# interface.py # Importar 'spaces' y decoradores antes que cualquier biblioteca que pueda inicializar CUDA from decorators import gpu_decorator # Luego importar cualquier cosa relacionada con PyTorch o el modelo que va a usar la GPU import torch from transformers import AutoTokenizer, AutoModelForCausalLM import pandas as pd import numpy as np import matplotlib.pyplot as plt from PIL import Image import io from sympy import symbols, lambdify, sympify # Importar otras partes necesarias del código (config, etc.) from config import DEVICE, MODEL_PATH, MAX_LENGTH, TEMPERATURE # Cargar el modelo fuera de la función para evitar la inicialización innecesaria cada vez que se llame a la función model_path = MODEL_PATH tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path) ############################### # bioprocess_model.py import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.integrate import odeint from scipy.optimize import curve_fit from sklearn.metrics import mean_squared_error import seaborn as sns 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 = [] @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)))) @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 def process_data(self, df): biomass_cols = [col for col in df.columns if 'Biomasa' in col] substrate_cols = [col for col in df.columns if 'Sustrato' in col] product_cols = [col for col in df.columns if 'Producto' in col] time_col = [col for col in df.columns if 'Tiempo' in col][0] time = df[time_col].values data_biomass = np.array([df[col].values for col in biomass_cols]) 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 = np.array([df[col].values for col in substrate_cols]) 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 = np.array([df[col].values for col in product_cols]) 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 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 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'): sns.set_style(style) fig, ax1 = plt.subplots(figsize=(10, 7)) ax1.set_xlabel('Tiempo') ax1.set_ylabel('Biomasa', color=line_color) ax1.plot(time, biomass, marker=marker_style, linestyle='', color=point_color, label='Biomasa (Datos)') ax1.plot(time, y_pred_biomass, linestyle=line_style, color=line_color, label='Biomasa (Modelo)') ax1.tick_params(axis='y', labelcolor=line_color) ax2 = ax1.twinx() ax2.set_ylabel('Sustrato', color='green') ax2.plot(time, substrate, marker=marker_style, linestyle='', color='green', label='Sustrato (Datos)') ax2.plot(time, y_pred_substrate, linestyle=line_style, color='green', label='Sustrato (Modelo)') ax2.tick_params(axis='y', labelcolor='green') ax3 = ax1.twinx() ax3.spines["right"].set_position(("axes", 1.1)) ax3.set_ylabel('Producto', color='red') ax3.plot(time, product, marker=marker_style, linestyle='', color='red', label='Producto (Datos)') ax3.plot(time, y_pred_product, linestyle=line_style, color='red', label='Producto (Modelo)') ax3.tick_params(axis='y', labelcolor='red') fig.tight_layout() return fig ############################### # Decorador GPU aplicado para manejar la ejecución en GPU si está disponible @gpu_decorator(duration=300) def generate_analysis(prompt, max_length=1024, device=None): try: # Si el dispositivo no se especifica, usa CPU por defecto if device is None: device = torch.device('cpu') # Mover el modelo al dispositivo adecuado (GPU o CPU) si es necesario if next(model.parameters()).device != device: model.to(device) # Preparar los datos de entrada en el dispositivo correcto input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device) max_gen_length = min(max_length + input_ids.size(1), model.config.max_position_embeddings) # Generar el texto generated_ids = model.generate( input_ids=input_ids, max_length=max_gen_length, temperature=0.7, num_return_sequences=1, no_repeat_ngram_size=2, early_stopping=True ) # Decodificar la respuesta generada output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) analysis = output_text[len(prompt):].strip() return analysis except RuntimeError as e: return f"Error durante la ejecución: {str(e)}" except Exception as e: return f"Ocurrió un error durante el análisis: {e}" def parse_bounds(bounds_str, num_params): try: bounds = eval(f"[{bounds_str}]") if len(bounds) != num_params: raise ValueError lower_bounds = [b[0] for b in bounds] upper_bounds = [b[1] for b in bounds] return lower_bounds, upper_bounds except: lower_bounds = [-np.inf] * num_params upper_bounds = [np.inf] * num_params return lower_bounds, upper_bounds 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 = [] # 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] model = BioprocessModel() model.set_model('biomass', equation, params_str) params = [param.strip() for param in params_str.split(',')] lower_bounds, upper_bounds = parse_bounds(bounds_str, len(params)) y_pred = model.fit_model( 'biomass', time, biomass_data, bounds=(lower_bounds, upper_bounds) ) biomass_results.append({ 'model': model, 'y_pred': y_pred, 'equation': equation }) # Usa el primer modelo de biomasa para X(t) biomass_model = biomass_results[0]['model'] X_t_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] model = BioprocessModel() t_symbol = symbols('t') expr_substrate = sympify(equation) substrate_params_symbols = symbols([param.strip() for param in params_str.split(',')]) substrate_func_expr = expr_substrate.subs('X(t)', X_t_func(t_symbol, *biomass_params_values)) substrate_func = lambdify( (t_symbol, *substrate_params_symbols), substrate_func_expr, 'numpy' ) model.models['substrate'] = { 'function': substrate_func, 'params': [param.strip() for param in params_str.split(',')] } params = model.models['substrate']['params'] lower_bounds, upper_bounds = parse_bounds(bounds_str, len(params)) y_pred = model.fit_model( 'substrate', time, substrate_data, bounds=(lower_bounds, upper_bounds) ) substrate_results.append({ 'model': model, 'y_pred': y_pred, 'equation': equation }) # 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] model = BioprocessModel() t_symbol = symbols('t') expr_product = sympify(equation) product_params_symbols = symbols([param.strip() for param in params_str.split(',')]) product_func_expr = expr_product.subs('X(t)', X_t_func(t_symbol, *biomass_params_values)) product_func = lambdify( (t_symbol, *product_params_symbols), product_func_expr, 'numpy' ) model.models['product'] = { 'function': product_func, 'params': [param.strip() for param in params_str.split(',')] } params = model.models['product']['params'] lower_bounds, upper_bounds = parse_bounds(bounds_str, len(params)) y_pred = model.fit_model( 'product', time, product_data, bounds=(lower_bounds, upper_bounds) ) product_results.append({ 'model': model, 'y_pred': y_pred, 'equation': equation }) # 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