# interface.py import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.optimize import curve_fit from PIL import Image import io from sympy import symbols, lambdify, sympify, Function from bioprocess_model import BioprocessModel from decorators import gpu_decorator # Importar el decorador personalizado def parse_bounds(bounds_str, num_params): try: # Reemplazar 'inf' por 'np.inf' si el usuario lo escribió así bounds_str = bounds_str.replace('inf', 'np.inf') # Evaluar la cadena de límites bounds = eval(f"[{bounds_str}]") if len(bounds) != num_params: raise ValueError("Número de límites no coincide con el número de parámetros.") lower_bounds = [b[0] for b in bounds] upper_bounds = [b[1] for b in bounds] return lower_bounds, upper_bounds except Exception as e: print(f"Error al parsear los límites: {e}. Usando límites por defecto.") lower_bounds = [-np.inf] * num_params upper_bounds = [np.inf] * num_params return lower_bounds, upper_bounds @gpu_decorator(duration=300) def generate_analysis(prompt, max_length=1024, device=None): # Implementación existente para generar análisis usando Hugging Face o similar # Por ejemplo, podrías usar un modelo de lenguaje para generar texto # Aquí se deja como placeholder analysis = "Análisis generado por el modelo de lenguaje." return analysis @gpu_decorator(duration=600) # Ajusta la duración según tus necesidades def process_and_plot( file, biomass_eq1, biomass_eq2, biomass_eq3, biomass_param1, biomass_param2, biomass_param3, biomass_bound1, biomass_bound2, biomass_bound3, substrate_eq1, substrate_eq2, substrate_eq3, substrate_param1, substrate_param2, substrate_param3, substrate_bound1, substrate_bound2, substrate_bound3, product_eq1, product_eq2, product_eq3, product_param1, product_param2, product_param3, product_bound1, product_bound2, product_bound3, legend_position, show_legend, show_params, biomass_eq_count, substrate_eq_count, product_eq_count, device=None ): # Leer el archivo Excel df = pd.read_excel(file.name) # Verificar que las columnas necesarias estén presentes expected_columns = ['Tiempo', 'Biomasa', 'Sustrato', 'Producto'] for col in expected_columns: if col not in df.columns: raise KeyError(f"La columna esperada '{col}' no se encuentra en el archivo Excel.") # Asignar los datos desde las columnas time = df['Tiempo'].values biomass_data = df['Biomasa'].values substrate_data = df['Sustrato'].values product_data = df['Producto'].values # Convierte los contadores a enteros biomass_eq_count = int(biomass_eq_count) substrate_eq_count = int(substrate_eq_count) product_eq_count = int(product_eq_count) # Recolecta las ecuaciones, parámetros y límites según los contadores biomass_eqs = [biomass_eq1, biomass_eq2, biomass_eq3][:biomass_eq_count] biomass_params = [biomass_param1, biomass_param2, biomass_param3][:biomass_eq_count] biomass_bounds = [biomass_bound1, biomass_bound2, biomass_bound3][:biomass_eq_count] substrate_eqs = [substrate_eq1, substrate_eq2, substrate_eq3][:substrate_eq_count] substrate_params = [substrate_param1, substrate_param2, substrate_param3][:substrate_eq_count] substrate_bounds = [substrate_bound1, substrate_bound2, substrate_bound3][:substrate_eq_count] product_eqs = [product_eq1, product_eq2, product_eq3][:product_eq_count] product_params = [product_param1, product_param2, product_param3][:product_eq_count] product_bounds = [product_bound1, product_bound2, product_bound3][:product_eq_count] biomass_results = [] substrate_results = [] product_results = [] # Inicializar el modelo principal main_model = BioprocessModel() # Ajusta los modelos de Biomasa for i in range(len(biomass_eqs)): equation = biomass_eqs[i] params_str = biomass_params[i] bounds_str = biomass_bounds[i] try: main_model.set_model('biomass', equation, params_str) except ValueError as ve: raise ValueError(f"Error en la configuración del modelo de biomasa {i+1}: {ve}") params = [param.strip() for param in params_str.split(',')] lower_bounds, upper_bounds = parse_bounds(bounds_str, len(params)) try: y_pred = main_model.fit_model( 'biomass', time, biomass_data, bounds=(lower_bounds, upper_bounds) ) biomass_results.append({ 'model': main_model, 'y_pred': y_pred, 'equation': equation, 'params': main_model.params['biomass'] }) except Exception as e: raise RuntimeError(f"Error al ajustar el modelo de biomasa {i+1}: {e}") # Usa el primer modelo de biomasa para X(t) biomass_model = biomass_results[0]['model'] biomass_func = biomass_model.models['biomass']['function'] biomass_params_values = list(biomass_model.params['biomass'].values()) # Ajusta los modelos de Sustrato for i in range(len(substrate_eqs)): equation = substrate_eqs[i] params_str = substrate_params[i] bounds_str = substrate_bounds[i] try: main_model.set_model('substrate', equation, params_str) except ValueError as ve: raise ValueError(f"Error en la configuración del modelo de sustrato {i+1}: {ve}") params = [param.strip() for param in params_str.split(',')] lower_bounds, upper_bounds = parse_bounds(bounds_str, len(params)) try: y_pred = main_model.fit_model( 'substrate', time, substrate_data, bounds=(lower_bounds, upper_bounds) ) substrate_results.append({ 'model': main_model, 'y_pred': y_pred, 'equation': equation, 'params': main_model.params['substrate'] }) except Exception as e: raise RuntimeError(f"Error al ajustar el modelo de sustrato {i+1}: {e}") # Ajusta los modelos de Producto for i in range(len(product_eqs)): equation = product_eqs[i] params_str = product_params[i] bounds_str = product_bounds[i] try: main_model.set_model('product', equation, params_str) except ValueError as ve: raise ValueError(f"Error en la configuración del modelo de producto {i+1}: {ve}") params = [param.strip() for param in params_str.split(',')] lower_bounds, upper_bounds = parse_bounds(bounds_str, len(params)) try: y_pred = main_model.fit_model( 'product', time, product_data, bounds=(lower_bounds, upper_bounds) ) product_results.append({ 'model': main_model, 'y_pred': y_pred, 'equation': equation, 'params': main_model.params['product'] }) except Exception as e: raise RuntimeError(f"Error al ajustar el modelo de producto {i+1}: {e}") # Genera las gráficas fig, axs = plt.subplots(3, 1, figsize=(10, 15)) # Gráfica de Biomasa axs[0].plot(time, biomass_data, 'o', label='Datos de Biomasa') for i, result in enumerate(biomass_results): axs[0].plot(time, result['y_pred'], '-', label=f'Modelo de Biomasa {i+1}') axs[0].set_xlabel('Tiempo') axs[0].set_ylabel('Biomasa') if show_legend: axs[0].legend(loc=legend_position) # Gráfica de Sustrato axs[1].plot(time, substrate_data, 'o', label='Datos de Sustrato') for i, result in enumerate(substrate_results): axs[1].plot(time, result['y_pred'], '-', label=f'Modelo de Sustrato {i+1}') axs[1].set_xlabel('Tiempo') axs[1].set_ylabel('Sustrato') if show_legend: axs[1].legend(loc=legend_position) # Gráfica de Producto axs[2].plot(time, product_data, 'o', label='Datos de Producto') for i, result in enumerate(product_results): axs[2].plot(time, result['y_pred'], '-', label=f'Modelo de Producto {i+1}') axs[2].set_xlabel('Tiempo') axs[2].set_ylabel('Producto') if show_legend: axs[2].legend(loc=legend_position) plt.tight_layout() buf = io.BytesIO() plt.savefig(buf, format='png') buf.seek(0) image = Image.open(buf) prompt = f""" Eres un experto en modelado de bioprocesos. Analiza los siguientes resultados experimentales y proporciona un veredicto sobre la calidad de los modelos, sugiriendo mejoras si es necesario. Biomasa: {biomass_results} Sustrato: {substrate_results} Producto: {product_results} """ analysis = generate_analysis(prompt, device=device) return image, analysis