Cleanup

app.py

@@ -43,14 +43,6 @@ def smiles_to_image(smiles):
     return None
 
 
-# Function to get canonical SMILES
-def get_canonical_smiles(smiles):
-    mol = Chem.MolFromSmiles(smiles)
-    if mol:
-        return Chem.MolToSmiles(mol, canonical=True)
-    return None
-
-
 # Dictionary for SMILES strings and corresponding images (you can replace with your actual image paths)
 smiles_image_mapping = {
     "Mol 1": {"smiles": "C=C(C)CC(=O)NC[C@H](CO)NC(=O)C=Cc1ccc(C)c(Cl)c1", "image": "img/img1.png"},
@@ -72,9 +64,6 @@ fusion_available = ["Concat"]
 
 
 # Function to handle evaluation and logging
-def save_rep(models, dataset, task_type, eval_output):
-    return
-
 def evaluate_and_log(models, dataset, task_type, eval_output, state):
     task_dic = {'Classification': 'CLS', 'Regression': 'RGR'}
     result = f"{eval_output}"
@@ -133,16 +122,12 @@ def generate(latent_vector, mask):
     decoder_output = gen_model.generate(encoder_outputs=encoder_outputs, attention_mask=mask,
                                         max_new_tokens=64, do_sample=True, top_k=5, top_p=0.95, num_return_sequences=1)
     selfies = gen_tokenizer.batch_decode(decoder_output, skip_special_tokens=True)
-    outs = []
-    for i in selfies:
-        outs.append(sf.decoder(re.sub(r'\]\s*(.*?)\s*\[', r']\1[', i)))
-    return outs
+    return [sf.decoder(re.sub(r'\]\s*(.*?)\s*\[', r']\1[', i)) for i in selfies]
 
 
 def perturb_latent(latent_vecs, noise_scale=0.5):
-    modified_vec = torch.tensor(np.random.uniform(0, 1, latent_vecs.shape) * noise_scale,
+    return torch.tensor(np.random.uniform(0, 1, latent_vecs.shape) * noise_scale,
                                 dtype=torch.float32) + latent_vecs
-    return modified_vec
 
 
 def encode(selfies):