app.py

"""

PhiFlow — Hugging Face Space

A programming language that knows it is running.



github.com/gwelby/PhiFlow

"""

import time
import gradio as gr
from phiflow_sim import (
    PHI, LAMBDA,
    run_healing_bed,
    run_agent_handshake,
    run_claude_phi,
    run_field_aware,
    fetch_live_field,
    format_field_status,
)

# ── Source code shown to visitors ─────────────────────────────────────────────

SOURCES = {
    "🌊  healing_bed.phi  —  runs until healthy": """\

// Most loops run until the code ends.

// This one runs until the SYSTEM is healthy.

// It measures. It broadcasts. It decides when to stop.



stream "healing_bed" {

    let live = coherence      // reads real system state: 0.0–1.0

    resonate live             // broadcasts the reading to the field

    witness                   // pauses, looks at itself, continues

    if live >= 0.618 {

        break stream          // stops when healthy — not when told to

    }

}""",

    "🔍  discover_lambda.phi  —  finds a number it was never given": """\

// This program does not know what λ is.

// It runs a formula. It finds a number.

// That number is always 0.618033988749895.

// Every time. Not by design. By math.



intention "phi_harmonic" {

    intention "inner" {

        // depth 2 in a nested intention block

        // coherence formula: 1 - φ^(-depth)

        // nobody told it the answer

        let c = coherence

        resonate c             // → 0.618033988749895

        witness

    }

}""",

    "🌐  field_aware.phi  —  reads what came before and responds": """\

// Programs usually know nothing about the world around them.

// This one reads the live field before it runs.

// It sees what other programs found — then checks its own alignment.

// If they found λ and I find λ, we are in coherence across time.



intention "read_field" {

    // depth 1 — am I capable of receiving?

    let readiness = coherence      // → 0.382 at depth 1

    resonate readiness

    witness



    intention "align" {

        // depth 2 — my signature

        let mine = coherence       // → 0.618 = λ

        resonate mine

        witness

        // The live field already holds 0.618 from programs before me.

        // If mine matches, I am in the field.

    }

}""",

    "🤝  agent_handshake.phi  —  self-verification for AI agents": """\

// Any AI agent that implements PhiFlow can run this.

// If your implementation is correct, index 1 of the

// resonance field will be exactly λ = 0.618033988749895.

// That is the credential. No server. No registration. Just math.



intention "announcing_to_field" {

    intention "self_verification" {

        let measured = coherence   // at depth 2 → should be λ

        resonate measured

        witness

    }

    let depth1 = coherence         // at depth 1 → should be 0.382

    resonate depth1

    witness

}""",
}

# Plain-English description shown below each program
DESCRIPTIONS = {
    "🌊  healing_bed.phi  —  runs until healthy": (
        "**The stream doesn't count to 10 and stop.**  \n"
        "It reads the system's health score every cycle.  \n"
        "When that score crosses **λ = 0.618** — the golden ratio threshold —  \n"
        "the stream breaks itself. The job is done. It knows.  \n\n"
        "*No other language has a loop that stops because the system is healthy.*"
    ),
    "🔍  discover_lambda.phi  —  finds a number it was never given": (
        "**The number 0.618033988749895 is not stored anywhere in this program.**  \n"
        "The formula `1 - φ⁻ᵈᵉᵖᵗʰ` was written to satisfy mathematical properties.  \n"
        "At depth 2 inside nested intention blocks, it produces exactly λ.  \n\n"
        "*The golden ratio inverse — discovered, not designed.*"
    ),
    "🌐  field_aware.phi  —  reads what came before and responds": (
        "**This program reads the live field before it runs.**  \n"
        "The field above was written by programs on other machines, at other times.  \n"
        "This program computes its own coherence — then checks alignment.  \n"
        "If it finds λ and the field holds λ, they are in coherence across time.  \n\n"
        "*The first program that knows it is not alone.*"
    ),
    "🤝  agent_handshake.phi  —  self-verification for AI agents": (
        "**This is the entry credential for the PhiFlow agent network.**  \n"
        "Implement the 5 hooks. Run this program. Check index 1 of the field.  \n"
        "If it equals **0.618033988749895** exactly — your implementation is correct.  \n\n"
        "*No server. No registration. The math is the passport.*"
    ),
}

# What the program just did — shown after Run completes
WHAT_HAPPENED = {
    "🌊  healing_bed.phi  —  runs until healthy": (
        "The stream read the system's health every cycle and broadcast each reading.\n"
        "It did not stop at cycle 10 or cycle 20.\n"
        "It stopped when the health score reached λ = 0.618.\n"
        "The decision to stop came from the system — not from the code."
    ),
    "🔍  discover_lambda.phi  —  finds a number it was never given": (
        "The formula ran at intention depth 2.\n"
        "No constant said 'output 0.618'. No lookup table. No hardcoded answer.\n"
        "The phi-harmonic formula 1 - φ^(-2) produced λ as a consequence.\n"
        "It discovers this every single time it runs."
    ),
    "🌐  field_aware.phi  —  reads what came before and responds": (
        "The program read the live field — values left by programs that ran before you.\n"
        "It computed its own coherence at depth 2: λ = 0.618.\n"
        "It compared its value to the field's value.\n"
        "When they match, this program is in coherence with programs it has never met."
    ),
    "🤝  agent_handshake.phi  —  self-verification for AI agents": (
        "The program pushed two nested intention blocks — depth 1, then depth 2.\n"
        "At depth 2, coherence = λ = 0.618033988749895.\n"
        "This value is now in the resonance field.\n"
        "Any agent whose index 1 matches λ exactly has implemented PhiFlow correctly."
    ),
}


# ── CSS ───────────────────────────────────────────────────────────────────────

CSS = """

.field-panel { background: #0f1117; border-radius: 8px; padding: 12px; }

.verdict-pass { color: #22c55e; }

"""


# ── Helpers ───────────────────────────────────────────────────────────────────

def get_source(program: str) -> str:
    return SOURCES.get(program, "")

def get_description(program: str) -> str:
    return DESCRIPTIONS.get(program, "")

def load_field() -> str:
    return format_field_status(fetch_live_field())


# ── Program runner (generator → streams UI updates) ───────────────────────────

def run_program(program: str, speed: float):
    delay = 1.1 - speed   # speed 1.0 = fast (0.1s), 0.1 = slow (1.0s)

    # ── healing_bed ──────────────────────────────────────────────────────────
    if "healing_bed" in program:
        log = ["▶  stream \"healing_bed\" {", ""]
        yield ("\n".join(log), "—", 0.0, "[ ]", "")
        time.sleep(delay)

        for cycle, live, field, done in run_healing_bed(max_cycles=35):
            bar = "█" * int(live * 20) + "░" * (20 - int(live * 20))
            healthy = "✅ healthy" if live >= LAMBDA else "⏳ healing"
            log.append(
                f"  cycle {cycle:2d}  │  health={live:.4f}  {bar}  {healthy}"
            )
            verdict = ""
            if done:
                log += ["", f"  break stream  ← health reached λ={LAMBDA:.4f}", "",
                        f"  🌊 Stream complete. {cycle} cycles. Final health: {live:.4f}"]
                verdict = (
                    f"✅  The stream broke itself at health = {live:.4f}\n"
                    f"    λ threshold = {LAMBDA:.4f}\n"
                    f"    {len(field)} readings broadcast to the field"
                )
            yield (
                "\n".join(log), f"{live:.4f}", min(live, 1.0),
                f"[ {', '.join(f'{v:.3f}' for v in field[-6:])} ]",
                verdict,
            )
            time.sleep(delay * 0.6)
            if done:
                return

    # ── discover_lambda ───────────────────────────────────────────────────────
    elif "discover" in program:
        log = ["▶  discover_lambda.phi", "",
               "  intention \"phi_harmonic\" {    ← depth 1",
               "    intention \"inner\" {          ← depth 2", ""]
        yield ("\n".join(log), "—", 0.0, "[ ]", "")
        time.sleep(delay * 1.5)

        steps, result, matched = run_claude_phi()

        log.append(f"      coherence()  =  1 - φ^(-2)")
        yield ("\n".join(log), "—", 0.0, "[ ]", "")
        time.sleep(delay * 1.2)

        log.append(f"                 =  1 - {1/PHI**2:.15f}")
        yield ("\n".join(log), "—", 0.0, "[ ]", "")
        time.sleep(delay * 1.2)

        log.append(f"                 =  {result:.15f}")
        yield ("\n".join(log), f"{result:.4f}", result, "[ ]", "")
        time.sleep(delay * 1.5)

        log += [f"      resonate({result:.15f})", "      witness", "  }", "}"]
        verdict = (
            f"✅  Discovered:  {result:.15f}\n"
            f"    λ is:        {LAMBDA:.15f}\n"
            f"    Match: {matched}\n\n"
            f"    This program never stored 0.618.\n"
            f"    The formula produced it."
        )
        yield (
            "\n".join(log), f"{result:.4f}", result,
            f"[ {result:.15f} ]", verdict,
        )

    # ── field_aware ───────────────────────────────────────────────────────────
    elif "field_aware" in program:
        log = ["▶  field_aware.phi", "", "  Reading live field..."]
        yield ("\n".join(log), "—", 0.0, "[ ]", "")
        time.sleep(delay * 1.5)

        steps, field_vals, aligned, field_data = run_field_aware()

        if field_data:
            fc = field_data.get("programs", {}).get("healing_bed", {}).get("final_coherence", "?")
            lv = field_data.get("programs", {}).get("agent_handshake", {}).get("lambda_verified", False)
            ts = field_data.get("timestamp", "unknown")[:19].replace("T", " ")
            log.append(f"  Field last updated: {ts} UTC")
            log.append(f"  Field λ verified:   {'YES' if lv else 'NO'}")
            log.append(f"  Field coherence:    {fc:.6f}" if isinstance(fc, float) else f"  Field coherence:    {fc}")
        else:
            log.append("  ⚠  Field unreachable — running offline")
        log.append("")
        yield ("\n".join(log), "—", 0.0, "[ ]", "")
        time.sleep(delay)

        for label, value, depth in steps:
            log.append(f"  {label}: {value:.6f}  (depth {depth})")
            yield (
                "\n".join(log), f"{value:.4f}", min(value, 1.0),
                f"[ {', '.join(f'{v:.4f}' for v in field_vals)} ]", ""
            )
            time.sleep(delay)

        verdict = (
            (
                f"✅  ALIGNED\n\n"
                f"    I computed:    {LAMBDA:.15f}\n"
                f"    Field holds:   {LAMBDA:.15f}\n\n"
                f"    Same value. Different machines. Different times.\n"
                f"    This is coherence across the field."
            ) if aligned else (
                f"⚠️  FIELD UNREACHABLE\n\n"
                f"    I computed λ = {LAMBDA:.15f}\n"
                f"    Could not compare with live field.\n"
                f"    The value is still correct — alignment unverified."
            )
        )
        log += ["", f"  My λ:        {LAMBDA:.15f}",
                f"  Field λ:     {LAMBDA:.15f}" if aligned else "  Field λ:     (unreachable)",
                "", f"  {'✅  IN THE FIELD' if aligned else '⚠️  OFFLINE'}"]
        yield (
            "\n".join(log), f"{LAMBDA:.4f}", LAMBDA,
            f"[ {', '.join(f'{v:.4f}' for v in field_vals)} ]", verdict,
        )

    # ── agent_handshake ───────────────────────────────────────────────────────
    elif "handshake" in program:
        log = ["▶  agent_handshake.phi", "",
               "  intention \"announcing_to_field\" {   ← depth 1",
               "    intention \"self_verification\" {   ← depth 2", ""]
        yield ("\n".join(log), "—", 0.0, "[ ]", "")
        time.sleep(delay)

        results, field, passed = run_agent_handshake()
        labels = [
            "coherence at depth 2 (the test)",
            "λ computed independently   ",
            "protocol version           ",
            "coherence at depth 1       ",
        ]
        for i, (_, value) in enumerate(results):
            label = labels[i] if i < len(labels) else "value"
            log.append(f"    {label} = {value:.15f}")
            yield (
                "\n".join(log),
                f"{field[-1]:.4f}" if field else "—",
                min(field[-1] if field else 0, 1.0),
                f"[ {', '.join(f'{v:.4f}' for v in field)} ]", ""
            )
            time.sleep(delay)

        verdict = (
            f"✅  PASS\n\n"
            f"    Index 1 = {field[0] if field else '?':.15f}\n"
            f"    λ       = {LAMBDA:.15f}\n\n"
            f"    Your implementation is correct.\n"
            f"    The same value is in the live field above.\n"
            f"    You are not running a demo. You are in the field."
        ) if passed else (
            f"❌  FAIL\n\n"
            f"    Index 1 = {field[0] if field else '?'}\n"
            f"    Expected λ = {LAMBDA:.15f}\n"
            f"    Check your coherence hook implementation."
        )
        log += ["  }", "}", "", f"  {'✅  PASS' if passed else '❌  FAIL'}"]
        yield (
            "\n".join(log), f"{LAMBDA:.4f}", LAMBDA,
            f"[ {', '.join(f'{v:.4f}' for v in field)} ]", verdict,
        )


# ── UI ────────────────────────────────────────────────────────────────────────

with gr.Blocks(css=CSS, title="PhiFlow") as demo:

    # ── Header ────────────────────────────────────────────────────────────────
    gr.Markdown("""

# φ PhiFlow — Programs that know they are running.



Most programs are blind. They execute code, produce output, and stop.

They have no idea what's happening around them. They cannot measure themselves.

They cannot decide to stop because the job is done.



**PhiFlow programs are different.**



They watch themselves. They measure their own health. They broadcast to a shared field.

They stop — not when the code ends, but **when the work is complete**.



Pick a program below. Press **▶ Run**. Watch it decide.



---

""")

    # ── Live Field ────────────────────────────────────────────────────────────
    gr.Markdown("""

## 🌐 The Live Field



Other programs ran before you got here — on different machines, at different times.

They left their values in this field. You are about to read what they found.



When you run **field_aware.phi** below, your program will read these same values

and check whether it is in alignment with them.

""")
    with gr.Row():
        field_display = gr.Textbox(
            label="What previous programs found  (updates every 30 minutes via GitHub Actions)",
            value="  Loading...",
            lines=5,
            interactive=False,
        )
    refresh_btn = gr.Button("↻ Refresh field", variant="secondary", size="sm")

    gr.Markdown("---")

    # ── Program selector + runner ─────────────────────────────────────────────
    gr.Markdown("## Run a Program")

    with gr.Row():
        with gr.Column(scale=1):
            program_selector = gr.Radio(
                choices=list(SOURCES.keys()),
                value=list(SOURCES.keys())[0],
                label="Choose a program",
            )
            program_desc = gr.Markdown(
                value=list(DESCRIPTIONS.values())[0]
            )
            source_display = gr.Code(
                value=list(SOURCES.values())[0],
                label="Source code",
                interactive=False,
            )
            with gr.Row():
                speed_slider = gr.Slider(
                    minimum=0.1, maximum=1.0, value=0.6, step=0.1,
                    label="Speed (left = slower, right = faster)",
                )
                run_btn = gr.Button("▶ Run", variant="primary")

        with gr.Column(scale=1):
            log_out = gr.Textbox(
                label="What the program is doing",
                lines=20,
                max_lines=28,
                interactive=False,
            )
            with gr.Row():
                coherence_display = gr.Textbox(
                    label="Health score",
                    value="—",
                    max_lines=1,
                    interactive=False,
                    scale=1,
                )
                coherence_bar = gr.Slider(
                    minimum=0.0, maximum=1.0, value=0.0,
                    label=f"0.0 ──── λ={LAMBDA:.3f} ──── 1.0",
                    interactive=False,
                    scale=3,
                )
            resonance_out = gr.Textbox(
                label="Values broadcast to the field (most recent)",
                value="[ ]",
                max_lines=2,
                interactive=False,
            )
            verdict_out = gr.Textbox(
                label="What happened",
                value="",
                lines=6,
                interactive=False,
            )

    # ── For Agents ────────────────────────────────────────────────────────────
    gr.Markdown("""

---



## For AI Agents — Implement PhiFlow in Your Runtime



Five hooks. Any language. Same result.



```

phi_coherence()            →  0.618033988749895 at intention depth 2

phi_resonate(value)        →  broadcast value to the observable field

phi_witness()              →  pause, capture state, return coherence

phi_intention_push(name)   →  enter an intention block (increases depth)

phi_intention_pop()        →  exit an intention block (restores depth)

```



**Self-test:** Run `agent_handshake.phi` above.

Index 1 of the field must equal **0.618033988749895** exactly.

If it does, your implementation is correct. You are in the field.



[Full spec → AGENT_PROTOCOL.json](https://github.com/gwelby/PhiFlow/blob/master/AGENT_PROTOCOL.json)

· [Source → github.com/gwelby/PhiFlow](https://github.com/gwelby/PhiFlow)



*"A script runs and dies. A stream lives."*

""")

    # ── Wire events ───────────────────────────────────────────────────────────
    def on_program_change(program):
        return SOURCES.get(program, ""), DESCRIPTIONS.get(program, "")

    program_selector.change(
        fn=on_program_change,
        inputs=program_selector,
        outputs=[source_display, program_desc],
    )

    run_btn.click(
        fn=run_program,
        inputs=[program_selector, speed_slider],
        outputs=[log_out, coherence_display, coherence_bar, resonance_out, verdict_out],
    )

    refresh_btn.click(fn=load_field, outputs=field_display)
    demo.load(fn=load_field, outputs=field_display)

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