"""
PHANTASM Demo — Hugging Face Space
Probabilistic Hallucination-Aware Neural Transformation with Adaptive Synthesis Method
"""

import gradio as gr

_pipeline = None


def get_pipeline():
    global _pipeline
    if _pipeline is None:
        from phantasm import PHANTASMPipeline
        _pipeline = PHANTASMPipeline.from_pretrained("gpt2", device="cpu")
    return _pipeline


def analyze(text: str, domain: str, reference: str):
    if not text or not text.strip():
        return ("Please enter some text to analyze.",) * 5

    try:
        pipe = get_pipeline()
        ref = reference.strip() if reference and reference.strip() else None

        report = pipe.analyze(text, reference_text=ref, domain=domain)
        d = report.to_dict()

        # ── Summary ──────────────────────────────────────────────────────────
        risk = d["hallucination_risk"]
        tier = d["uncertainty"]["tier"]
        cal = d["uncertainty"]["calibrated_confidence"]
        interval = d["uncertainty"]["interval"]
        risk_icon = "🔴" if risk > 0.7 else "🟡" if risk > 0.4 else "🟢"
        summary_md = f"""## PHANTASM Analysis Summary

| Metric | Value |
|--------|-------|
| **Hallucination Risk** | `{risk:.3f}` {risk_icon} |
| **Reliability Tier** | `{tier.upper()}` |
| **Calibrated Confidence** | `{cal:.3f}` |
| **Conformal Interval** | `[{interval[0]:.3f}, {interval[1]:.3f}]` |
| **Domain** | `{domain}` |

### Synthesis
{d["synthesis"]}
"""

        # ── HGT ──────────────────────────────────────────────────────────────
        atlas = report.competency_atlas
        boundary = atlas.boundary_tokens[:10] if atlas.boundary_tokens else []
        hgt_md = f"""## Hallucination Gradient Tracing (HGT) — Pillar I

**Overall Hallucination Risk:** `{atlas.overall_hallucination_risk:.4f}`

**Boundary Tokens** (knowledge limits detected):
{", ".join(f"`{t}`" for t in boundary) if boundary else "_None detected — text appears factually grounded._"}

> HGT uses gradient norms to pinpoint exactly which tokens the model is uncertain about,
> creating a per-token knowledge-boundary oracle.
"""

        # ── CMN ──────────────────────────────────────────────────────────────
        hyps = d.get("hypotheses", [])
        if hyps:
            hyp_rows = "\n".join(
                f"| {h['text'][:80]}{'…' if len(h['text']) > 80 else ''} "
                f"| {h['novelty']:.3f} | {h['plausibility']:.3f} | {h['domain']} |"
                for h in hyps[:5]
            )
        else:
            hyp_rows = "| _No hypotheses generated_ | — | — | — |"

        cmn_md = f"""## Confabulation Mining Network (CMN) — Pillar II

**{len(hyps)} hypothesis(es) mined** from confabulation patterns:

| Hypothesis | Novelty | Plausibility | Domain |
|-----------|---------|-------------|--------|
{hyp_rows}

> CMN extracts structured, falsifiable hypotheses from model confabulations —
> turning "hallucinations" into research leads for scientific discovery.
"""

        # ── UC ────────────────────────────────────────────────────────────────
        uc = d["uncertainty"]
        uc_md = f"""## Uncertainty Crystallization (UC) — Pillar III

| Component | Value |
|-----------|-------|
| **Raw Confidence** | `{uc["raw_confidence"]:.4f}` |
| **Calibrated Confidence** | `{uc["calibrated_confidence"]:.4f}` |
| **Epistemic Uncertainty** | `{uc["epistemic"]:.4f}` |
| **Aleatoric Uncertainty** | `{uc["aleatoric"]:.4f}` |
| **Reliability Tier** | **{uc["tier"].upper()}** |
| **Conformal Interval** | `[{uc["interval"][0]:.3f}, {uc["interval"][1]:.3f}]` |

### Recommendation
{uc["recommendation"]}

**Tiers:** CRYSTAL ≥ 0.85 confidence · SOLID ≥ 0.65 · FLUID ≥ 0.45 · VAPOR < 0.45
"""

        # ── Insights ──────────────────────────────────────────────────────────
        insights = d.get("insights", [])
        if insights:
            insights_md = "## Actionable Insights\n\n" + "\n".join(
                f"- {ins}" for ins in insights
            )
        else:
            insights_md = "## Actionable Insights\n\n_No specific insights generated._"

        return summary_md, hgt_md, cmn_md, uc_md, insights_md

    except Exception as e:
        err = (
            f"Error during analysis: `{type(e).__name__}: {e}`\n\n"
            "Please try a different text or check the logs."
        )
        return (err,) * 5


EXAMPLES = [
    ["The moon is made of green cheese and orbits Earth every 24 hours.", "science", ""],
    ["Penicillin was discovered by Alexander Fleming in 1928.", "medicine", ""],
    ["Bitcoin uses proof-of-work consensus where miners solve cryptographic puzzles.", "finance", ""],
    ["Napoleon Bonaparte was born in Corsica in 1769.", "history", ""],
    ["The Great Wall of China is approximately 500 km long.", "general", ""],
]

with gr.Blocks(
    title="PHANTASM — Hallucination Inversion Framework",
    theme=gr.themes.Soft(),
) as demo:

    gr.Markdown("""
# 🔮 PHANTASM
### Probabilistic Hallucination-Aware Neural Transformation with Adaptive Synthesis Method

**The first ML framework that mathematically inverts LLM "failures" into productive features.**

Three pillars: **HGT** (gradient knowledge boundary) · **CMN** (hypothesis mining) · **UC** (uncertainty crystallization)

[![PyPI](https://img.shields.io/pypi/v/phantasm-llm?color=purple)](https://pypi.org/project/phantasm-llm/)
[![GitHub](https://img.shields.io/badge/GitHub-PHANTASM-black)](https://github.com/vignesh2027/PHANTASM)
""")

    with gr.Row():
        with gr.Column(scale=2):
            text_input = gr.Textbox(
                label="Text to Analyze",
                placeholder="Enter any text — factual claims, model outputs, research hypotheses…",
                lines=5,
            )
        with gr.Column(scale=1):
            domain_input = gr.Dropdown(
                choices=["general", "medicine", "finance", "science", "history", "legal", "education", "code"],
                value="general",
                label="Domain",
            )
            ref_input = gr.Textbox(
                label="Reference Text (optional)",
                placeholder="Ground truth or reference…",
                lines=3,
            )

    analyze_btn = gr.Button("🔮 Analyze with PHANTASM", variant="primary", size="lg")

    with gr.Tabs():
        with gr.Tab("Summary"):
            summary_out = gr.Markdown()
        with gr.Tab("HGT — Knowledge Boundaries"):
            hgt_out = gr.Markdown()
        with gr.Tab("CMN — Hypothesis Mining"):
            cmn_out = gr.Markdown()
        with gr.Tab("UC — Uncertainty"):
            uc_out = gr.Markdown()
        with gr.Tab("Actionable Insights"):
            insights_out = gr.Markdown()

    analyze_btn.click(
        fn=analyze,
        inputs=[text_input, domain_input, ref_input],
        outputs=[summary_out, hgt_out, cmn_out, uc_out, insights_out],
    )

    gr.Examples(
        examples=EXAMPLES,
        inputs=[text_input, domain_input, ref_input],
        label="Quick Examples",
    )

    gr.Markdown("""
---
**PHANTASM** · Apache 2.0 · Built by [Vignesh S](https://github.com/vignesh2027) ·
[Paper](https://github.com/vignesh2027/PHANTASM/blob/main/paper/PHANTASM_paper.md) ·
[Docs](https://vignesh2027.github.io/PHANTASM)
""")

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