Update app.py

app.py

@@ -171,7 +171,8 @@ def run_inference(mode: str, model_name: str, num_molecules: int, seed_num: str,
 
 
 with gr.Blocks(theme=gr.themes.Ocean()) as demo:
-    with gr.Column():
+    
+    with gr.Column(scale=1):
         # Add custom CSS for styling
         gr.HTML("""
         <style>
@@ -220,47 +221,47 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
     
         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).
+                ## 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)
+                ## 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():
@@ -274,7 +275,6 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
         # 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,
@@ -296,19 +296,20 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
                         size="lg"
                     )
     
-        with gr.TabItem("Custom Input SMILES"):
-            with gr.Row():
-                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.TabItem("Custom Input SMILES"):
+                    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"
+                    )
+
+        gr.Markdown("### Created by the HUBioDataLab | [GitHub](https://github.com/HUBioDataLab/DrugGEN) | [Paper](https://arxiv.org/abs/2302.07868)")
 
     with gr.Column(scale=2):
         basic_metrics_df = gr.Dataframe(
@@ -326,7 +327,7 @@ with gr.Blocks(theme=gr.themes.Ocean()) as demo:
         image_output = gr.Image(label="Structures of Randomly Selected Generated Molecules",
                         elem_id="molecule_display")
 
-    gr.Markdown("### Created by the HUBioDataLab | [GitHub](https://github.com/HUBioDataLab/DrugGEN) | [Paper](https://arxiv.org/abs/2302.07868)")
+
 
     # Set up the click actions for each tab.
     classical_submit.click(