Delete json_leaderboard.py

json_leaderboard.py

@@ -1,121 +0,0 @@
-import json
-import pandas as pd
-from pathlib import Path
-
-
-def load_leaderboard_from_json(json_path="leaderboard_data.json"):
-    """Load leaderboard data from JSON file"""
-    try:
-        with open(json_path, 'r', encoding='utf-8') as f:
-            data = json.load(f)
-        return data['leaderboard']
-    except FileNotFoundError:
-        print(f"JSON file {json_path} not found")
-        return []
-    except json.JSONDecodeError:
-        print(f"Error decoding JSON file {json_path}")
-        return []
-
-
-def create_leaderboard_df(json_path="leaderboard_data.json"):
-    """Create a pandas DataFrame from JSON leaderboard data"""
-    leaderboard_data = load_leaderboard_from_json(json_path)
-    
-    if not leaderboard_data:
-        return pd.DataFrame()
-    
-    # Convert to DataFrame
-    df = pd.DataFrame(leaderboard_data)
-    
-    # Sort by ACC score (descending)
-    df = df.sort_values('Overall', ascending=False).reset_index(drop=True)
-    
-    # Add ranking icons and make model names clickable links to papers
-    def add_ranking_icon_and_link(index, model_name, paper_link):
-        if index == 0:
-            return f'🥇 <a href="{paper_link}" target="_blank">{model_name}</a>'
-        elif index == 1:
-            return f'🥈 <a href="{paper_link}" target="_blank">{model_name}</a>'
-        elif index == 2:
-            return f'🥉 <a href="{paper_link}" target="_blank">{model_name}</a>'
-        else:
-            return f'<a href="{paper_link}" target="_blank">{model_name}</a>'
-    
-    # Format the DataFrame for display
-    display_df = pd.DataFrame({
-    'Model': [add_ranking_icon_and_link(i, model, link) for i, (model, link) in enumerate(zip(df['model'], df['link']))],
-    'Release Date': df['release_date'],
-    'HF Model': df['hf'].apply(lambda x: f'<a href="{x}" target="_blank">🤗</a>' if x != "-" else "-"),
-    'Open Source': df['open_source'].apply(lambda x: '✓' if x else '✗'),
-
-    'Overall': df['Overall'].apply(lambda x: f"{x:.2f}"),
-
-    'Style': df['Style'].apply(lambda x: f"{x:.2f}"),
-
-    'World Knowledge': df['World Knowledge'].apply(lambda x: f"{x:.2f}"),
-    'Logical Reasoning': df['Logical Reasoning'].apply(lambda x: f"{x:.2f}"),
-
-
-    'Text': df['Text'].apply(lambda x: f"{x:.2f}"),
-
-
-
-    'Attribute-Overall': df['Attribute-Overall'].apply(lambda x: f"{x:.2f}"),
-    'Quantity': df['Quantity'].apply(lambda x: f"{x:.2f}"),
-    'Expression': df['Expression'].apply(lambda x: f"{x:.2f}"),
-    'Material': df['Material'].apply(lambda x: f"{x:.2f}"),
-    'Size': df['Size'].apply(lambda x: f"{x:.2f}"),
-    'Shape': df['Shape'].apply(lambda x: f"{x:.2f}"),
-    'Color': df['Color'].apply(lambda x: f"{x:.2f}"),
-
-    'Action-Overall': df['Action-Overall'].apply(lambda x: f"{x:.2f}"),
-    'Hand': df['Hand'].apply(lambda x: f"{x:.2f}"),
-    'Full body': df['Full body'].apply(lambda x: f"{x:.2f}"),
-    'Animal': df['Animal'].apply(lambda x: f"{x:.2f}"),
-    'Non Contact': df['Non Contact'].apply(lambda x: f"{x:.2f}"),
-    'Contact': df['Contact'].apply(lambda x: f"{x:.2f}"),
-    'State': df['State'].apply(lambda x: f"{x:.2f}"),
-        
-    'Relationship-Overall': df['Relationship-Overall'].apply(lambda x: f"{x:.2f}"),
-    'Composition': df['Composition'].apply(lambda x: f"{x:.2f}"),
-    'Similarity': df['Similarity'].apply(lambda x: f"{x:.2f}"),
-    'Inclusion': df['Inclusion'].apply(lambda x: f"{x:.2f}"),
-    'Comparison': df['Comparison'].apply(lambda x: f"{x:.2f}"),
-
-
-    'Compound-Overall': df['Compound-Overall'].apply(lambda x: f"{x:.2f}"),
-    'Imagination': df['Imagination'].apply(lambda x: f"{x:.2f}"),
-    'Feature matching': df['Feature matching'].apply(lambda x: f"{x:.2f}"),
-
-
-    'Grammar-Overall': df['Grammar-Overall'].apply(lambda x: f"{x:.2f}"),
-    'Pronoun Reference': df['Pronoun Reference'].apply(lambda x: f"{x:.2f}"),
-    'Consistency': df['Consistency'].apply(lambda x: f"{x:.2f}"),
-    'Negation': df['Negation'].apply(lambda x: f"{x:.2f}"),
-
-
-    'Layout-Overall': df['Layout-Overall'].apply(lambda x: f"{x:.2f}"),
-    '2D': df['2D'].apply(lambda x: f"{x:.2f}"),
-    '3D': df['3D'].apply(lambda x: f"{x:.2f}"),
-
-
-})
-    
-    return display_df
-
-
-def get_leaderboard_stats(json_path="leaderboard_data.json"):
-    """Get statistics about the leaderboard"""
-    leaderboard_data = load_leaderboard_from_json(json_path)
-    
-    if not leaderboard_data:
-        return {}
-    
-    df = pd.DataFrame(leaderboard_data)
-    
-    stats = {
-        'total_models': len(df),
-        'open_source_models': df['open_source'].sum(),
-    }
-    
-    return stats