Update app.py

app.py

@@ -227,16 +227,23 @@ def plot_properties_dashboard(df: pd.DataFrame):
     if df.empty:
         return None, "Cannot plot: No analysis data."
     
+    # Clear any existing plots to prevent interference
+    plt.clf()
+    plt.close('all')
+    
     plt.style.use('dark_background')
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
     fig.suptitle("Molecular Properties Analysis", fontsize=16)
 
+    # Set transparent backgrounds
     fig.patch.set_facecolor('none')
     for ax_row in axes:
         for ax in ax_row:
             ax.set_facecolor('none')
 
-    axes[0,0].scatter(df['MW'], df['LogP'], c=df['Drug_Like'].map({True: 'green', False: 'red'}), s=80, alpha=0.7)
+    # Plot 1: MW vs LogP
+    drug_like_colors = df['Drug_Like'].map({True: 'green', False: 'red'})
+    axes[0,0].scatter(df['MW'], df['LogP'], c=drug_like_colors, s=80, alpha=0.7)
     axes[0,0].set_title('Molecular Weight vs LogP')
     axes[0,0].set_xlabel('Molecular Weight (Da)')
     axes[0,0].set_ylabel('LogP')
@@ -244,7 +251,8 @@ def plot_properties_dashboard(df: pd.DataFrame):
     axes[0,0].axhline(5, color='r', linestyle='--', alpha=0.6, label='LogP < 5')
     axes[0,0].legend()
 
-    axes[0,1].scatter(df['HBD'], df['HBA'], c=df['Drug_Like'].map({True: 'green', False: 'red'}), s=80, alpha=0.7)
+    # Plot 2: Hydrogen bonding
+    axes[0,1].scatter(df['HBD'], df['HBA'], c=drug_like_colors, s=80, alpha=0.7)
     axes[0,1].set_title('Hydrogen Bonding Properties')
     axes[0,1].set_xlabel('Hydrogen Bond Donors')
     axes[0,1].set_ylabel('Hydrogen Bond Acceptors')
@@ -252,11 +260,13 @@ def plot_properties_dashboard(df: pd.DataFrame):
     axes[0,1].axhline(10, color='r', linestyle='--', alpha=0.6, label='HBA < 10')
     axes[0,1].legend()
 
-    axes[1,0].scatter(df['TPSA'], df['RotBonds'], c=df['Drug_Like'].map({True: 'green', False: 'red'}), s=80, alpha=0.7)
+    # Plot 3: TPSA vs Flexibility
+    axes[1,0].scatter(df['TPSA'], df['RotBonds'], c=drug_like_colors, s=80, alpha=0.7)
     axes[1,0].set_title('TPSA vs Flexibility')
     axes[1,0].set_xlabel('Topological Polar Surface Area (Ų)')
     axes[1,0].set_ylabel('Rotatable Bonds')
 
+    # Plot 4: Drug-likeness pie chart
     drug_like_counts = df['Drug_Like'].value_counts()
     labels = ['Drug-like' if i else 'Non-drug-like' for i in drug_like_counts.index]
     colors = ['green' if i else 'red' for i in drug_like_counts.index]
@@ -264,8 +274,10 @@ def plot_properties_dashboard(df: pd.DataFrame):
     axes[1,1].set_title('Drug-likeness Distribution')
     
     plt.tight_layout(rect=[0, 0, 1, 0.96])
+    
+    # Return the figure without calling plt.show()
     return fig, "✅ Generated properties dashboard."
-
+    
 # ===== Phase 2 Functions =====
 def get_phase2_molecules():
     return {
@@ -426,9 +438,14 @@ def simulate_rwd_analysis(adverse_event_text):
     event_counts = pd.Series(all_events).value_counts()
     log = f"✅ Analyzed {len(all_events)} simulated adverse event reports.\n"
     
+    # Clear any existing plots
+    plt.clf()
+    plt.close('all')
+    
     plt.style.use('dark_background')
     fig_bar, ax_bar = plt.subplots(figsize=(10, 6))
     
+    # Set transparent backgrounds
     fig_bar.patch.set_facecolor('none')
     ax_bar.set_facecolor('none')
     
@@ -440,7 +457,7 @@ def simulate_rwd_analysis(adverse_event_text):
     plt.tight_layout()
 
     return event_counts.reset_index().rename(columns={'index': 'Event', 0: 'Count'}), fig_bar, log
-
+    
 def get_ethical_framework():
     framework = {'Pillar': ['1. Beneficence & Non-Maleficence', '2. Justice & Fairness', '3. Transparency & Explainability', '4. Accountability & Governance'],
                  'Description': ['AI should help patients and do no harm. Requires rigorous validation and safety monitoring.',
@@ -537,9 +554,12 @@ with tab1:
             st.subheader("Calculated Molecular Properties")
             st.dataframe(res1.get('props_df', pd.DataFrame()), use_container_width=True)
         with p1_tabs[2]:
-            st.subheader("Molecular Properties Dashboard")
-            if res1.get('props_plot'):
-                st.pyplot(res1['props_plot'])
+                    st.subheader("Molecular Properties Dashboard")
+                    if res1.get('props_plot'):
+                        # Display the plot and then close it to prevent memory leaks
+                        st.pyplot(res1['props_plot'], clear_figure=True)
+                        # Explicitly close the figure after displaying
+                        plt.close(res1['props_plot'])
 
 # ===== TAB 2: LEAD GENERATION & OPTIMIZATION =====
 with tab2:
@@ -682,9 +702,12 @@ with tab4:
         res4 = st.session_state.results_p4
         p4_tabs = st.tabs(["Pharmacovigilance Analysis", "Regulatory & Ethical Frameworks"])
         with p4_tabs[0]:
-            st.subheader("Simulated Adverse Event Analysis")
-            st.pyplot(res4['plot_bar'])
-            st.dataframe(res4['rwd_df'], use_container_width=True)
+                    st.subheader("Simulated Adverse Event Analysis")
+                    if 'plot_bar' in res4 and res4['plot_bar'] is not None:
+                        st.pyplot(res4['plot_bar'], clear_figure=True)
+                        # Explicitly close the figure after displaying
+                        plt.close(res4['plot_bar'])
+                    st.dataframe(res4['rwd_df'], use_container_width=True)
             
         with p4_tabs[1]:
             col1, col2 = st.columns(2)