Update app.py

app.py

@@ -5,13 +5,7 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-# --- FIX FOR FLICKERING PLOTS IN DOCKER ---
-# Explicitly set the Matplotlib backend to 'Agg' for headless environments.
-# This must be done *before* importing pyplot.
-import matplotlib
-matplotlib.use('Agg')
 import matplotlib.pyplot as plt
-# -----------------------------------------
 import seaborn as sns
 import requests
 import io
@@ -237,15 +231,12 @@ def plot_properties_dashboard(df: pd.DataFrame):
     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')
 
-    # 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].scatter(df['MW'], df['LogP'], c=df['Drug_Like'].map({True: 'green', False: 'red'}), 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')
@@ -253,8 +244,7 @@ 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()
 
-    # Plot 2: Hydrogen bonding
-    axes[0,1].scatter(df['HBD'], df['HBA'], c=drug_like_colors, s=80, alpha=0.7)
+    axes[0,1].scatter(df['HBD'], df['HBA'], c=df['Drug_Like'].map({True: 'green', False: 'red'}), 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')
@@ -262,13 +252,11 @@ 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()
 
-    # 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].scatter(df['TPSA'], df['RotBonds'], c=df['Drug_Like'].map({True: 'green', False: 'red'}), 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]
@@ -276,10 +264,8 @@ 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 {
@@ -437,13 +423,12 @@ def simulate_rwd_analysis(adverse_event_text):
     base_events = list(np.random.choice(['headache', 'nausea', 'fatigue', 'dizziness', 'rash', 'fever'], 100, p=[0.25, 0.2, 0.15, 0.15, 0.15, 0.1]))
     user_events = [e.strip().lower() for e in adverse_event_text.split(',') if e.strip()]
     all_events = base_events + user_events
-    event_.value_counts()
+    event_counts = pd.Series(all_events).value_counts()
     log = f"✅ Analyzed {len(all_events)} simulated adverse event reports.\n"
     
     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')
     
@@ -455,7 +440,7 @@ 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.',
@@ -545,16 +530,7 @@ with tab1:
             st.subheader("Protein 3D Structure (Interactive)")
             st.components.v1.html(res1.get('protein_view_html', '<p>No data</p>'), height=600, scrolling=False)
             st.subheader("FASTA Sequence Information")
-            # --- ACCESSIBILITY FIX ---
-            # Added a label and set visibility to "collapsed" to remove the warning
-            # without changing the visual layout.
-            st.text_area(
-                "FASTA log output", 
-                res1.get('fasta_log', 'No data'), 
-                height=200, 
-                key="fasta_info_area",
-                label_visibility="collapsed"
-            )
+            st.text_area("", res1.get('fasta_log', 'No data'), height=200, key="fasta_info_area")
         with p1_tabs[1]:
             st.subheader("Drug-Likeness Assessment (Lipinski's Rule of Five)")
             st.dataframe(res1.get('lipinski_subset_df', pd.DataFrame()), use_container_width=True)
@@ -563,7 +539,7 @@ with tab1:
         with p1_tabs[2]:
             st.subheader("Molecular Properties Dashboard")
             if res1.get('props_plot'):
-                st.pyplot(res1['props_plot'], clear_figure=True)
+                st.pyplot(res1['props_plot'])
 
 # ===== TAB 2: LEAD GENERATION & OPTIMIZATION =====
 with tab2:
@@ -707,8 +683,7 @@ with tab4:
         p4_tabs = st.tabs(["Pharmacovigilance Analysis", "Regulatory & Ethical Frameworks"])
         with p4_tabs[0]:
             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)
+            st.pyplot(res4['plot_bar'])
             st.dataframe(res4['rwd_df'], use_container_width=True)
             
         with p4_tabs[1]: