Update app.py

app.py

@@ -71,7 +71,7 @@ model_configs = {
 def function(model_name: str, num_molecules: int, seed_num: int):
     '''
     Returns:
-    image, score_df, file_path, and individual metrics
+    image, metrics_df, file_path, basic_metrics, advanced_metrics
     '''
     if model_name == "DrugGEN-NoTarget":
         model_name = "NoTarget"
@@ -90,10 +90,9 @@ def function(model_name: str, num_molecules: int, seed_num: int):
         except ValueError:
             raise gr.Error("The seed must be an integer value!")
 
-
     inferer = Inference(config)
     start_time = time.time()
-    scores = inferer.inference() # create scores_df out of this
+    scores = inferer.inference()  # This returns a DataFrame with specific columns
     et = time.time() - start_time
 
     score_df = pd.DataFrame({
@@ -166,7 +165,7 @@ def function(model_name: str, num_molecules: int, seed_num: int):
         highlightBondLists=None,
     )
 
-    return molecule_image, score_df, new_path, basic_metrics, advanced_metrics
+    return molecule_image, new_path, basic_metrics, advanced_metrics
 
 
 
@@ -283,12 +282,14 @@ For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
             with gr.Row():
                 with gr.Column():
                     basic_metrics_df = gr.Dataframe(
+                        label="Basic Metrics",
                         headers=["Validity", "Uniqueness", "Novelty (Train)", "Novelty (Test)", "Drug Novelty", "Runtime (s)"],
                         elem_id="basic-metrics"
                     )
                 
                 with gr.Column():
                     advanced_metrics_df = gr.Dataframe(
+                        label="Advanced Metrics",
                         headers=["QED", "SA Score", "Internal Diversity", "SNN ChEMBL", "SNN Drug", "Max Length"],
                         elem_id="advanced-metrics"
                     )
@@ -301,26 +302,7 @@ For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
             file_download = gr.File(
                 label="Download All Generated Molecules (SMILES format)",
             )
-            
-            with gr.Group(elem_id="metrics-container"):
-                gr.Markdown("### Performance Metrics")
-                
-                with gr.Row():
-                    with gr.Column():
-                        validity = gr.Number(label="Validity", precision=3)
-                        uniqueness = gr.Number(label="Uniqueness", precision=3)
-                        novelty_train = gr.Number(label="Novelty (Train)", precision=3)
-                        novelty_test = gr.Number(label="Novelty (Test)", precision=3)
-                        drug_novelty = gr.Number(label="Drug Novelty", precision=3)
-                        runtime = gr.Number(label="Runtime (seconds)", precision=2)
-                    
-                    with gr.Column():
-                        qed = gr.Number(label="QED Score", precision=3, info="Higher is more drug-like (0-1)")
-                        sa = gr.Number(label="SA Score", precision=3, info="Lower is easier to synthesize (1-10)")
-                        int_div = gr.Number(label="Internal Diversity", precision=3)
-                        snn_chembl = gr.Number(label="SNN ChEMBL", precision=3)
-                        snn_drug = gr.Number(label="SNN Drug", precision=3)
-                        max_len = gr.Number(label="Max Length", precision=3)
+
 
     gr.Markdown("### Created by the HUBioDataLab | [GitHub](https://github.com/HUBioDataLab/DrugGEN) | [Paper](https://arxiv.org/abs/2302.07868)")
 
@@ -329,24 +311,12 @@ For more details, see our [paper on arXiv](https://arxiv.org/abs/2302.07868).
         inputs=[model_name, num_molecules, seed_num], 
         outputs=[
             image_output, 
-            scores_df, 
             file_download,
-            validity,
-            uniqueness,
-            novelty_train,
-            novelty_test,
-            drug_novelty,
-            runtime,
-            qed,
-            sa,
-            int_div,
-            snn_chembl,
-            snn_drug,
-            max_len
+            basic_metrics_df,
+            advanced_metrics_df
         ], 
         api_name="inference"
     )
-
+#demo.queue(concurrency_count=1)
 demo.queue()
-demo.launch()
-
+demo.launch()