Update app.py

app.py

@@ -1,6 +1,5 @@
 #import os
 #!pip install gradio seaborn scipy scikit-learn openpyxl pydantic==1.10.0 -q
-
 from pydantic import BaseModel, ConfigDict
 import numpy as np
 import pandas as pd
@@ -80,28 +79,23 @@ class BioprocessModel:
         biomass_cols = [col for col in df.columns if col[1] == 'Biomasa']
         substrate_cols = [col for col in df.columns if col[1] == 'Sustrato']
         product_cols = [col for col in df.columns if col[1] == 'Producto']
-
         time_col = [col for col in df.columns if col[1] == 'Tiempo'][0]
         time = df[time_col].values
-
         data_biomass = [df[col].values for col in biomass_cols]
         data_biomass = np.array(data_biomass)
         self.datax.append(data_biomass)
         self.dataxp.append(np.mean(data_biomass, axis=0))
         self.datax_std.append(np.std(data_biomass, axis=0, ddof=1))
-
         data_substrate = [df[col].values for col in substrate_cols]
         data_substrate = np.array(data_substrate)
         self.datas.append(data_substrate)
         self.datasp.append(np.mean(data_substrate, axis=0))
         self.datas_std.append(np.std(data_substrate, axis=0, ddof=1))
-
         data_product = [df[col].values for col in product_cols]
         data_product = np.array(data_product)
         self.datap.append(data_product)
         self.datapp.append(np.mean(data_product, axis=0))
         self.datap_std.append(np.std(data_product, axis=0, ddof=1))
-
         self.time = time
 
     def fit_model(self):
@@ -132,7 +126,6 @@ class BioprocessModel:
                 popt, _ = curve_fit(self.moser, time, biomass, p0=p0, maxfev=self.maxfev)
                 self.params['biomass'] = {'Xm': popt[0], 'um': popt[1], 'Ks': popt[2]}
                 y_pred = self.moser(time, *popt)
-
             self.r2['biomass'] = 1 - (np.sum((biomass - y_pred) ** 2) / np.sum((biomass - np.mean(biomass)) ** 2))
             self.rmse['biomass'] = np.sqrt(mean_squared_error(biomass, y_pred))
             return y_pred
@@ -212,7 +205,6 @@ class BioprocessModel:
 
     def system(self, y, t, biomass_params, substrate_params, product_params, model_type):
         X, S, P = y
-
         if model_type == 'logistic':
             dXdt = self.logistic_diff(X, t, biomass_params)
         elif model_type == 'gompertz':
@@ -221,10 +213,8 @@ class BioprocessModel:
             dXdt = self.moser_diff(X, t, biomass_params)
         else:
             dXdt = 0.0
-
         so, p, q = substrate_params
         po, alpha, beta = product_params
-
         dSdt = -p * dXdt - q * X
         dPdt = alpha * dXdt + beta * X
         return [dXdt, dSdt, dPdt]
@@ -246,26 +236,22 @@ class BioprocessModel:
                 X0 = Xm*(1 - np.exp(-um*(0 - Ks)))
         else:
             X0 = biomass[0]
-
         if 'substrate' in self.params:
             so = self.params['substrate']['so']
             S0 = so
         else:
             S0 = substrate[0]
-
         if 'product' in self.params:
             po = self.params['product']['po']
             P0 = po
         else:
             P0 = product[0]
-
         return [X0, S0, P0]
 
     def solve_differential_equations(self, time, biomass, substrate, product):
         if 'biomass' not in self.params or not self.params['biomass']:
             print("No hay parámetros de biomasa, no se pueden resolver las EDO.")
             return None, None, None, time
-
         if self.model_type == 'logistic':
             biomass_params = [self.params['biomass']['xo'], self.params['biomass']['xm'], self.params['biomass']['um']]
         elif self.model_type == 'gompertz':
@@ -274,26 +260,21 @@ class BioprocessModel:
             biomass_params = [self.params['biomass']['Xm'], self.params['biomass']['um'], self.params['biomass']['Ks']]
         else:
             biomass_params = [0,0,0]
-
         if 'substrate' in self.params:
             substrate_params = [self.params['substrate']['so'], self.params['substrate']['p'], self.params['substrate']['q']]
         else:
             substrate_params = [0,0,0]
-
         if 'product' in self.params:
             product_params = [self.params['product']['po'], self.params['product']['alpha'], self.params['product']['beta']]
         else:
             product_params = [0,0,0]
-
         initial_conditions = self.get_initial_conditions(time, biomass, substrate, product)
         time_fine = self.generate_fine_time_grid(time)
         sol = odeint(self.system, initial_conditions, time_fine,
                      args=(biomass_params, substrate_params, product_params, self.model_type))
-
         X = sol[:, 0]
         S = sol[:, 1]
         P = sol[:, 2]
-
         return X, S, P, time_fine
 
     def plot_results(self, time, biomass, substrate, product,
@@ -303,15 +284,11 @@ class BioprocessModel:
                      show_legend=True, show_params=True,
                      style='whitegrid',
                      line_color='#0000FF', point_color='#000000', line_style='-', marker_style='o',
-                     use_differential=False,
-                     x_label='Tiempo', y_label_biomass='Biomasa', y_label_substrate='Sustrato', y_label_product='Producto'):
-
+                     use_differential=False):
         if y_pred_biomass is None:
             print(f"No se pudo ajustar biomasa para {experiment_name} con {self.model_type}. Omitiendo figura.")
             return None
-
         sns.set_style(style)
-
         if use_differential and 'biomass' in self.params and self.params['biomass']:
             X, S, P, time_to_plot = self.solve_differential_equations(time, biomass, substrate, product)
             if X is not None:
@@ -320,36 +297,30 @@ class BioprocessModel:
                 time_to_plot = time
         else:
             time_to_plot = time
-
         fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 15))
         fig.suptitle(f'{experiment_name}', fontsize=16)
-
         plots = [
-            (ax1, biomass, y_pred_biomass, biomass_std, y_label_biomass, 'Modelo', self.params.get('biomass', {}),
+            (ax1, biomass, y_pred_biomass, biomass_std, 'Biomass', 'Model', self.params.get('biomass', {}),
              self.r2.get('biomass', np.nan), self.rmse.get('biomass', np.nan)),
-            (ax2, substrate, y_pred_substrate, substrate_std, y_label_substrate, 'Modelo', self.params.get('substrate', {}),
+            (ax2, substrate, y_pred_substrate, substrate_std, 'Substrate', 'Model', self.params.get('substrate', {}),
              self.r2.get('substrate', np.nan), self.rmse.get('substrate', np.nan)),
-            (ax3, product, y_pred_product, product_std, y_label_product, 'Modelo', self.params.get('product', {}),
+            (ax3, product, y_pred_product, product_std, 'Product', 'Model', self.params.get('product', {}),
              self.r2.get('product', np.nan), self.rmse.get('product', np.nan))
         ]
-
         for idx, (ax, data, y_pred, data_std, ylabel, model_name, params, r2, rmse) in enumerate(plots):
             if data_std is not None:
                 ax.errorbar(time, data, yerr=data_std, fmt=marker_style, color=point_color,
-                            label='Datos experimentales', capsize=5)
+                            label='Experimental Data', capsize=5)
             else:
                 ax.plot(time, data, marker=marker_style, linestyle='', color=point_color,
-                        label='Datos experimentales')
-
+                        label='Experimental Data')
             if y_pred is not None:
                 ax.plot(time_to_plot, y_pred, linestyle=line_style, color=line_color, label=model_name)
-
-            ax.set_xlabel(x_label)
+            ax.set_xlabel('Time')
             ax.set_ylabel(ylabel)
             if show_legend:
                 ax.legend(loc=legend_position)
             ax.set_title(f'{ylabel}')
-
             if show_params and params and all(np.isfinite(list(map(float, params.values())))):
                 param_text = '\n'.join([f"{k} = {v:.3f}" for k, v in params.items()])
                 text = f"{param_text}\nR² = {r2:.3f}\nRMSE = {rmse:.3f}"
@@ -364,26 +335,21 @@ class BioprocessModel:
                     else:
                         text_x = 0.05
                         ha = 'left'
-
                     if params_position in ['upper right', 'upper left']:
                         text_y = 0.95
                         va = 'top'
                     else:
                         text_y = 0.05
                         va = 'bottom'
-
                     ax.text(text_x, text_y, text, transform=ax.transAxes,
                             verticalalignment=va, horizontalalignment=ha,
                             bbox={'boxstyle': 'round', 'facecolor':'white', 'alpha':0.5})
-
         plt.tight_layout(rect=[0, 0.03, 1, 0.95])
-
         buf = io.BytesIO()
         fig.savefig(buf, format='png')
         buf.seek(0)
         image = Image.open(buf).convert("RGB")
         plt.close(fig)
-
         return image
 
     def plot_combined_results(self, time, biomass, substrate, product,
@@ -393,15 +359,11 @@ class BioprocessModel:
                               show_legend=True, show_params=True,
                               style='whitegrid',
                               line_color='#0000FF', point_color='#000000', line_style='-', marker_style='o',
-                              use_differential=False,
-                              x_label='Tiempo', y_label_biomass='Biomasa', y_label_substrate='Sustrato', y_label_product='Producto'):
-
+                              use_differential=False):
         if y_pred_biomass is None:
             print(f"No se pudo ajustar biomasa para {experiment_name} con {self.model_type}. Omitiendo figura.")
             return None
-
         sns.set_style(style)
-
         if use_differential and 'biomass' in self.params and self.params['biomass']:
             X, S, P, time_to_plot = self.solve_differential_equations(time, biomass, substrate, product)
             if X is not None:
@@ -410,79 +372,67 @@ class BioprocessModel:
                 time_to_plot = time
         else:
             time_to_plot = time
-
         fig, ax1 = plt.subplots(figsize=(10, 7))
         fig.suptitle(f'{experiment_name}', fontsize=16)
-
-        colors = {'Biomasa': 'blue', 'Sustrato': 'green', 'Producto': 'red'}
-
-        ax1.set_xlabel(x_label)
-        ax1.set_ylabel(y_label_biomass, color=colors['Biomasa'])
+        colors = {'Biomass': 'blue', 'Substrate': 'green', 'Product': 'red'}
+        ax1.set_xlabel('Time')
+        ax1.set_ylabel('Biomass', color=colors['Biomass'])
         if biomass_std is not None:
-            ax1.errorbar(time, biomass, yerr=biomass_std, fmt=marker_style, color=colors['Biomasa'],
-                         label='Biomasa (Datos)', capsize=5)
+            ax1.errorbar(time, biomass, yerr=biomass_std, fmt=marker_style, color=colors['Biomass'],
+                         label='Biomass (Data)', capsize=5)
         else:
-            ax1.plot(time, biomass, marker=marker_style, linestyle='', color=colors['Biomasa'],
-                     label='Biomasa (Datos)')
-        ax1.plot(time_to_plot, y_pred_biomass, linestyle=line_style, color=colors['Biomasa'],
-                 label='Biomasa (Modelo)')
-        ax1.tick_params(axis='y', labelcolor=colors['Biomasa'])
-
+            ax1.plot(time, biomass, marker=marker_style, linestyle='', color=colors['Biomass'],
+                     label='Biomass (Data)')
+        ax1.plot(time_to_plot, y_pred_biomass, linestyle=line_style, color=colors['Biomass'],
+                 label='Biomass (Model)')
+        ax1.tick_params(axis='y', labelcolor=colors['Biomass'])
         ax2 = ax1.twinx()
-        ax2.set_ylabel(y_label_substrate, color=colors['Sustrato'])
+        ax2.set_ylabel('Substrate', color=colors['Substrate'])
         if substrate_std is not None:
-            ax2.errorbar(time, substrate, yerr=substrate_std, fmt=marker_style, color=colors['Sustrato'],
-                         label='Sustrato (Datos)', capsize=5)
+            ax2.errorbar(time, substrate, yerr=substrate_std, fmt=marker_style, color=colors['Substrate'],
+                         label='Substrate (Data)', capsize=5)
         else:
-            ax2.plot(time, substrate, marker=marker_style, linestyle='', color=colors['Sustrato'],
-                     label='Sustrato (Datos)')
+            ax2.plot(time, substrate, marker=marker_style, linestyle='', color=colors['Substrate'],
+                     label='Substrate (Data)')
         if y_pred_substrate is not None:
-            ax2.plot(time_to_plot, y_pred_substrate, linestyle=line_style, color=colors['Sustrato'],
-                     label='Sustrato (Modelo)')
-        ax2.tick_params(axis='y', labelcolor=colors['Sustrato'])
-
+            ax2.plot(time_to_plot, y_pred_substrate, linestyle=line_style, color=colors['Substrate'],
+                     label='Substrate (Model)')
+        ax2.tick_params(axis='y', labelcolor=colors['Substrate'])
         ax3 = ax1.twinx()
         ax3.spines["right"].set_position(("axes", 1.2))
         ax3.set_frame_on(True)
         ax3.patch.set_visible(False)
         for sp in ax3.spines.values():
             sp.set_visible(True)
-
-        ax3.set_ylabel(y_label_product, color=colors['Producto'])
+        ax3.set_ylabel('Product', color=colors['Product'])
         if product_std is not None:
-            ax3.errorbar(time, product, yerr=product_std, fmt=marker_style, color=colors['Producto'],
-                         label='Producto (Datos)', capsize=5)
+            ax3.errorbar(time, product, yerr=product_std, fmt=marker_style, color=colors['Product'],
+                         label='Product (Data)', capsize=5)
         else:
-            ax3.plot(time, product, marker=marker_style, linestyle='', color=colors['Producto'],
-                     label='Producto (Datos)')
+            ax3.plot(time, product, marker=marker_style, linestyle='', color=colors['Product'],
+                     label='Product (Data)')
         if y_pred_product is not None:
-            ax3.plot(time_to_plot, y_pred_product, linestyle=line_style, color=colors['Producto'],
-                     label='Producto (Modelo)')
-        ax3.tick_params(axis='y', labelcolor=colors['Producto'])
-
+            ax3.plot(time_to_plot, y_pred_product, linestyle=line_style, color=colors['Product'],
+                     label='Product (Model)')
+        ax3.tick_params(axis='y', labelcolor=colors['Product'])
         lines_labels = [ax.get_legend_handles_labels() for ax in [ax1, ax2, ax3]]
         lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
         if show_legend:
             ax1.legend(lines, labels, loc=legend_position)
-
         if show_params:
             param_text_biomass = ''
             if 'biomass' in self.params:
                 param_text_biomass = '\n'.join([f"{k} = {v:.3f}" for k, v in self.params['biomass'].items()])
-            text_biomass = f"Biomasa:\n{param_text_biomass}\nR² = {self.r2.get('biomass', np.nan):.3f}\nRMSE = {self.rmse.get('biomass', np.nan):.3f}"
-
+            text_biomass = f"Biomass:\n{param_text_biomass}\nR² = {self.r2.get('biomass', np.nan):.3f}\nRMSE = {self.rmse.get('biomass', np.nan):.3f}"
             param_text_substrate = ''
             if 'substrate' in self.params:
                 param_text_substrate = '\n'.join([f"{k} = {v:.3f}" for k, v in self.params['substrate'].items()])
-            text_substrate = f"Sustrato:\n{param_text_substrate}\nR² = {self.r2.get('substrate', np.nan):.3f}\nRMSE = {self.rmse.get('substrate', np.nan):.3f}"
-
+            text_substrate = f"Substrate:\n{param_text_substrate}\nR² = {self.r2.get('substrate', np.nan):.3f}\nRMSE = {self.rmse.get('substrate', np.nan):.3f}"
             param_text_product = ''
             if 'product' in self.params:
                 param_text_product = '\n'.join([f"{k} = {v:.3f}" for k, v in self.params['product'].items()])
-            text_product = f"Producto:\n{param_text_product}\nR² = {self.r2.get('product', np.nan):.3f}\nRMSE = {self.rmse.get('product', np.nan):.3f}"
-
-            total_text = f"{text_biomass}\n\n{text_substrate}\n\n{text_product}"
-
+            text_product = f"Product:\n{param_text_product}\nR² = {self.r2.get('product', np.nan):.3f}\nRMSE = {self.rmse.get('product', np.nan):.3f}"
+            total_text = f"{text_biomass}\n{text_substrate}\n{text_product}"
             if params_position == 'outside right':
                 bbox_props = dict(boxstyle='round', facecolor='white', alpha=0.5)
                 ax3.annotate(total_text, xy=(1.2, 0.5), xycoords='axes fraction',
@@ -494,55 +444,44 @@ class BioprocessModel:
                 else:
                     text_x = 0.05
                     ha = 'left'
-
                 if params_position in ['upper right', 'upper left']:
                     text_y = 0.95
                     va = 'top'
                 else:
                     text_y = 0.05
                     va = 'bottom'
-
                 ax1.text(text_x, text_y, total_text, transform=ax1.transAxes,
                          verticalalignment=va, horizontalalignment=ha,
                          bbox={'boxstyle':'round', 'facecolor':'white', 'alpha':0.5})
-
         plt.tight_layout(rect=[0, 0.03, 1, 0.95])
-
         buf = io.BytesIO()
         fig.savefig(buf, format='png')
         buf.seek(0)
         image = Image.open(buf).convert("RGB")
         plt.close(fig)
-
         return image
 
 def process_all_data(file, legend_position, params_position, model_types, experiment_names, lower_bounds, upper_bounds,
                      mode='independent', style='whitegrid', line_color='#0000FF', point_color='#000000',
-                     line_style='-', marker_style='o', show_legend=True, show_params=True, use_differential=False, maxfev_val=50000,
-                     x_label='Tiempo', y_label_biomass='Biomasa', y_label_substrate='Sustrato', y_label_product='Producto'):
-
+                     line_style='-', marker_style='o', show_legend=True, show_params=True, use_differential=False, maxfev_val=50000):
     try:
         xls = pd.ExcelFile(file.name)
     except Exception as e:
         print(f"Error al leer el archivo Excel: {e}")
         return [], pd.DataFrame()
-
     sheet_names = xls.sheet_names
     figures = []
     comparison_data = []
     experiment_counter = 0
-
     for sheet_name in sheet_names:
         try:
             df = pd.read_excel(file.name, sheet_name=sheet_name, header=[0, 1])
         except Exception as e:
             print(f"Error al leer la hoja '{sheet_name}': {e}")
             continue
-
         model_dummy = BioprocessModel()
         model_dummy.process_data(df)
         time = model_dummy.time
-
         if mode == 'independent':
             num_experiments = len(df.columns.levels[0])
             for idx in range(num_experiments):
@@ -555,7 +494,6 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                 except KeyError as e:
                     print(f"Error al procesar el experimento '{col}': {e}")
                     continue
-
                 biomass_std = None
                 substrate_std = None
                 product_std = None
@@ -568,14 +506,11 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                 if product.ndim > 1:
                     product_std = np.std(product, axis=0, ddof=1)
                     product = np.mean(product, axis=0)
-
                 experiment_name = (experiment_names[experiment_counter] if experiment_counter < len(experiment_names)
                                    else f"Tratamiento {experiment_counter + 1}")
-
                 for model_type in model_types:
                     model = BioprocessModel(model_type=model_type, maxfev=maxfev_val)
                     model.fit_model()
-
                     y_pred_biomass = model.fit_biomass(time_exp, biomass)
                     if y_pred_biomass is None:
                         comparison_data.append({
@@ -596,7 +531,6 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                         else:
                             y_pred_substrate = None
                             y_pred_product = None
-
                         comparison_data.append({
                             'Experimento': experiment_name,
                             'Modelo': model_type.capitalize(),
@@ -607,7 +541,6 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                             'R² Producto': model.r2.get('product', np.nan),
                             'RMSE Producto': model.rmse.get('product', np.nan)
                         })
-
                         if mode == 'combinado':
                             fig = model.plot_combined_results(time_exp, biomass, substrate, product,
                                                               y_pred_biomass, y_pred_substrate, y_pred_product,
@@ -617,8 +550,7 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                                                               show_legend, show_params,
                                                               style,
                                                               line_color, point_color, line_style, marker_style,
-                                                              use_differential,
-                                                              x_label, y_label_biomass, y_label_substrate, y_label_product)
+                                                              use_differential)
                         else:
                             fig = model.plot_results(time_exp, biomass, substrate, product,
                                                      y_pred_biomass, y_pred_substrate, y_pred_product,
@@ -628,13 +560,10 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                                                      show_legend, show_params,
                                                      style,
                                                      line_color, point_color, line_style, marker_style,
-                                                     use_differential,
-                                                     x_label, y_label_biomass, y_label_substrate, y_label_product)
+                                                     use_differential)
                         if fig is not None:
                             figures.append(fig)
-
                 experiment_counter += 1
-
         elif mode in ['average', 'combinado']:
             try:
                 time_exp = df[(df.columns.levels[0][0], 'Tiempo')].dropna().values
@@ -644,18 +573,14 @@ def process_all_data(file, legend_position, params_position, model_types, experi
             except IndexError as e:
                 print(f"Error al obtener los datos promedio de la hoja '{sheet_name}': {e}")
                 continue
-
             biomass_std = model_dummy.datax_std[-1]
             substrate_std = model_dummy.datas_std[-1]
             product_std = model_dummy.datap_std[-1]
-
             experiment_name = (experiment_names[experiment_counter] if experiment_counter < len(experiment_names)
                                else f"Tratamiento {experiment_counter + 1}")
-
             for model_type in model_types:
                 model = BioprocessModel(model_type=model_type, maxfev=maxfev_val)
                 model.fit_model()
-
                 y_pred_biomass = model.fit_biomass(time_exp, biomass)
                 if y_pred_biomass is None:
                     comparison_data.append({
@@ -676,7 +601,6 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                     else:
                         y_pred_substrate = None
                         y_pred_product = None
-
                     comparison_data.append({
                         'Experimento': experiment_name,
                         'Modelo': model_type.capitalize(),
@@ -687,7 +611,6 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                         'R² Producto': model.r2.get('product', np.nan),
                         'RMSE Producto': model.rmse.get('product', np.nan)
                     })
-
                     if mode == 'combinado':
                         fig = model.plot_combined_results(time_exp, biomass, substrate, product,
                                                           y_pred_biomass, y_pred_substrate, y_pred_product,
@@ -697,8 +620,7 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                                                           show_legend, show_params,
                                                           style,
                                                           line_color, point_color, line_style, marker_style,
-                                                          use_differential,
-                                                          x_label, y_label_biomass, y_label_substrate, y_label_product)
+                                                          use_differential)
                     else:
                         fig = model.plot_results(time_exp, biomass, substrate, product,
                                                  y_pred_biomass, y_pred_substrate, y_pred_product,
@@ -708,15 +630,11 @@ def process_all_data(file, legend_position, params_position, model_types, experi
                                                  show_legend, show_params,
                                                  style,
                                                  line_color, point_color, line_style, marker_style,
-                                                 use_differential,
-                                                 x_label, y_label_biomass, y_label_substrate, y_label_product)
+                                                 use_differential)
                     if fig is not None:
                         figures.append(fig)
-
             experiment_counter += 1
-
     comparison_df = pd.DataFrame(comparison_data)
-
     if not comparison_df.empty:
         comparison_df_sorted = comparison_df.sort_values(
             by=['R² Biomasa', 'R² Sustrato', 'R² Producto', 'RMSE Biomasa', 'RMSE Sustrato', 'RMSE Producto'],
@@ -724,54 +642,42 @@ def process_all_data(file, legend_position, params_position, model_types, experi
         ).reset_index(drop=True)
     else:
         comparison_df_sorted = comparison_df
-
     return figures, comparison_df_sorted
 
 def create_interface():
     with gr.Blocks() as demo:
         gr.Markdown("# Modelos de Bioproceso: Logístico, Gompertz, Moser y Luedeking-Piret")
-
         gr.Markdown(r"""
 ## Ecuaciones Diferenciales Utilizadas
-
 **Biomasa:**
-
 - Logístico:
 $$
 \frac{dX}{dt} = \mu_m X\left(1 - \frac{X}{X_m}\right)
 $$
-
 - Gompertz:
 $$
 X(t) = X_m \exp\left(-\exp\left(\left(\frac{\mu_m e}{X_m}\right)(\text{lag}-t)+1\right)\right)
 $$
-
 Ecuación diferencial:
 $$
 \frac{dX}{dt} = X(t)\left(\frac{\mu_m e}{X_m}\right)\exp\left(\left(\frac{\mu_m e}{X_m}\right)(\text{lag}-t)+1\right)
 $$
-
 - Moser (simplificado):
 $$
 X(t)=X_m(1-e^{-\mu_m(t-K_s)})
 $$
-
 $$
 \frac{dX}{dt}=\mu_m(X_m - X)
 $$
-
 **Sustrato y Producto (Luedeking-Piret):**
 $$
 \frac{dS}{dt} = -p \frac{dX}{dt} - q X
 $$
-
 $$
 \frac{dP}{dt} = \alpha \frac{dX}{dt} + \beta X
 $$
         """)
-
         file_input = gr.File(label="Subir archivo Excel")
-
         with gr.Row():
             with gr.Column():
                 legend_position = gr.Radio(
@@ -780,7 +686,6 @@ $$
                     value="best"
                 )
                 show_legend = gr.Checkbox(label="Mostrar Leyenda", value=True)
-
             with gr.Column():
                 params_positions = ["upper left", "upper right", "lower left", "lower right", "outside right"]
                 params_position = gr.Radio(
@@ -789,7 +694,6 @@ $$
                     value="upper right"
                 )
                 show_params = gr.Checkbox(label="Mostrar Parámetros", value=True)
-
         model_types = gr.CheckboxGroup(
             choices=["logistic", "gompertz", "moser"],
             label="Tipo(s) de Modelo",
@@ -797,19 +701,11 @@ $$
         )
         mode = gr.Radio(["independent", "average", "combinado"], label="Modo de Análisis", value="independent")
         use_differential = gr.Checkbox(label="Usar ecuaciones diferenciales para graficar", value=False)
-
         experiment_names = gr.Textbox(
             label="Nombres de los experimentos (uno por línea)",
             placeholder="Experimento 1\nExperimento 2\n...",
             lines=5
         )
-
-        with gr.Row():
-            x_label = gr.Textbox(label="Etiqueta del eje X", value="Tiempo")
-            y_label_biomass = gr.Textbox(label="Etiqueta del eje Y (Biomasa)", value="Biomasa")
-            y_label_substrate = gr.Textbox(label="Etiqueta del eje Y (Sustrato)", value="Sustrato")
-            y_label_product = gr.Textbox(label="Etiqueta del eje Y (Producto)", value="Producto")
-
         with gr.Row():
             with gr.Column():
                 lower_bounds = gr.Textbox(
@@ -817,30 +713,22 @@ $$
                     placeholder="0,0,0\n0,0,0\n...",
                     lines=5
                 )
-
             with gr.Column():
                 upper_bounds = gr.Textbox(
                     label="Upper Bounds (uno por línea, formato: param1,param2,param3)",
                     placeholder="inf,inf,inf\ninf,inf,inf\n...",
                     lines=5
                 )
-
         styles = ['white', 'dark', 'whitegrid', 'darkgrid', 'ticks']
         style_dropdown = gr.Dropdown(choices=styles, label="Selecciona el estilo de gráfico", value='whitegrid')
-
         line_color_picker = gr.ColorPicker(label="Color de la línea", value='#0000FF')
         point_color_picker = gr.ColorPicker(label="Color de los puntos", value='#000000')
-
         line_style_options = ['-', '--', '-.', ':']
         line_style_dropdown = gr.Dropdown(choices=line_style_options, label="Estilo de línea", value='-')
-
         marker_style_options = ['o', 's', '^', 'v', 'D', 'x', '+', '*']
         marker_style_dropdown = gr.Dropdown(choices=marker_style_options, label="Estilo de punto", value='o')
-
         maxfev_input = gr.Number(label="maxfev (Máx. evaluaciones para el ajuste)", value=50000)
-
         simulate_btn = gr.Button("Simular")
-
         output_gallery = gr.Gallery(label="Resultados", columns=2, height='auto')
         output_table = gr.Dataframe(
             label="Tabla Comparativa de Modelos",
@@ -848,15 +736,12 @@ $$
                      "R² Sustrato", "RMSE Sustrato", "R² Producto", "RMSE Producto"],
             interactive=False
         )
-
         state_df = gr.State()
 
         def process_and_plot(file, legend_position, params_position, model_types, mode, experiment_names,
                              lower_bounds, upper_bounds, style,
                              line_color, point_color, line_style, marker_style,
-                             show_legend, show_params, use_differential, maxfev_input,
-                             x_label, y_label_biomass, y_label_substrate, y_label_product):
-
+                             show_legend, show_params, use_differential, maxfev_input):
             experiment_names_list = experiment_names.strip().split('\n') if experiment_names.strip() else []
             lower_bounds_list = []
             if lower_bounds.strip():
@@ -886,13 +771,10 @@ $$
                             except ValueError:
                                 ub_values.append(np.inf)
                     upper_bounds_list.append(tuple(ub_values))
-
             figures, comparison_df = process_all_data(file, legend_position, params_position, model_types, experiment_names_list,
                                                      lower_bounds_list, upper_bounds_list, mode, style,
                                                      line_color, point_color, line_style, marker_style,
-                                                     show_legend, show_params, use_differential, maxfev_val=int(maxfev_input),
-                                                     x_label="Tiempo", y_label_biomass="Biomasa", y_label_substrate="Sustrato", y_label_product="Producto")
-
+                                                     show_legend, show_params, use_differential, maxfev_val=int(maxfev_input))
             return figures, comparison_df, comparison_df
 
         simulate_output = simulate_btn.click(
@@ -913,11 +795,7 @@ $$
                     show_legend,
                     show_params,
                     use_differential,
-                    maxfev_input,
-                    x_label,
-                    y_label_biomass,
-                    y_label_substrate,
-                    y_label_product],
+                    maxfev_input],
             outputs=[output_gallery, output_table, state_df]
         )
 
@@ -930,7 +808,6 @@ $$
 
         export_btn = gr.Button("Exportar Tabla a Excel")
         file_output = gr.File()
-
         export_btn.click(
             fn=export_excel,
             inputs=state_df,