import gradio as gr
import pandas as pd

# Static data - reordered columns: Method, #Param., Input Type, Control Type, Model Type, Mean Traj. ↓, Acc. ↑
STATIC_DATA = [
    ["w/o WM", "72B", "RGB", "–", "VLM", 6.24, 50.27],
    ["PathDreamer [36]", "0.69B", "RGB-D; Sem; Pano", "Viewpoint", "Image Gen.", 5.28, 56.99],
    ["SE3DS [11]", "1.1B", "RGB-D; Pano", "Viewpoint", "Image Gen.", 5.29, 57.53],
    ["NWM [25]", "1B", "RGB", "Trajectory", "Video Gen.", 5.68, 57.35],
    ["SVD [6]", "1.5B", "RGB", "Image", "Video Gen.", 5.29, 57.71],
    ["LTX-Video [5]", "2B", "RGB", "Text", "Video Gen.", 5.37, 56.08],
    ["Hunyuan [4]", "13B", "RGB", "Text", "Video Gen.", 5.21, 57.71],
    ["Wan2.1 [23]", "14B", "RGB", "Text", "Video Gen.", 5.24, 58.26],
    ["Cosmos [1]", "2B", "RGB", "Text", "Video Gen.", 5.898, 52.27],
    ["Runway", "–", "–", "Text", "Video Gen.", "–", "–"],
    ["SVD† [6]", "1.5B", "RGB; Pano", "Action", "Video Gen. Post-Train", 5.02, 60.98],
    ["LTX† [5]", "2B", "RGB; Pano", "Action", "Video Gen. Post-Train", 5.49, 57.53],
    ["WAN2.1† [23]", "14B", "RGB; Pano", "Action", "Video Gen. Post-Train", "XXX", "XXX"],
    ["Cosmos† [1]", "2B", "RGB; Pano", "Action", "Video Gen. Post-Train", 5.08, 60.25],
]

COLUMNS = ["Method", "#Param.", "Input Type", "Control Type", "Model Type", "Mean Traj. ↓", "Acc. ↑"]

def create_leaderboard():
    df = pd.DataFrame(STATIC_DATA, columns=COLUMNS)
    # Sort by accuracy in descending order (highest first), handling non-numeric values
    df_clean = df.copy()
    # Replace non-numeric values with -1 for sorting (so they appear at bottom)
    df_clean['Acc. ↑'] = pd.to_numeric(df_clean['Acc. ↑'], errors='coerce').fillna(-1)
    df_sorted = df_clean.sort_values('Acc. ↑', ascending=False)
    # Return original df with the sorted order but original values
    return df.iloc[df_sorted.index].reset_index(drop=True)

with gr.Blocks(title="World-in-World: Building a Closed-Loop World Interface to Evaluate World Models", theme=gr.themes.Soft()) as demo:
    gr.HTML("<h1 style='text-align: center; margin-bottom: 1rem'>🏆 World-in-World: Building a Closed-Loop World Interface to Evaluate World Models</h1>")
    
    with gr.Tabs():
        with gr.TabItem("📊 Leaderboard"):
            leaderboard_table = gr.DataFrame(
                value=create_leaderboard(),
                headers=COLUMNS,
                datatype=["str", "str", "str", "str", "str", "number", "number"],
                interactive=False,
                wrap=True
            )
        
        with gr.TabItem("📝 About"):
            gr.Markdown("""
            # World-in-World: Building a Closed-Loop World Interface to Evaluate World Models
            
            This leaderboard showcases performance metrics across different types of AI models in world modeling tasks:
            
            ## Model Categories
            - **VLM**: Vision-Language Models
            - **Image Gen.**: Image Generation Models  
            - **Video Gen.**: Video Generation Models
            - **Video Gen. Post-Train**: Post-training specialized Video Generation Models
            
            ## Metrics Explained
            - **Acc. ↑**: Accuracy score (higher values indicate better performance)
            - **Mean Traj. ↓**: Mean trajectory error (lower values indicate better performance)
            
            ## Notes
            - † indicates post-training specialized models
            - XXX indicates results pending/unavailable
            - – indicates not applicable or not available
            
            *Results represent performance on world modeling evaluation benchmarks and may vary across different evaluation settings.*
            """)

if __name__ == "__main__":
    demo.launch()