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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
import gradio as gr
class RSM_BoxBehnken:
# ... (Tu c贸digo de la clase RSM_BoxBehnken se mantiene igual) ...
# Crear un DataFrame a partir de la tabla
data = pd.DataFrame({
'Exp.': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
'Glucosa': [-1, 1, -1, 1, -1, 1, -1, 1, 0, 0, 0, 0, 0, 0, 0],
'Extracto de Levadura': [-1, -1, 1, 1, 0, 0, 0, 0, -1, 1, -1, 1, 0, 0, 0],
'Tript贸fano': [0, 0, 0, 0, -1, -1, 1, 1, -1, -1, 1, 1, 0, 0, 0],
'AIA (ppm)': [166.594, 177.557, 127.261, 147.573, 188.883, 224.527, 190.238, 226.483, 195.550, 149.493, 187.683, 148.621, 278.951, 297.238, 280.896]
})
# Crear una instancia de la clase RSM_BoxBehnken
rsm = RSM_BoxBehnken(data)
# --- Funciones para la interfaz de Gradio ---
def fit_full_model():
rsm.fit_model()
return "Modelo completo ajustado. Revisa la consola para ver el resumen."
def fit_simplified_model():
rsm.fit_simplified_model()
return "Modelo simplificado ajustado. Revisa la consola para ver el resumen."
def optimize_model(method):
rsm.optimize(method)
return (f"Optimizaci贸n realizada con {method}. Revisa la consola para ver los niveles 贸ptimos.\n"
f"Niveles 贸ptimos (codificados): {rsm.optimal_levels}\n"
f"Valor m谩ximo de {rsm.y_name}: {-rsm.optimized_results.fun:.4f}")
def generate_plot(fixed_variable, fixed_level_natural):
fig = rsm.plot_rsm_individual(fixed_variable, fixed_level_natural)
if fig is not None:
return fig
else:
return "Ajusta el modelo simplificado primero."
# --- Creaci贸n de la interfaz de Gradio ---
with gr.Blocks() as demo:
gr.Markdown("# An谩lisis de Superficie de Respuesta (RSM) - Dise帽o Box-Behnken")
with gr.Tab("Ajuste de Modelos"):
with gr.Row():
full_model_button = gr.Button("Ajustar Modelo Completo")
simplified_model_button = gr.Button("Ajustar Modelo Simplificado")
model_output = gr.Textbox(label="Resultado del Ajuste")
full_model_button.click(fn=fit_full_model, outputs=model_output)
simplified_model_button.click(fn=fit_simplified_model, outputs=model_output)
with gr.Tab("Optimizaci贸n"):
method_dropdown = gr.Dropdown(
choices=['Nelder-Mead', 'Powell', 'BFGS'],
value='Nelder-Mead',
label="M茅todo de Optimizaci贸n"
)
optimize_button = gr.Button("Optimizar")
optimization_output = gr.Textbox(label="Resultado de la Optimizaci贸n")
optimize_button.click(fn=optimize_model, inputs=method_dropdown, outputs=optimization_output)
with gr.Tab("Gr谩ficos de Superficie de Respuesta"):
with gr.Row():
fixed_variable_dropdown = gr.Dropdown(
choices=[rsm.x1_name, rsm.x2_name, rsm.x3_name],
value=rsm.x1_name,
label="Variable Fija"
)
fixed_level_slider = gr.Slider(
minimum=min(rsm.get_levels(rsm.x1_name)),
maximum=max(rsm.get_levels(rsm.x1_name)),
step=0.01,
value=rsm.get_levels(rsm.x1_name)[1],
label="Nivel de Variable Fija (Natural)"
)
plot_button = gr.Button("Generar Gr谩fico")
plot_output = gr.Plot(label="Gr谩fico RSM")
def update_slider_range(fixed_variable):
levels = rsm.get_levels(fixed_variable)
return gr.Slider.update(minimum=min(levels), maximum=max(levels), value=levels[1])
fixed_variable_dropdown.change(
fn=update_slider_range,
inputs=fixed_variable_dropdown,
outputs=fixed_level_slider
)
plot_button.click(
fn=generate_plot,
inputs=[fixed_variable_dropdown, fixed_level_slider],
outputs=plot_output
)
demo.launch() |