Merge branch 'main' of https://huggingface.co/spaces/Neuralk-AI/tabbench

app.py

@@ -778,9 +778,7 @@ with gr.Blocks(css=css, theme=gr.themes.Default()) as demo:
                 type="pandas",
             )
             gr.Markdown('### Features')
-            # -------------------------
-            # Top row: Models & Datasets
-            # -------------------------
+
             with gr.Row():
                 with gr.Column():
                     with gr.Accordion("📂 Datasets & use case", open=True):
@@ -789,9 +787,7 @@ with gr.Blocks(css=css, theme=gr.themes.Default()) as demo:
                         with gr.Accordion("🤖 Models", open=False):
                             gr.Markdown(PRIVATE_MODEL_TEXT)
         
-            # -------------------------
-            # Second row: Metrics & Evaluation Workflow
-            # -------------------------
+
             with gr.Row():
                     with gr.Accordion("⚙️ Benchmarking procedure", open=False):
                         gr.Markdown(PRIVATE_BENCHMARKING_TEXT) 
@@ -1074,13 +1070,11 @@ with gr.Blocks(css=css, theme=gr.themes.Default()) as demo:
 
 
         def show_content(visible_content, selected_btn):
-            # All content blocks and buttons
             content_blocks = [home_intro, public_content, retail_domain_content, finance_domain_content, healthcare_domain_content, private_content, datamap_content]
             ## btns = [home_btn, public_btn, domain_retail_btn, domain_finance_btn, domain_healthcare_btn, private_btn, datamap_btn]
             btns = [public_btn_home, home_btn_public, home_btn_retail, home_btn_finance, home_btn_health, home_btn_private, home_btn_datamap]
             # Set visibility
             updates = {block: gr.update(visible=(block == visible_content)) for block in content_blocks}
-            # Set button classes
             for btn in btns:
                 if btn == selected_btn:
                     updates[btn] = gr.update(elem_classes=["white-btn", "selected-btn"])

run.py

@@ -5,7 +5,6 @@ import json
 with open("complete_results_acad.json", "r") as f:
     public_per_dataset = pd.json_normalize(json.load(f))
 
-# Load dataset metadata (including industries)
 with open("public_datasets_info.json", "r") as f:
     public_datasets_info = pd.json_normalize(json.load(f))
 
@@ -16,23 +15,14 @@ merged_df = public_per_dataset.merge(
     how='left'
 )
 
-# Ensure consistent column name
 merged_df = merged_df.rename(columns={"dataset_industry": "industry"})
 
-# Columns to average
 metrics_cols = ['Accuracy', 'Precision', 'Recall', 'F1_score', 'AUC', 'Elo_score']
 
-# Group by industry and model, compute mean
 industry_model_avg = merged_df.groupby(['industry', 'model'])[metrics_cols].mean().reset_index()
 
-# Round for readability
 industry_model_avg = industry_model_avg.round(3)
-
-# Optional: sort by industry and then by model
 industry_model_avg = industry_model_avg.sort_values(by=['industry', 'model']).reset_index(drop=True)
 
-# Save or display
 print(industry_model_avg)
-
-# Save to CSV if needed
 industry_model_avg.to_csv("industry_model_average_performance.csv", index=False)