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Parent(s):
0c350fd
refactor: streamline DataFrame column selection in app.py, update logo image for branding consistency, and enhance "About" section with additional links for improved user engagement
Browse files
app.py
CHANGED
@@ -24,11 +24,7 @@ df["Model"] = df.apply(
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)
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df = df[
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["Model", "Median Inference Time", "Price per Image"]
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-
+ [
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col
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for col in df.columns.tolist()
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if col not in ["URL", "Model", "Median Inference Time", "Price per Image"]
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]
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]
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df = df.sort_values(by="GenEval", ascending=False)
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@@ -36,7 +32,7 @@ with gr.Blocks("ParityError/Interstellar", css=custom_css) as demo:
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gr.HTML(
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"""
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<div style="text-align: center;">
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<img src="https://huggingface.co/datasets/PrunaAI/documentation-images/resolve/main/inferbench/
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<h1>ποΈ InferBench ποΈ</h1>
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<h2>A cost/quality/speed Leaderboard for Inference Providers!</h2>
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</div>
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@@ -45,12 +41,8 @@ with gr.Blocks("ParityError/Interstellar", css=custom_css) as demo:
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with gr.Tabs():
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with gr.TabItem("FLUX.1 [dev] Leaderboard"):
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median_inference_time_min = math.floor(
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-
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)
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median_inference_time_max = math.ceil(
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float(df["Median Inference Time"].max())
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)
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price_per_image_min = math.floor(float(df["Price per Image"].min()))
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price_per_image_max = math.ceil(float(df["Price per Image"].max()))
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Leaderboard(
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@@ -81,7 +73,7 @@ with gr.Blocks("ParityError/Interstellar", css=custom_css) as demo:
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gr.Markdown(
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"""
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# π About Pruna AI
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We are Pruna AI, an open source AI optimisation engine and we simply make your models cheaper, faster, smaller, greener!
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# π About InferBench
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InferBench is a leaderboard for inference providers, focusing on cost, quality, and speed.
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@@ -97,11 +89,16 @@ with gr.Blocks("ParityError/Interstellar", css=custom_css) as demo:
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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.
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This combination is available in our Pruna Pro package and can be applied to almost every image generation model.
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A full blogpost on the
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"""
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)
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with gr.Column(scale=1):
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gr.
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with gr.Accordion("π Join the Pruna AI community!", open=False):
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gr.HTML(
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@@ -111,7 +108,7 @@ with gr.Blocks("ParityError/Interstellar", css=custom_css) as demo:
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<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>
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<a rel="nofollow" href="https://discord.com/invite/rskEr4BZJx"><img alt="Discord" src="https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord"></a>
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<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>
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-
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)
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with gr.Accordion("Citation", open=True):
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gr.Markdown(
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)
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df = df[
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["Model", "Median Inference Time", "Price per Image"]
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+ [col for col in df.columns.tolist() if col not in ["URL", "Model", "Median Inference Time", "Price per Image"]]
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]
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df = df.sort_values(by="GenEval", ascending=False)
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gr.HTML(
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"""
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<div style="text-align: center;">
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<img src="https://huggingface.co/datasets/PrunaAI/documentation-images/resolve/main/inferbench/logo2-cropped.png" style="width: 200px; height: auto; max-width: 100%; margin: 0 auto;">
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<h1>ποΈ InferBench ποΈ</h1>
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<h2>A cost/quality/speed Leaderboard for Inference Providers!</h2>
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</div>
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with gr.Tabs():
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with gr.TabItem("FLUX.1 [dev] Leaderboard"):
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median_inference_time_min = math.floor(float(df["Median Inference Time"].min()))
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median_inference_time_max = math.ceil(float(df["Median Inference Time"].max()))
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price_per_image_min = math.floor(float(df["Price per Image"].min()))
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price_per_image_max = math.ceil(float(df["Price per Image"].max()))
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Leaderboard(
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gr.Markdown(
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"""
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# π About Pruna AI
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We are [Pruna AI, an open source AI optimisation engine](https://github.com/PrunaAI/pruna) and we simply make your models cheaper, faster, smaller, greener!
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# π About InferBench
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InferBench is a leaderboard for inference providers, focusing on cost, quality, and speed.
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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.
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This combination is available in our Pruna Pro package and can be applied to almost every image generation model.
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- A full blogpost on the methodology can be found [here](https://pruna.ai/blog/flux-juiced).
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- A website that compares the outputs of the different models can be found [here](https://www.notion.so/FLUX-juiced-1d270a039e5f80c6a2a3c00fc0d75ef0?pvs=4).
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"""
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)
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with gr.Column(scale=1):
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gr.HTML(
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"""
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<iframe src="https://www.notion.so/FLUX-juiced-1d270a039e5f80c6a2a3c00fc0d75ef0?pvs=4" width="100%" height="100%" frameborder="0"></iframe>
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"""
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)
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with gr.Accordion("π Join the Pruna AI community!", open=False):
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gr.HTML(
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<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>
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<a rel="nofollow" href="https://discord.com/invite/rskEr4BZJx"><img alt="Discord" src="https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord"></a>
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<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>
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"""
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)
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with gr.Accordion("Citation", open=True):
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gr.Markdown(
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