Update app.py

app.py

@@ -171,110 +171,110 @@ def run_inference(mode: str, model_name: str, num_molecules: int, seed_num: str,
 
 
 with gr.Blocks(theme=gr.themes.Ocean()) as demo:
-    # Add custom CSS for styling
-    gr.HTML("""
-    <style>
-    #metrics-container {
-        border: 1px solid rgba(128, 128, 128, 0.3);
-        border-radius: 8px;
-        padding: 15px;
-        margin-top: 15px;
-        margin-bottom: 15px;
-        background-color: rgba(255, 255, 255, 0.05);
-    }
-    </style>
-    """)
-
-    gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
+    with gr.Column():
+        # Add custom CSS for styling
+        gr.HTML("""
+        <style>
+        #metrics-container {
+            border: 1px solid rgba(128, 128, 128, 0.3);
+            border-radius: 8px;
+            padding: 15px;
+            margin-top: 15px;
+            margin-bottom: 15px;
+            background-color: rgba(255, 255, 255, 0.05);
+        }
+        </style>
+        """)
     
-    gr.HTML("""
-    <div style="display: flex; gap: 10px; margin-bottom: 15px;">
-        <!-- arXiv badge -->
-        <a href="https://arxiv.org/abs/2302.07868" target="_blank" style="text-decoration: none;">
-            <div style="
-                display: inline-block; 
-                background-color: #b31b1b; 
-                color: #ffffff !important; 
-                padding: 5px 10px; 
-                border-radius: 5px; 
-                font-size: 14px;">
-                <span style="font-weight: bold;">arXiv</span> 2302.07868
-            </div>
-        </a>
+        gr.Markdown("# DrugGEN: Target Centric De Novo Design of Drug Candidate Molecules with Graph Generative Deep Adversarial Networks")
         
-        <!-- GitHub badge -->
-        <a href="https://github.com/HUBioDataLab/DrugGEN" target="_blank" style="text-decoration: none;">
-            <div style="
-                display: inline-block; 
-                background-color: #24292e; 
-                color: #ffffff !important; 
-                padding: 5px 10px; 
-                border-radius: 5px; 
-                font-size: 14px;">
-                <span style="font-weight: bold;">GitHub</span> Repository
-            </div>
-        </a>
-    </div>
-    """)
-
-    with gr.Accordion("About DrugGEN Models", open=False):
-        gr.Markdown("""
-## Model Variations
-
-### DrugGEN-AKT1
-This model is designed to generate molecules targeting the human AKT1 protein (UniProt ID: P31749).
-
-### DrugGEN-CDK2
-This model is designed to generate molecules targeting the human CDK2 protein (UniProt ID: P24941).
-
-### DrugGEN-NoTarget
-This is a general-purpose model that generates diverse drug-like molecules without targeting a specific protein.
-- Useful for exploring chemical space, generating diverse scaffolds, and creating molecules with drug-like properties.
-
-For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
+        gr.HTML("""
+        <div style="display: flex; gap: 10px; margin-bottom: 15px;">
+            <!-- arXiv badge -->
+            <a href="https://arxiv.org/abs/2302.07868" target="_blank" style="text-decoration: none;">
+                <div style="
+                    display: inline-block; 
+                    background-color: #b31b1b; 
+                    color: #ffffff !important; 
+                    padding: 5px 10px; 
+                    border-radius: 5px; 
+                    font-size: 14px;">
+                    <span style="font-weight: bold;">arXiv</span> 2302.07868
+                </div>
+            </a>
+            
+            <!-- GitHub badge -->
+            <a href="https://github.com/HUBioDataLab/DrugGEN" target="_blank" style="text-decoration: none;">
+                <div style="
+                    display: inline-block; 
+                    background-color: #24292e; 
+                    color: #ffffff !important; 
+                    padding: 5px 10px; 
+                    border-radius: 5px; 
+                    font-size: 14px;">
+                    <span style="font-weight: bold;">GitHub</span> Repository
+                </div>
+            </a>
+        </div>
         """)
     
-    with gr.Accordion("Understanding the Metrics", open=False):
-        gr.Markdown("""
-## Evaluation Metrics
-
-### Basic Metrics
-- **Validity**: Percentage of generated molecules that are chemically valid
-- **Uniqueness**: Percentage of unique molecules among valid ones
-- **Runtime**: Time taken to generate or evaluate the molecules
-
-### Novelty Metrics
-- **Novelty (Train)**: Percentage of molecules not found in the training set
-- **Novelty (Inference)**: Percentage of molecules not found in the test set
-- **Novelty (Real Inhibitors)**: Percentage of molecules not found in known inhibitors of the target protein
-
-### Structural Metrics
-- **Average Length**: Average component length in the generated molecules
-- **Mean Atom Type**: Average distribution of atom types
-- **Internal Diversity**: Diversity within the generated set (higher is more diverse)
-
-### Drug-likeness Metrics
-- **QED (Quantitative Estimate of Drug-likeness)**: Score from 0-1 measuring how drug-like a molecule is (higher is better)
-- **SA Score (Synthetic Accessibility)**: Score from 1-10 indicating ease of synthesis (lower is better)
-
-### Similarity Metrics
-- **SNN ChEMBL**: Similarity to ChEMBL molecules (higher means more similar to known drug-like compounds)
-- **SNN Real Inhibitors**: Similarity to known drugs (higher means more similar to approved drugs)
-        """)
-    with gr.Row():
-        with gr.Column(scale=1):
+        with gr.Accordion("About DrugGEN Models", open=False):
+            gr.Markdown("""
+    ## Model Variations
+    
+    ### DrugGEN-AKT1
+    This model is designed to generate molecules targeting the human AKT1 protein (UniProt ID: P31749).
+    
+    ### DrugGEN-CDK2
+    This model is designed to generate molecules targeting the human CDK2 protein (UniProt ID: P24941).
+    
+    ### DrugGEN-NoTarget
+    This is a general-purpose model that generates diverse drug-like molecules without targeting a specific protein.
+    - Useful for exploring chemical space, generating diverse scaffolds, and creating molecules with drug-like properties.
+    
+    For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
+            """)
+        
+        with gr.Accordion("Understanding the Metrics", open=False):
+            gr.Markdown("""
+    ## Evaluation Metrics
+    
+    ### Basic Metrics
+    - **Validity**: Percentage of generated molecules that are chemically valid
+    - **Uniqueness**: Percentage of unique molecules among valid ones
+    - **Runtime**: Time taken to generate or evaluate the molecules
+    
+    ### Novelty Metrics
+    - **Novelty (Train)**: Percentage of molecules not found in the training set
+    - **Novelty (Inference)**: Percentage of molecules not found in the test set
+    - **Novelty (Real Inhibitors)**: Percentage of molecules not found in known inhibitors of the target protein
+    
+    ### Structural Metrics
+    - **Average Length**: Average component length in the generated molecules
+    - **Mean Atom Type**: Average distribution of atom types
+    - **Internal Diversity**: Diversity within the generated set (higher is more diverse)
+    
+    ### Drug-likeness Metrics
+    - **QED (Quantitative Estimate of Drug-likeness)**: Score from 0-1 measuring how drug-like a molecule is (higher is better)
+    - **SA Score (Synthetic Accessibility)**: Score from 1-10 indicating ease of synthesis (lower is better)
+    
+    ### Similarity Metrics
+    - **SNN ChEMBL**: Similarity to ChEMBL molecules (higher means more similar to known drug-like compounds)
+    - **SNN Real Inhibitors**: Similarity to known drugs (higher means more similar to approved drugs)
+            """)
+        
+        with gr.Row():
             model_name = gr.Radio(
                 choices=("DrugGEN-AKT1", "DrugGEN-CDK2", "DrugGEN-NoTarget"),
                 value="DrugGEN-AKT1",
                 label="Select Target Model",
                 info="Choose which protein target or general model to use for molecule generation"
             )
-            
-    # Use Gradio Tabs to separate the two modes.
-    with gr.Tabs():
-        with gr.TabItem("Classical Generation"):
-            with gr.Row():
-                with gr.Column(scale=1):
+                
+        # Use Gradio Tabs to separate the two modes.
+        with gr.Tabs():
+            with gr.TabItem("Classical Generation"):
+                with gr.Row():
                     num_molecules = gr.Slider(
                         minimum=10,
                         maximum=200,
@@ -283,33 +283,32 @@ For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
                         label="Number of Molecules to Generate",
                         info="This space runs on a CPU, which may result in slower performance. Generating 100 molecules takes approximately 6 minutes. Therefore, we set a 200-molecule cap."
                     )
-
+    
                     seed_num = gr.Textbox(
                         label="Random Seed (Optional)",
                         value="",
                         info="Set a specific seed for reproducible results, or leave empty for random generation"
                     )
-
+    
                     classical_submit = gr.Button(
                         value="Generate Molecules",
                         variant="primary",
                         size="lg"
                     )
-
+    
         with gr.TabItem("Custom Input SMILES"):
             with gr.Row():
-                with gr.Column(scale=1):
-                    # Reuse model selection for custom input
-                    custom_smiles = gr.Textbox(
-                        label="Input SMILES (one per line, maximum 100 molecules)",
-                        placeholder="C(C(=O)O)N\nCCO\n...",
-                        lines=10
-                    )
-                    custom_submit = gr.Button(
-                        value="Generate Molecules using Custom SMILES",
-                        variant="primary",
-                        size="lg"
-                    )
+                custom_smiles = gr.Textbox(
+                    label="Input SMILES (one per line, maximum 100 molecules)",
+                    placeholder="C(C(=O)O)N\nCCO\n...",
+                    lines=10
+                )
+                
+                custom_submit = gr.Button(
+                    value="Generate Molecules using Custom SMILES",
+                    variant="primary",
+                    size="lg"
+                )
 
     with gr.Column(scale=2):
         basic_metrics_df = gr.Dataframe(