Update app.py

app.py

@@ -206,10 +206,10 @@ def calculate_molecular_properties(smiles_list: list):
 
 def assess_drug_likeness(df: pd.DataFrame):
     """
-    Assesses drug-likeness based on Lipinski's Rule of Five.
+    Assesses drug-likeness based on Lipinski's Rule of Five. 
     """
     if df.empty:
-        return pd.DataFrame(), "Cannot assess drug-likeness: No properties data."
+        return pd.DataFrame(), "Cannot assess drug-likeness: No properties data." [cite: 26]
     df_copy = df.copy()
     df_copy['MW_OK'] = df_copy['MW'] <= 500
     df_copy['LogP_OK'] = df_copy['LogP'] <= 5
@@ -218,15 +218,15 @@ def assess_drug_likeness(df: pd.DataFrame):
     df_copy['Lipinski_Violations'] = (~df_copy[['MW_OK', 'LogP_OK', 'HBD_OK', 'HBA_OK']]).sum(axis=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
+    log = "✅ Assessed drug-likeness using Lipinski's Rule of Five.\n" [cite: 27]
+    return df_copy, log [cite: 27]
     
 def plot_properties_dashboard(df: pd.DataFrame):
     """
-    Creates a 2x2 dashboard of molecular property visualizations.
+    Creates a 2x2 dashboard of molecular property visualizations. 
     """
     if df.empty:
-        return None, "Cannot plot: No analysis data."
+        return None, "Cannot plot: No analysis data." [cite: 28]
     
     plt.style.use('dark_background')
     fig, axes = plt.subplots(2, 2, figsize=(12, 10))
@@ -237,15 +237,17 @@ def plot_properties_dashboard(df: pd.DataFrame):
         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)
+    # FIXED: Correctly map string values to colors
+    axes[0,0].scatter(df['MW'], df['LogP'], c=df['Drug_Like'].map({'✅ Yes': 'green', '❌ No': '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')
-    axes[0,0].axvline(500, color='r', linestyle='--', alpha=0.6, label='MW < 500')
-    axes[0,0].axhline(5, color='r', linestyle='--', alpha=0.6, label='LogP < 5')
+    axes[0,0].set_xlabel('Molecular Weight (Da)') [cite: 29]
+    axes[0,0].set_ylabel('LogP') [cite: 29]
+    axes[0,0].axvline(500, color='r', linestyle='--', alpha=0.6, label='MW < 500') [cite: 29]
+    axes[0,0].axhline(5, color='r', linestyle='--', alpha=0.6, label='LogP < 5') [cite: 29]
     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)
+    # FIXED: Correctly map string values to colors
+    axes[0,1].scatter(df['HBD'], df['HBA'], c=df['Drug_Like'].map({'✅ Yes': 'green', '❌ No': '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')
@@ -253,20 +255,22 @@ 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)
-    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')
+    # FIXED: Correctly map string values to colors
+    axes[1,0].scatter(df['TPSA'], df['RotBonds'], c=df['Drug_Like'].map({'✅ Yes': 'green', '❌ No': 'red'}), s=80, alpha=0.7)
+    axes[1,0].set_title('TPSA vs Flexibility') [cite: 30]
+    axes[1,0].set_xlabel('Topological Polar Surface Area (Ų)') [cite: 30]
+    axes[1,0].set_ylabel('Rotatable Bonds') [cite: 30]
 
     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]
+    # FIXED: Correctly map string values to labels and colors
+    labels = drug_like_counts.index
+    colors = ['green' if i == '✅ Yes' else 'red' for i in drug_like_counts.index]
     axes[1,1].pie(drug_like_counts.values, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
     axes[1,1].set_title('Drug-likeness Distribution')
     
     plt.tight_layout(rect=[0, 0, 1, 0.96])
     return fig, "✅ Generated properties dashboard."
-
+    
 # ===== Phase 2 Functions =====
 def get_phase2_molecules():
     return {