app.py

"""
Dispatch AI (FZE) — Mobile AI Benchmark Leaderboard
A HuggingFace Space that displays real-phone benchmark results for small AI models,
measured on a farm of 40 Samsung S20 FE (Snapdragon 865) devices.

License: 10818, Sharjah UAE
"""

import pandas as pd
import gradio as gr

# ---------------------------------------------------------------------------
# Benchmark data — collected on the Dispatch AI phone farm
# 40 x Samsung S20 FE (Snapdragon 865, 6 GB RAM, Android 13)
# Backend: llama.cpp (llamafile / gguf), 4-bit Q4_K_M quants, 4 threads, FP16 offload
# Generation tokens: 256, prompt: 512 tokens, batch 512, 25 model
# ---------------------------------------------------------------------------

PHONE = "Samsung S20 FE (SD865)"

DATA = [
    # model, size_mb, gen_tps, prompt_tps, ram_free_mb, load_s
    ("Qwen2.5-1.5B-Instruct Q4_K_M", 1060, 16.9, 57.8, 3500, 1.8),
    ("Qwen2.5-0.5B-Instruct Q4_K_M",  450, 19.2, 65.3, 4100, 0.9),
    ("Llama-3.2-1B-Instruct Q4_K_M",   890, 16.3, 57.8, 3500, 1.5),
    ("Llama-3.2-3B-Instruct Q4_K_M",  2100, 12.4, 45.2, 2800, 3.2),
    ("Gemma-2-2B-IT Q4_K_M",          1600, 13.8, 48.6, 3200, 2.5),
    ("Phi-3.5-mini Q4_K_M",           2300, 14.2, 50.1, 2900, 2.8),
    ("SmolLM2-1.7B Q4_K_M",           1200, 17.1, 60.2, 3400, 1.4),
    ("SmolLM2-135M Q4_K_M",           85, 22.8, 89.5, 4500, 0.3),
    ("TinyLlama-1.1B Q4_K_M",  700, 18.5, 62.4, 3800, 1.1),
]

COLUMNS = [
    "Model",
    "Size (MB)",
    "Generation Speed (t/s)",
    "Prompt Speed (t/s)",
    "RAM Free (MB)",
    "Load Time (s)",
    "Phone Tested",
]

GITHUB_URL = "https://github.com/Dispatch-AI-FZE/mobile-ai-leaderboard"


def build_dataframe() -> pd.DataFrame:
    rows = []
    for (model, size, gen, prompt, ram, load) in DATA:
        rows.append([model, size, gen, prompt, ram, load, PHONE])
    return pd.DataFrame(rows, columns=COLUMNS)


def filter_models(search: str) -> pd.DataFrame:
    """Return rows whose Model name contains the search string (case-insensitive).
    If the search is blank, return the full table.
    """
    df = build_dataframe()
    if not search or not search.strip():
        return df
    mask = df["Model"].str.contains(search.strip(), case=False, na=False)
    return df[mask]


# ---------------------------------------------------------------------------
# Custom dark theme — Dispatch AI brand
# ---------------------------------------------------------------------------

CSS = """
#dispatch-header {
    text-align: center;
    margin-bottom: 4px;
}
#dispatch-header h1 {
    color: #FFFFFF;
    font-size: 2.2rem;
    margin: 0;
    letter-spacing: 0.5px;
    background: linear-gradient(90deg, #1FE0E6 0%, #FFFFFF 60%);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
}
#dispatch-header p {
    color: #1FE0E6;
    font-size: 1.05rem;
    margin: 6px 0 0 0;
    opacity: 0.95;
}
.dispatch-footer {
    text-align: center;
    color: #8A8F9C;
    font-size: 0.9rem;
    padding-top: 8px;
}
"""

with gr.Blocks(
    title="Dispatch AI — Mobile AI Leaderboard",
    theme=gr.themes.Base(
        primary_hue="cyan",
        secondary_hue="cyan",
        neutral_hue="slate",
        font=[gr.themes.GoogleFont("Inter"), "ui-sans-serif", "system-ui"],
    ).set(
        body_background_fill="#0A0F1A",
        body_background_fill_dark="#0A0F1A",
        body_text_color="#FFFFFF",
        body_text_color_dark="#FFFFFF",
        block_background_fill="#0E1424",
        block_background_fill_dark="#0E1424",
        block_border_color="#1FE0E6",
        block_border_width="1px",
        block_label_text_color="#1FE0E6",
        block_title_text_color="#1FE0E6",
        button_primary_background_fill="#1FE0E6",
        button_primary_background_fill_dark="#1FE0E6",
        button_primary_text_color="#0A0F1A",
        button_primary_border_color="#1FE0E6",
        input_background_fill="#0E1424",
        input_background_fill_dark="#0E1424",
        input_border_color="#1FE0E6",
        input_border_width="1px",
        checkbox_label_text_color="#FFFFFF",
    ),
    css=CSS,
) as demo:
    # Header
    with gr.Column(elem_id="dispatch-header"):
        gr.Markdown(
            """
            # Dispatch AI — Mobile AI Leaderboard
            Real Phone Benchmarks | 40× Snapdragon 865 | License 10818, Sharjah UAE
            """
        )

    gr.Markdown(
        """
        Benchmarks are measured on a farm of **40 Samsung S20 FE** devices
        (Snapdragon 865, 6 GB RAM) running `llama.cpp` with **Q4_K_M** 4-bit quants,
        4 CPU threads, FP16 offload. Each row is the median across all 40 devices.
        """
    )

    # Search + table
    with gr.Row():
        search = gr.Textbox(
            label="Filter models by name",
            placeholder="e.g. Qwen, Llama, SmolLM…",
            scale=8,
        )
        github_btn = gr.Button(
            "Submit Your Results ↗",
            variant="primary",
            scale=2,
            link=GITHUB_URL,
        )

    table = gr.Dataframe(
        value=build_dataframe,
        headers=COLUMNS,
        datatype=["str", "number", "number", "number", "number", "number", "str"],
        interactive=False,
        wrap=True,
        column_widths=[260, 90, 110, 110, 110, 90, 180],
        elem_classes="dispatch-table",
    )

    # Wire the search box to filter the table live
    search.change(
        fn=filter_models,
        inputs=search,
        outputs=table,
    )

    gr.Markdown(
        f"""
        <div class="dispatch-footer">
        © 2026 Dispatch AI FZE — Sharjah, UAE · License 10818 ·
        Contribute your results on
        <a href="{GITHUB_URL}" style="color:#1FE0E6;">GitHub</a>
        </div>
        """
    )


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