app.py

# -*- coding: utf-8 -*-
"""PrediLectia - Gradio Final v2 with Multiple Y-Axes in Combined Plot.ipynb"""

# Instalación de librerías necesarias
#!pip install gradio seaborn scipy -q
import os
os.system('pip install gradio seaborn scipy scikit-learn openpyxl pydantic==1.10.0')

from pydantic import BaseModel, ConfigDict

class YourModel(BaseModel):
    class Config:
        arbitrary_types_allowed = True

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.integrate import odeint
from scipy.interpolate import interp1d
from scipy.optimize import curve_fit
from sklearn.metrics import mean_squared_error
import gradio as gr
import io
from PIL import Image

# Definición de la clase BioprocessModel
class BioprocessModel:
    def __init__(self):
        self.params = {}
        self.r2 = {}
        self.rmse = {}
        self.datax = []
        self.datas = []
        self.datap = []
        self.dataxp = []
        self.datasp = []
        self.datapp = []
        self.datax_std = []
        self.datas_std = []
        self.datap_std = []

    # Funciones modelo analíticas
    @staticmethod
    def logistic(time, xo, xm, um):
        return (xo * np.exp(um * time)) / (1 - (xo / xm) * (1 - np.exp(um * time)))

    @staticmethod
    def substrate(time, so, p, q, xo, xm, um):
        return so - (p * xo * ((np.exp(um * time)) / (1 - (xo / xm) * (1 - np.exp(um * time))) - 1)) - \
               (q * (xm / um) * np.log(1 - (xo / xm) * (1 - np.exp(um * time))))

    @staticmethod
    def product(time, po, alpha, beta, xo, xm, um):
        return po + (alpha * xo * ((np.exp(um * time) / (1 - (xo / xm) * (1 - np.exp(um * time)))) - 1)) + \
               (beta * (xm / um) * np.log(1 - (xo / xm) * (1 - np.exp(um * time))))

    # Funciones modelo diferenciales
    @staticmethod
    def logistic_diff(X, t, params):
        xo, xm, um = params
        dXdt = um * X * (1 - X / xm)
        return dXdt

    def substrate_diff(self, S, t, params, biomass_params, X_func):
        so, p, q = params
        xo, xm, um = biomass_params
        X_t = X_func(t)
        dSdt = -p * (um * X_t * (1 - X_t / xm)) - q * X_t
        return dSdt

    def product_diff(self, P, t, params, biomass_params, X_func):
        po, alpha, beta = params
        xo, xm, um = biomass_params
        X_t = X_func(t)
        dPdt = alpha * (um * X_t * (1 - X_t / xm)) + beta * X_t
        return dPdt

    # Métodos de procesamiento y ajuste de datos
    def process_data(self, df):
        # Obtener todas las columnas que contengan "Biomasa", "Sustrato", y "Producto"
        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']

        # Procesar los datos de tiempo
        time_col = [col for col in df.columns if col[1] == 'Tiempo'][0]
        time = df[time_col].values

        # Procesar los datos de biomasa
        data_biomass = [df[col].values for col in biomass_cols]
        data_biomass = np.array(data_biomass)  # shape (num_experiments, num_time_points)
        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))

        # Procesar los datos de sustrato
        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))

        # Procesar los datos de producto
        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, model_type='logistic'):
        if model_type == 'logistic':
            self.fit_biomass = self.fit_biomass_logistic
            self.fit_substrate = self.fit_substrate_logistic
            self.fit_product = self.fit_product_logistic
        # Puedes agregar más modelos aquí si los necesitas.

    def fit_biomass_logistic(self, time, biomass, bounds):
        popt, _ = curve_fit(self.logistic, time, biomass, bounds=bounds, maxfev=10000)
        self.params['biomass'] = {'xo': popt[0], 'xm': popt[1], 'um': popt[2]}
        y_pred = self.logistic(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

    def fit_substrate_logistic(self, time, substrate, biomass_params, bounds):
        popt, _ = curve_fit(lambda t, so, p, q: self.substrate(t, so, p, q, *biomass_params.values()),
                            time, substrate, bounds=bounds)
        self.params['substrate'] = {'so': popt[0], 'p': popt[1], 'q': popt[2]}
        y_pred = self.substrate(time, *popt, *biomass_params.values())
        self.r2['substrate'] = 1 - (np.sum((substrate - y_pred) ** 2) / np.sum((substrate - np.mean(substrate)) ** 2))
        self.rmse['substrate'] = np.sqrt(mean_squared_error(substrate, y_pred))
        return y_pred

    def fit_product_logistic(self, time, product, biomass_params, bounds):
        popt, _ = curve_fit(lambda t, po, alpha, beta: self.product(t, po, alpha, beta, *biomass_params.values()),
                            time, product, bounds=bounds)
        self.params['product'] = {'po': popt[0], 'alpha': popt[1], 'beta': popt[2]}
        y_pred = self.product(time, *popt, *biomass_params.values())
        self.r2['product'] = 1 - (np.sum((product - y_pred) ** 2) / np.sum((product - np.mean(product)) ** 2))
        self.rmse['product'] = np.sqrt(mean_squared_error(product, y_pred))
        return y_pred

    # Métodos de visualización de resultados
    def generate_fine_time_grid(self, time):
        # Generar una malla temporal más fina para curvas suaves
        time_fine = np.linspace(time.min(), time.max(), 500)
        return time_fine

    def solve_differential_equations(self, time, initial_conditions, params):
        # Resolver la ecuación diferencial para biomasa
        xo, xm, um = params['biomass'].values()
        biomass_params = [xo, xm, um]
        time_fine = self.generate_fine_time_grid(time)

        # Resolver biomasa
        X0 = xo
        X = odeint(self.logistic_diff, X0, time_fine, args=(biomass_params,)).flatten()

        # Crear función de interpolación para X(t)
        X_func = interp1d(time_fine, X, kind='linear', fill_value="extrapolate")

        # Resolver sustrato
        so, p, q = params['substrate'].values()
        substrate_params = [so, p, q]
        S0 = so
        S = odeint(self.substrate_diff, S0, time_fine, args=(substrate_params, biomass_params, X_func)).flatten()

        # Resolver producto
        po, alpha, beta = params['product'].values()
        product_params = [po, alpha, beta]
        P0 = po
        P = odeint(self.product_diff, P0, time_fine, args=(product_params, biomass_params, X_func)).flatten()

        return X, S, P, time_fine

    def plot_results(self, time, biomass, substrate, product,
                     y_pred_biomass, y_pred_substrate, y_pred_product,
                     biomass_std=None, substrate_std=None, product_std=None,
                     experiment_name='', legend_position='best', params_position='upper right',
                     show_legend=True, show_params=True,
                     style='whitegrid',
                     line_color='#0000FF', point_color='#000000', line_style='-', marker_style='o',
                     use_differential=False):
        sns.set_style(style)  # Establecer el estilo seleccionado

        if use_differential:
            y_pred_biomass, y_pred_substrate, y_pred_product, time_to_plot = self.solve_differential_equations(
                time, [biomass[0], substrate[0], product[0]], self.params)
        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, 'Biomasa', 'Modelo', self.params['biomass'],
             self.r2['biomass'], self.rmse['biomass']),
            (ax2, substrate, y_pred_substrate, substrate_std, 'Sustrato', 'Modelo', self.params['substrate'],
             self.r2['substrate'], self.rmse['substrate']),
            (ax3, product, y_pred_product, product_std, 'Producto', 'Modelo', self.params['product'],
             self.r2['product'], self.rmse['product'])
        ]

        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)
            else:
                ax.plot(time, data, marker=marker_style, linestyle='', color=point_color,
                        label='Datos experimentales')
            if use_differential:
                ax.plot(time_to_plot, y_pred, linestyle=line_style, color=line_color, label=model_name)
            else:
                ax.plot(time, y_pred, linestyle=line_style, color=line_color, label=model_name)
            ax.set_xlabel('Tiempo')
            ax.set_ylabel(ylabel)
            if show_legend:
                ax.legend(loc=legend_position)
            ax.set_title(f'{ylabel}')

            if show_params:
                param_text = '\n'.join([f"{k} = {v:.4f}" for k, v in params.items()])
                text = f"{param_text}\nR² = {r2:.4f}\nRMSE = {rmse:.4f}"

                # Si la posición es 'outside right', ajustar la posición del texto
                if params_position == 'outside right':
                    bbox_props = dict(boxstyle='round', facecolor='white', alpha=0.5)
                    ax.annotate(text, xy=(1.05, 0.5), xycoords='axes fraction',
                                verticalalignment='center', bbox=bbox_props)
                else:
                    if params_position in ['upper right', 'lower right']:
                        text_x = 0.95
                        ha = 'right'
                    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()
        return fig

    def plot_combined_results(self, time, biomass, substrate, product,
                              y_pred_biomass, y_pred_substrate, y_pred_product,
                              biomass_std=None, substrate_std=None, product_std=None,
                              experiment_name='', legend_position='best', params_position='upper right',
                              show_legend=True, show_params=True,
                              style='whitegrid',
                              line_color='#0000FF', point_color='#000000', line_style='-', marker_style='o',
                              use_differential=False):
        sns.set_style(style)  # Establecer el estilo seleccionado

        if use_differential:
            y_pred_biomass, y_pred_substrate, y_pred_product, time_to_plot = self.solve_differential_equations(
                time, [biomass[0], substrate[0], product[0]], self.params)
        else:
            time_to_plot = time

        fig, ax1 = plt.subplots(figsize=(10, 7))
        fig.suptitle(f'{experiment_name}', fontsize=16)

        # Colores específicos para cada variable
        colors = {'Biomasa': 'blue', 'Sustrato': 'green', 'Producto': 'red'}

        # Plot Biomasa en ax1
        ax1.set_xlabel('Tiempo')
        ax1.set_ylabel('Biomasa', color=colors['Biomasa'])
        if biomass_std is not None:
            ax1.errorbar(time, biomass, yerr=biomass_std, fmt=marker_style, color=colors['Biomasa'],
                         label='Biomasa (Datos)', capsize=5)
        else:
            ax1.plot(time, biomass, marker=marker_style, linestyle='', color=colors['Biomasa'],
                     label='Biomasa (Datos)')
        if use_differential:
            ax1.plot(time_to_plot, y_pred_biomass, linestyle=line_style, color=colors['Biomasa'],
                     label='Biomasa (Modelo)')
        else:
            ax1.plot(time, y_pred_biomass, linestyle=line_style, color=colors['Biomasa'],
                     label='Biomasa (Modelo)')
        ax1.tick_params(axis='y', labelcolor=colors['Biomasa'])

        # Crear segundo eje y para Sustrato
        ax2 = ax1.twinx()
        ax2.set_ylabel('Sustrato', color=colors['Sustrato'])
        if substrate_std is not None:
            ax2.errorbar(time, substrate, yerr=substrate_std, fmt=marker_style, color=colors['Sustrato'],
                         label='Sustrato (Datos)', capsize=5)
        else:
            ax2.plot(time, substrate, marker=marker_style, linestyle='', color=colors['Sustrato'],
                     label='Sustrato (Datos)')
        if use_differential:
            ax2.plot(time_to_plot, y_pred_substrate, linestyle=line_style, color=colors['Sustrato'],
                     label='Sustrato (Modelo)')
        else:
            ax2.plot(time, y_pred_substrate, linestyle=line_style, color=colors['Sustrato'],
                     label='Sustrato (Modelo)')
        ax2.tick_params(axis='y', labelcolor=colors['Sustrato'])

        # Crear tercer eje y para Producto
        ax3 = ax1.twinx()
        # Desplazar el tercer eje para evitar superposición
        ax3.spines["right"].set_position(("axes", 1.1))
        ax3.set_frame_on(True)
        ax3.patch.set_visible(False)
        for sp in ax3.spines.values():
            sp.set_visible(True)

        ax3.set_ylabel('Producto', color=colors['Producto'])
        if product_std is not None:
            ax3.errorbar(time, product, yerr=product_std, fmt=marker_style, color=colors['Producto'],
                         label='Producto (Datos)', capsize=5)
        else:
            ax3.plot(time, product, marker=marker_style, linestyle='', color=colors['Producto'],
                     label='Producto (Datos)')
        if use_differential:
            ax3.plot(time_to_plot, y_pred_product, linestyle=line_style, color=colors['Producto'],
                     label='Producto (Modelo)')
        else:
            ax3.plot(time, y_pred_product, linestyle=line_style, color=colors['Producto'],
                     label='Producto (Modelo)')
        ax3.tick_params(axis='y', labelcolor=colors['Producto'])

        # Manejo de leyendas
        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)

        # Mostrar parámetros y estadísticas en el gráfico
        if show_params:
            param_text_biomass = '\n'.join([f"{k} = {v:.4f}" for k, v in self.params['biomass'].items()])
            text_biomass = f"Biomasa:\n{param_text_biomass}\nR² = {self.r2['biomass']:.4f}\nRMSE = {self.rmse['biomass']:.4f}"

            param_text_substrate = '\n'.join([f"{k} = {v:.4f}" for k, v in self.params['substrate'].items()])
            text_substrate = f"Sustrato:\n{param_text_substrate}\nR² = {self.r2['substrate']:.4f}\nRMSE = {self.rmse['substrate']:.4f}"

            param_text_product = '\n'.join([f"{k} = {v:.4f}" for k, v in self.params['product'].items()])
            text_product = f"Producto:\n{param_text_product}\nR² = {self.r2['product']:.4f}\nRMSE = {self.rmse['product']:.4f}"

            total_text = f"{text_biomass}\n\n{text_substrate}\n\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',
                             verticalalignment='center', bbox=bbox_props)
            else:
                if params_position in ['upper right', 'lower right']:
                    text_x = 0.95
                    ha = 'right'
                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()
        return fig

# Función de procesamiento de datos
def process_data(file, legend_position, params_position, model_type, 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):
    # Leer todas las hojas del archivo Excel
    xls = pd.ExcelFile(file.name)
    sheet_names = xls.sheet_names

    model = BioprocessModel()
    model.fit_model(model_type)
    figures = []

    # Si no se proporcionan suficientes límites, usar valores predeterminados
    default_lower_bounds = (0, 0, 0)
    default_upper_bounds = (np.inf, np.inf, np.inf)

    experiment_counter = 0  # Contador global de experimentos

    for sheet_name in sheet_names:
        df = pd.read_excel(file.name, sheet_name=sheet_name, header=[0, 1])

        # Procesar datos
        model.process_data(df)
        time = model.time

        if mode == 'independent':
            # Modo independiente: iterar sobre cada experimento
            num_experiments = len(df.columns.levels[0])
            for idx in range(num_experiments):
                col = df.columns.levels[0][idx]
                time = df[(col, 'Tiempo')].dropna().values
                biomass = df[(col, 'Biomasa')].dropna().values
                substrate = df[(col, 'Sustrato')].dropna().values
                product = df[(col, 'Producto')].dropna().values

                # Si hay replicados en el experimento, calcular la desviación estándar
                biomass_std = None
                substrate_std = None
                product_std = None
                if biomass.ndim > 1:
                    biomass_std = np.std(biomass, axis=0, ddof=1)
                    biomass = np.mean(biomass, axis=0)
                if substrate.ndim > 1:
                    substrate_std = np.std(substrate, axis=0, ddof=1)
                    substrate = np.mean(substrate, axis=0)
                if product.ndim > 1:
                    product_std = np.std(product, axis=0, ddof=1)
                    product = np.mean(product, axis=0)

                # Obtener límites o usar valores predeterminados
                lower_bound = lower_bounds[experiment_counter] if experiment_counter < len(lower_bounds) else default_lower_bounds
                upper_bound = upper_bounds[experiment_counter] if experiment_counter < len(upper_bounds) else default_upper_bounds
                bounds = (lower_bound, upper_bound)

                # Ajustar el modelo
                y_pred_biomass = model.fit_biomass(time, biomass, bounds)
                y_pred_substrate = model.fit_substrate(time, substrate, model.params['biomass'], bounds)
                y_pred_product = model.fit_product(time, product, model.params['biomass'], bounds)

                # Usar el nombre del experimento proporcionado o un nombre por defecto
                experiment_name = experiment_names[experiment_counter] if experiment_counter < len(experiment_names) else f"Tratamiento {experiment_counter + 1}"

                if mode == 'combinado':
                    fig = model.plot_combined_results(time, biomass, substrate, product,
                                                      y_pred_biomass, y_pred_substrate, y_pred_product,
                                                      biomass_std, substrate_std, product_std,
                                                      experiment_name, legend_position, params_position,
                                                      show_legend, show_params,
                                                      style,
                                                      line_color, point_color, line_style, marker_style,
                                                      use_differential)
                else:
                    fig = model.plot_results(time, biomass, substrate, product,
                                             y_pred_biomass, y_pred_substrate, y_pred_product,
                                             biomass_std, substrate_std, product_std,
                                             experiment_name, legend_position, params_position,
                                             show_legend, show_params,
                                             style,
                                             line_color, point_color, line_style, marker_style,
                                             use_differential)
                figures.append(fig)

                experiment_counter += 1

        elif mode == 'average':
            # Modo promedio: usar dataxp, datasp y datapp
            time = df[(df.columns.levels[0][0], 'Tiempo')].dropna().values
            biomass = model.dataxp[-1]
            substrate = model.datasp[-1]
            product = model.datapp[-1]

            # Obtener las desviaciones estándar
            biomass_std = model.datax_std[-1]
            substrate_std = model.datas_std[-1]
            product_std = model.datap_std[-1]

            # Obtener límites o usar valores predeterminados
            lower_bound = lower_bounds[experiment_counter] if experiment_counter < len(lower_bounds) else default_lower_bounds
            upper_bound = upper_bounds[experiment_counter] if experiment_counter < len(upper_bounds) else default_upper_bounds
            bounds = (lower_bound, upper_bound)

            # Ajustar el modelo
            y_pred_biomass = model.fit_biomass(time, biomass, bounds)
            y_pred_substrate = model.fit_substrate(time, substrate, model.params['biomass'], bounds)
            y_pred_product = model.fit_product(time, product, model.params['biomass'], bounds)

            # Usar el nombre del experimento proporcionado o un nombre por defecto
            experiment_name = experiment_names[experiment_counter] if experiment_counter < len(experiment_names) else f"Tratamiento {experiment_counter + 1}"

            if mode == 'combinado':
                fig = model.plot_combined_results(time, biomass, substrate, product,
                                                  y_pred_biomass, y_pred_substrate, y_pred_product,
                                                  biomass_std, substrate_std, product_std,
                                                  experiment_name, legend_position, params_position,
                                                  show_legend, show_params,
                                                  style,
                                                  line_color, point_color, line_style, marker_style,
                                                  use_differential)
            else:
                fig = model.plot_results(time, biomass, substrate, product,
                                         y_pred_biomass, y_pred_substrate, y_pred_product,
                                         biomass_std, substrate_std, product_std,
                                         experiment_name, legend_position, params_position,
                                         show_legend, show_params,
                                         style,
                                         line_color, point_color, line_style, marker_style,
                                         use_differential)
            figures.append(fig)

            experiment_counter += 1

        elif mode == 'combinado':
            # Modo combinado: combinar las gráficas en una sola
            time = df[(df.columns.levels[0][0], 'Tiempo')].dropna().values
            biomass = model.dataxp[-1]
            substrate = model.datasp[-1]
            product = model.datapp[-1]

            # Obtener las desviaciones estándar
            biomass_std = model.datax_std[-1]
            substrate_std = model.datas_std[-1]
            product_std = model.datap_std[-1]

            # Obtener límites o usar valores predeterminados
            lower_bound = lower_bounds[experiment_counter] if experiment_counter < len(lower_bounds) else default_lower_bounds
            upper_bound = upper_bounds[experiment_counter] if experiment_counter < len(upper_bounds) else default_upper_bounds
            bounds = (lower_bound, upper_bound)

            # Ajustar el modelo
            y_pred_biomass = model.fit_biomass(time, biomass, bounds)
            y_pred_substrate = model.fit_substrate(time, substrate, model.params['biomass'], bounds)
            y_pred_product = model.fit_product(time, product, model.params['biomass'], bounds)

            # Usar el nombre del experimento proporcionado o un nombre por defecto
            experiment_name = experiment_names[experiment_counter] if experiment_counter < len(experiment_names) else f"Tratamiento {experiment_counter + 1}"

            fig = model.plot_combined_results(time, biomass, substrate, product,
                                              y_pred_biomass, y_pred_substrate, y_pred_product,
                                              biomass_std, substrate_std, product_std,
                                              experiment_name, legend_position, params_position,
                                              show_legend, show_params,
                                              style,
                                              line_color, point_color, line_style, marker_style,
                                              use_differential)
            figures.append(fig)

            experiment_counter += 1

    return figures

def create_interface():
    with gr.Blocks(theme='upsatwal/mlsc_tiet') as demo:
        gr.Markdown("# Modelos de Bioproceso: Logístico y Luedeking-Piret")
        gr.Markdown(
            "Sube un archivo Excel con múltiples pestañas. Cada pestaña debe contener columnas 'Tiempo', 'Biomasa', 'Sustrato' y 'Producto' para cada experimento.")

        file_input = gr.File(label="Subir archivo Excel")

        with gr.Row():
            with gr.Column():
                legend_position = gr.Radio(
                    choices=["upper left", "upper right", "lower left", "lower right", "best"],
                    label="Posición de la leyenda",
                    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(
                    choices=params_positions,
                    label="Posición de los parámetros",
                    value="upper right"
                )
                show_params = gr.Checkbox(label="Mostrar Parámetros", value=True)

        model_type = gr.Radio(["logistic"], label="Tipo de Modelo", value="logistic")
        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():
            with gr.Column():
                lower_bounds = gr.Textbox(
                    label="Lower Bounds (uno por línea, formato: xo,xm,um)",
                    placeholder="0,0,0\n0,0,0\n...",
                    lines=5
                )

            with gr.Column():
                upper_bounds = gr.Textbox(
                    label="Upper Bounds (uno por línea, formato: xo,xm,um)",
                    placeholder="inf,inf,inf\ninf,inf,inf\n...",
                    lines=5
                )

        # Añadir un desplegable para seleccionar el estilo del gráfico
        styles = ['white', 'dark', 'whitegrid', 'darkgrid', 'ticks']
        style_dropdown = gr.Dropdown(choices=styles, label="Selecciona el estilo de gráfico", value='whitegrid')

        # Añadir color pickers para líneas y puntos
        line_color_picker = gr.ColorPicker(label="Color de la línea", value='#0000FF')
        point_color_picker = gr.ColorPicker(label="Color de los puntos", value='#000000')

        # Añadir listas desplegables para tipo de línea y tipo de punto
        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')

        simulate_btn = gr.Button("Simular")

        # Definir un componente gr.Gallery para las salidas
        output_gallery = gr.Gallery(label="Resultados", columns=2, height='auto')

        def process_and_plot(file, legend_position, params_position, model_type, mode, experiment_names,
                             lower_bounds, upper_bounds, style,
                             line_color, point_color, line_style, marker_style,
                             show_legend, show_params, use_differential):
            # Dividir los nombres de experimentos y límites en listas
            experiment_names_list = experiment_names.strip().split('\n') if experiment_names.strip() else []
            lower_bounds_list = [tuple(map(float, lb.split(','))) for lb in
                                 lower_bounds.strip().split('\n')] if lower_bounds.strip() else []
            upper_bounds_list = [tuple(map(float, ub.split(','))) for ub in
                                 upper_bounds.strip().split('\n')] if upper_bounds.strip() else []

            # Procesar los datos y generar gráficos
            figures = process_data(file, legend_position, params_position, model_type, 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)

            # Convertir las figuras a imágenes y devolverlas como lista
            image_list = []
            for fig in figures:
                buf = io.BytesIO()
                fig.savefig(buf, format='png')
                buf.seek(0)
                image = Image.open(buf)
                image_list.append(image)

            return image_list

        simulate_btn.click(
            fn=process_and_plot,
            inputs=[file_input,
                    legend_position,
                    params_position,
                    model_type,
                    mode,
                    experiment_names,
                    lower_bounds,
                    upper_bounds,
                    style_dropdown,
                    line_color_picker,
                    point_color_picker,
                    line_style_dropdown,
                    marker_style_dropdown,
                    show_legend,
                    show_params,
                    use_differential],
            outputs=output_gallery
        )

    return demo

# Crear y lanzar la interfaz
demo = create_interface()
demo.launch(share=True)