Update app.py

app.py

@@ -42,297 +42,266 @@ interaction_checkboxes = gr.CheckboxGroup(["x1x2", "x1x3", "x2x3"], label="Térm
 # --- Clase RSM_BoxBehnken ---
 class RSM_BoxBehnken:
     def __init__(self, data, x1_name, x2_name, x3_name, y_name, x1_levels, x2_levels, x3_levels):
+        """
+        Inicializa la clase con los datos del diseño Box-Behnken.
+        """
         self.data = data.copy()
         self.model = None
         self.model_simplified = None
-        self.model_personalized = None
+        self.model_personalized = None # For personalized model
         self.optimized_results = None
         self.optimal_levels = None
-        self.all_figures_full = []
+        self.all_figures_full = [] # Separate lists for different model plots
         self.all_figures_simplified = []
         self.all_figures_personalized = []
         self.x1_name = x1_name
         self.x2_name = x2_name
         self.x3_name = x3_name
         self.y_name = y_name
+
+        # Niveles originales de las variables
         self.x1_levels = x1_levels
         self.x2_levels = x2_levels
         self.x3_levels = x3_levels
 
     def get_levels(self, variable_name):
+        """
+        Obtiene los niveles para una variable específica.
+        """
         levels = {self.x1_name: self.x1_levels, self.x2_name: self.x2_levels, self.x3_name: self.x3_levels}
         return levels.get(variable_name)
 
     def fit_model(self):
-        formula = f'{self.y_name} ~ {self.x1_name} + {self.x2_name} + {self.x3_name} + I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**2) + {self.x1_name}:{self.x2_name} + {self.x1_name}:{self.x3_name} + {self.x2_name}:{self.x3_name}'
+        """
+        Ajusta el modelo de segundo orden completo a los datos.
+        """
+        formula = f'{self.y_name} ~ {self.x1_name} + {self.x2_name} + {self.x3_name} + ' \
+                  f'I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**2) + ' \
+                  f'{self.x1_name}:{self.x2_name} + {self.x1_name}:{self.x3_name} + {self.x2_name}:{self.x3_name}'
         self.model = smf.ols(formula, data=self.data).fit()
+        print("Modelo Completo:")
+        print(self.model.summary())
         return self.model, self.pareto_chart(self.model, "Pareto - Modelo Completo")
 
     def fit_simplified_model(self):
-        formula = f'{self.y_name} ~ {self.x1_name} + {self.x2_name} + I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**2)'
+        """
+        Ajusta el modelo de segundo orden a los datos, eliminando términos no significativos.
+        """
+        formula = f'{self.y_name} ~ {self.x1_name} + {self.x2_name} + ' \
+                  f'I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**2)' # Adjusted formula to include x3^2
         self.model_simplified = smf.ols(formula, data=self.data).fit()
+        print("\nModelo Simplificado:")
+        print(self.model_simplified.summary())
         return self.model_simplified, self.pareto_chart(self.model_simplified, "Pareto - Modelo Simplificado")
 
     def optimize(self, method='Nelder-Mead'):
-        if self.model_simplified is None: return
+        """
+        Encuentra los niveles óptimos de los factores para maximizar la respuesta usando el modelo simplificado.
+        """
+        if self.model_simplified is None:
+            print("Error: Ajusta el modelo simplificado primero.")
+            return
+
         def objective_function(x):
-            return -self.model_simplified.predict(pd.DataFrame({self.x1_name: [x[0]], self.x2_name: [x[1]], self.x3_name: [x[2]]})).values[0]
+            return -self.model_simplified.predict(pd.DataFrame({
+                self.x1_name: [x[0]],
+                self.x2_name: [x[1]],
+                self.x3_name: [x[2]]
+            })).values[0]
+
         bounds = [(-1, 1), (-1, 1), (-1, 1)]
         x0 = [0, 0, 0]
+
         self.optimized_results = minimize(objective_function, x0, method=method, bounds=bounds)
-        optimal_levels_natural = [self.coded_to_natural(self.optimal_levels[0], self.x1_name), self.coded_to_natural(self.optimal_levels[1], self.x2_name), self.coded_to_natural(self.optimal_levels[2], self.x3_name)]
-        optimization_table = pd.DataFrame({'Variable': [self.x1_name, self.x2_name, self.x3_name], 'Nivel Óptimo (Natural)': optimal_levels_natural, 'Nivel Óptimo (Codificado)': self.optimal_levels})
-        return optimization_table.round(3)
+        self.optimal_levels = self.optimized_results.x
+
+        # Convertir niveles óptimos de codificados a naturales
+        optimal_levels_natural = [
+            self.coded_to_natural(self.optimal_levels[0], self.x1_name),
+            self.coded_to_natural(self.optimal_levels[1], self.x2_name),
+            self.coded_to_natural(self.optimal_levels[2], self.x3_name)
+        ]
+        # Crear la tabla de optimización
+        optimization_table = pd.DataFrame({
+            'Variable': [self.x1_name, self.x2_name, self.x3_name],
+            'Nivel Óptimo (Natural)': optimal_levels_natural,
+            'Nivel Óptimo (Codificado)': self.optimal_levels
+        })
+
+        return optimization_table.round(3)  # Redondear a 3 decimales
 
     def fit_personalized_model(self, formula):
+        """
+        Ajusta un modelo personalizado de segundo orden a los datos, usando la formula dada.
+        """
         self.model_personalized = smf.ols(formula, data=self.data).fit()
+        print("\nModelo Personalizado:")
+        print(self.model_personalized.summary())
         return self.model_personalized, self.pareto_chart(self.model_personalized, "Pareto - Modelo Personalizado")
 
     def generate_all_plots(self):
-        if self.model_simplified is None: return
-        self.all_figures_full = []
+        """
+        Genera todas las gráficas de RSM para todos los modelos.
+        """
+        if self.model_simplified is None:
+            print("Error: Ajusta el modelo simplificado primero.")
+            return
+
+        self.all_figures_full = [] # Reset lists for each model type
         self.all_figures_simplified = []
         self.all_figures_personalized = []
-        levels_to_plot_natural = {self.x1_name: self.x1_levels, self.x2_name: self.x2_levels, self.x3_name: self.x3_levels}
+
+        levels_to_plot_natural = { # Levels from data, as before
+            self.x1_name: sorted(list(set(self.data[self.x1_name]))),
+            self.x2_name: sorted(list(set(self.data[self.x2_name]))),
+            self.x3_name: sorted(list(set(self.data[self.x3_name])))
+        }
+
         for fixed_variable in [self.x1_name, self.x2_name, self.x3_name]:
             for level in levels_to_plot_natural[fixed_variable]:
-                fig_full = self.plot_rsm_individual(fixed_variable, level, model_type='full')
-                if fig_full: self.all_figures_full.append(fig_full)
-                fig_simplified = self.plot_rsm_individual(fixed_variable, level, model_type='simplified')
-                if fig_simplified: self.all_figures_simplified.append(fig_simplified)
-                if self.model_personalized is not None:
-                    fig_personalized = self.plot_rsm_individual(fixed_variable, level, model_type='personalized')
-                    if fig_personalized: self.all_figures_personalized.append(fig_personalized)
-
-    def plot_rsm_individual(self, fixed_variable, fixed_level, model_type='simplified'):
-        model_to_use = self.model_simplified if model_type == 'simplified' else self.model if model_type == 'full' else self.model_personalized
-        if model_to_use is None: return None
-        model_title_suffix = "(Modelo Simplificado)" if model_type == 'simplified' else "(Modelo Completo)" if model_type == 'full' else "(Modelo Personalizado)"
+                fig_full = self.plot_rsm_individual(fixed_variable, level, model_type='full') # Pass model_type
+                if fig_full is not None:
+                    self.all_figures_full.append(fig_full)
+                fig_simplified = self.plot_rsm_individual(fixed_variable, level, model_type='simplified') # Pass model_type
+                if fig_simplified is not None:
+                    self.all_figures_simplified.append(fig_simplified)
+                if self.model_personalized is not None: # Generate personalized plots only if model exists
+                    fig_personalized = self.plot_rsm_individual(fixed_variable, level, model_type='personalized') # Pass model_type
+                    if fig_personalized is not None:
+                        self.all_figures_personalized.append(fig_personalized)
+
+    def plot_rsm_individual(self, fixed_variable, fixed_level, model_type='simplified'): # Added model_type parameter
+        """
+        Genera un gráfico de superficie de respuesta (RSM) individual para una configuración específica y modelo.
+        """
+        model_to_use = self.model_simplified # Default to simplified model
+        model_title_suffix = "(Modelo Simplificado)"
+        if model_type == 'full':
+            model_to_use = self.model
+            model_title_suffix = "(Modelo Completo)"
+        elif model_type == 'personalized':
+            if self.model_personalized is None:
+                print("Error: Modelo personalizado no ajustado.")
+                return None
+            model_to_use = self.model_personalized
+            model_title_suffix = "(Modelo Personalizado)"
+
+        if model_to_use is None: # Use model_to_use instead of self.model_simplified
+            print(f"Error: Ajusta el modelo {model_type} primero.") # More informative error message
+            return None
+
+        # Determinar las variables que varían y sus niveles naturales
         varying_variables = [var for var in [self.x1_name, self.x2_name, self.x3_name] if var != fixed_variable]
+
+        # Establecer los niveles naturales para las variables que varían
         x_natural_levels = self.get_levels(varying_variables[0])
         y_natural_levels = self.get_levels(varying_variables[1])
+
+        # Crear una malla de puntos para las variables que varían (en unidades naturales)
         x_range_natural = np.linspace(x_natural_levels[0], x_natural_levels[-1], 100)
         y_range_natural = np.linspace(y_natural_levels[0], y_natural_levels[-1], 100)
         x_grid_natural, y_grid_natural = np.meshgrid(x_range_natural, y_range_natural)
+
+        # 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])
-        prediction_data = pd.DataFrame({varying_variables[0]: x_grid_coded.flatten(), varying_variables[1]: y_grid_coded.flatten()})
+        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({
+            varying_variables[0]: x_grid_coded.flatten(),
+            varying_variables[1]: y_grid_coded.flatten(),
+        })
         prediction_data[fixed_variable] = self.natural_to_coded(fixed_level, fixed_variable)
-        z_pred = model_to_use.predict(prediction_data).values.reshape(x_grid_coded.shape)
+
+        # Fijar la variable fija en el DataFrame de predicción
+        fixed_var_levels = self.get_levels(fixed_variable)
+        if len(fixed_var_levels) == 3:  # Box-Behnken design levels
+            prediction_data[fixed_variable] = self.natural_to_coded(fixed_level, fixed_variable)
+        elif len(fixed_var_levels) > 0: # Use the closest level if not Box-Behnken
+            closest_level_coded = self.natural_to_coded(min(fixed_var_levels, key=lambda x:abs(x-fixed_level)), fixed_variable)
+            prediction_data[fixed_variable] = closest_level_coded
+
+
+        # Calcular los valores predichos
+        z_pred = model_to_use.predict(prediction_data).values.reshape(x_grid_coded.shape) # Use model_to_use here
+
+        # Filtrar por el nivel de la variable fija (en codificado)
         fixed_level_coded = self.natural_to_coded(fixed_level, fixed_variable)
         subset_data = self.data[np.isclose(self.data[fixed_variable], fixed_level_coded)]
+
+        # Filtrar por niveles válidos en las variables que varían
         valid_levels = [-1, 0, 1]
-        experiments_data = subset_data[subset_data[varying_variables[0]].isin(valid_levels) & subset_data[varying_variables[1]].isin(valid_levels)]
+        experiments_data = subset_data[
+            subset_data[varying_variables[0]].isin(valid_levels) &
+            subset_data[varying_variables[1]].isin(valid_levels)
+        ]
+
+        # Convertir coordenadas de experimentos a naturales
         experiments_x_natural = experiments_data[varying_variables[0]].apply(lambda x: self.coded_to_natural(x, varying_variables[0]))
         experiments_y_natural = experiments_data[varying_variables[1]].apply(lambda x: self.coded_to_natural(x, varying_variables[1]))
 
+        # Crear el gráfico de superficie con variables naturales en los ejes y transparencia
         fig = go.Figure(data=[go.Surface(z=z_pred, x=x_grid_natural, y=y_grid_natural, colorscale='Viridis', opacity=0.7, showscale=True)])
+
+        # --- Añadir cuadrícula a la superficie ---
+        # Líneas en la dirección x
         for i in range(x_grid_natural.shape[0]):
-            fig.add_trace(go.Scatter3d(x=x_grid_natural[i, :], y=y_grid_natural[i, :], z=z_pred[i, :], mode='lines', line=dict(color='gray', width=2), showlegend=False, hoverinfo='skip'))
+            fig.add_trace(go.Scatter3d(
+                x=x_grid_natural[i, :],
+                y=y_grid_natural[i, :],
+                z=z_pred[i, :],
+                mode='lines',
+                line=dict(color='gray', width=2),
+                showlegend=False,
+                hoverinfo='skip'
+            ))
+        # Líneas en la dirección y
         for j in range(x_grid_natural.shape[1]):
-            fig.add_trace(go.Scatter3d(x=x_grid_natural[:, j], y=y_grid_natural[:, j], z=z_pred[:, j], mode='lines', line=dict(color='gray', width=2), showlegend=False, hoverinfo='skip'))
-
+            fig.add_trace(go.Scatter3d(
+                x=x_grid_natural[:, j],
+                y=y_grid_natural[:, j],
+                z=z_pred[:, j],
+                mode='lines',
+                line=dict(color='gray', width=2),
+                showlegend=False,
+                hoverinfo='skip'
+            ))
+
+        # --- Fin de la adición de la cuadrícula ---
+
+        # Añadir los puntos de los experimentos en la superficie de respuesta con diferentes colores y etiquetas
         colors = px.colors.qualitative.Safe
         point_labels = [f"{row[self.y_name]:.3f}" for _, row in experiments_data.iterrows()]
-        fig.add_trace(go.Scatter3d(x=experiments_x_natural, y=experiments_y_natural, z=experiments_data[self.y_name].round(3), mode='markers+text', marker=dict(size=4, color=colors[:len(experiments_x_natural)]), text=point_labels, textposition='top center', name='Experimentos'))
-        fig.update_layout(scene=dict(xaxis_title=f"{varying_variables[0]} ({self.get_units(varying_variables[0])})", yaxis_title=f"{varying_variables[1]} ({self.get_units(varying_variables[1])})", zaxis_title=self.y_name), title=f"{self.y_name} vs {varying_variables[0]} y {varying_variables[1]}<br><sup>{fixed_variable} fijo en {fixed_level:.3f} ({self.get_units(fixed_variable)}) {model_title_suffix}</sup>", height=800, width=1000, showlegend=True)
-        return fig
 
-    def get_units(self, variable_name):
-        units = {'Glucosa_g_L': 'g/L', 'Proteina_Pescado_g_L': 'g/L', 'Sulfato_Manganeso_g_L': 'g/L', 'Abs_600nm': ''}
-        return units.get(variable_name, '')
-
-    def coded_to_natural(self, coded_value, variable_name):
-        levels = self.get_levels(variable_name)
-        return levels[0] + (coded_value + 1) * (levels[-1] - levels[0]) / 2
-
-    def natural_to_coded(self, natural_value, variable_name):
-        levels = self.get_levels(variable_name)
-        return -1 + 2 * (natural_value - levels[0]) / (levels[-1] - levels[0])
-
-    def pareto_chart(self, model, title):
-        tvalues = model.tvalues[1:]
-        abs_tvalues = np.abs(tvalues)
-        sorted_idx = np.argsort(abs_tvalues)[::-1]
-        sorted_tvalues = abs_tvalues[sorted_idx]
-        sorted_names = tvalues.index[sorted_idx]
-        alpha = 0.05
-        dof = model.df_resid
-        t_critical = t.ppf(1 - alpha / 2, dof)
-        fig = px.bar(x=sorted_tvalues.round(3), y=sorted_names, orientation='h', labels={'x': 'Efecto Estandarizado', 'y': 'Término'}, title=title)
-        fig.update_yaxes(autorange="reversed")
-        fig.add_vline(x=t_critical, line_dash="dot", annotation_text=f"t crítico = {t_critical:.3f}", annotation_position="bottom right")
+        fig.add_trace(go.Scatter3d(
+            x=experiments_x_natural,
+            y=experiments_y_natural,
+            z=experiments_data[self.y_name].round(3),
+            mode='markers+text',
+            marker=dict(size=4, color=colors[:len(experiments_x_natural)]),
+            text=point_labels,
+            textposition='top center',
+            name='Experimentos'
+        ))
+
+        # Añadir etiquetas y título con variables naturales
+        fig.update_layout(
+            scene=dict(
+                xaxis_title=f"{varying_variables[0]} ({self.get_units(varying_variables[0])})",
+                yaxis_title=f"{varying_variables[1]} ({self.get_units(varying_variables[1])})",
+                zaxis_title=self.y_name,
+            ),
+            title=f"{self.y_name} vs {varying_variables[0]} y {varying_variables[1]}<br><sup>{fixed_variable} fijo en {fixed_level:.3f} ({self.get_units(fixed_variable)}) {model_title_suffix}</sup>", # Updated title
+            height=800,
+            width=1000,
+            showlegend=True
+        )
         return fig
 
-    def get_simplified_equation(self):
-        if self.model_simplified is None: return None
-        coefficients = self.model_simplified.params
-        equation = f"{self.y_name} = {coefficients['Intercept']:.3f}"
-        for term, coef in coefficients.items():
-            if term != 'Intercept':
-                if term == f'{self.x1_name}': equation += f" + {coef:.3f}*{self.x1_name}"
-                elif term == f'{self.x2_name}': equation += f" + {coef:.3f}*{self.x2_name}"
-                elif term == f'{self.x3_name}': equation += f" + {coef:.3f}*{self.x3_name}"
-                elif term == f'I({self.x1_name} ** 2)': equation += f" + {coef:.3f}*{self.x1_name}^2"
-                elif term == f'I({self.x2_name} ** 2)': equation += f" + {coef:.3f}*{self.x2_name}^2"
-                elif term == f'I({self.x3_name} ** 2)': equation += f" + {coef:.3f}*{self.x3_name}^2"
-        return equation
-
-    def generate_prediction_table(self):
-        if self.model_simplified is None: return None
-        self.data['Predicho'] = self.model_simplified.predict(self.data)
-        self.data['Residual'] = self.data[self.y_name] - self.data['Predicho']
-        return self.data[[self.y_name, 'Predicho', 'Residual']].round(3)
-
-    def calculate_contribution_percentage(self):
-        if self.model_simplified is None: return None
-        anova_table = sm.stats.anova_lm(self.model_simplified, typ=2)
-        ss_total = anova_table['sum_sq'].sum()
-        contribution_table = pd.DataFrame({'Factor': [], 'Suma de Cuadrados': [], '% Contribución': []})
-        for index, row in anova_table.iterrows():
-            if index != 'Residual':
-                factor_name = index
-                if factor_name == f'I({self.x1_name} ** 2)': factor_name = f'{self.x1_name}^2'
-                elif factor_name == f'I({self.x2_name} ** 2)': factor_name = f'{self.x2_name}^2'
-                elif factor_name == f'I({self.x3_name} ** 2)': factor_name = f'{self.x3_name}^2'
-                ss_factor = row['sum_sq']
-                contribution_percentage = (ss_factor / ss_total) * 100
-                contribution_table = pd.concat([contribution_table, pd.DataFrame({'Factor': [factor_name], 'Suma de Cuadrados': [ss_factor], '% Contribución': [contribution_percentage]})], ignore_index=True)
-        return contribution_table.round(3)
-
-    def calculate_detailed_anova(self):
-        if self.model_simplified is None: return None
-        formula_reduced = f'{self.y_name} ~ {self.x1_name} + {self.x2_name} + {self.x3_name} + I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**2)'
-        model_reduced = smf.ols(formula_reduced, data=self.data).fit()
-        anova_reduced = sm.stats.anova_lm(model_reduced, typ=2)
-        ss_total = np.sum((self.data[self.y_name] - self.data[self.y_name].mean())**2)
-        df_total = len(self.data) - 1
-        ss_regression = anova_reduced['sum_sq'][:-1].sum()
-        df_regression = len(anova_reduced) - 1
-        ss_residual = self.model_simplified.ssr
-        df_residual = self.model_simplified.df_resid
-        replicas = self.data[self.data.duplicated(subset=[self.x1_name, self.x2_name, self.x3_name], keep=False)]
-        ss_pure_error = replicas.groupby([self.x1_name, self.x2_name, self.x3_name])[self.y_name].var().sum() * replicas.groupby([self.x1_name, self.x2_name, self.x3_name]).ngroups if not replicas.empty else np.nan
-        df_pure_error = len(replicas) - replicas.groupby([self.x1_name, self.x2_name, self.x3_name]).ngroups if not replicas.empty else np.nan
-        ss_lack_of_fit = ss_residual - ss_pure_error if not np.isnan(ss_pure_error) else np.nan
-        df_lack_of_fit = df_residual - df_pure_error if not np.isnan(df_pure_error) else np.nan
-        ms_regression = ss_regression / df_regression
-        ms_residual = ss_residual / df_residual
-        ms_lack_of_fit = np.nan
-        if not np.isnan(df_lack_of_fit) and df_lack_of_fit != 0:
-            ms_lack_of_fit = ss_lack_of_fit / df_lack_of_fit
-        ms_pure_error = ss_pure_error / df_pure_error if not np.isnan(df_pure_error) else np.nan
-        f_lack_of_fit = ms_lack_of_fit / ms_pure_error if not np.isnan(ms_lack_of_fit) and not np.isnan(ms_pure_error) and ms_pure_error != 0 else np.nan
-        p_lack_of_fit = 1 - f.cdf(f_lack_of_fit, df_lack_of_fit, df_pure_error) if not np.isnan(f_lack_of_fit) and not np.isnan(df_lack_of_fit) and not np.isnan(df_pure_error) else np.nan
-
-        detailed_anova_table = pd.DataFrame({
-            'Fuente de Variación': ['Regresión', 'Curvatura', 'Residual', 'Falta de Ajuste', 'Error Puro', 'Total'], # Curvature added here
-            'Suma de Cuadrados': [ss_regression, np.nan, ss_residual, ss_lack_of_fit, ss_pure_error, ss_total], # ss_curvature removed from here
-            'Grados de Libertad': [df_regression, np.nan, df_residual, df_lack_of_fit, df_pure_error, df_total], # df_curvature removed from here
-            'Cuadrado Medio': [ms_regression, np.nan, ms_residual, ms_lack_of_fit, ms_pure_error, np.nan],
-            'F': [np.nan, np.nan, np.nan, f_lack_of_fit, np.nan, np.nan],
-            'Valor p': [np.nan, np.nan, np.nan, p_lack_of_fit, np.nan, np.nan]
-        })
-        ss_curvature = anova_reduced['sum_sq'][f'I({self.x1_name} ** 2)'] + anova_reduced['sum_sq'][f'I({self.x2_name} ** 2)'] + anova_reduced['sum_sq'][f'I({self.x3_name} ** 2)']
-        df_curvature = 3
-        detailed_anova_table.loc[1, ['Fuente de Variación', 'Suma de Cuadrados', 'Grados de Libertad', 'Cuadrado Medio']] = ['Curvatura', ss_curvature, df_curvature, ss_curvature / df_curvature] # Curvature row added here
-
-        return detailed_anova_table.round(3)
-
-    def get_all_tables(self):
-        prediction_table = self.generate_prediction_table()
-        contribution_table = self.calculate_contribution_percentage()
-        detailed_anova_table = self.calculate_detailed_anova()
-        return {'Predicciones': prediction_table, '% Contribución': contribution_table, 'ANOVA Detallada': detailed_anova_table}
-
-    def save_figures_to_zip(self):
-        if not self.all_figures_simplified and not self.all_figures_full and not self.all_figures_personalized: return None
-        zip_buffer = io.BytesIO()
-        with zipfile.ZipFile(zip_buffer, 'w') as zip_file:
-            for idx, fig in enumerate(self.all_figures_simplified, start=1):
-                img_bytes = fig.to_image(format="png")
-                zip_file.writestr(f'Grafico_Simplificado_{idx}.png', img_bytes)
-            for idx, fig in enumerate(self.all_figures_full, start=1):
-                img_bytes = fig.to_image(format="png")
-                zip_file.writestr(f'Grafico_Completo_{idx}.png', img_bytes)
-            for idx, fig in enumerate(self.all_figures_personalized, start=1):
-                img_bytes = fig.to_image(format="png")
-                zip_file.writestr(f'Grafico_Personalizado_{idx}.png', img_bytes)
-        zip_buffer.seek(0)
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".zip") as temp_file:
-            temp_file.write(zip_buffer.read())
-            temp_path = temp_file.name
-        return temp_path
-
-    def save_fig_to_bytes(self, fig):
-        return fig.to_image(format="png")
-
-    def save_all_figures_png(self):
-        png_paths = []
-        for idx, fig in enumerate(self.all_figures_simplified, start=1):
-            img_bytes = fig.to_image(format="png")
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-                temp_file.write(img_bytes)
-                png_paths.append(temp_file.name)
-        for idx, fig in enumerate(self.all_figures_full, start=1):
-            img_bytes = fig.to_image(format="png")
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-                temp_file.write(img_bytes)
-                png_paths.append(temp_file.name)
-        for idx, fig in enumerate(self.all_figures_personalized, start=1):
-            img_bytes = fig.to_image(format="png")
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
-                temp_file.write(img_bytes)
-                png_paths.append(temp_file.name)
-        return png_paths
-
-    def save_tables_to_excel(self):
-        tables = self.get_all_tables()
-        excel_buffer = io.BytesIO()
-        with pd.ExcelWriter(excel_buffer, engine='xlsxwriter') as writer:
-            for sheet_name, table in tables.items():
-                table.to_excel(writer, sheet_name=sheet_name, index=False)
-        excel_buffer.seek(0)
-        excel_bytes = excel_buffer.read()
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx") as temp_file:
-            temp_file.write(excel_bytes)
-            temp_path = temp_file.name
-        return temp_path
-
-    def export_tables_to_word(self, tables_dict):
-        if not tables_dict: return None
-        doc = docx.Document()
-        style = doc.styles['Normal']
-        font = style.font
-        font.name = 'Times New Roman'
-        font.size = Pt(12)
-        titulo = doc.add_heading('Informe de Optimización de Producción de Absorbancia', 0)
-        titulo.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-        doc.add_paragraph(f"Fecha: {datetime.now().strftime('%d/%m/%Y %H:%M')}").alignment = WD_PARAGRAPH_ALIGNMENT.CENTER
-        doc.add_paragraph('\n')
-        for sheet_name, table in tables_dict.items():
-            doc.add_heading(sheet_name, level=1)
-            if table.empty:
-                doc.add_paragraph("No hay datos disponibles para esta tabla.")
-                continue
-            table_doc = doc.add_table(rows=1, cols=len(table.columns))
-            table_doc.style = 'Light List Accent 1'
-            hdr_cells = table_doc.rows[0].cells
-            for idx, col_name in enumerate(table.columns):
-                hdr_cells[idx].text = col_name
-            for _, row in table.iterrows():
-                row_cells = table_doc.add_row().cells
-                for idx, item in enumerate(row):
-                    row_cells[idx].text = str(item)
-            doc.add_paragraph('\n')
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".docx") as tmp:
-            doc.save(tmp.name)
-            tmp_path = tmp.name
-        return tmp_path
-
 
 # --- Funciones para la Interfaz de Gradio ---
 
+
 def load_data(x1_name, x2_name, x3_name, y_name, x1_levels_str, x2_levels_str, x3_levels_str, data_str):
     try:
         x1_levels = [float(x.strip()) for x in x1_levels_str.split(',')]
@@ -340,9 +309,10 @@ def load_data(x1_name, x2_name, x3_name, y_name, x1_levels_str, x2_levels_str, x
         x3_levels = [float(x.strip()) for x in x3_levels_str.split(',')]
         data_list = [row.split(',') for row in data_str.strip().split('\n')]
         column_names = ['Exp.', x1_name, x2_name, x3_name, y_name]
-        data = pd.DataFrame(data_list, columns=column_names).apply(pd.to_numeric, errors='coerce')
-        if not all(col in data.columns for col in column_names): raise ValueError("Data format incorrect.")
-        global rsm
+        data_loaded = pd.DataFrame(data_list, columns=column_names).apply(pd.to_numeric, errors='coerce')
+        if not all(col in data_loaded.columns for col in column_names): raise ValueError("Data format incorrect.")
+        global rsm, data
+        data = data_loaded # Assign loaded data to global data variable
         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)
     except Exception as e:
@@ -364,7 +334,7 @@ def fit_and_optimize_model():
     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.summary().as_html(), pareto_simplificado, equation_formatted, optimization_table, prediction_table, contribution_table, anova_table, zip_path, excel_path)
+    return (model_completo.summary().as_html(), pareto_completo, model_simplificado_output, 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
@@ -393,7 +363,7 @@ def navigate_plot(direction, current_index, all_figures, model_type):
     new_index = (current_index - 1) % len(figure_list) if direction == 'left' else (current_index + 1) % len(figure_list)
     selected_fig = figure_list[new_index]
     plot_info_text = f"Gráfico {new_index + 1} de {len(figure_list)} (Modelo {model_type.capitalize()})"
-    return selected_fig, plot_info_text, new_index
+    return selected_fig, plot_info_text, current_index
 
 def download_current_plot(all_figures, current_index, model_type):
     figure_list = rsm.all_figures_full if model_type == 'full' else rsm.all_figures_simplified if model_type == 'simplified' else rsm.all_figures_personalized
@@ -479,7 +449,7 @@ def create_gradio_interface():
 38,1,0,1,1.810
 39,0,-1,-1,1.852
 40,0,1,-1,1.694
-41,0,-1,1,1.831
+41,0,1,1,1.831
 42,0,1,1,0.347
 43,0,0,0,1.752
 44,0,0,0,1.367
@@ -531,7 +501,7 @@ def create_gradio_interface():
                 rsm_plot_output_comp = rsm_plot_output
                 plot_info_comp = plot_info
                 with gr.Row():
-                    download_plot_button_comp = download_plot_button
+                    download_plot_button_comp = download_plot_button # Correctly assigned here now
                     download_all_plots_button_comp = download_all_plots_button
                 current_index_state_comp = current_index_state
                 all_figures_state_comp = all_figures_state