Update app.py

app.py

@@ -216,10 +216,11 @@ def assess_drug_likeness(df: pd.DataFrame):
     df_copy['HBD_OK'] = df_copy['HBD'] <= 5
     df_copy['HBA_OK'] = df_copy['HBA'] <= 10
     df_copy['Lipinski_Violations'] = (~df_copy[['MW_OK', 'LogP_OK', 'HBD_OK', 'HBA_OK']]).sum(axis=1)
-    df_copy['Drug_Like'] = df_copy['Lipinski_Violations'] <= 1
+    # Fixed: Use proper colored emojis instead of boolean values
+    df_copy['Drug_Like'] = df_copy['Lipinski_Violations'].apply(lambda x: '✅ Yes' if x <= 1 else '❌ No')
     log = "✅ Assessed drug-likeness using Lipinski's Rule of Five.\n"
     return df_copy, log
-
+    
 def plot_properties_dashboard(df: pd.DataFrame):
     """
     Creates a 2x2 dashboard of molecular property visualizations.
@@ -447,7 +448,9 @@ def calculate_comprehensive_properties(smiles_dict: dict):
         analysis.append({'Compound': name, 'Molecular_Weight': mw, 'LogP': logp, 'HBD': hbd, 'HBA': hba,
                          'TPSA': Descriptors.TPSA(mol), 'Rotatable_Bonds': Descriptors.NumRotatableBonds(mol),
                          'Aromatic_Rings': Descriptors.NumAromaticRings(mol),
-                         'Lipinski_Violations': violations, 'Drug_Like': 'Yes' if violations <= 1 else 'No'})
+                         'Lipinski_Violations': violations, 
+                         # Fixed: Use proper colored emojis instead of text
+                         'Drug_Like': '✅ Yes' if violations <= 1 else '❌ No'})
     df = pd.DataFrame(analysis).round(2)
     log += f"✅ Calculated comprehensive properties for {len(df)} compounds.\n"
     return df, log
@@ -471,9 +474,10 @@ def predict_toxicity(properties_df: pd.DataFrame):
     toxicity_prob = rf_model.predict_proba(X_pred)[:, 1]
     results_df = properties_df[['Compound']].copy()
     results_df['Toxicity_Probability'] = np.round(toxicity_prob, 3)
+    # Fixed: Use proper colored emojis that will display correctly
     results_df['Predicted_Risk'] = ["🟢 LOW" if p < 0.3 else "🟡 MODERATE" if p < 0.7 else "🔴 HIGH" for p in toxicity_prob]
     return results_df, "✅ Predicted toxicity using a pre-trained simulation model.\n"
-
+    
 # ===== Phase 4 Functions =====
 def get_regulatory_summary():
     summary = {'Component': ['Data Governance', 'Model Architecture', 'Model Validation', 'Interpretability'],