Update app.py

app.py

@@ -433,12 +433,15 @@ class RSM_BoxBehnken:
         # 10. Cuadrados medios
         ms_regression = ss_regression / df_regression
         ms_residual = ss_residual / df_residual
-        ms_lack_of_fit = ms_lack_of_fit / df_lack_of_fit if not np.isnan(ss_lack_of_fit) else np.nan
-        ms_pure_error = ss_pure_error / df_pure_error if not np.isnan(ss_pure_error) else np.nan
+        ms_lack_of_fit = np.nan # Initialize ms_lack_of_fit to nan
+        if not np.isnan(df_lack_of_fit) and df_lack_of_fit != 0: # Check df_lack_of_fit is valid
+            ms_lack_of_fit = ss_lack_of_fit / df_lack_of_fit
+        ms_pure_error = ss_pure_error / df_pure_error if not np.isnan(df_pure_error) else np.nan
 
         # 11. Estadístico F y valor p para la falta de ajuste
-        f_lack_of_fit = ms_lack_of_fit / ms_pure_error if not np.isnan(ms_lack_of_fit) else np.nan
-        p_lack_of_fit = 1 - f.cdf(f_lack_of_fit, df_lack_of_fit, df_pure_error) if not np.isnan(f_lack_of_fit) else np.nan
+        f_lack_of_fit = ms_lack_of_fit / ms_pure_error if not np.isnan(ms_lack_of_fit) and not np.isnan(ms_pure_error) and ms_pure_error != 0 else np.nan # Added nan checks and zero division check
+        p_lack_of_fit = 1 - f.cdf(f_lack_of_fit, df_lack_of_fit, df_pure_error) if not np.isnan(f_lack_of_fit) and not np.isnan(df_lack_of_fit) and not np.isnan(df_pure_error) else np.nan # Added nan checks
+
 
         # 12. Crear la tabla ANOVA detallada
         detailed_anova_table = pd.DataFrame({
@@ -665,9 +668,9 @@ def fit_and_optimize_model():
     zip_path = rsm.save_figures_to_zip()
 
     return (
-        model_completo.summary().as_html(),
+        model_completo_output,
         pareto_completo,
-        model_simplificado.summary().as_html(),
+        model_simplificado_output,
         pareto_simplificado,
         equation_output,
         optimization_table,
@@ -796,7 +799,7 @@ def create_gradio_interface():
             with gr.Column():
                 gr.Markdown("## Generar Gráficos de Superficie de Respuesta")
                 fixed_variable_input = gr.Dropdown(label="Variable Fija", choices=["Glucosa_g_L", "Proteina_Pescado_g_L", "Sulfato_Manganeso_g_L"], value="Glucosa_g_L") # Updated choices
-                fixed_level_input = gr.Slider(label="Nivel de Variable Fija (Natural Units)", minimum=0, maximum=10, step=0.1, value=5.0) # Updated Slider
+                fixed_level_input = gr.Slider(label="Nivel de Variable Fija (Natural Units)", minimum=min(data['Glucosa_g_L']), maximum=max(data['Glucosa_g_L']), step=0.1, value=5.0) # Updated Slider - Using data min/max
                 plot_button = gr.Button("Generar Gráficos")
                 with gr.Row():
                     left_button = gr.Button("<")