Update app.py

app.py

@@ -23,30 +23,23 @@ def add_new_eval(
     skempi_file,
     model_name_textbox: str,
     revision_name_textbox: str,
-    benchmark_type,
+    benchmark_typse,
     similarity_tasks,
     function_prediction_aspect,
     function_prediction_dataset,
     family_prediction_dataset,
+    save,
 ):
     representation_name = model_name_textbox if revision_name_textbox == '' else revision_name_textbox
     results = run_probe(benchmark_type, representation_name, human_file, skempi_file, similarity_tasks, function_prediction_aspect, function_prediction_dataset, family_prediction_dataset)
     
     print(results)
 
-    save_similarity_output(results['similarity'], representation_name)
+    if save:
+        save_results(representation_name, benchmark_types, results)
+        print("Results are saved!")
 
-    return results
-
-    for benchmark_type in results:
-        if benchmark_type == 'similarity':
-            save_similarity_output(results['similarity'], representation_name)
-        elif benchmark_type == 'function':
-            save_function_output(results['function'], representation_name)
-        elif benchmark_type == 'family':
-            save_family_output(results['family'], representation_name)
-        elif benchmark_type == "affinity":
-            save_affinity_output(results['affinity', representation_name])
+    return 0
 
 # Function to update leaderboard dynamically based on user selection
 def update_leaderboard(selected_methods, selected_metrics):
@@ -58,7 +51,6 @@ with block:
     gr.Markdown(LEADERBOARD_INTRODUCTION)
     
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        # table jmmmu bench
         with gr.TabItem("🏅 PROBE Leaderboard", elem_id="probe-benchmark-tab-table", id=1):
 
 
@@ -163,9 +155,9 @@ with block:
                         label="Revision Model Name",
                     )
                     
-                    benchmark_type = gr.CheckboxGroup(
+                    benchmark_types = gr.CheckboxGroup(
                         choices=TASK_INFO,
-                        label="Benchmark Type",
+                        label="Benchmark Types",
                         interactive=True,
                     )
                     similarity_tasks = gr.CheckboxGroup(
@@ -176,23 +168,28 @@ with block:
                 
                     function_prediction_aspect = gr.Radio(
                         choices=function_prediction_aspect_options,
-                        label="Function Prediction Aspect",
+                        label="Function Prediction Aspects",
                         interactive=True,
                     )
                 
                     family_prediction_dataset = gr.CheckboxGroup(
                         choices=family_prediction_dataset_options,
-                        label="Family Prediction Dataset",
+                        label="Family Prediction Datasets",
                         interactive=True,
                     )
 
 
                     function_dataset = gr.Textbox(
-                        label="Function Prediction Dataset",
+                        label="Function Prediction Datasets",
                         visible=False,
                         value="All_Data_Sets"
                     )
 
+                    save_checkbox = gr.Checkbox(
+                        label="Save results for leaderboard and visualization",
+                        value=True
+                    )
+
             with gr.Column():
                 human_file = gr.components.File(label="Click to Upload the representation file (csv) for Human dataset", file_count="single", type='filepath')
                 skempi_file = gr.components.File(label="Click to Upload the representation file (csv) for SKEMPI dataset", file_count="single", type='filepath')
@@ -206,11 +203,12 @@ with block:
                         skempi_file,
                         model_name_textbox,
                         revision_name_textbox,
-                        benchmark_type,
+                        benchmark_types,
                         similarity_tasks,
                         function_prediction_aspect,
                         function_dataset,
                         family_prediction_dataset,
+                        save_checkbox,
                     ],
                 )