app.py

import math
from pathlib import Path

import gradio as gr
import pandas as pd
from gradio_leaderboard import ColumnFilter, Leaderboard

abs_path = Path(__file__).parent

# Any pandas-compatible data
df = pd.read_csv(str(abs_path / "data.csv"))
df["Model"] = df.apply(
    lambda row: f'<a target="_blank" href="{row["URL"]}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{row["Provider"]}</a>',
    axis=1,
)
df = df[["Model"] + [col for col in df.columns.tolist() if col not in ["URL", "Provider", "Model"]]]

with gr.Blocks("ParityError/Interstellar") as demo:
    gr.Markdown(
        """
        <h1 style="margin: 0;">InfraBench - A Leaderboard for Inference Providers</h1>
        <br>
        <div style="margin-bottom: 20px;">
            <p>Welcome to InfraBench, the ultimate leaderboard for evaluating inference providers. Our platform focuses on key metrics such as cost, quality, and compression to help you make informed decisions. Whether you're a developer, researcher, or business looking to optimize your inference processes, InfraBench provides the insights you need to choose the best provider for your needs.</p>
        </div>
    """
    )
    with gr.Tabs():
        with gr.TabItem("FLUX.1 [dev] Leaderboard"):
            median_inference_time_min = math.floor(float(df["Median Inference Time (in s)"].min()))
            median_inference_time_max = math.ceil(float(df["Median Inference Time (in s)"].max()))
            price_per_image_min = math.floor(float(df["Price per Image"].min()))
            price_per_image_max = math.ceil(float(df["Price per Image"].max()))
            Leaderboard(
                value=df,
                search_columns=["Model"],
                filter_columns=[
                    ColumnFilter(
                        column="Median Inference Time (in s)",
                        type="slider",
                        default=[median_inference_time_min, median_inference_time_max],
                        min=median_inference_time_min,
                        max=median_inference_time_max,
                    ),
                    ColumnFilter(
                        column="Price per Image",
                        type="slider",
                        default=[price_per_image_min, price_per_image_max],
                        min=price_per_image_min,
                        max=price_per_image_max,
                    ),
                ],
                select_columns=df.columns.tolist(),
                datatype="markdown",
            )
        with gr.TabItem("About"):
            gr.Markdown(
                """
                # 💜 About Pruna AI
                We are Pruna AI, an open source AI optimisation engine and we simply make your models cheaper, faster, smaller, greener!

                # 📊 About InfraBench
                InfraBench is a leaderboard for inference providers, focusing on cost, quality, and compression.
                Over the past few years, we’ve observed outstanding progress in image generation models fueled by ever-larger architectures.
                Due to their size, state-of-the-art models such as FLUX take more than 6 seconds to generate a single image on a high-end H100 GPU.
                While compression techniques can reduce inference time, their impact on quality often remains unclear.

                <a href="https://huggingface.co/datasets/PrunaAI/documentation-images/resolve/main/inferbench/spider_comparison.png" target="_blank">Spider Comparison</a>

                To bring more transparency around the quality of compressed models:

                - We release “juiced” endpoints for popular image generation models on Replicate, making it easy to play around with our compressed models.
                - We assess the quality of compressed FLUX-APIs from Replicate, fal, Fireworks AI and Together AI according to different benchmarks.

                <a href="https://huggingface.co/datasets/PrunaAI/documentation-images/resolve/main/inferbench/speed_comparison.png" target="_blank">Speed Comparison</a>

                FLUX-juiced was obtained using a combination of compilation and caching algorithms and we are proud to say that it consistently outperforms alternatives, while delivering performance on par with the original model.
                This combination is available in our Pruna Pro package and can be applied to almost every image generation model.

                # 🌍 Join the Pruna AI community!
                <p><a rel="nofollow" href="https://twitter.com/PrunaAI"><img alt="Twitter" src="https://img.shields.io/twitter/follow/PrunaAI?style=social"></a>
                <a rel="nofollow" href="https://github.com/PrunaAI/pruna"><img alt="GitHub" src="https://img.shields.io/github/stars/prunaai/pruna"></a>
                <a rel="nofollow" href="https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following"><img alt="LinkedIn" src="https://img.shields.io/badge/LinkedIn-Connect-blue"></a>
                <a rel="nofollow" href="https://discord.com/invite/rskEr4BZJx"><img alt="Discord" src="https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&amp;logo=discord"></a>
                <a rel="nofollow" href="https://www.reddit.com/r/PrunaAI/"><img alt="Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/PrunaAI?style=social"></a></p>
                """
            )
        with gr.Accordion("Citation", open=True):
            gr.Markdown(
                """
                    ```bibtex
                    @article{InfraBench,
                        title={InfraBench: A Leaderboard for Inference Providers},
                        author={PrunaAI},
                        year={2025},
                        howpublished={\\url{https://huggingface.co/spaces/PrunaAI/InferBench}}
                    }
                    ```
                    """
            )

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