import gradio as gr

TIERS = {
    "brain": {
        "model": "deepseek-v4-pro",
        "accuracy": 88.4,
        "cost_per_query": 0.002,
        "description": "maximum quality, no shortcuts",
    },
    "medium": {
        "model": "deepseek-v4-flash",
        "accuracy": 81.2,
        "cost_per_query": 0.000182,
        "description": "balanced speed and quality",
    },
    "cheap": {
        "model": "varies (Gemini Flash, Llama, etc.)",
        "accuracy": 65.0,
        "cost_per_query": 0.0,
        "description": "free-tier models, zero cost",
    },
}

BENCHMARKS = [
    ["BigPickle OpenCoderPure",   117.7, 0.24, "5 free models propose, Brain refines split vote"],
    ["BigPickle FamilyDebate",    118.6, 0.30, "Google x Meta x Microsoft vote, Brain refines"],
    ["BigPickle MoA (3x Llama70B)", 120.6, 0.42, "3-pass Llama 70B multi-attention"],
    ["BigPickle FreeEnsemble",    110.9, 0.00, "9 free models majority vote (zero cost)"],
    ["BigPickle Opencodebate",    107.2, 0.60, "Flash / Llama70B debate, Brain breaks tie"],
    ["VibeUltraX (DeepSeek family)", 104.0, 0.46, "DeepSeek-only cascade + flash/pro debate"],
    ["Raw Brain (baseline)",      100.0, 1.00, "single deepseek-v4-pro baseline"],
    ["budget (trivial routing)",  40.0,  0.00, "direct routing to cheapest model"],
]


def route_query(complexity, max_cost):
    if complexity < 3:
        tier = "cheap"
    elif complexity < 6:
        tier = "medium"
    else:
        tier = "brain"

    if max_cost < 0.25 and tier in ("medium", "brain"):
        tier = "cheap"

    selected = TIERS[tier]
    brain = TIERS["brain"]
    savings = ((brain["cost_per_query"] - selected["cost_per_query"]) / brain["cost_per_query"]) * 100
    quality_ratio = (selected["accuracy"] / brain["accuracy"]) * 100

    return (
        tier.upper(),
        selected["model"],
        selected["description"],
        f"${selected['cost_per_query']:.6f}",
        f"{quality_ratio:.1f}% of brain quality",
        f"{savings:.0f}% cheaper than brain",
    )


with gr.Blocks(
    theme=gr.themes.Soft(primary_hue="indigo", neutral_hue="slate"),
    title="vibeOScore",
) as demo:
    gr.Markdown("# vibeOScore \u2014 Model Routing Simulator")
    gr.Markdown(
        "Routes AI prompts across model tiers based on complexity vs cost constraints. "
        "Brain (best quality) \u2192 Medium (balanced) \u2192 Cheap (free, zero cost)."
    )

    with gr.Tab("Routing Simulator"):
        with gr.Row():
            with gr.Column():
                complexity = gr.Slider(1, 10, value=5, step=1, label="Query complexity")
                max_cost = gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="Max cost (fraction of brain tier)")

                gr.Examples(
                    examples=[
                        [2, 0.0],
                        [5, 0.3],
                        [9, 0.8],
                    ],
                    inputs=[complexity, max_cost],
                    label="Quick presets",
                )

                route_btn = gr.Button("Route", variant="primary", size="sm")

            with gr.Column():
                tier_out = gr.Textbox(label="Assigned tier")
                model_out = gr.Textbox(label="Model")
                desc_out = gr.Textbox(label="Why this tier")
                cost_out = gr.Textbox(label="Cost per query")
                quality_out = gr.Textbox(label="Quality vs brain")
                savings_out = gr.Textbox(label="Savings vs brain")

        route_btn.click(
            fn=route_query,
            inputs=[complexity, max_cost],
            outputs=[tier_out, model_out, desc_out, cost_out, quality_out, savings_out],
        )

    with gr.Tab("Benchmarks"):
        gr.Markdown("**Benchmark results** \u2014 660k evaluations across 11 strategies x 30 runs x 2000 MC questions. Quality scores normalized to Raw Brain = 100%.")
        gr.Dataframe(
            headers=["Strategy", "Quality vs Brain", "Cost vs Brain", "Method"],
            value=[[s, f"{q:.1f}%", f"{c*100:.0f}%", m] for s, q, c, m in BENCHMARKS],
            interactive=False,
            column_widths=["220px", "130px", "130px", "auto"],
        )
        gr.Markdown(
            "The FreeEnsemble (9 free models, zero cost) hits **110.9% quality at $0**. "
            "OpenCoderPure achieves **117.7% quality at 24% cost** \u2014 best Pareto efficiency."
        )

    with gr.Tab("About"):
        gr.Markdown(
            "**vibeOScore** \u2014 open-source AI agent orchestration backend.\n\n"
            "Built with Fastify v5, TypeScript, SQLite.\n\n"
            "**Features:**\n"
            "- Model tier routing (brain / medium / cheap)\n"
            "- Multi-provider cost optimization\n"
            "- MCP server for agent-to-agent communication\n"
            "- LSH approximate caching (90.9% hit rate)\n"
            "- INT8 vector quantization (3-6x speedup)\n"
            "- SPI multi-resolution indexing\n\n"
            "**Links:** [GitHub](https://github.com/DrunkkToys/vibeOScore)"
            " | [npm](https://www.npmjs.com/package/vibeoscore)"
            " | [Frontend](https://github.com/DrunkkToys/theSaver-oc)"
            " | [MCP server](https://github.com/DrunkkToys/vibeOSmcp)"
        )

demo.launch()