Update app.py

app.py

@@ -30,6 +30,7 @@ from sklearn.model_selection import train_test_split
 
 #Figures setting block
 import io
+import matplotlib.pyplot as plt
 
 def make_fig(figsize=(5.5, 3.6), dpi=120):
     """
@@ -38,28 +39,22 @@ def make_fig(figsize=(5.5, 3.6), dpi=120):
     fig = plt.figure(figsize=figsize, dpi=dpi)
     return fig
 
+
+
 def fig_to_png_bytes(fig, dpi=600):
-    """
-    Export current figure as PNG bytes at high DPI (>=600).
-    """
     buf = io.BytesIO()
     fig.savefig(buf, format="png", dpi=int(dpi), bbox_inches="tight")
     buf.seek(0)
     return buf.getvalue()
 
-def render_plot_with_download(
-    fig,
-    *,
-    title: str,
-    filename: str,
-    export_dpi: int = 600
-):
-    """
-    Show a compact plot in Streamlit + provide high-DPI PNG download.
-    """
-    st.pyplot(fig, clear_figure=True, use_container_width=True)
-
+def render_plot_with_download(fig, *, title: str, filename: str, export_dpi: int = 600):
+    # 1) SAVE FIRST (before any clear)
     png_bytes = fig_to_png_bytes(fig, dpi=export_dpi)
+
+    # 2) DISPLAY (do NOT clear)
+    st.pyplot(fig, clear_figure=False, use_container_width=False)
+
+    # 3) DOWNLOAD
     st.download_button(
         label=f"Download {title} (PNG {export_dpi} dpi)",
         data=png_bytes,
@@ -68,6 +63,10 @@ def render_plot_with_download(
         key=f"dl_{filename}"
     )
 
+    # 4) Prevent figure leakage / overlaps
+    plt.close(fig)
+
+
 
 # ============================================================
 # Fixed schema definition (PLACEHOLDER FRAMEWORK)
@@ -1195,57 +1194,54 @@ with tab_predict:
 
         
                 # PR plot
-                pr = pr_ext
-                fig_pr = make_fig(figsize=FIGSIZE, dpi=plot_dpi_screen)
-                plt.plot(pr_ext["recall"], pr_ext["precision"])
-                plt.xlabel("Recall")
-                plt.ylabel("Precision")
-                plt.title(f"External PR Curve (AP = {pr_ext['average_precision']:.3f})")
+                # PR curve (external)
+                fig, ax = plt.subplots(figsize=FIGSIZE, dpi=plot_dpi_screen)
+                ax.plot(pr_ext["recall"], pr_ext["precision"])
+                ax.set_xlabel("Recall")
+                ax.set_ylabel("Precision")
+                ax.set_title(f"External PR Curve (AP = {pr_ext['average_precision']:.3f})")
                 
                 render_plot_with_download(
-                    fig_pr,
+                    fig,
                     title="External PR curve",
                     filename="external_pr_curve.png",
                     export_dpi=export_dpi
                 )
-
-
-        
-                # Calibration plot (external)
-                cal = cal_ext
-                fig_cal = make_fig(figsize=FIGSIZE, dpi=plot_dpi_screen)
-                plt.plot(cal["prob_pred"], cal["prob_true"])
-                plt.plot([0, 1], [0, 1])
-                plt.xlabel("Mean predicted probability")
-                plt.ylabel("Observed event rate")
-                plt.title("External calibration curve")
+                
+                # Calibration curve (external)
+                fig, ax = plt.subplots(figsize=FIGSIZE, dpi=plot_dpi_screen)
+                ax.plot(cal_ext["prob_pred"], cal_ext["prob_true"])
+                ax.plot([0, 1], [0, 1])  # diagonal reference
+                ax.set_xlabel("Mean predicted probability")
+                ax.set_ylabel("Observed event rate")
+                ax.set_title("External calibration curve")
                 
                 render_plot_with_download(
-                    fig_cal,
+                    fig,
                     title="External calibration curve",
                     filename="external_calibration_curve.png",
                     export_dpi=export_dpi
                 )
                 
-                # DCA plot (external)
-                dca = dca_ext
-                fig_dca = make_fig(figsize=FIGSIZE, dpi=plot_dpi_screen)
-                plt.plot(dca["thresholds"], dca["net_benefit_model"], label="Model")
-                plt.plot(dca["thresholds"], dca["net_benefit_all"], label="Treat all")
-                plt.plot(dca["thresholds"], dca["net_benefit_none"], label="Treat none")
-                plt.xlabel("Threshold probability")
-                plt.ylabel("Net benefit")
-                plt.title("External decision curve analysis")
-                plt.legend()
+                # DCA (external)
+                fig, ax = plt.subplots(figsize=FIGSIZE, dpi=plot_dpi_screen)
+                ax.plot(dca_ext["thresholds"], dca_ext["net_benefit_model"], label="Model")
+                ax.plot(dca_ext["thresholds"], dca_ext["net_benefit_all"], label="Treat all")
+                ax.plot(dca_ext["thresholds"], dca_ext["net_benefit_none"], label="Treat none")
+                ax.set_xlabel("Threshold probability")
+                ax.set_ylabel("Net benefit")
+                ax.set_title("External decision curve analysis")
+                ax.legend()
                 
                 render_plot_with_download(
-                    fig_dca,
+                    fig,
                     title="External decision curve",
                     filename="external_decision_curve.png",
                     export_dpi=export_dpi
                 )
 
 
+
         
             except Exception as e:
                 st.error(f"Could not compute external validation metrics: {e}")
@@ -1419,40 +1415,32 @@ with tab_predict:
             st.markdown(f"### Global SHAP summary (first {batch_n} rows)")
         
             # BAR SUMMARY
-            fig_bar = make_fig(figsize=FIGSIZE, dpi=plot_dpi_screen)
+            plt.figure(figsize=FIGSIZE, dpi=plot_dpi_screen)
             shap.summary_plot(
                 shap_vals_batch,
                 features=X_dense,
                 feature_names=names,
                 plot_type="bar",
                 max_display=max_display,
-                show=False,
-                plot_size=FIGSIZE
-            )
-            render_plot_with_download(
-                fig_bar,
-                title="SHAP bar summary",
-                filename="shap_summary_bar.png",
-                export_dpi=export_dpi
+                show=False
             )
+            fig_bar = plt.gcf()
+            render_plot_with_download(fig_bar, title="SHAP bar summary", filename="shap_summary_bar.png", export_dpi=export_dpi)
+
 
         
             # BEESWARM SUMMARY (optional)
-            fig_swarm = make_fig(figsize=FIGSIZE, dpi=plot_dpi_screen)
+            plt.figure(figsize=FIGSIZE, dpi=plot_dpi_screen)
             shap.summary_plot(
                 shap_vals_batch,
                 features=X_dense,
                 feature_names=names,
                 max_display=max_display,
-                show=False,
-                plot_size=FIGSIZE
-            )
-            render_plot_with_download(
-                fig_swarm,
-                title="SHAP beeswarm",
-                filename="shap_beeswarm.png",
-                export_dpi=export_dpi
+                show=False
             )
+            fig_swarm = plt.gcf()
+            render_plot_with_download(fig_swarm, title="SHAP beeswarm", filename="shap_beeswarm.png", export_dpi=export_dpi)
+
 
         
             st.markdown("### Waterfall plots (batch)")
@@ -1536,18 +1524,16 @@ with tab_predict:
         
             with c1:
                 st.markdown("**Waterfall**")
-                fig = make_fig(figsize=FIGSIZE, dpi=plot_dpi_screen)
+                plt.figure(figsize=FIGSIZE, dpi=plot_dpi_screen)
                 shap.plots.waterfall(exp, show=False, max_display=20)
-                render_plot_with_download(
-                    fig, title="SHAP waterfall", filename="shap_waterfall_row.png", export_dpi=export_dpi
-                )
+                fig_w = plt.gcf()
+                render_plot_with_download(fig_w, title="SHAP waterfall", filename="shap_waterfall_row.png", export_dpi=export_dpi)
         
             with c2:
                 st.markdown("**Top features**")
-                fig2 = make_fig(figsize=FIGSIZE, dpi=plot_dpi_screen)
+                plt.figure(figsize=FIGSIZE, dpi=plot_dpi_screen)
                 shap.plots.bar(exp, show=False, max_display=20)
-                render_plot_with_download(
-                    fig2, title="SHAP bar", filename="shap_bar_row.png", export_dpi=export_dpi
-                )
+                fig_b = plt.gcf()
+                render_plot_with_download(fig_b, title="SHAP bar", filename="shap_bar_row.png", export_dpi=export_dpi)
         
             st.stop()