Update app.py

app.py

@@ -13,20 +13,20 @@ import pandas as pd
 import streamlit.components.v1 as components
 import json  # For safely encoding text in JavaScript
 
-# **Page Configuration**
+# Page Configuration
 st.set_page_config(
     page_title='Beta-Lactam Molecule Generator',
     layout='wide'
 )
 
-# **Load Models**
+# Load Models
 @st.cache_resource(show_spinner="Loading Models...", ttl=600)
 def load_models():
     """
     Load the molecule generation model and the ADMET-AI model.
     Caches the models to avoid reloading on every run.
     """
-    # **Load your molecule generation model**
+    # Load your molecule generation model
     model_name = "bcadkins01/beta_lactam_generator"  # Replace with your actual model path
     access_token = os.getenv("HUGGING_FACE_TOKEN")
     if access_token is None:
@@ -35,24 +35,24 @@ def load_models():
     model = BartForConditionalGeneration.from_pretrained(model_name, token=access_token)
     tokenizer = BartTokenizer.from_pretrained(model_name, token=access_token)
     
-    # **Load ADMET-AI model**
+    # Load ADMET-AI model
     admet_model = ADMETModel()
     
     return model, tokenizer, admet_model
 
-# **Load models once and reuse**
+# Load models once and reuse
 model, tokenizer, admet_model = load_models()
 
-# **Set Generation Parameters in Sidebar**
+# Set Generation Parameters in Sidebar
 st.sidebar.header('Generation Parameters')
 
-# **Creativity Slider (Temperature)**
+# Creativity Slider (Temperature)
 creativity = st.sidebar.slider(
     'Creativity (Temperature):',
     min_value=0.0,
-    max_value=2.5,
+    max_value=2.4,
     value=1.0,
-    step=0.1,
+    step=0.2,
     help="Higher values lead to more diverse outputs."
 )
 
@@ -65,7 +65,7 @@ num_molecules = st.sidebar.number_input(
     help="Select the number of molecules you want to generate (up to 3)."
 )
 
-# **Function to Generate Molecule Images**
+# Function to Generate Molecule Images
 def generate_molecule_image(input_string, use_safe=False):
     """
     Generates an image of the molecule from the input string.
@@ -91,10 +91,10 @@ def generate_molecule_image(input_string, use_safe=False):
         st.error(f"Error generating molecule image: {e}")
         return None
 
-# **Function to Create Copy-to-Clipboard Button**
+# Function to Create Copy-to-Clipboard Button
 def st_copy_button(text, key):
-    """Creates a copy-to-clipboard button placed appropriately."""
-    # Safely encode the text for JavaScript
+    """Creates a copy-to-clipboard button."""
+    # Encode the text for JavaScript
     escaped_text = json.dumps(text)
     button_html = f"""
     <div style="text-align: right; margin-top: -10px; margin-bottom: 10px;">
@@ -105,17 +105,17 @@ def st_copy_button(text, key):
     """
     components.html(button_html, height=35)
 
-# **Generate Molecules Button**
+# Generate Molecules Button
 if st.button('Generate Molecules'):
     st.info("Generating molecules... Please wait.")
     
-    # **Beta-lactam core structure**
+    # Beta-lactam core structure
     core_smiles = "C1C(=O)N(C)C(=O)C1"
     
-    # **Tokenize the core SMILES**
+    # Tokenize the core SMILES
     input_ids = tokenizer(core_smiles, return_tensors='pt').input_ids
     
-    # **Generate molecules using the model**
+    # Generate molecules using the model
     output_ids = model.generate(
         input_ids=input_ids,
         max_length=128,
@@ -126,25 +126,25 @@ if st.button('Generate Molecules'):
         num_beams=max(num_molecules, 5)  # Ensure num_beams >= num_return_sequences
     )
     
-    # **Decode generated molecule SMILES**
+    # Decode generated molecule SMILES
     generated_smiles = [
         tokenizer.decode(ids, skip_special_tokens=True)
         for ids in output_ids
     ]
     
-    # **Create molecule names**
+    # Create generic molecule names for demo
     molecule_names = [
         f"Mol{str(i).zfill(2)}"
         for i in range(1, len(generated_smiles) + 1)
     ]
     
-    # **Create DataFrame for generated molecules**
+    # Create df for generated molecules
     df_molecules = pd.DataFrame({
         'Molecule Name': molecule_names,
         'SMILES': generated_smiles
     })
     
-    # **Invalid SMILES Check**
+    # Invalid SMILES Check
     # Function to validate SMILES
     def is_valid_smile(smile):
         return Chem.MolFromSmiles(smile) is not None