changed formatting

about/description.md

@@ -10,8 +10,8 @@ The leaderboard offers a comprehensive assessment of each model's classification
 
 <h2 style="color: #00ff00;">Evaluation Criteria:</h2>
 The primary metric used for evaluation is accuracy, which measures the proportion of correct predictions made by the model. We used two levels of accruacy <br>
-1. <b> Label-level accuracy <b>: Accuracy is measured in terms of total labels.<br>
-2. <b> Record-level accuracy <b>: Accuracy is measured if a report is classified accurately across all labels. 
+1. <b> Label-level accuracy </b>: Accuracy is measured in terms of total labels.<br>
+2. <b> Record-level accuracy </b>: Accuracy is measured if a report is classified accurately across all labels. 
 
 <h2 style="color: #00ff00;">Different Parameters:</h2> The leaderboard displays the different type of settings explored to get various results <br>
 1. <b> Different shots prompting </b>: 0 shot, 1 shot, 5 shots. <br>

app.py

@@ -15,6 +15,41 @@ with open(DESCRIPTION_FILE, "r") as f:
 html_description = markdown.markdown(md_text, extensions=["tables"])
 columns_fixed = ["Model Name", "Average Label", "Average Record"]
 
+df = load_leaderboard()
+all_columns = list(df.columns)
+columns_variable = [i for i in all_columns if i not in columns_fixed]
+
+shot_options = ["0 shot", "1 shot", "5 shots"]
+
+
+def get_columns_for_shots(selected_shots):
+    if not selected_shots:
+        return []
+    return [col for col in all_columns if any(shot in col for shot in selected_shots)]
+
+def get_columns_for_data(selected_data):
+    if not selected_data:
+        return []
+    return [col for col in all_columns if any(data in col for data in selected_data)]
+
+# data_types = sorted(df["data_type"].dropna().unique())
+parameter_options = sorted(df["Parameters"].dropna().unique())
+
+def filter_leaderboard(selected_params, selected_shots, selected_data):
+    filtered = df.copy()
+    print("Selected Shots:", selected_shots)
+    if selected_params:
+        filtered = filtered[filtered["Parameters"].isin(selected_params)]
+
+    columns_by_shot = get_columns_for_shots(selected_shots)
+    columns_by_data = get_columns_for_data(selected_data)
+    additional_columns = [col for col in columns_by_shot + columns_by_data if col in df.columns]
+    cols_to_show = list(dict.fromkeys(columns_fixed + additional_columns))
+
+    print("COLUMNS TO SHOW:", cols_to_show)
+
+    return filtered[cols_to_show]
+
 with gr.Blocks() as demo:
     gr.Markdown("<h1 style='text-align: center;'>🏆 Medical Classification Leaderboard - Beta</h1>")
     gr.Image("./about/linguist.png", elem_id="linguist-image", show_label=False)
@@ -31,41 +66,6 @@ with gr.Blocks() as demo:
 
     gr.HTML(html_description)
 
-    df = load_leaderboard()
-    all_columns = list(df.columns)
-    columns_variable = [i for i in all_columns if i not in columns_fixed]
-    
-    shot_options = ["0 shot", "1 shot", "5 shots"]
-    
-
-    def get_columns_for_shots(selected_shots):
-        if not selected_shots:
-            return []
-        return [col for col in all_columns if any(shot in col for shot in selected_shots)]
-    
-    def get_columns_for_data(selected_data):
-        if not selected_data:
-            return []
-        return [col for col in all_columns if any(data in col for data in selected_data)]
-
-    # data_types = sorted(df["data_type"].dropna().unique())
-    parameter_options = sorted(df["Parameters"].dropna().unique())
-    
-    def filter_leaderboard(selected_params, selected_shots, selected_data):
-        filtered = df.copy()
-        print("Selected Shots:", selected_shots)
-        if selected_params:
-            filtered = filtered[filtered["Parameters"].isin(selected_params)]
-
-        columns_by_shot = get_columns_for_shots(selected_shots)
-        columns_by_data = get_columns_for_data(selected_data)
-        additional_columns = [col for col in columns_by_shot + columns_by_data if col in df.columns]
-        cols_to_show = list(dict.fromkeys(columns_fixed + additional_columns))
-
-        print("COLUMNS TO SHOW:", cols_to_show)
-
-        return filtered[cols_to_show]
-
 
     with gr.Row():
         # dataset_filter = gr.Dropdown(label="📂 Select Benchmark Dataset", choices=dataset_options, value="All")
@@ -99,6 +99,5 @@ with gr.Blocks() as demo:
     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"])
-    print(leaderboard_table.value)
 
 demo.launch()