-
-    if direction == 'left':
-        new_index = (current_index - 1) % len(figure_list)
-    elif direction == 'right':
-        new_index = (current_index + 1) % len(figure_list)
-    else:
-        new_index = current_index
-
-    selected_fig = figure_list[new_index]
-    plot_info_text = f"Gráfico {new_index + 1} de {len(figure_list)} (Modelo {model_type.capitalize()})" # Updated plot info
-
-    return selected_fig, plot_info_text, new_index
-
-def download_current_plot(all_figures, current_index, model_type): # Modified to accept model_type
-    figure_list = []
-    if model_type == 'full':
-        figure_list = rsm.all_figures_full
-    elif model_type == 'simplified':
-        figure_list = rsm.all_figures_simplified
-    elif model_type == 'personalized':
-        figure_list = rsm.all_figures_personalized
-    if not figure_list:
-        return None
-    fig = figure_list[current_index]
-    img_bytes = rsm.save_fig_to_bytes(fig)
-    filename = f"Grafico_RSM_{model_type}_{current_index + 1}.png" # Added model type to filename
-
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-        temp_file.write(img_bytes)
-        temp_path = temp_file.name
-    return temp_path
-
-def download_all_plots_zip(model_type): # Modified to accept model_type
-    if 'rsm' not in globals():
-        return None
-    if model_type == 'full':
-        rsm.all_figures = rsm.all_figures_full # Set current figures to download
-    elif model_type == 'simplified':
-        rsm.all_figures = rsm.all_figures_simplified
-    elif model_type == 'personalized':
-        rsm.all_figures = rsm.all_figures_personalized
-
-    zip_path = rsm.save_figures_to_zip()
-    if zip_path:
-        filename = f"Graficos_RSM_{model_type}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip" # Added model type to filename
-        return zip_path
-    return None
-
-def download_all_tables_excel():
-    """
-    Descarga todas las tablas en un archivo Excel con múltiples hojas.
-    """
-    if 'rsm' not in globals():
-        return None
-    excel_path = rsm.save_tables_to_excel()
-    if excel_path:
-        filename = f"Tablas_RSM_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
-        # Gradio no permite renombrar directamente, por lo que retornamos la ruta del archivo
-        return excel_path
-    return None
-
-def exportar_word(rsm_instance, tables_dict):
-    """
-    Función para exportar las tablas a un documento de Word.
-    """
-    word_path = rsm_instance.export_tables_to_word(tables_dict)
-    if word_path and os.path.exists(word_path):
-        return word_path
-    return None
-
-
-# --- Crear la interfaz de Gradio ---
+model_completo_output = gr.HTML()
+pareto_completo_output = gr.Plot()
+model_simplificado_output = gr.HTML()
+pareto_simplificado_output = gr.Plot()
+equation_output = gr.HTML()
+optimization_table_output = gr.Dataframe(label="Tabla de Optimización", interactive=False)
+prediction_table_output = gr.Dataframe(label="Tabla de Predicciones", interactive=False)
+contribution_table_output = gr.Dataframe(label="Tabla de % de Contribución", interactive=False)
+anova_table_output = gr.Dataframe(label="Tabla ANOVA Detallada", interactive=False)
+download_all_plots_button = gr.DownloadButton("Descargar Todos los Gráficos (ZIP)")
+download_excel_button = gr.DownloadButton("Descargar Tablas en Excel")
+rsm_plot_output = gr.Plot()
+plot_info = gr.Textbox(label="Información del Gráfico", value="Gráfico 1 de 9", interactive=False)
+current_index_state = gr.State(0)
+all_figures_state = gr.State([])
+current_model_type_state = gr.State('simplified')
+model_personalized_output = gr.HTML() # Output for personalized model summary
+pareto_personalized_output = gr.Plot() # Pareto for personalized model
+factor_checkboxes = gr.CheckboxGroup(["factors", "x1_sq", "x2_sq", "x3_sq"], label="Términos de Factores", value=["factors", "x1_sq", "x2_sq", "x3_sq"]) # Factor Checkboxes
+interaction_checkboxes = gr.CheckboxGroup(["x1x2", "x1x3", "x2x3"], label="Términos de Interacción") # Interaction Checkboxes
+
 
 def create_gradio_interface():
+    global model_completo_output, pareto_completo_output, model_simplificado_output, pareto_simplificado_output, equation_output, optimization_table_output, prediction_table_output, contribution_table_output, anova_table_output, download_all_plots_button, download_excel_button, rsm_plot_output, plot_info, current_index_state, all_figures_state, current_model_type_state, model_personalized_output, pareto_personalized_output, factor_checkboxes, interaction_checkboxes
+
     with gr.Blocks() as demo:
         gr.Markdown("# Optimización de la Absorbancia usando RSM")
 
@@ -493,28 +345,28 @@ def create_gradio_interface():
             with gr.Column():
                 fit_button = gr.Button("Ajustar Modelo Simplificado y Completo") # Button label changed
                 gr.Markdown("**Modelo Completo**")
-                model_completo_output = gr.HTML()
-                pareto_completo_output = gr.Plot()
+                model_completo_output = model_completo_output # gr.HTML() # output_components are now global
+                pareto_completo_output = pareto_completo_output # gr.Plot()
                 gr.Markdown("**Modelo Simplificado**")
-                model_simplificado_output = gr.HTML()
-                pareto_simplificado_output = gr.Plot()
+                model_simplificado_output = model_simplificado_output # gr.HTML()
+                pareto_simplificado_output = pareto_simplificado_output # gr.Plot()
 
                 gr.Markdown("## Modelo Personalizado") # Personalized Model Section
-                factor_checkboxes = gr.CheckboxGroup(["factors", "x1_sq", "x2_sq", "x3_sq"], label="Términos de Factores", value=["factors", "x1_sq", "x2_sq", "x3_sq"]) # Factor Checkboxes
-                interaction_checkboxes = gr.CheckboxGroup(["x1x2", "x1x3", "x2x3"], label="Términos de Interacción") # Interaction Checkboxes
+                factor_checkboxes_comp = factor_checkboxes # gr.CheckboxGroup(["factors", "x1_sq", "x2_sq", "x3_sq"], label="Términos de Factores", value=["factors", "x1_sq", "x2_sq", "x3_sq"]) # Factor Checkboxes
+                interaction_checkboxes_comp = interaction_checkboxes # gr.CheckboxGroup(["x1x2", "x1x3", "x2x3"], label="Términos de Interacción") # Interaction Checkboxes
                 custom_model_button = gr.Button("Ajustar Modelo Personalizado") # Fit Custom Model Button
-                model_personalized_output = gr.HTML() # Output for personalized model summary
-                pareto_personalized_output = gr.Plot() # Pareto for personalized model
+                model_personalized_output_comp = model_personalized_output # gr.HTML() # Output for personalized model summary
+                pareto_personalized_output_comp = pareto_personalized_output # gr.Plot() # Pareto for personalized model
 
                 gr.Markdown("**Ecuación del Modelo Simplificado**")
-                equation_output = gr.HTML()
-                optimization_table_output = gr.Dataframe(label="Tabla de Optimización", interactive=False)
-                prediction_table_output = gr.Dataframe(label="Tabla de Predicciones", interactive=False)
-                contribution_table_output = gr.Dataframe(label="Tabla de % de Contribución", interactive=False)
-                anova_table_output = gr.Dataframe(label="Tabla ANOVA Detallada", interactive=False)
+                equation_output = equation_output # gr.HTML()
+                optimization_table_output = optimization_table_output # gr.Dataframe(label="Tabla de Optimización", interactive=False)
+                prediction_table_output = prediction_table_output # gr.Dataframe(label="Tabla de Predicciones", interactive=False)
+                contribution_table_output = contribution_table_output # gr.Dataframe(label="Tabla de % de Contribución", interactive=False)
+                anova_table_output = anova_table_output # gr.Dataframe(label="Tabla ANOVA Detallada", interactive=False)
 
                 gr.Markdown("## Descargar Todas las Tablas")
-                download_excel_button = gr.DownloadButton("Descargar Tablas en Excel")
+                download_excel_button_comp = download_excel_button # gr.DownloadButton("Descargar Tablas en Excel")
                 download_word_button = gr.DownloadButton("Descargar Tablas en Word")
 
             with gr.Column():
@@ -526,14 +378,14 @@ def create_gradio_interface():
                 with gr.Row():
                     left_button = gr.Button("<")
                     right_button = gr.Button(">")
-                rsm_plot_output = gr.Plot()
-                plot_info = gr.Textbox(label="Información del Gráfico", value="Gráfico 1 de 9", interactive=False)
+                rsm_plot_output = rsm_plot_output # gr.Plot()
+                plot_info = plot_info # gr.Textbox(label="Información del Gráfico", value="Gráfico 1 de 9", interactive=False)
                 with gr.Row():
-                    download_plot_button = gr.DownloadButton("Descargar Gráfico Actual (PNG)")
-                    download_all_plots_button = gr.DownloadButton("Descargar Todos los Gráficos (ZIP)")
-                current_index_state = gr.State(0)
-                all_figures_state = gr.State([])
-                current_model_type_state = gr.State('simplified') # State to track selected model type for plots
+                    download_plot_button_comp = download_plot_button # gr.DownloadButton("Descargar Gráfico Actual (PNG)")
+                    download_all_plots_button_comp = download_all_plots_button # gr.DownloadButton("Descargar Todos los Gráficos (ZIP)")
+                current_index_state = current_index_state # gr.State(0)
+                all_figures_state = all_figures_state # gr.State([])
+                current_model_type_state = current_model_type_state # gr.State('simplified')
 
 
         # Cargar datos
@@ -565,8 +417,8 @@ def create_gradio_interface():
         # Ajustar modelo personalizado
         custom_model_button.click( # New event for custom model fitting
             fit_custom_model,
-            inputs=[factor_checkboxes, interaction_checkboxes, model_personalized_output, pareto_personalized_output], # pass output components
-            outputs=[model_personalized_output, pareto_personalized_output] # return values for outputs
+            inputs=[factor_checkboxes_comp, interaction_checkboxes_comp],
+            outputs=[model_personalized_output_comp, pareto_personalized_output_comp] # pass output components
         )