added another model andmed42 and changed df style

app.py

@@ -15,7 +15,7 @@ def load_description(DESCRIPTION_FILE):
         md_text = f.read()
         html_description = markdown.markdown(md_text, extensions=["tables"])
     return html_description
-columns_fixed = ["Model Name", "Average Label", "Average Record"]
+columns_fixed = ["Model Name", "Parameters", "Average Label", "Average Record"]
 
 df = load_leaderboard()
 all_columns = list(df.columns)
@@ -56,7 +56,7 @@ with gr.Blocks() as demo:
     gr.HTML("<h1 style='text-align: center;'>🏆 Medical Classification Leaderboard - Beta</h1>")
     gr.Image("./about/linguist.png", elem_id="linguist-image", show_label=False)
 
-    # Add CSS to restrict the size of the image
+    # Add CSS to style the leaderboard table
     demo.css = """
     #linguist-image img {
         max-width: 300px;
@@ -64,42 +64,75 @@ with gr.Blocks() as demo:
         margin: 0 auto;
         display: block;
     }
+
+    /* Style the leaderboard label */
+    label[for*="leaderboard_table"] {
+        font-size: 24px;
+        font-weight: bold;
+        color: #2c3e50;
+        text-align: center;
+        margin-bottom: 10px;
+        display: block;
+    }
+
+    /* Table headers */
+    .dataframe thead th {
+        background-color: #4CAF50;
+        color: white;
+        font-size: 16px;
+        font-weight: bold;
+        padding: 12px;
+        text-align: left;
+        border-bottom: 2px solid #ccc;
+    }
+
+    /* Table cells */
+    .dataframe tbody td {
+        font-size: 14px;
+        padding: 10px 15px; /* Add horizontal spacing */
+        color: #333;
+    }
+
+    /* First and second columns spacing */
+    .dataframe tbody td:first-child,
+    .dataframe tbody td:nth-child(2) {
+        padding-left: 20px;
+        padding-right: 20px;
+    }
+
+    /* Zebra striping */
+    .dataframe tbody tr:nth-child(even) {
+        background-color: #f9f9f9;
+    }
+
+    /* Add hover effect */
+    .dataframe tbody tr:hover {
+        background-color: #e6f7ff;
+    }
+
+    /* Make sure the table is not squished */
+    .dataframe {
+        table-layout: auto;
+        width: 100%;
+        border-collapse: collapse;
+    }
     """
 
     gr.HTML(load_description(DESCRIPTION_FILE))
 
-
     with gr.Row():
-        # dataset_filter = gr.Dropdown(label="📂 Select Benchmark Dataset", choices=dataset_options, value="All")
         with gr.Column():
-            # list all columns here except model name and average
             column_selector_data = gr.CheckboxGroup(label="📊 Select Columns to Display - Dataset", choices=["Chexpert Plus", "CT Rate"], value=["Chexpert Plus", "CT Rate"])
     
         with gr.Column():
-            # first list columns of different shots
             shot_filter = gr.CheckboxGroup(label="Select Shot Type", choices=shot_options, value=shot_options)
-            # then list parameters
-            param_filter = gr.CheckboxGroup(choices=parameter_options, label="Filter by Parameter Count", value = parameter_options)
-            # rest to be added
-            # data_type_filter = gr.CheckboxGroup(choices=data_types, label="Filter by Data Type")
-            
+            param_filter = gr.CheckboxGroup(choices=parameter_options, label="Filter by Parameter Count", value=parameter_options)
 
     initial_data = filter_leaderboard(parameter_options, shot_options, ["Chexpert Plus", "CT Rate"])
     leaderboard_table = gr.Dataframe(value=initial_data, label="🏅 Leaderboard", interactive=False)
-    # Initialize table
-    
-    # dataset_filter.change(
-    #     fn=update_leaderboard,
-    #     inputs=[column_selector, dataset_filter],
-    #     outputs=leaderboard_table
-    # )
-    # Trigger update
-    column_selector_data.change(filter_leaderboard, inputs=[param_filter, shot_filter, column_selector_data,], outputs=leaderboard_table)
-    
-    # data_type_filter.change(filter_leaderboard, inputs=[column_selector, [], param_filter], outputs=leaderboard_table)
-    param_filter.change(fn=filter_leaderboard, inputs=[param_filter, shot_filter, column_selector_data,], outputs=leaderboard_table)
-    shot_filter.change(fn=filter_leaderboard, inputs=[param_filter, shot_filter, column_selector_data,], outputs=leaderboard_table)
 
-    # leaderboard_table.value = filter_leaderboard(parameter_options, shot_options, ["Chexpert Plus", "CT Rate"])
+    column_selector_data.change(filter_leaderboard, inputs=[param_filter, shot_filter, column_selector_data], outputs=leaderboard_table)
+    param_filter.change(fn=filter_leaderboard, inputs=[param_filter, shot_filter, column_selector_data], outputs=leaderboard_table)
+    shot_filter.change(fn=filter_leaderboard, inputs=[param_filter, shot_filter, column_selector_data], outputs=leaderboard_table)
 
 demo.launch()

leaderboard_files/leaderboard_data.csv

@@ -1,8 +1,9 @@
 Model Name,Parameters,Average Label,Average Record,CT Rate 0 Shot Label,CT Rate 0 Shot Record,CT Rate 1 Shot Label,CT Rate 1 Shot Record,CT Rate 5 Shots Label,CT Rate 5 Shots Record,Chexpert Plus 0 Shot Label,Chexpert Plus 0 Shot Record,Chexpert Plus 1 Shot Label,Chexpert Plus 1 Shot Record,Chexpert Plus 5 Shots Label,Chexpert Plus 5 Shots Record
-LLama 3.1,8B,0.8743599999999999,0.23858000000000001,0.9534,0.4965,0.9376,0.363,,,0.7857,0.0749,0.8365,0.0963,0.8586,0.1622
-Deepseek llama 3.1,8B,0.78456,0.17608,0.9365,0.4524,0.9032,0.2308,,,0.5289,0.0112,0.7746,0.1,0.7796,0.086
-Qwen 2.5,7B,0.75136,0.23757999999999999,0.9486,0.4797,0.9523,0.4774,,,0.2125,0.0,0.8085,0.1195,0.8349,0.1113
-Qwen 2.5,14B,0.818,0.28376,0.9609,0.5459,0.9573,0.5163,,,0.4354,0.0228,0.8607,0.1677,0.8757,0.1661
-Qwen 2.5,32B,0.82796,0.29425999999999997,0.9618,0.55,0.9569,0.5145,,,0.5125,0.0,0.8153,0.1309,0.8933,0.2759
-Gemma 2,9B,0.86126,0.25356,0.9429,0.4712,0.9457,0.4369,,,0.7042,0.0726,0.8498,0.1121,0.8637,0.175
-Gemma 2,27B,0.8253999999999999,0.25256,0.9506,0.4828,0.9583,0.5698,,,0.5916,0.0161,0.7673,0.0417,0.8592,0.1524
+LLama 3.1,8B,0.8744,0.2386,0.9534,0.4965,0.9376,0.363,,,0.7857,0.0749,0.8365,0.0963,0.8586,0.1622
+Deepseek llama 3.1,8B,0.7841,0.1817,0.9391,0.4722,0.8983,0.2377,,,0.5286,0.04,0.7684,0.0699,0.7861,0.0889
+Qwen 2.5,7B,0.7514,0.2376,0.9486,0.4797,0.9523,0.4774,,,0.2125,0.0,0.8085,0.1195,0.8349,0.1113
+Qwen 2.5,14B,0.818,0.2838,0.9609,0.5459,0.9573,0.5163,,,0.4354,0.0228,0.8607,0.1677,0.8757,0.1661
+Qwen 2.5,32B,0.828,0.2943,0.9618,0.55,0.9569,0.5145,,,0.5125,0.0,0.8153,0.1309,0.8933,0.2759
+Gemma 2,9B,0.8613,0.2536,0.9429,0.4712,0.9457,0.4369,,,0.7042,0.0726,0.8498,0.1121,0.8637,0.175
+Gemma 2,27B,0.8254,0.2526,0.9506,0.4828,0.9583,0.5698,,,0.5916,0.0161,0.7673,0.0417,0.8592,0.1524
+Med42 Llama 3.1,8B,0.8658,0.2756,0.9513,0.546,0.9353,0.432,,,0.7338,0.0752,0.8619,0.166,0.8468,0.1589