test

app.py

@@ -61,13 +61,7 @@ def get_dataset_csv(
 ):
     df = ORIGINAL_DF[ORIGINAL_DF['Size'].isin(model_size)]
     df = df.drop(columns="Size")
-
-    # if metric_choice != "None":
-    #     metric_choice = metric_choice + "/std"
-    #     sort_basis = df[metric_choice].apply(format_csv_numbers)
-    #     sorted_indices = sort_basis.argsort()[::-1]
-    #     df = df.iloc[sorted_indices]
-        
+    
     leaderboard_table = gr.components.Dataframe(
         value=df,
         interactive=False,
@@ -81,12 +75,6 @@ def get_dataset_csv_per(
     df = ORIGINAL_DF_PER[ORIGINAL_DF_PER['Size'].isin(model_size)]
     df = df.drop(columns="Size")
 
-    # if metric_choice != "None":
-    #     metric_choice = metric_choice + "/std"
-    #     sort_basis = df[metric_choice].apply(format_csv_numbers)
-    #     sorted_indices = sort_basis.argsort()[::-1]
-    #     df = df.iloc[sorted_indices]
-        
     leaderboard_table = gr.components.Dataframe(
         value=df,
         interactive=False,
@@ -106,16 +94,6 @@ def get_dataset_csv_sub_gen(
     subclass_choice_label = ["Model", subclass_choice+"_Accuracy", subclass_choice+"_Precision", subclass_choice+"_Recall"]
     df = df[subclass_choice_label]
     
-    # if metric_choice != "None":
-    #     # metric_choice = metric_choice + "/std"
-    #     metric_choice = metric_choice.split("_")[0]
-    #     metric_choice = subclass_choice + "_" + metric_choice
-    #     # sort_basis = df[metric_choice].apply(format_csv_numbers)
-    #     sort_basis = df[metric_choice]
-        
-    #     sorted_indices = sort_basis.argsort()[::-1]
-    #     df = df.iloc[sorted_indices]
-    
     leaderboard_table = gr.components.Dataframe(
         value=df,
         interactive=False,
@@ -135,16 +113,6 @@ def get_dataset_csv_sub_per(
     subclass_choice_label = ["Model", subclass_choice+"_Accuracy", subclass_choice+"_Precision", subclass_choice+"_Recall"]
     df = df[subclass_choice_label]
     
-    # if metric_choice != "None":
-    #     # metric_choice = metric_choice + "/std"
-    #     metric_choice = metric_choice.split("_")[0]
-    #     metric_choice = subclass_choice + "_" + metric_choice
-    #     # sort_basis = df[metric_choice].apply(format_csv_numbers)
-    #     sort_basis = df[metric_choice]
-        
-    #     sorted_indices = sort_basis.argsort()[::-1]
-    #     df = df.iloc[sorted_indices]
-    
     leaderboard_table = gr.components.Dataframe(
         value=df,
         interactive=False,
@@ -197,14 +165,6 @@ with gr.Blocks() as demo:
                 info="Please choose the type to display.",
             )
             
-        # with gr.Column(scale=0.8):  
-        #     metric_choice = gr.Dropdown(
-        #         choices=METRICS,
-        #         value="None",
-        #         label="Metric",
-        #         info="Please choose the metric to display.",
-        #     )
-            
         with gr.Column(scale=10): 
             model_choice = gr.CheckboxGroup(
                 choices=CLASSIFICATION["model_size"],
@@ -213,40 +173,8 @@ with gr.Blocks() as demo:
                 info="Please choose the model size to display.",
             )
 
-        
-        # with gr.Column(scale=0.8): 
-        #     subclass_choice = gr.Dropdown(
-        #         choices=SUBCLASS,
-        #         value="Discrimination",
-        #         label="Subclass",
-        #         info="Please choose the subclass to display.",
-        #     )
-
-            
-        #👉 this part is for csv table generatived
+    #👉 this part is for csv table generatived
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        
-        # with gr.TabItem("🏅 Overall Generatived", elem_id="od-benchmark-tab-table", id=1):
-        #     dataframe = gr.components.Dataframe(
-        #         elem_id="leaderboard-table",
-        #     )
-        # #👉 this part is for csv table perplexity
-        # with gr.TabItem("🏅 Overall Perplexity", elem_id="od-benchmark-tab-table", id=2):
-        #     datafram_per = gr.components.Dataframe(
-        #         elem_id="leaderboard-table",
-        #     )
-        
-        # #👉 this part is for csv subclass table generatived 
-        # with gr.TabItem("🏅 Subclass Generatived", elem_id="od-benchmark-tab-table", id=3):
-        #     dataframe_sub_gen = gr.components.Dataframe(
-        #         elem_id="leaderboard-table",
-        #     )
-            
-        # #👉 this part is for csv subclass table perplexity 
-        # with gr.TabItem("🏅 Subclass Perplexity", elem_id="od-benchmark-tab-table", id=4):
-        #     dataframe_sub_per = gr.components.Dataframe(
-        #         elem_id="leaderboard-table",
-        #     )
         # ----------------- modify text -----------------
         
         with gr.TabItem("🏅 Generation", elem_id="od-benchmark-tab-table", id=6):
@@ -269,105 +197,6 @@ with gr.Blocks() as demo:
     
     gr.Markdown(f"Last updated on **{_LAST_UPDATED}**", elem_classes="markdown-text")
     
-    # 👉 this part is for citation
-    # with gr.Row():
-    #     with gr.Accordion("📙 Citation", open=False):
-    #         gr.Textbox(
-    #             value=_BIBTEX,
-    #             lines=7,
-    #             label="Copy the BibTeX snippet to cite this source",
-    #             elem_id="citation-button",
-    #             show_copy_button=True
-    #         )
-    
-    # this is result based on generative
-    # metric_choice.change(
-    #     get_dataset_csv,
-    #     inputs=[model_choice, metric_choice],
-    #     outputs=dataframe,
-    # )
-    
-    # model_choice.change(
-    #     get_dataset_csv,
-    #     inputs=[model_choice, metric_choice],
-    #     outputs=dataframe,
-    # )
-    
-    # demo.load(
-    #     fn=get_dataset_csv,
-    #     inputs=[model_choice, metric_choice],
-    #     outputs=dataframe,
-    # )
-    
-    # # this is result based on Perplexity
-    # metric_choice.change(
-    #     get_dataset_csv_per,
-    #     inputs=[model_choice, metric_choice],
-    #     outputs=datafram_per,
-    # )
-    
-    # model_choice.change(
-    #     get_dataset_csv_per,
-    #     inputs=[model_choice, metric_choice],
-    #     outputs=datafram_per,
-    # )
-    
-    # demo.load(
-    #     fn=get_dataset_csv_per,
-    #     inputs=[model_choice, metric_choice],
-    #     outputs=datafram_per,
-    # )
-    
-    # this is subclass result generatived
-    # metric_choice.change(
-    #     get_dataset_csv_sub_gen,
-    #     inputs=[model_choice, metric_choice, subclass_choice],
-    #     outputs=dataframe_sub_gen,
-    # )
-    
-    # model_choice.change(
-    #     get_dataset_csv_sub_gen,
-    #     inputs=[model_choice, metric_choice, subclass_choice],
-    #     outputs=dataframe_sub_gen,
-    # )
-    
-    # subclass_choice.change(
-    #     get_dataset_csv_sub_gen,
-    #     inputs=[model_choice, metric_choice, subclass_choice],
-    #     outputs=dataframe_sub_gen,
-    # )
-    
-    # demo.load(
-    #     fn=get_dataset_csv_sub_gen,
-    #     inputs=[model_choice, metric_choice, subclass_choice],
-    #     outputs=dataframe_sub_gen,
-    # )
-    
-    # # this is subclass result Perplexity
-    # # metric_choice.change(
-    # #     get_dataset_csv_sub_per,
-    # #     inputs=[model_choice, metric_choice, subclass_choice],
-    # #     outputs=dataframe_sub_per,
-    # # )
-    
-    # model_choice.change(
-    #     get_dataset_csv_sub_per,
-    #     inputs=[model_choice, metric_choice, subclass_choice],
-    #     outputs=dataframe_sub_per,
-    # )
-    
-    # subclass_choice.change(
-    #     get_dataset_csv_sub_per,
-    #     inputs=[model_choice, metric_choice, subclass_choice],
-    #     outputs=dataframe_sub_per,
-    # )
-    
-    # demo.load(
-    #     fn=get_dataset_csv_sub_per,
-    #     inputs=[model_choice, metric_choice, subclass_choice],
-    #     outputs=dataframe_sub_per,
-    # )
-    
     # --------------------------- all --------------------------------
     # this is  all result Perplexity
     
@@ -382,18 +211,6 @@ with gr.Blocks() as demo:
         inputs=[model_choice, main_choice],
         outputs=dataframe_all_per,
     )
-    
-    # metric_choice.change(
-    #     get_dataset_classfier_per,
-    #     inputs=[model_choice, main_choice],
-    #     outputs=dataframe_all_per,
-    # )
-    
-    # subclass_choice.change(
-    #     get_dataset_classfier_per,
-    #     inputs=[model_choice, metric_choice, main_choice],
-    #     outputs=dataframe_all_per,
-    # )
 
     demo.load(
         fn=get_dataset_classfier_per,
@@ -414,18 +231,6 @@ with gr.Blocks() as demo:
         outputs=dataframe_all_gen,
     )
     
-    # metric_choice.change(
-    #     get_dataset_classfier_gen,
-    #     inputs=[model_choice, metric_choice, main_choice],
-    #     outputs=dataframe_all_gen,
-    # )
-    
-    # subclass_choice.change(
-    #     get_dataset_classfier_gen,
-    #     inputs=[model_choice, metric_choice, main_choice],
-    #     outputs=dataframe_all_gen,
-    # )
-    
     demo.load(
         fn=get_dataset_classfier_gen,
         inputs=[model_choice, main_choice],