Update app.py

app.py

@@ -95,21 +95,36 @@ def batch_predict(file_obj):
 def generate_and_screen(num_to_gen):
     candidates = []
     for _ in range(num_to_gen):
-        # ... (keep your generation and prediction code the same) ...
+        # 1. Generation
+        input_seq = torch.tensor([[char_to_int['<SOS>']]])
+        h, generated = None, ""
+        for _ in range(100):
+            output, h = gen_model(input_seq, h)
+            probs = F.softmax(output[0, -1], dim=0)
+            idx = torch.multinomial(probs, 1).item()
+            char = int_to_char[idx]
+            if char == '<EOS>': break
+            generated += char
+            input_seq = torch.tensor([[idx]])
+        
+        # --- FIX: Create the 'mol' object here before checking 'if mol:' ---
+        mol = Chem.MolFromSmiles(generated.replace('*', 'C'))
         
         if mol:
-            # 1. Check Benchmarks
-            # Pass Yield Strength Target (>0.075 GPa) [cite: 236]
+            feats = get_expanded_features(generated)
+            strength_log = judge.predict(feats.reshape(1, -1))
+            strength = np.expm1(strength_log)[0]
+            
+            sa_val = sascorer.calculateScore(mol) if sascorer else 5.0
+            deg_val = degradability_model.predict(feats.reshape(1, -1))[0] if degradability_model else 0.5
+            
+            # Check Benchmarks
             pass_strength = strength > 0.075 
-            # Pass SA Score Target (<5.0) [cite: 224]
             pass_sa = sa_val < 5.0 
-            # Pass Degradability Target (>0.303) [cite: 231, 236]
             pass_deg = deg_val > 0.303 
 
-            # 2. Calculate Number of Passes
             num_passes = sum([pass_strength, pass_sa, pass_deg])
 
-            # 3. Assign 3-Category Classification
             if num_passes == 3:
                 status = "Excellent"
             elif num_passes == 2:
@@ -170,26 +185,23 @@ with gr.Blocks(title="PolymerDesignAI") as demo:
     with gr.Tab("Autonomous Design"):
         gr.Markdown("### Stage C & D: Integrated Generation + Screening")
         
-        gr.Markdown("""
-        **Goal:** Discover novel polymers that balance High Strength, Ease of Synthesis, and Environmental Impact.
-        
-        ---
+        # Adding 'r' before the triple quotes fixes the LaTeX display issues
+        gr.Markdown(r"""
+        **Goal:** Discover novel polymers that balance High Strength, Ease of Synthesis, and Environmental Impact. 
         
         ### 📊 Classification Logic
-        Candidates are filtered based on the triple-objective criteria outlined in the **RLPolyG** paper
-        
         | Category | Criteria |
         | :--- | :--- |
-        | **✅ Excellent** | **Passes ALL 3 benchmarks**: High Strength, Easy to Make, and Eco-Friendly.  |
-        | **⚠️ Review** | **Passes 2 benchmarks**: Fails exactly one criteria. Requires expert adjustment.  |
-        | **❌ Bad** | **Passes 1 or 0 benchmarks**: Fails two or more criteria. High risk of failure.  |
+        | **✅ Excellent** | **Passes ALL 3 benchmarks**: High Strength, Easy to Make, and Eco-Friendly. |
+        | **⚠️ Review** | **Passes 2 benchmarks**: Fails exactly one criteria.  |
+        | **❌ Bad** | **Passes 1 or 0 benchmarks**: Fails two or more criteria.  |
         
         ---
         
         ### 🧪 Targets & Benchmarks
-        * **Strength Target**: $\sigma_y > 0.075$ GPa (High performance) 
-        * **SA Score Target**: $< 5.0$ (Synthetically accessible) 
-        * **Degradability Target**: $> 0.303$ (Non-persistent in environment) 
+        * **Strength Target**: $\sigma_y > 0.075$ GPa 
+        * **SA Score Target**: $< 5.0$ 
+        * **Degradability Target**: $> 0.303$ 
         
         **Degradability Interpretation:** 
         * **Undegradable**: $\leq 0.303$