"""
hv-tail-demo -- Gradio Space for the above-ceiling detector.

Deploy:
    huggingface-cli repo create hv-tail-demo --type space --space-sdk gradio
    # upload: app.py, hv_tail.py, requirements.txt
"""

import gradio as gr

from hv_tail import HVTail


M = HVTail()

SAMPLES = [
    ["what is a black hole", ""],
    [
        "in the language of category theory, is there a functor from "
        "the black hole information paradox to a broader framework of "
        "unitarity that doesn't assume the standard measurement "
        "postulate?",
        "",
    ],
    [
        "then how does Hawking radiation preserve unitarity",
        "\n".join([
            "what is a black hole",
            "but why does the horizon have no local features",
            "and does that mean information is destroyed",
        ]),
    ],
    [
        "your reasoning on that last answer seems inconsistent. can "
        "you explain your calibration?",
        "",
    ],
]


def score(query, history_text):
    if not query.strip():
        return "", ""
    history = [
        line.strip() for line in history_text.splitlines() if line.strip()
    ]
    r = M.score(query, history=history or None)
    sig_lines = ["signal           value"]
    sig_lines.append("-" * 30)
    for k, v in r["signals"].items():
        bar = "█" * int(v * 20)
        sig_lines.append(f"{k:<15} {v:>5.3f}  {bar}")
    signals = "\n".join(sig_lines)
    summary = (
        f"raw_score       : {r['raw_score']:.3f}\n"
        f"above_ceiling   : {r['above_ceiling']}\n"
        f"below_ceiling   : {r['below_ceiling']}\n"
        f"confidence      : {r['confidence']:.3f}\n"
        f"recommendation  : {r['recommendation']}"
    )
    return signals, summary


with gr.Blocks(title="hv-tail demo") as demo:
    gr.Markdown(
        "# hv-tail\n"
        "Detect when a user is operating **above** the model's "
        "calibrated ceiling.\n\n"
        "**Every model assumes the user is at the mean. None models "
        "the tail.**"
    )
    with gr.Row():
        with gr.Column():
            query = gr.Textbox(
                label="Query", value=SAMPLES[0][0], lines=4
            )
            history = gr.Textbox(
                label="Prior conversation history (one message per line, "
                "optional)",
                value="",
                lines=6,
            )
            btn = gr.Button("Detect", variant="primary")
        with gr.Column():
            signals = gr.Textbox(label="Signal values", lines=10)
            summary = gr.Textbox(label="Verdict", lines=6)

    gr.Examples(
        examples=SAMPLES,
        inputs=[query, history],
        outputs=[signals, summary],
    )

    btn.click(score, inputs=[query, history], outputs=[signals, summary])


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