update leaderboard

app.py

@@ -16,6 +16,107 @@ head_style = """
 </style>
 """
 
+
+def math_main_tab(results):
+    _, check_box = BUILD_L1_DF(results)
+    table = generate_table(results)
+    table['Rank'] = list(range(1, len(table) + 1))
+    type_map = check_box['type_map']
+    type_map['Rank'] = 'number'
+
+    checkbox_group = gr.CheckboxGroup(choices=check_box['all'], value=check_box['required'], label='Evaluation Dimension')
+
+    headers = ['Rank'] + check_box['essential'] + checkbox_group.value
+    with gr.Row():
+        model_name = gr.Textbox(value='Input the Model Name (fuzzy)', label='Model Name')
+        model_size = gr.CheckboxGroup(choices=MODEL_SIZE, value=MODEL_SIZE, label='Model Size')
+        model_type = gr.CheckboxGroup(choices=MODEL_TYPE, value=MODEL_TYPE, label='Model Type')
+
+    data_component = gr.components.DataFrame(value=table[headers], datatype=[type_map[x] for x in headers])
+
+    def filter_df(fields, model_name, model_size, model_type):
+        results = load_results()['results']
+        headers = ['Rank'] + check_box['essential'] + fields
+
+        df = generate_table(results)
+
+        df['flag'] = [model_size_flag(x, model_size) for x in df['Param (B)']]
+        df = df[df['flag']]
+        df.pop('flag')
+        if len(df):
+            df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))]
+            df = df[df['flag']]
+            df.pop('flag')
+        df['Rank'] = list(range(1, len(df) + 1))
+        default_val = 'Input the Model Name (fuzzy)'
+        if model_name != default_val:
+            method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Method']]
+            flag = [model_name.lower() in name for name in method_names]
+            df['TEMP'] = flag
+            df = df[df['TEMP'] == True] 
+            df.pop('TEMP')
+
+        comp = gr.components.DataFrame(value=df[headers], datatype=[type_map[x] for x in headers])
+        return comp
+
+    for cbox in [checkbox_group, model_size, model_type]:
+        cbox.change(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type], outputs=data_component)
+    model_name.submit(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type], outputs=data_component)
+
+
+def dataset_tab(results, struct, dataset):
+    s = struct
+    s.table, s.check_box = BUILD_L2_DF(results, dataset)
+    s.type_map = s.check_box['type_map']
+    s.type_map['Rank'] = 'number'
+
+    s.checkbox_group = gr.CheckboxGroup(choices=s.check_box['all'], value=s.check_box['required'], label=f'{dataset} CheckBoxes')
+    s.headers = ['Rank'] + s.check_box['essential'] + s.checkbox_group.value
+    s.table['Rank'] = list(range(1, len(s.table) + 1))
+
+    with gr.Row():
+        s.model_name = gr.Textbox(value='Input the Model Name (fuzzy)', label='Model Name')
+        s.model_size = gr.CheckboxGroup(choices=MODEL_SIZE, value=MODEL_SIZE, label='Model Size')
+        s.model_type = gr.CheckboxGroup(choices=MODEL_TYPE, value=MODEL_TYPE, label='Model Type')
+
+    s.data_component = gr.components.DataFrame(value=s.table[s.headers], datatype=[s.type_map[x] for x in s.headers])
+    s.dataset = gr.Textbox(value=dataset, label=dataset, visible=False)
+
+    def filter_df_l2(dataset_name, fields, model_name, model_size, model_type):
+        results = load_results()['results']
+        s = structs[DATASETS.index(dataset_name)]
+        headers = ['Rank'] + s.check_box['essential'] + fields
+        df = cp.deepcopy(s.table)
+        df['flag'] = [model_size_flag(x, model_size) for x in df['Param (B)']]
+        df = df[df['flag']]
+        df.pop('flag')
+        if len(df):
+            df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))]
+            df = df[df['flag']]
+            df.pop('flag')
+        df['Rank'] = list(range(1, len(df) + 1))
+        default_val = 'Input the Model Name (fuzzy)'
+        if model_name != default_val:
+            method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Method']]
+            flag = [model_name.lower() in name for name in method_names]
+            df['TEMP'] = flag
+            df = df[df['TEMP'] == True] 
+            df.pop('TEMP')
+
+        comp = gr.components.DataFrame(value=df[headers], datatype=[s.type_map[x] for x in headers])
+        return comp
+
+    for cbox in [s.checkbox_group, s.model_size, s.model_type]:
+        cbox.change(
+            fn=filter_df_l2,
+            inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type],
+            outputs=s.data_component)
+    s.model_name.submit(
+        fn=filter_df_l2, 
+        inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type],
+        outputs=s.data_component)
+
+    
 with gr.Blocks(title="Math Leaderboard", head=head_style) as demo:
     results = load_results()['results']
     N_MODEL = len(results)
@@ -32,84 +133,8 @@ with gr.Blocks(title="Math Leaderboard", head=head_style) as demo:
 
     with gr.Tabs(elem_classes='tab-buttons') as tabs:
         with gr.TabItem('🏅 LMM Math Leaderboard', elem_id='main', id=0):
-            _, check_box = BUILD_L1_DF(results)
-
-            table = generate_table(results)
-            table['Rank'] = list(range(1, len(table) + 1))
-
-            type_map = check_box['type_map']
-            type_map['Rank'] = 'number'
-
-            checkbox_group = gr.CheckboxGroup(
-                choices=check_box['all'],
-                value=check_box['required'],
-                label='Evaluation Dimension',
-                interactive=True,
-            )
-
-            headers = ['Rank'] + check_box['essential'] + checkbox_group.value
-            with gr.Row():
-                model_name = gr.Textbox(
-                    value='Input the Model Name (fuzzy)', 
-                    label='Model Name', 
-                    interactive=True,
-                    visible=True)
-                model_size = gr.CheckboxGroup(
-                    choices=MODEL_SIZE,
-                    value=MODEL_SIZE,
-                    label='Model Size',
-                    interactive=True
-                )
-                model_type = gr.CheckboxGroup(
-                    choices=MODEL_TYPE,
-                    value=MODEL_TYPE,
-                    label='Model Type',
-                    interactive=True
-                )
-
-            data_component = gr.components.DataFrame(
-                value=table[headers],
-                type='pandas',
-                datatype=[type_map[x] for x in headers],
-                interactive=False,
-                visible=True)
-
-            def filter_df(fields, model_name, model_size, model_type):
-                results = load_results()['results']
-                headers = ['Rank'] + check_box['essential'] + fields
-
-                df = generate_table(results)
-
-                df['flag'] = [model_size_flag(x, model_size) for x in df['Param (B)']]
-                df = df[df['flag']]
-                df.pop('flag')
-                if len(df):
-                    df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))]
-                    df = df[df['flag']]
-                    df.pop('flag')
-                df['Rank'] = list(range(1, len(df) + 1))
-                default_val = 'Input the Model Name (fuzzy)'
-                if model_name != default_val:
-                    print(model_name)
-                    model_name = model_name.lower()
-                    method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Method']]
-                    flag = [model_name in name for name in method_names]
-                    df['TEMP_FLAG'] = flag
-                    df = df[df['TEMP_FLAG'] == True] 
-                    df.pop('TEMP_FLAG')
-
-                comp = gr.components.DataFrame(
-                    value=df[headers],
-                    type='pandas',
-                    datatype=[type_map[x] for x in headers],
-                    interactive=False,
-                    visible=True)
-                return comp
-
-            for cbox in [checkbox_group, model_size, model_type]:
-                cbox.change(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type], outputs=data_component)
-            model_name.submit(fn=filter_df, inputs=[checkbox_group, model_name, model_size, model_type], outputs=data_component)
-
+            math_main_tab(results)
+            
         for i, dataset in enumerate(DATASETS):
             tab_name_map = {
                 'MathVista': 'MathVista (Test Mini)',
@@ -118,87 +143,7 @@ with gr.Blocks(title="Math Leaderboard", head=head_style) as demo:
 
             with gr.TabItem(
                 f'📊 {dataset if dataset not in tab_name_map else tab_name_map[dataset]}', elem_id=dataset, id=i + 2):
-
-                s = structs[i]
-                s.table, s.check_box = BUILD_L2_DF(results, dataset)
-                s.type_map = s.check_box['type_map']
-                s.type_map['Rank'] = 'number'
-
-                s.checkbox_group = gr.CheckboxGroup(
-                    choices=s.check_box['all'],
-                    value=s.check_box['required'],
-                    label=f'{dataset} CheckBoxes',
-                    interactive=True,
-                )
-                s.headers = ['Rank'] + s.check_box['essential'] + s.checkbox_group.value
-                s.table['Rank'] = list(range(1, len(s.table) + 1))
-
-                with gr.Row():
-                    s.model_name = gr.Textbox(
-                        value='Input the Model Name (fuzzy)', 
-                        label='Model Name', 
-                        interactive=True,
-                        visible=True)
-                    s.model_size = gr.CheckboxGroup(
-                        choices=MODEL_SIZE,
-                        value=MODEL_SIZE,
-                        label='Model Size',
-                        interactive=True
-                    )
-                    s.model_type = gr.CheckboxGroup(
-                        choices=MODEL_TYPE,
-                        value=MODEL_TYPE,
-                        label='Model Type',
-                        interactive=True
-                    )
-                s.data_component = gr.components.DataFrame(
-                    value=s.table[s.headers],
-                    type='pandas',
-                    datatype=[s.type_map[x] for x in s.headers],
-                    interactive=False,
-                    visible=True)
-                s.dataset = gr.Textbox(value=dataset, label=dataset, visible=False)
-
-                def filter_df_l2(dataset_name, fields, model_name, model_size, model_type):
-                    results = load_results()['results']
-                    s = structs[DATASETS.index(dataset_name)]
-                    headers = ['Rank'] + s.check_box['essential'] + fields
-                    df = cp.deepcopy(s.table)
-                    df['flag'] = [model_size_flag(x, model_size) for x in df['Param (B)']]
-                    df = df[df['flag']]
-                    df.pop('flag')
-                    if len(df):
-                        df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))]
-                        df = df[df['flag']]
-                        df.pop('flag')
-                    df['Rank'] = list(range(1, len(df) + 1))
-                    default_val = 'Input the Model Name (fuzzy)'
-                    if model_name != default_val:
-                        print(model_name)
-                        model_name = model_name.lower()
-                        method_names = [x.split('</a>')[0].split('>')[-1].lower() for x in df['Method']]
-                        flag = [model_name in name for name in method_names]
-                        df['TEMP_FLAG'] = flag
-                        df = df[df['TEMP_FLAG'] == True] 
-                        df.pop('TEMP_FLAG')
-
-                    comp = gr.components.DataFrame(
-                        value=df[headers],
-                        type='pandas',
-                        datatype=[s.type_map[x] for x in headers],
-                        interactive=False,
-                        visible=True)
-                    return comp
-
-                for cbox in [s.checkbox_group, s.model_size, s.model_type]:
-                    cbox.change(
-                        fn=filter_df_l2,
-                        inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type],
-                        outputs=s.data_component)
-                s.model_name.submit(
-                    fn=filter_df_l2, 
-                    inputs=[s.dataset, s.checkbox_group, s.model_name, s.model_size, s.model_type],
-                    outputs=s.data_component)
+                dataset_tab(results, structs[i], dataset)
 
     with gr.Row():
         with gr.Accordion('Citation', open=False):