Update app.py

app.py

@@ -489,7 +489,7 @@ def load_data(x1_name, x2_name, x3_name, y_name, x1_levels_str, x2_levels_str, x
         data_str (str): Datos del experimento en formato CSV, separados por comas.
 
     Returns:
-        tuple: (pd.DataFrame, str, str, str, str, list, list, list, gr.update)
+        tuple: (pd.DataFrame, str, str, str, str, list, list, list, gr.update, list)
     """
     try:
         # Convertir los niveles a listas de números
@@ -516,11 +516,10 @@ def load_data(x1_name, x2_name, x3_name, y_name, x1_levels_str, x2_levels_str, x
     except Exception as e:
         return None, "", "", "", "", [], [], [], gr.update(visible=False), f"Error: {e}"
 
-def fit_and_optimize_model():
+def fit_and_optimize_model(rsm_plots_state=None):
     if 'rsm' not in globals():
-        return None, None, None, None, None, None, None, None, None, "Error: Carga los datos primero."
+        return (None, None, None, None, None, None, None, None, None, None, [], "Error: Carga los datos primero.")
     
-    global rsm_plots
     model_completo, pareto_completo = rsm.fit_model()
     model_simplificado, pareto_simplificado = rsm.fit_simplified_model()
     optimization_table = rsm.optimize()
@@ -536,22 +535,35 @@ def fit_and_optimize_model():
     equation_formatted = equation.replace(" + ", "<br>+ ").replace(" ** ", "^").replace("*", " × ")
     equation_formatted = f"### Ecuación del Modelo Simplificado:<br>{equation_formatted}"
     
-    return model_completo.summary().tables[0].as_html(), pareto_completo, model_completo.summary().tables[1].as_html(), model_simplificado.summary().as_html(), pareto_simplificado, equation_formatted, optimization_table, prediction_table, contribution_table, anova_table, rsm_plots, gr.update(visible=True, maximum=len(rsm_plots) -1)
+    return (
+        model_completo.summary().tables[0].as_html(),
+        pareto_completo,
+        model_completo.summary().tables[1].as_html(),
+        model_simplificado.summary().as_html(),
+        pareto_simplificado,
+        equation_formatted,
+        optimization_table,
+        prediction_table,
+        contribution_table,
+        anova_table,
+        rsm_plots,  # Esto se asignará al estado
+        gr.update(visible=True, maximum=len(rsm_plots) -1) if rsm_plots else gr.update()
+    )
 
-def generate_rsm_plot(plot_index):
-    if 'rsm_plots' not in globals() or not rsm_plots:
+def generate_rsm_plot(plot_index, rsm_plots_state):
+    if not rsm_plots_state:
         return None, gr.update(visible=False), "Error: Genera los gráficos primero."
     
     plot_index = int(plot_index)
-    if 0 <= plot_index < len(rsm_plots):
-        selected_plot = rsm_plots[plot_index]
-        return selected_plot, gr.update(visible=True, value=plot_index)
+    if 0 <= plot_index < len(rsm_plots_state):
+        selected_plot = rsm_plots_state[plot_index]
+        return selected_plot, gr.update(visible=True, value=plot_index), ""
     else:
         return None, gr.update(visible=False), "Error: Índice de gráfico fuera de rango."
 
 def download_excel():
     if 'rsm' not in globals():
-        return None, "Error: Carga los datos y ajusta el modelo primero."
+        return "Error: Carga los datos y ajusta el modelo primero."
 
     output = io.BytesIO()
     with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
@@ -567,27 +579,27 @@ def download_excel():
     href = f'<a download="resultados_rsm.xlsx" href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}">Descargar Excel</a>'
     return href
 
-def download_selected_image(plot_index):
-    if 'rsm_plots' not in globals() or not rsm_plots:
-        return None, "Error: Genera los gráficos primero."
+def download_selected_image(plot_index, rsm_plots_state):
+    if not rsm_plots_state:
+        return "Error: Genera los gráficos primero."
 
     plot_index = int(plot_index)
-    if 0 <= plot_index < len(rsm_plots):
-        selected_plot = rsm_plots[plot_index]
+    if 0 <= plot_index < len(rsm_plots_state):
+        selected_plot = rsm_plots_state[plot_index]
         img_bytes = selected_plot.to_image(format="png")
         b64 = b64encode(img_bytes).decode('utf-8')
         href = f'<a download="grafico_rsm_{plot_index}.png" href="data:image/png;base64,{b64}">Descargar Gráfico {plot_index}</a>'
         return href
     else:
-        return None, "Error: Índice de gráfico fuera de rango."
+        return "Error: Índice de gráfico fuera de rango."
 
-def download_all_images():
-    if 'rsm_plots' not in globals() or not rsm_plots:
-        return None, "Error: Genera los gráficos primero."
+def download_all_images(rsm_plots_state):
+    if not rsm_plots_state:
+        return "Error: Genera los gráficos primero."
 
     zip_output = io.BytesIO()
     with zipfile.ZipFile(zip_output, 'w') as zipf:
-        for i, fig in enumerate(rsm_plots):
+        for i, fig in enumerate(rsm_plots_state):
             img_bytes = fig.to_image(format="png")
             zipf.writestr(f"grafico_rsm_{i}.png", img_bytes)
 
@@ -633,6 +645,9 @@ with gr.Blocks() as demo:
             gr.Markdown("## Datos Cargados")
             data_output = gr.Dataframe(label="Tabla de Datos")
 
+    # Definir el estado para rsm_plots
+    rsm_plots_state = gr.State([])
+
     # Hacer que la sección de análisis y gráficos sea visible solo después de cargar los datos
     with gr.Row(visible=False) as analysis_row:
         with gr.Column():
@@ -674,30 +689,52 @@ with gr.Blocks() as demo:
 
     fit_button.click(
         fit_and_optimize_model, 
-        outputs=[model_completo_output1, pareto_completo_output, model_completo_output2, model_simplificado_output, pareto_simplificado_output, equation_output, optimization_table_output, prediction_table_output, contribution_table_output, anova_table_output, rsm_plots, plot_index_slider]
+        inputs=None,
+        outputs=[
+            model_completo_output1, 
+            pareto_completo, 
+            model_completo_output2, 
+            model_simplificado_output, 
+            pareto_simplificado_output, 
+            equation_output, 
+            optimization_table_output, 
+            prediction_table_output, 
+            contribution_table_output, 
+            anova_table_output, 
+            rsm_plots_state, 
+            plot_index_slider
+        ]
     )
 
     previous_plot_button.click(
+        lambda x: x - 1 if x > 0 else x,
+        inputs=plot_index_slider,
+        outputs=plot_index_slider
+    ).then(
         generate_rsm_plot,
-        inputs=[plot_index_slider],
-        outputs=[rsm_plot_output, plot_index_slider]
-    ).then(lambda x: x - 1 if x > 0 else x, plot_index_slider, plot_index_slider)
+        inputs=[plot_index_slider, rsm_plots_state],
+        outputs=[rsm_plot_output, plot_index_slider, gr.Textbox()]
+    )
 
     next_plot_button.click(
+        lambda x: x + 1 if x < 8 else x,
+        inputs=plot_index_slider,
+        outputs=plot_index_slider
+    ).then(
         generate_rsm_plot,
-        inputs=[plot_index_slider],
-        outputs=[rsm_plot_output, plot_index_slider]
-    ).then(lambda x: x + 1 if x < 8 else x, plot_index_slider, plot_index_slider)
+        inputs=[plot_index_slider, rsm_plots_state],
+        outputs=[rsm_plot_output, plot_index_slider, gr.Textbox()]
+    )
 
     plot_index_slider.change(
         generate_rsm_plot,
-        inputs=[plot_index_slider],
-        outputs=[rsm_plot_output, plot_index_slider]
+        inputs=[plot_index_slider, rsm_plots_state],
+        outputs=[rsm_plot_output, plot_index_slider, gr.Textbox()]
     )
     
-    download_excel_button.click(download_excel, outputs=[download_excel_button])
-    download_image_button.click(download_selected_image, inputs=[plot_index_slider], outputs=[download_image_button])
-    download_all_images_button.click(download_all_images, outputs=[download_all_images_button])
+    download_excel_button.click(download_excel, outputs=download_excel_button)
+    download_image_button.click(download_selected_image, inputs=[plot_index_slider, rsm_plots_state], outputs=download_image_button)
+    download_all_images_button.click(download_all_images, inputs=[rsm_plots_state], outputs=download_all_images_button)
 
     # Ejemplo de uso
     gr.Markdown("## Ejemplo de uso")
@@ -710,4 +747,4 @@ with gr.Blocks() as demo:
     gr.Markdown("7. Haz clic en 'Descargar Gráfico' para descargar la imagen del gráfico actual.")
     gr.Markdown("8. Haz clic en 'Descargar Todos los Gráficos' para descargar un archivo zip con todas las imágenes de los gráficos.")
 
-demo.launch()
+demo.launch()