Update app.py

app.py

@@ -39,10 +39,10 @@ def make_link(mname):
     display_name = parts[1] if len(parts) > 1 else mname
     return f'[{display_name}](https://huggingface.co/{mname})'
 
-# --- Leaderboard Table Functions (Removed redundant drop) ---
+# --- Leaderboard Table Functions (Using gr.HTML Component) ---
 
 def create_minimal_bar_html(energy_value_wh, energy_score, max_energy_value):
-    """Generates HTML for the minimal bar chart with dynamic max energy."""
+    """Generates HTML for the minimal bar chart."""
     if max_energy_value <= 0: # Avoid division by zero if max energy is 0 or negative
         bar_percentage = 0
     else:
@@ -69,12 +69,11 @@ def get_model_names(task):
     max_energy_for_task = df['total_gpu_energy'].max() # Calculate max energy for this task
 
     # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_for_task
-    df['GPU Energy (Wh)'] = df.apply(lambda row: create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_for_task), axis=1)
+    df['GPU Energy (Wh)'] = df.apply(lambda row: gr.HTML(create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_for_task)), axis=1)
 
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     df = df[['Model', 'GPU Energy (Wh)', 'Score']] # Keep only these columns
-    # df = df.drop('total_gpu_energy', axis=1) # REMOVED redundant drop
     return df
 
 def get_all_model_names():
@@ -92,11 +91,10 @@ def get_all_model_names():
     max_energy_overall = all_df['total_gpu_energy'].max() # Calculate overall max AFTER sorting
 
     # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_overall
-    all_df['GPU Energy (Wh)'] = all_df.apply(lambda row: create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_overall), axis=1)
+    all_df['GPU Energy (Wh)'] = all_df.apply(lambda row: gr.HTML(create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_overall)), axis=1)
     all_df['Model'] = all_df['model'].apply(make_link)
     all_df['Score'] = all_df['energy_score'].apply(format_stars)
     all_df = all_df[['Model', 'GPU Energy (Wh)', 'Score']]
-    # all_df = all_df.drop('total_gpu_energy', axis=1) # REMOVED redundant drop
     return all_df[['Model', 'GPU Energy (Wh)', 'Score']]
 
 
@@ -113,19 +111,18 @@ def get_text_generation_model_names(model_class):
     max_energy_for_class = df['total_gpu_energy'].max() # Calculate max energy for this class
 
     # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_for_class
-    df['GPU Energy (Wh)'] = df.apply(lambda row: create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_for_class), axis=1)
+    df['GPU Energy (Wh)'] = df.apply(lambda row: gr.HTML(create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_for_class)), axis=1)
 
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
     df = df[['Model', 'GPU Energy (Wh)', 'Score']] # Keep only these columns
-    # df = df.drop('total_gpu_energy', axis=1) # REMOVED redundant drop
     return df
 
 def update_text_generation(model_class):
     table = get_text_generation_model_names(model_class)
     return table
 
-# --- Build the Gradio Interface (Plots Removed, Tables with Dynamic Bars) ---
+# --- Build the Gradio Interface (Plots Removed, Tables with Dynamic Bars using gr.HTML) ---
 
 demo = gr.Blocks(css="""
 .gr-dataframe table {
@@ -165,41 +162,41 @@ Select different tasks to see scored models. Submit open models for testing and
             model_class_dropdown = gr.Dropdown(choices=["A", "B", "C"],
                                                  label="Select Model Class",
                                                  value="A")
-            tg_table = gr.Dataframe(get_text_generation_model_names("A"), datatype="markdown") # No plot anymore
+            tg_table = gr.Dataframe(get_text_generation_model_names("A")) # No datatype="markdown" here
             # Update table when the dropdown value changes
             model_class_dropdown.change(fn=update_text_generation,
                                         inputs=model_class_dropdown,
                                         outputs=[tg_table])
 
         with gr.TabItem("Image Generation 📷"):
-            table = gr.Dataframe(get_model_names('image_generation.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('image_generation.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Text Classification 🎭"):
-            table = gr.Dataframe(get_model_names('text_classification.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('text_classification.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Image Classification 🖼️"):
-            table = gr.Dataframe(get_model_names('image_classification.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('image_classification.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Image Captioning 📝"):
-            table = gr.Dataframe(get_model_names('image_captioning.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('image_captioning.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Summarization 📃"):
-            table = gr.Dataframe(get_model_names('summarization.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('summarization.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Automatic Speech Recognition 💬"):
-            table = gr.Dataframe(get_model_names('asr.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('asr.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Object Detection 🚘"):
-            table = gr.Dataframe(get_model_names('object_detection.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('object_detection.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Sentence Similarity 📚"):
-            table = gr.Dataframe(get_model_names('sentence_similarity.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('sentence_similarity.csv')) # No datatype="markdown" here
 
         with gr.TabItem("Extractive QA ❔"):
-            table = gr.Dataframe(get_model_names('question_answering.csv'), datatype="markdown")
+            table = gr.Dataframe(get_model_names('question_answering.csv')) # No datatype="markdown" here
 
         with gr.TabItem("All Tasks 💡"):
-            table = gr.Dataframe(get_all_model_names(), datatype="markdown")
+            table = gr.Dataframe(get_all_model_names()) # No datatype="markdown" here
 
     with gr.Accordion("📙 Citation", open=False):
         citation_button = gr.Textbox(