Update inference_app.py

inference_app.py

@@ -104,6 +104,30 @@ def get_metrics(
         columns=mapping,
         inplace=True,
     )
+    if store:
+        with tempfile.NamedTemporaryFile as temp:
+            metrics.to_csv(temp.name)
+            API.upload_file(
+                    path_or_fileobj=temp.name,
+                    path_in_repo=f"{dataset}/{methodname}/{system_id}/",
+                    repo_id=QUEUE_REPO,
+                    repo_type="dataset",
+                    commit_message=f"Add {model_name} to eval queue",
+            )
+        API.upload_file(
+                path_or_fileobj=receptor_file.name,
+                path_in_repo=f"{dataset}/{methodname}/{system_id}/",
+                repo_id=QUEUE_REPO,
+                repo_type="dataset",
+                commit_message=f"Add {model_name} to eval queue",
+        )
+        API.upload_file(
+                path_or_fileobj=ligand_file.name,
+                path_in_repo=f"{dataset}/{methodname}/{system_id}/",
+                repo_id=QUEUE_REPO,
+                repo_type="dataset",
+                commit_message=f"Add {model_name} to eval queue",
+        )
     end_time = time.time()
     run_time = end_time - start_time
     return gr.DataFrame(metrics, visible=True), run_time
@@ -138,23 +162,24 @@ def get_metrics_pinder(
         metrics = pd.DataFrame([failed_metrics])
         metrics["system"] = native.stem
         gr.Error(f"Failed to evaluate prediction [{prediction}]:\n{e}")
-    # Upload to hub
-    with tempfile.NamedTemporaryFile as temp:
-        metrics.to_csv(temp.name)
+    if store:
+        # Upload to hub
+        with tempfile.NamedTemporaryFile as temp:
+            metrics.to_csv(temp.name)
+            API.upload_file(
+                    path_or_fileobj=temp.name,
+                    path_in_repo=f"{dataset}/{methodname}/{system_id}/",
+                    repo_id=QUEUE_REPO,
+                    repo_type="dataset",
+                    commit_message=f"Add {model_name} to eval queue",
+            )
         API.upload_file(
-                path_or_fileobj=temp.name,
+                path_or_fileobj=complex_file.name,
                 path_in_repo=f"{dataset}/{methodname}/{system_id}/",
                 repo_id=QUEUE_REPO,
                 repo_type="dataset",
                 commit_message=f"Add {model_name} to eval queue",
         )
-    API.upload_file(
-            path_or_fileobj=complex_file.name,
-            path_in_repo=f"{dataset}/{methodname}/{system_id}/",
-            repo_id=QUEUE_REPO,
-            repo_type="dataset",
-            commit_message=f"Add {model_name} to eval queue",
-    )
     end_time = time.time()
     run_time = end_time - start_time
     return gr.DataFrame(metrics, visible=True), run_time
@@ -220,7 +245,7 @@ with gr.Blocks() as app:
 
         eval_btn.click(
             get_metrics,
-            inputs=[input_system_id, input_receptor_file, input_ligand_file, flexible, posebusters],
+            inputs=[input_system_id, input_receptor_file, input_ligand_file, flexible, posebusters, methodname, store],
             outputs=[metric_table, eval_run_time],
         )
         eval_btn_pinder.click(