import gradio as gr
import pandas as pd

def value_func():
    return pd.DataFrame({"Name": ["[SD-XL](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)", "[SSD-1B](https://huggingface.co/segmind/SSD-1B)", "[Delibarate-V3](https://huggingface.co/stablediffusionapi/deliberate-v3)", "[DALLE•3-XL](https://huggingface.co/openskyml/dalle-3-xl)", "[Midjourney-V4-XL](https://huggingface.co/openskyml/midjourney-v4-xl)"], 
                         "LCM-LoRA": ["[True](https://huggingface.co/openskyml/lcm-lora-stable-diffusion-xl)", "[True](https://huggingface.co/openskyml/lcm-lora-ssd-1b)", "False", "False", "False"],
                         "Dev by": ["[StabilityAI](https://huggingface.co/stabilityai)", "[Segmind AI](https://huggingface.co/segmind)", "[XpucT](https://huggingface.co/XpucT)", "[OpenSkyML](https://huggingface.co/openskyml)", "[OpenSkyML](https://huggingface.co/openskyml)"],
                        
                        })

with gr.Blocks() as demo:
    gr.Markdown("""
    # 🎖️ Diffusion Models Leaderboard

    🤗 Using this simple table you can familiarize yourself with good Diffusion models on HuggingFace.

    🤩 The Leaderboard is made by OpenSkyML and is based on subjective opinion.
    
    """)
    
    gr.DataFrame(value=value_func)

demo.launch(show_api=False)