Update interface.py

interface.py

@@ -156,10 +156,13 @@ class BioprocessModel:
         """
         if model_type not in self.models:
             raise ValueError(f"Model type '{model_type}' is not set. Please use set_model first.")
-
+    
         func = self.models[model_type]['function']
         params = self.models[model_type]['params']
         
+        # Depuración: Asegurarse de que los parámetros estén bien definidos
+        print(f"Fitting {model_type} model with function: {func} and parameters: {params}")
+        
         # Define the fitting function based on model type
         if model_type == 'biomass':
             def fit_func(t, *args):
@@ -167,13 +170,24 @@ class BioprocessModel:
         else:
             def fit_func(t, *args):
                 return func(t, *args)
-
-        popt, _ = curve_fit(fit_func, time, data, bounds=bounds, maxfev=10000)
-        self.params[model_type] = {param: val for param, val in zip(params, popt)}
-        y_pred = fit_func(time, *popt)
-        self.r2[model_type] = 1 - (np.sum((data - y_pred) ** 2) / np.sum((data - np.mean(data)) ** 2))
-        self.rmse[model_type] = np.sqrt(mean_squared_error(data, y_pred))
-        return y_pred
+        
+        # Depuración: Verificar el número de parámetros que se espera ajustar
+        print(f"Number of parameters to fit: {len(params)}")
+    
+        try:
+            # Ajustar el modelo usando curve_fit
+            popt, _ = curve_fit(fit_func, time, data, bounds=bounds, maxfev=10000)
+            print(f"Optimal parameters found: {popt}")
+    
+            # Guardar los parámetros ajustados en el modelo
+            self.params[model_type] = {param: val for param, val in zip(params, popt)}
+            y_pred = fit_func(time, *popt)
+            self.r2[model_type] = 1 - (np.sum((data - y_pred) ** 2) / np.sum((data - np.mean(data)) ** 2))
+            self.rmse[model_type] = np.sqrt(mean_squared_error(data, y_pred))
+            return y_pred
+        except Exception as e:
+            print(f"Error while fitting {model_type} model: {str(e)}")
+            raise
 
     def plot_combined_results(self, time, biomass, substrate, product,
                               y_pred_biomass, y_pred_substrate, y_pred_product,