app.py

import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from scipy.optimize import minimize
import plotly.express as px
from scipy.stats import t, f
import gradio as gr
import io
import os
from zipfile import ZipFile

class RSM_BoxBehnken:
    def __init__(self, data, x1_name, x2_name, x3_name, y_name, x1_levels, x2_levels, x3_levels):
        # ... (El código de la clase RSM_BoxBehnken se mantiene igual, solo se modifican las funciones que generan dataframes o strings)
        self.data = data.copy()
        self.model = None
        self.model_simplified = None
        self.optimized_results = None
        self.optimal_levels = None

        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):
        if variable_name == self.x1_name:
            return self.x1_levels
        elif variable_name == self.x2_name:
            return self.x2_levels
        elif variable_name == self.x3_name:
            return self.x3_levels
        else:
            raise ValueError(f"Variable desconocida: {variable_name}")

    def fit_model(self):
        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} + ' \
                  f'I({self.x1_name}**2) + I({self.x2_name}**2) + I({self.x3_name}**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:
            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]]}))

        bounds = [(-1, 1), (-1, 1), (-1, 1)]
        x0 = [0, 0, 0]

        self.optimized_results = minimize(objective_function, x0, method=method, bounds=bounds)
        self.optimal_levels = self.optimized_results.x
        
        optimal_levels_natural = [
            round(self.coded_to_natural(self.optimal_levels[0], self.x1_name), 3),
            round(self.coded_to_natural(self.optimal_levels[1], self.x2_name), 3),
            round(self.coded_to_natural(self.optimal_levels[2], self.x3_name), 3)
        ]
        optimization_table = pd.DataFrame({
            'Variable': [self.x1_name, self.x2_name, self.x3_name],
            'Nivel Óptimo (Natural)': optimal_levels_natural,
            'Nivel Óptimo (Codificado)': [round(x, 3) for x in self.optimal_levels]
        })

        return optimization_table

    def plot_rsm_individual(self, fixed_variable, fixed_level):
        if self.model_simplified is None:
            print("Error: Ajusta el modelo simplificado primero.")
            return None

        varying_variables = [var for var in [self.x1_name, self.x2_name, self.x3_name] if var != fixed_variable]
        
        x_natural_levels = self.get_levels(varying_variables[0])
        y_natural_levels = self.get_levels(varying_variables[1])

        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)

        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(),
        })
        prediction_data[fixed_variable] = self.natural_to_coded(fixed_level, fixed_variable)

        z_pred = self.model_simplified.predict(prediction_data).values.reshape(x_grid_coded.shape)

        varying_variables = [var for var in [self.x1_name, self.x2_name, self.x3_name] if var != fixed_variable]

        fixed_level_coded = self.natural_to_coded(fixed_level, fixed_variable)
        subset_data = self.data[np.isclose(self.data[fixed_variable], fixed_level_coded)]

        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_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]))

        fig = go.Figure(data=[go.Surface(z=z_pred, x=x_grid_natural, y=y_grid_natural, colorscale='Viridis', opacity=0.7, showscale=True)])

        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'
            ))
        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'
            ))

        colors = ['red', 'blue', 'green', 'purple', 'orange', 'yellow', 'cyan', 'magenta']
        point_labels = []
        for i, row in experiments_data.iterrows():
            point_labels.append(f"{row[self.y_name]:.2f}")

        fig.add_trace(go.Scatter3d(
            x=experiments_x_natural,
            y=experiments_y_natural,
            z=experiments_data[self.y_name],
            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=varying_variables[0] + " (g/L)",
                yaxis_title=varying_variables[1] + " (g/L)",
                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:.2f} (g/L) (Modelo Simplificado)</sup>",
            height=800,
            width=1000,
            showlegend=True
        )
        return fig

    def generate_all_plots(self):
        """
        Genera todas las gráficas de RSM, variando la variable fija y sus niveles usando el modelo simplificado.
        """
        if self.model_simplified is None:
            print("Error: Ajusta el modelo simplificado primero.")
            return

        # Niveles naturales para graficar
        levels_to_plot_natural = {
            self.x1_name: self.x1_levels,
            self.x2_name: self.x2_levels,
            self.x3_name: self.x3_levels
        }
        
        figs = []

        # Generar y mostrar gráficos individuales
        for fixed_variable in [self.x1_name, self.x2_name, self.x3_name]:
            for level in levels_to_plot_natural[fixed_variable]:
                fig = self.plot_rsm_individual(fixed_variable, level)
                if fig is not None:
                    figs.append(fig)
        return figs

    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,
            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:.2f}",
                      annotation_position="bottom right")

        return fig

    def get_simplified_equation(self):
        if self.model_simplified is None:
            print("Error: Ajusta el modelo simplificado primero.")
            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:
          print("Error: Ajusta el modelo simplificado primero.")
          return None

      self.data['Predicho'] = self.model_simplified.predict(self.data)
      self.data['Residual'] = self.data[self.y_name] - self.data['Predicho']

      # Redondear a 3 decimales en la tabla de predicciones
      prediction_table = self.data[[self.y_name, 'Predicho', 'Residual']].copy()
      prediction_table[self.y_name] = prediction_table[self.y_name].round(3)
      prediction_table['Predicho'] = prediction_table['Predicho'].round(3)
      prediction_table['Residual'] = prediction_table['Residual'].round(3)

      return prediction_table

    def calculate_contribution_percentage(self):
      if self.model_simplified is None:
          print("Error: Ajusta el modelo simplificado primero.")
          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': [round(ss_factor, 3)],
                '% Contribución': [round(contribution_percentage, 3)]
            })], ignore_index=True)

      return contribution_table

    def calculate_detailed_anova(self):
        if self.model_simplified is None:
            print("Error: Ajusta el modelo simplificado primero.")
            return None

        formula_reduced = 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)'
        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()
        df_pure_error = len(replicas) - len(replicas.groupby([self.x1_name, self.x2_name, self.x3_name]))

        ss_lack_of_fit = ss_residual - ss_pure_error
        df_lack_of_fit = df_residual - df_pure_error

        ms_regression = ss_regression / df_regression
        ms_residual = ss_residual / df_residual
        ms_lack_of_fit = ss_lack_of_fit / df_lack_of_fit
        ms_pure_error = ss_pure_error / df_pure_error

        f_lack_of_fit = ms_lack_of_fit / ms_pure_error
        p_lack_of_fit = 1 - f.cdf(f_lack_of_fit, df_lack_of_fit, df_pure_error)

        detailed_anova_table = pd.DataFrame({
            'Fuente de Variación': ['Regresión', 'Residual', 'Falta de Ajuste', 'Error Puro', 'Total'],
            'Suma de Cuadrados': [round(ss_regression, 3), round(ss_residual, 3), round(ss_lack_of_fit, 3), round(ss_pure_error, 3), round(ss_total, 3)],
            'Grados de Libertad': [df_regression, df_residual, df_lack_of_fit, df_pure_error, df_total],
            'Cuadrado Medio': [round(ms_regression, 3), round(ms_residual, 3), round(ms_lack_of_fit, 3), round(ms_pure_error, 3), np.nan],
            'F': [np.nan, np.nan, round(f_lack_of_fit, 3), np.nan, np.nan],
            'Valor p': [np.nan, np.nan, round(p_lack_of_fit, 3), 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[len(detailed_anova_table)] = ['Curvatura', round(ss_curvature, 3), df_curvature, round(ss_curvature / df_curvature, 3), np.nan, np.nan]

        detailed_anova_table = detailed_anova_table.reindex([0, 5, 1, 2, 3, 4])
        
        detailed_anova_table = detailed_anova_table.reset_index(drop=True)

        return detailed_anova_table

# --- 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(',')]
        x2_levels = [float(x.strip()) for x in x2_levels_str.split(',')]
        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)
        data = data.apply(pd.to_numeric, errors='coerce')

        if not all(col in data.columns for col in column_names):
            raise ValueError("El formato de los datos no es correcto.")

        global rsm
        rsm = RSM_BoxBehnken(data, x1_name, x2_name, x3_name, y_name, x1_levels, x2_levels, x3_levels)

        return data, x1_name, x2_name, x3_name, y_name, x1_levels, x2_levels, x3_levels, gr.update(visible=True)

    except Exception as e:
        return None, "", "", "", "", [], [], [], gr.update(visible=False), f"Error: {e}"

def fit_and_optimize_model():
    if 'rsm' not in globals():
        return None, None, None, None, None, None, "Error: Carga los datos primero."
    
    model_completo, pareto_completo = rsm.fit_model()
    model_simplificado, pareto_simplificado = rsm.fit_simplified_model()
    optimization_table = rsm.optimize()
    equation = rsm.get_simplified_equation()
    prediction_table = rsm.generate_prediction_table()
    contribution_table = rsm.calculate_contribution_percentage()
    anova_table = rsm.calculate_detailed_anova()

    equation_formatted = equation.replace(" + ", "<br>+ ").replace(" ** ", "^").replace("*", " × ")
    equation_formatted = f"### Ecuación del Modelo Simplificado:<br>{equation_formatted}"

    
    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

def generate_rsm_plot(fixed_variable, fixed_level):
    if 'rsm' not in globals():
        return None, "Error: Carga los datos primero."
    
    # Generar todas las gráficas
    all_figs = rsm.generate_all_plots()

    # Crear una lista de figuras para la salida
    plot_outputs = []
    for fig in all_figs:
        # Convertir la figura a una imagen en formato PNG
        img_bytes = fig.to_image(format="png")
        plot_outputs.append(img_bytes)

    # Retornar la lista de imágenes
    return plot_outputs

def download_excel():
    if 'rsm' not in globals():
        return None, "Error: Carga los datos y ajusta el modelo primero."

    output = io.BytesIO()
    with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
        rsm.data.to_excel(writer, sheet_name='Datos', index=False)
        rsm.generate_prediction_table().to_excel(writer, sheet_name='Predicciones', index=False)
        rsm.optimize().to_excel(writer, sheet_name='Optimizacion', index=False)
        rsm.calculate_contribution_percentage().to_excel(writer, sheet_name='Contribucion', index=False)
        rsm.calculate_detailed_anova().to_excel(writer, sheet_name='ANOVA', index=False)

    output.seek(0)
    return gr.File.update(value=output, visible=True, filename="resultados_rsm.xlsx")

def download_images():
    if 'rsm' not in globals():
        return None, "Error: Carga los datos y ajusta el modelo primero."

    # Crear un directorio temporal para guardar las imágenes
    temp_dir = "temp_images"
    os.makedirs(temp_dir, exist_ok=True)

    # Generar todas las gráficas y guardarlas como imágenes PNG
    all_figs = rsm.generate_all_plots()
    for i, fig in enumerate(all_figs):
        img_path = os.path.join(temp_dir, f"plot_{i}.png")
        fig.write_image(img_path)

    # Comprimir las imágenes en un archivo ZIP
    zip_buffer = io.BytesIO()
    with ZipFile(zip_buffer, "w") as zip_file:
        for filename in os.listdir(temp_dir):
            file_path = os.path.join(temp_dir, filename)
            zip_file.write(file_path, arcname=filename)

    # Eliminar el directorio temporal
    for filename in os.listdir(temp_dir):
        file_path = os.path.join(temp_dir, filename)
        os.remove(file_path)
    os.rmdir(temp_dir)

    zip_buffer.seek(0)
    return gr.File.update(value=zip_buffer, visible=True, filename="graficos_rsm.zip")

# --- Crear la interfaz de Gradio ---

with gr.Blocks() as demo:
    gr.Markdown("# Optimización de la producción de AIA usando RSM Box-Behnken")

    with gr.Row():
        with gr.Column():
            gr.Markdown("## Configuración del Diseño")
            x1_name_input = gr.Textbox(label="Nombre de la Variable X1 (ej. Glucosa)", value="Glucosa")
            x2_name_input = gr.Textbox(label="Nombre de la Variable X2 (ej. Extracto de Levadura)", value="Extracto_de_Levadura")
            x3_name_input = gr.Textbox(label="Nombre de la Variable X3 (ej. Triptófano)", value="Triptofano")
            y_name_input = gr.Textbox(label="Nombre de la Variable Dependiente (ej. AIA (ppm))", value="AIA_ppm")
            x1_levels_input = gr.Textbox(label="Niveles de X1 (separados por comas)", value="1, 3.5, 5.5")
            x2_levels_input = gr.Textbox(label="Niveles de X2 (separados por comas)", value="0.03, 0.2, 0.3")
            x3_levels_input = gr.Textbox(label="Niveles de X3 (separados por comas)", value="0.4, 0.65, 0.9")
            data_input = gr.Textbox(label="Datos del Experimento (formato CSV)", lines=5, value="""1,-1,-1,0,166.594
2,1,-1,0,177.557
3,-1,1,0,127.261
4,1,1,0,147.573
5,-1,0,-1,188.883
6,1,0,-1,224.527
7,-1,0,1,190.238
8,1,0,1,226.483
9,0,-1,-1,195.550
10,0,1,-1,149.493
11,0,-1,1,187.683
12,0,1,1,148.621
13,0,0,0,278.951
14,0,0,0,297.238
15,0,0,0,280.896""")
            load_button = gr.Button("Cargar Datos")
            

        with gr.Column():
            gr.Markdown("## Datos Cargados")
            data_output = gr.Dataframe(label="Tabla de Datos")

    # Hacer que la sección de análisis sea visible solo después de cargar los datos
    with gr.Row(visible=False) as analysis_row:
        with gr.Column():
            fit_button = gr.Button("Ajustar Modelo y Optimizar")
            download_excel_button = gr.Button("Descargar Tablas en Excel")
            download_images_button = gr.Button("Descargar Gráficos en ZIP")
            gr.Markdown("**Modelo Completo**")
            model_completo_output = gr.HTML()
            pareto_completo_output = gr.Plot()
            gr.Markdown("**Modelo Simplificado**")
            model_simplificado_output = gr.HTML()
            pareto_simplificado_output = gr.Plot()
            equation_output = gr.HTML()
            optimization_table_output = gr.Dataframe(label="Tabla de Optimización")
            prediction_table_output = gr.Dataframe(label="Tabla de Predicciones")
            contribution_table_output = gr.Dataframe(label="Tabla de % de Contribución")
            anova_table_output = gr.Dataframe(label="Tabla ANOVA Detallada")
        with gr.Column():
            gr.Markdown("## Generar Gráficos de Superficie de Respuesta")
            fixed_variable_input = gr.Dropdown(label="Variable Fija", choices=["Glucosa", "Extracto_de_Levadura", "Triptofano"], value="Glucosa")
            fixed_level_input = gr.Slider(label="Nivel de Variable Fija", minimum=0, maximum=1, step=0.01, value=0.5)
            plot_button = gr.Button("Generar Gráfico")
            rsm_plot_output = gr.Gallery(label="Gráficos RSM", columns=3, preview=True, height="auto")

    load_button.click(
        load_data,
        inputs=[x1_name_input, x2_name_input, x3_name_input, y_name_input, x1_levels_input, x2_levels_input, x3_levels_input, data_input],
        outputs=[data_output, x1_name_input, x2_name_input, x3_name_input, y_name_input, x1_levels_input, x2_levels_input, x3_levels_input, analysis_row]
    )

    fit_button.click(fit_and_optimize_model, outputs=[model_completo_output, pareto_completo_output, model_simplificado_output, pareto_simplificado_output, equation_output, optimization_table_output, prediction_table_output, contribution_table_output, anova_table_output])
    
    plot_button.click(generate_rsm_plot, inputs=[fixed_variable_input, fixed_level_input], outputs=[rsm_plot_output])

    download_excel_button.click(download_excel, outputs=[gr.File()])
    download_images_button.click(download_images, outputs=[gr.File()])

    # Ejemplo de uso
    gr.Markdown("## Ejemplo de uso")
    gr.Markdown("1. Introduce los nombres de las variables y sus niveles en las cajas de texto correspondientes.")
    gr.Markdown("2. Copia y pega los datos del experimento en la caja de texto 'Datos del Experimento'.")
    gr.Markdown("3. Haz clic en 'Cargar Datos' para cargar los datos en la tabla.")
    gr.Markdown("4. Haz clic en 'Ajustar Modelo y Optimizar' para ajustar el modelo y encontrar los niveles óptimos de los factores.")
    gr.Markdown("5. Selecciona una variable fija y su nivel en los controles deslizantes.")
    gr.Markdown("6. Haz clic en 'Generar Gráfico' para generar un gráfico de superficie de respuesta.")
    gr.Markdown("7. Haz clic en 'Descargar Tablas en Excel' para obtener un archivo Excel con todas las tablas generadas.")
    gr.Markdown("8. Haz clic en 'Descargar Gráficos en ZIP' para obtener un archivo ZIP con todos los gráficos generados.")

demo.launch()