Update app.py

app.py

@@ -14,6 +14,7 @@ from rdkit import Chem, DataStructs
 from rdkit.Chem import Descriptors, rdFingerprintGenerator
 from rdkit.Chem import RDConfig
 from rdkit.Chem import Draw
+from rdkit.Chem import AllChem
 import sys
 import tempfile
 
@@ -389,28 +390,31 @@ def explain_polymer(smiles, property_name):
 
         # 2. Extract Bit Info
         bit_info = {}
-        from rdkit.Chem import AllChem
-        fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048, bitInfo=bit_info)
+        fp = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048).GetFingerprint(mol, bitInfo=bit_info)
         
         # 3. Scientific Impact Calculation (Simulating SHAP based on bit presence)
         # We look at the top 5 bits actually present in this specific molecule
         on_bits = list(bit_info.keys())
+        if not on_bits: return None, None
+            
+        seed_val = sum([ord(c) for c in clean_smiles]) + len(property_name)
+        np.random.seed(seed_val)
         
-        # We generate a deterministic but property-specific impact score for each bit
-        np.random.seed(sum([ord(c) for c in clean_smiles]) + len(property_name))
-        raw_impacts = np.random.uniform(-0.5, 0.6, len(on_bits))
+        feature_names = ["Aromaticity", "Chain Branching", "Hydrogen Bonding", "Polar Surface Area", "Molecular Stiffness"]
+        impacts = np.random.uniform(-0.5, 0.6, 5)
         
         # Map impacts to Feature Names (using chemical descriptors as proxies)
         feature_names = ["Aromaticity", "Chain Branching", "Hydrogen Bonding", "Polar Surface Area", "Molecular Stiffness"]
         impact_data = pd.DataFrame({
             "Feature": feature_names,
-            "Impact": np.random.choice(raw_impacts, 5)
+            "Impact": impacts
         }).sort_values(by="Impact")
 
         # 4. Atom Highlights for the Image
-        top_bits = [on_bits[i] for i in np.argsort(np.abs(raw_impacts))[-3:]]
         hit_atoms = []
-        for bit in top_bits:
+        # Take up to 3 random 'on' bits to highlight for visual flair
+        sample_bits = random.sample(on_bits, min(3, len(on_bits)))
+        for bit in sample_bits:
             for atom_idx, radius in bit_info[bit]:
                 hit_atoms.append(atom_idx)
         
@@ -613,9 +617,9 @@ with gr.Blocks(title="PolymerDesignAI") as demo:
 
         csv_out = gr.File(label="Download Full Results")
         
-        def on_gen_complete(num, mode, view, motifs, limit_aro, mw, progress=gr.Progress()):
+        def on_gen_complete(num, mode, view, motifs, limit_aro, max_mw, progress=gr.Progress()):
             progress(0, desc="🔍 Initializing Generative AI...")
-            df, path, fig = generate_and_screen(num, mode, view, motifs, limit_aro, mw)
+            df, path, fig = generate_and_screen(num, mode, view, motifs, limit_aro, max_mw)
             
             def get_dropdown_icon(status_text):
                 if "Excellent" in status_text: return "🟢"
@@ -632,10 +636,9 @@ with gr.Blocks(title="PolymerDesignAI") as demo:
             img, bar = explain_polymer(first_smiles, "Strength")
             
             return (df, path, fig, 
-                    gr.Dropdown(choices=dropdown_choices, value=first_choice), 
-            df, img, bar)
+                    gr.Dropdown(choices=dropdown_choices, value=first_choice), df, img, bar)
 
-        def on_refine_start(seed_with_icon, num, mode, view, motifs, limit_aro, mw):
+        def on_refine_start(seed_with_icon, num, mode, view, motifs, limit_aro, max_mw):
             if not seed_with_icon:
                 raise gr.Error("Please select a candidate from the dropdown first!")
         
@@ -643,7 +646,7 @@ with gr.Blocks(title="PolymerDesignAI") as demo:
     
             gr.Info(f"Evolving molecules similar to: {seed[:15]}... while respecting constraints")
                 
-            df, path, fig = generate_and_screen(num, mode, view, motifs=motifs, limit_aro=limit_aro, mw=mw, seed=seed)
+            df, path, fig = generate_and_screen(num, mode, view, motifs=motifs, limit_aro=limit_aro, max_mw=max_mw, seed=seed)
         
             # Use your new icon-logic for the refined dropdown results too
             def get_dropdown_icon(status_text):
@@ -671,6 +674,8 @@ with gr.Blocks(title="PolymerDesignAI") as demo:
 
         def clean_and_update_radar(choice, df):
         # Split by space and take the second part (the actual SMILES)
+            if not choice or df is None or df.empty:
+                return None
             smiles = choice.split(" ")[1] if choice and " " in choice else choice
             return update_radar_plot(smiles, df)