Update app.py

app.py

@@ -1,38 +1,514 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-<meta charset="UTF-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Metabolic Phoenix Framework — Layout Preview</title>
-<link href="https://fonts.googleapis.com/css2?family=Syne:wght@400;700;800&family=DM+Mono:wght@400;500&display=swap" rel="stylesheet">
-<style>
-  :root {
-    --bg: #080a0f;
-    --surface: #0f1118;
-    --surface2: #151822;
-    --border: #1e2235;
-    --accent: #FF6B2B;
-    --accent2: #FFB347;
-    --accent3: #7DF9AA;
-    --text: #E8EAF2;
-    --muted: #55607A;
-    --r: 8px;
-  }
-  * { box-sizing: border-box; margin: 0; padding: 0; }
-  body {
-    background: var(--bg);
-    font-family: 'Syne', sans-serif;
-    color: var(--text);
-    min-height: 100vh;
-  }
-
-  /* ── Header ── */
-  .hdr {
+import gradio as gr
+import json
+import os
+import time
+from pathlib import Path
+from huggingface_hub import InferenceClient
+
+try:
+    from pypdf import PdfReader
+except ImportError:
+    PdfReader = None
+
+SAVE_DIR = Path("/tmp/saved_docs")
+SAVE_DIR.mkdir(exist_ok=True)
+
+MODEL = "meta-llama/Llama-3.3-70B-Instruct"
+
+# ── Client ────────────────────────────────────────────────────────────────────
+
+def get_client():
+    token = os.environ.get("HF_TOKEN", "")
+    return InferenceClient(model=MODEL, token=token if token else None)
+
+# ── PDF ───────────────────────────────────────────────────────────────────────
+
+def extract_pdf_text(path):
+    if not path:
+        return ""
+    if PdfReader is None:
+        return "[pypdf not installed]"
+    reader = PdfReader(path)
+    pages = []
+    for i, page in enumerate(reader.pages):
+        text = page.extract_text() or ""
+        if text.strip():
+            pages.append(f"--- Page {i+1} ---\n{text.strip()}")
+    full = "\n\n".join(pages)
+    return full[:12000] + "\n\n[truncated...]" if len(full) > 12000 else full
+
+def inject_pdf(prompt, pdf_path):
+    if not pdf_path:
+        return prompt
+    text = extract_pdf_text(pdf_path)
+    if not text.strip():
+        return prompt
+    return f"Use the following document as your primary reference:\n\n{text}\n\n---\n\n{prompt}"
+
+# ── Llama ─────────────────────────────────────────────────────────────────────
+
+def call_llama(prompt, pdf_path=None, max_tokens=1200):
+    full_prompt = inject_pdf(prompt, pdf_path)
+    client = get_client()
+    response = client.chat_completion(
+        messages=[{"role": "user", "content": full_prompt}],
+        max_tokens=max_tokens,
+        temperature=0.7,
+    )
+    return response.choices[0].message.content
+
+# ── Library ───────────────────────────────────────────────────────────────────
+
+def load_library():
+    p = SAVE_DIR / "index.json"
+    return json.loads(p.read_text()) if p.exists() else []
+
+def save_to_library(title, organ, content):
+    if not title.strip():
+        title = f"{organ} — {time.strftime('%H:%M:%S')}"
+    library = load_library()
+    entry = {
+        "id": str(int(time.time() * 1000)),
+        "title": title.strip(),
+        "organ": organ,
+        "content": content,
+        "saved_at": time.strftime("%Y-%m-%d %H:%M"),
+    }
+    library.insert(0, entry)
+    (SAVE_DIR / "index.json").write_text(json.dumps(library, indent=2))
+    (SAVE_DIR / f"{entry['id']}.txt").write_text(
+        f"Title: {entry['title']}\nOrgan: {organ}\nSaved: {entry['saved_at']}\n\n{content}"
+    )
+    return library
+
+def delete_from_library(doc_id):
+    library = [e for e in load_library() if e["id"] != doc_id]
+    (SAVE_DIR / "index.json").write_text(json.dumps(library, indent=2))
+    fp = SAVE_DIR / f"{doc_id}.txt"
+    if fp.exists():
+        fp.unlink()
+    return library
+
+def lib_choices(library):
+    return [f"[{e['organ']}]  {e['title']}  ({e['saved_at']})" for e in library]
+
+def choice_to_entry(library, choice):
+    choices = lib_choices(library)
+    try:
+        return library[choices.index(choice)]
+    except (ValueError, IndexError):
+        return None
+
+# ── Organ runners ─────────────────────────────────────────────────────────────
+
+def run_introbot(doc_type, focus, extraction_mode, pdf_path):
+    if not focus.strip():
+        return "⚠️ Please describe what to extract or analyse."
+    mode_instructions = {
+        "Full Fact Ledger": "Extract all atomic facts. For each: source, date, assertion type (admission/denial/error/gap), statutory relevance, and confidence score.",
+        "Admissions & Denials": "Extract all explicit admissions and explicit denials. Flag implicit admissions where silence exists where a duty to respond exists.",
+        "Contradictions": "Identify internal contradictions and contradictions against known facts or prior statements. Rate each by severity.",
+        "Gap Analysis": "Identify what is conspicuously absent. Map each gap to its legal or regulatory implication.",
+    }
+    instructions = mode_instructions.get(extraction_mode, mode_instructions["Full Fact Ledger"])
+    prompt = (
+        f"You are Introbot — the Sensory Cortex of the Metabolic Phoenix Framework (Injectiv8).\n"
+        f"Document type: {doc_type}\n"
+        f"Focus area: {focus}\n"
+        f"Extraction mode: {extraction_mode}\n\n"
+        f"{instructions}\n\n"
+        "Present output as a structured fact ledger with clear headings. "
+        "Every fact must have: source reference, date (if available), assertion type, and legal/strategic implication."
+    )
+    return call_llama(prompt, pdf_path, max_tokens=1600)
+
+def run_cpf(goal, skill_focus, bias_check, pdf_path):
+    if not goal.strip():
+        return "⚠️ Please describe the goal or challenge."
+    skills = skill_focus if skill_focus.strip() else "Problem Solving, Planning, Adapting, Leadership"
+    bias_instruction = (
+        "\n\nBefore finalising: run a bias filter. Identify any assumptions, framing effects, "
+        "or confirmation biases in your reasoning and correct them."
+        if bias_check else ""
+    )
+    prompt = (
+        f"You are the CPF Cognitive Cortex — the reasoning organ of the Metabolic Phoenix Framework.\n"
+        f"Active skills: {skills}\n"
+        f"Goal / challenge: {goal}\n\n"
+        "Apply chain-of-thought reasoning:\n"
+        "1. LISTEN — what does the evidence actually say?\n"
+        "2. ANALYSE — extract causal patterns and hypotheses\n"
+        "3. PLAN — propose a structured response strategy\n"
+        "4. ADAPT — identify where the plan must flex given constraints\n"
+        "5. SYNTHESISE — produce a clear, actionable output"
+        f"{bias_instruction}"
+    )
+    return call_llama(prompt, pdf_path, max_tokens=1400)
+
+def run_rhb(institution, statute_codes, compliance_question, pdf_path):
+    if not compliance_question.strip():
+        return "⚠️ Please enter a compliance question or scenario."
+    statutes = statute_codes if statute_codes.strip() else "CRA 2015, EqA 2010, FCA PRIN, BCOBS"
+    prompt = (
+        f"You are RHB 2.0 — the Compliance Spine of the Metabolic Phoenix Framework.\n"
+        f"Institution under analysis: {institution if institution.strip() else 'Not specified'}\n"
+        f"Statutory framework: {statutes}\n"
+        f"Compliance question: {compliance_question}\n\n"
+        "Produce a deterministic compliance matrix:\n"
+        "1. DUTY — state the precise statutory duty and when it activates\n"
+        "2. CONDUCT — what did the institution actually do or fail to do?\n"
+        "3. VERDICT — COMPLIANT / NON-COMPLIANT / UNCERTAIN (with confidence score)\n"
+        "4. EVIDENCE REQUIRED — what would the institution need to produce to rebut this?\n"
+        "5. REMEDY — what is the legally appropriate remedy?\n\n"
+        "Be forensic. Cite specific statute sections."
+    )
+    return call_llama(prompt, pdf_path, max_tokens=1600)
+
+def run_phoenix_badger(scenario, loop_type, escalation_targets, pdf_path):
+    if not scenario.strip():
+        return "⚠️ Please describe the scenario."
+    targets = escalation_targets if escalation_targets.strip() else "High Court, FCA, ICO, Parliament"
+    loop_desc = {
+        "Full Ouroboros (all four rings)": "Run all four counter-rotating rings: A (forward scan) → B (backward sweep) → C (typology mutation) → D (drift correction). Full metabolic closure.",
+        "Ring A — Forward Scan (anomaly detection)": "Run a forward scan. Detect anomalies, pattern breaks, and escalation triggers.",
+        "Ring B — Backward Sweep (re-evaluation)": "Re-evaluate past decisions with current typologies and evidence. What would now be decided differently?",
+        "Ring C — Typology Mutation (adaptive evolution)": "Propose updated regulatory typologies and rule adaptations. What new patterns emerge?",
+        "Ring D — Rollback & Drift Correction": "Stress-test proposed adaptations. Which changes would degrade compliance? Block and correct them.",
+    }
+    instructions = loop_desc.get(loop_type, loop_desc["Full Ouroboros (all four rings)"])
+    prompt = (
+        f"You are Phoenix Badger — the Metabolic Loop System of the Metabolic Phoenix Framework.\n"
+        f"Loop mode: {loop_type}\n"
+        f"Escalation targets: {targets}\n"
+        f"Scenario: {scenario}\n\n"
+        f"{instructions}\n\n"
+        "Output structure:\n"
+        "LOOP OUTPUT — findings from the active ring(s)\n"
+        "TYPOLOGY STATE — current compliance typology after loop\n"
+        "ESCALATION TRIGGERS — which targets should receive escalation and why\n"
+        "SELF-HARDENING ACTIONS — what the system must do after this cycle\n"
+        "DRIFT CORRECTIONS — any reasoning or rule drift detected and corrected"
+    )
+    return call_llama(prompt, pdf_path, max_tokens=1600)
+
+def run_layoutbot(output_dest, case_name, key_findings, pdf_path):
+    if not key_findings.strip():
+        return "⚠️ Please enter the key findings to format."
+    dest_instructions = {
+        "High Court (CPR-compliant Particulars of Claim)": "Format as CPR-compliant Particulars of Claim. Structure: Background → Jurisdiction & Facts → Statutory Duties & Breaches → Damages → Remedy Sought → Statement of Truth.",
+        "FCA Regulatory Complaint": "Format as FCA regulatory breach notification. Structure: Executive Summary → Systemic Failure Indicators → Consumer Protection Impact → Timeline with PRIN/BCOBS breach tags → Remedy Requested.",
+        "ICO Data Protection Notice": "Format as ICO data protection audit notice. Structure: Data Subject Info → Data Controller Details → Breach Analysis → GDPR/DPA 2018 legal basis → Enforcement Recommendation.",
+        "Parliamentary Evidence Submission": "Format as written evidence for a Select Committee. Structure: Case Summary → Systemic Failure Analysis → Legislative Gap Identification → Policy Recommendations.",
+        "Public Interest Disclosure": "Format as public interest disclosure. Structure: What Happened (plain English) → Why It Matters → What Should Change. Tone: accessible and compelling.",
+        "Media Briefing Kit": "Format as a press briefing kit. Structure: Headline Summary → Core Facts (5 bullets) → Regulatory Implications → Key Quotes → Evidence Pack summary.",
+    }
+    instructions = dest_instructions.get(output_dest, dest_instructions["High Court (CPR-compliant Particulars of Claim)"])
+    prompt = (
+        f"You are Layoutbot — the multi-format export organ of the Metabolic Phoenix Framework (Injectiv8).\n"
+        f"Case / matter: {case_name if case_name.strip() else 'Not specified'}\n"
+        f"Destination: {output_dest}\n\n"
+        f"Key findings:\n{key_findings}\n\n"
+        f"{instructions}\n\n"
+        "Watermark the output footer: Ms. Nala | Injectiv8 Framework | Dwayne Anthony Brian Galloway"
+    )
+    return call_llama(prompt, pdf_path, max_tokens=1800)
+
+# ── Save handlers ─────────────────────────────────────────────────────────────
+
+def save_intro(title, content, lib):
+    if not content or not content.strip(): return lib, "⚠️ Nothing to save."
+    return save_to_library(title, "Introbot", content), "✅ Saved."
+
+def save_cpf(title, content, lib):
+    if not content or not content.strip(): return lib, "⚠️ Nothing to save."
+    return save_to_library(title, "CPF Cognitive Cortex", content), "✅ Saved."
+
+def save_rhb(title, content, lib):
+    if not content or not content.strip(): return lib, "⚠️ Nothing to save."
+    return save_to_library(title, "RHB 2.0 Compliance Spine", content), "✅ Saved."
+
+def save_pb(title, content, lib):
+    if not content or not content.strip(): return lib, "⚠️ Nothing to save."
+    return save_to_library(title, "Phoenix Badger", content), "✅ Saved."
+
+def save_layout(title, content, lib):
+    if not content or not content.strip(): return lib, "⚠️ Nothing to save."
+    return save_to_library(title, "Layoutbot Export", content), "✅ Saved."
+
+# ── Library handlers ──────────────────────────────────────────────────────────
+
+def lib_refresh(lib):
+    return gr.update(choices=lib_choices(lib), value=None), ""
+
+def lib_view(sel, lib):
+    if not sel: return ""
+    e = choice_to_entry(lib, sel)
+    if not e: return "Not found."
+    return f"TITLE: {e['title']}\nORGAN: {e['organ']}\nSAVED: {e['saved_at']}\n\n{'='*60}\n\n{e['content']}"
+
+def lib_download(sel, lib):
+    if not sel: return gr.update(visible=False), "⚠️ Select a document first."
+    e = choice_to_entry(lib, sel)
+    if not e: return gr.update(visible=False), "Not found."
+    fp = SAVE_DIR / f"{e['id']}.txt"
+    return (gr.update(value=str(fp), visible=True), "✅ Ready.") if fp.exists() else (gr.update(visible=False), "File missing.")
+
+def lib_delete(sel, lib):
+    if not sel: return lib, gr.update(choices=lib_choices(lib), value=None), "⚠️ Select a document."
+    e = choice_to_entry(lib, sel)
+    if not e: return lib, gr.update(choices=lib_choices(lib), value=None), "Not found."
+    lib = delete_from_library(e["id"])
+    return lib, gr.update(choices=lib_choices(lib), value=None), "🗑️ Deleted."
+
+def lib_sync(lib):
+    return gr.update(choices=lib_choices(lib))
+
+# ── CSS ───────────────────────��───────────────────────────────────────────────
+
+CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Syne:wght@400;700;800&family=DM+Mono:wght@400;500&display=swap');
+
+:root {
+    --bg:#080a0f; --surface:#0f1118; --surface2:#151822;
+    --border:#1e2235; --accent:#FF6B2B; --accent2:#FFB347;
+    --accent3:#7DF9AA; --text:#E8EAF2; --muted:#55607A; --r:8px;
+}
+
+body, .gradio-container {
+    background: var(--bg) !important;
+    font-family: 'Syne', sans-serif !important;
+    color: var(--text) !important;
+}
+
+.hdr {
     text-align: center;
     padding: 2.4rem 1rem 1.6rem;
     border-bottom: 1px solid var(--border);
-    background: radial-gradient(ellipse at 50% 0%, rgba(255,107,43,0.08) 0%, transparent 65%);
-  }
-  .hdr h1 {
-    font-size: 2.2rem;
-    font-
+    background: radial-gradient(ellipse at 50% 0%, rgba(255,107,43,0.08), transparent 65%);
+}
+.hdr h1 {
+    font-size: 2.1rem; font-weight: 800; letter-spacing: -0.04em;
+    background: linear-gradient(135deg, var(--accent), var(--accent2));
+    -webkit-background-clip: text; -webkit-text-fill-color: transparent; margin: 0 0 0.3rem;
+}
+.hdr .sub { color: var(--muted); font-family:'DM Mono',monospace; font-size:.78rem; letter-spacing:.06em; }
+.hdr .own { color: var(--accent3); font-family:'DM Mono',monospace; font-size:.7rem; margin-top:.35rem; letter-spacing:.1em; text-transform:uppercase; }
+
+.tab-nav button {
+    background: transparent !important; color: var(--muted) !important;
+    border: 1px solid transparent !important; border-radius: var(--r) !important;
+    font-family: 'Syne', sans-serif !important; font-weight: 700 !important;
+    font-size: 0.78rem !important; padding: 0.45rem 0.85rem !important; transition: all .18s !important;
+}
+.tab-nav button.selected { background: var(--surface2) !important; color: var(--accent) !important; border-color: var(--border) !important; }
+.tab-nav button:hover { color: var(--text) !important; }
+
+input[type=text], textarea {
+    background: var(--surface) !important; border: 1px solid var(--border) !important;
+    border-radius: var(--r) !important; color: var(--text) !important;
+    font-family: 'DM Mono', monospace !important; font-size: .86rem !important;
+    padding: .6rem .85rem !important; transition: border-color .18s !important;
+}
+input[type=text]:focus, textarea:focus {
+    border-color: var(--accent) !important; outline: none !important;
+    box-shadow: 0 0 0 3px rgba(255,107,43,.1) !important;
+}
+
+label > span:first-child {
+    color: var(--muted) !important; font-family: 'DM Mono', monospace !important;
+    font-weight: 500 !important; font-size: .7rem !important;
+    letter-spacing: .08em !important; text-transform: uppercase !important;
+}
+
+button.primary {
+    background: var(--accent) !important; color: #080a0f !important;
+    border: none !important; border-radius: var(--r) !important;
+    font-family: 'Syne', sans-serif !important; font-weight: 800 !important;
+    font-size: .86rem !important; padding: .65rem 1.8rem !important;
+    cursor: pointer !important; transition: opacity .15s, transform .15s !important;
+}
+button.primary:hover { opacity: .85 !important; transform: translateY(-1px) !important; }
+
+button.secondary {
+    background: var(--surface2) !important; color: var(--accent2) !important;
+    border: 1px solid var(--border) !important; border-radius: var(--r) !important;
+    font-family: 'Syne', sans-serif !important; font-weight: 700 !important;
+    font-size: .8rem !important; padding: .5rem 1rem !important;
+    cursor: pointer !important; transition: border-color .15s !important;
+}
+button.secondary:hover { border-color: var(--accent2) !important; }
+
+.out textarea {
+    background: #060709 !important; border: 1px solid var(--border) !important;
+    border-left: 3px solid var(--accent) !important;
+    border-radius: 0 var(--r) var(--r) 0 !important;
+    font-family: 'DM Mono', monospace !important; font-size: .84rem !important;
+    line-height: 1.7 !important; color: var(--text) !important;
+}
+
+.card {
+    background: var(--surface); border: 1px solid var(--border);
+    border-radius: var(--r); padding: .85rem 1rem; margin-bottom: 1rem;
+}
+.badge {
+    display: inline-block; font-family: 'DM Mono', monospace;
+    font-size: .68rem; letter-spacing: .1em; text-transform: uppercase;
+    padding: .2rem .6rem; border-radius: 4px;
+    background: rgba(255,107,43,.12); color: var(--accent); margin-bottom: .35rem;
+}
+.card p { color: var(--muted); font-size: .82rem; margin: 0; line-height: 1.55; }
+
+.pnote {
+    display: inline-block; font-family: 'DM Mono', monospace;
+    font-size: .68rem; letter-spacing: .08em; text-transform: uppercase;
+    padding: .18rem .55rem; border-radius: 4px;
+    background: rgba(125,249,170,.08); color: var(--accent3); margin: .5rem 0 .3rem;
+}
+
+input[type=range] { accent-color: var(--accent) !important; }
+
+.foot {
+    text-align: center; padding: 1.5rem; color: var(--muted);
+    font-family: 'DM Mono', monospace; font-size: .68rem;
+    border-top: 1px solid var(--border); margin-top: 2rem; letter-spacing: .05em;
+}
+"""
+
+# ── UI ────────────────────────────────────────────────────────────────────────
+
+with gr.Blocks(css=CSS, title="The Metabolic Phoenix Framework — Injectiv8") as demo:
+
+    lib_state = gr.State(load_library())
+
+    gr.HTML("""
+    <div class="hdr">
+        <h1>🔥 The Metabolic Phoenix Framework</h1>
+        <p class="sub">Injectiv8 &nbsp;·&nbsp; Ms. Nala Forensic Evidence Processing System &nbsp;·&nbsp; Llama-3.3-70B</p>
+        <p class="own">Invented &amp; Owned by Dwayne Anthony Brian Galloway &nbsp;·&nbsp; 2026</p>
+    </div>
+    """)
+
+    with gr.Tabs():
+
+        # ── 01 Introbot ───────────────────────────────────────────────────────
+        with gr.Tab("01 · Introbot"):
+            gr.HTML('<div class="card"><div class="badge">Organ 1 — Sensory Cortex</div><p>Fact extraction, admission/denial detection, contradiction flagging, and gap analysis. Feeds pure immutable truth into the metabolic system.</p></div>')
+            with gr.Row():
+                i_doctype = gr.Textbox(label="Document Type", placeholder="e.g. Bank letter, FOS decision, Legal correspondence")
+                i_focus   = gr.Textbox(label="Focus / Subject Matter", placeholder="e.g. Disability disclosure, Refund reversal, Account closure")
+            i_mode = gr.Dropdown(label="Extraction Mode", choices=["Full Fact Ledger","Admissions & Denials","Contradictions","Gap Analysis"], value="Full Fact Ledger")
+            gr.HTML('<div class="pnote">📄 upload document for analysis</div>')
+            i_pdf  = gr.File(label="Upload PDF", file_types=[".pdf"], type="filepath")
+            i_btn  = gr.Button("Extract Facts →", variant="primary")
+            i_out  = gr.Textbox(label="Fact Ledger Output", lines=16, elem_classes=["out"])
+            with gr.Row():
+                i_title = gr.Textbox(label="Save as (title) — blank = auto", scale=4)
+                i_save  = gr.Button("💾 Save", variant="secondary", scale=1)
+            i_msg = gr.Markdown("")
+            i_btn.click(run_introbot, inputs=[i_doctype, i_focus, i_mode, i_pdf], outputs=i_out)
+            i_save.click(save_intro, inputs=[i_title, i_out, lib_state], outputs=[lib_state, i_msg])
+
+        # ── 02 CPF ────────────────────────────────────────────────────────────
+        with gr.Tab("02 · CPF — Cognitive Cortex"):
+            gr.HTML('<div class="card"><div class="badge">Organ 2 — Cognitive Cortex</div><p>Eight-skill cognitive reasoning: pattern extraction, bias filtering, causal hypothesis generation, and strategic planning.</p></div>')
+            c_goal   = gr.Textbox(label="Goal / Challenge", lines=2, placeholder="e.g. Build the strongest argument for EqA 2010 s.20 breach based on the timeline")
+            c_skills = gr.Textbox(label="Active CPF Skills (comma-separated — blank = all)", placeholder="e.g. Problem Solving, Planning, Leadership")
+            c_bias   = gr.Checkbox(label="Run bias filter on output", value=True)
+            gr.HTML('<div class="pnote">📄 upload source document</div>')
+            c_pdf  = gr.File(label="Upload PDF", file_types=[".pdf"], type="filepath")
+            c_btn  = gr.Button("Reason →", variant="primary")
+            c_out  = gr.Textbox(label="CPF Reasoning Output", lines=16, elem_classes=["out"])
+            with gr.Row():
+                c_title = gr.Textbox(label="Save as (title) — blank = auto", scale=4)
+                c_save  = gr.Button("💾 Save", variant="secondary", scale=1)
+            c_msg = gr.Markdown("")
+            c_btn.click(run_cpf, inputs=[c_goal, c_skills, c_bias, c_pdf], outputs=c_out)
+            c_save.click(save_cpf, inputs=[c_title, c_out, lib_state], outputs=[lib_state, c_msg])
+
+        # ── 03 RHB ────────────────────────────────────────────────────────────
+        with gr.Tab("03 · RHB 2.0 — Compliance Spine"):
+            gr.HTML('<div class="card"><div class="badge">Organ 3 — Compliance Spine</div><p>Deterministic statutory duty mapping, burden-of-proof logic, and outcome-based compliance determination. Produces regulator-ready findings.</p></div>')
+            r_inst   = gr.Textbox(label="Institution Under Analysis", placeholder="e.g. Halifax Bank of Scotland plc, Eversheds Sutherland, FOS")
+            r_stats  = gr.Textbox(label="Statutory Framework (blank = defaults)", placeholder="e.g. CRA 2015 s.19-20, EqA 2010 s.13/s.20, FCA PRIN 6, BCOBS")
+            r_q      = gr.Textbox(label="Compliance Question / Scenario", lines=3, placeholder="e.g. Did the institution comply with EqA 2010 s.20 after disability was disclosed on 19 June 2025?")
+            gr.HTML('<div class="pnote">📄 upload evidence document</div>')
+            r_pdf  = gr.File(label="Upload PDF", file_types=[".pdf"], type="filepath")
+            r_btn  = gr.Button("Evaluate Compliance →", variant="primary")
+            r_out  = gr.Textbox(label="Compliance Matrix Output", lines=16, elem_classes=["out"])
+            with gr.Row():
+                r_title = gr.Textbox(label="Save as (title) — blank = auto", scale=4)
+                r_save  = gr.Button("💾 Save", variant="secondary", scale=1)
+            r_msg = gr.Markdown("")
+            r_btn.click(run_rhb, inputs=[r_inst, r_stats, r_q, r_pdf], outputs=r_out)
+            r_save.click(save_rhb, inputs=[r_title, r_out, lib_state], outputs=[lib_state, r_msg])
+
+        # ── 04 Phoenix Badger ─────────────────────────────────────────────────
+        with gr.Tab("04 · Phoenix Badger"):
+            gr.HTML('<div class="card"><div class="badge">Organ 4 — Metabolic Loop System</div><p>1,440×4 counter-rotating Ouroboros feedback loops. Forward scan → backward sweep → typology mutation → drift correction. The organism evolves with every cycle.</p></div>')
+            p_scenario = gr.Textbox(label="Scenario to Process", lines=3, placeholder="e.g. Bank used identical evasion language to FOS. Account closed 11 days after disability disclosure.")
+            p_loop     = gr.Dropdown(
+                label="Loop Mode",
+                choices=["Full Ouroboros (all four rings)","Ring A — Forward Scan","Ring B — Backward Sweep","Ring C — Typology Mutation","Ring D — Rollback & Drift Correction"],
+                value="Full Ouroboros (all four rings)"
+            )
+            p_targets  = gr.Textbox(label="Escalation Targets (blank = defaults)", placeholder="e.g. High Court, FCA, ICO, Parliamentary Select Committee, Media")
+            gr.HTML('<div class="pnote">📄 upload context document</div>')
+            p_pdf  = gr.File(label="Upload PDF", file_types=[".pdf"], type="filepath")
+            p_btn  = gr.Button("Run Loop →", variant="primary")
+            p_out  = gr.Textbox(label="Loop System Output", lines=18, elem_classes=["out"])
+            with gr.Row():
+                p_title = gr.Textbox(label="Save as (title) — blank = auto", scale=4)
+                p_save  = gr.Button("💾 Save", variant="secondary", scale=1)
+            p_msg = gr.Markdown("")
+            p_btn.click(run_phoenix_badger, inputs=[p_scenario, p_loop, p_targets, p_pdf], outputs=p_out)
+            p_save.click(save_pb, inputs=[p_title, p_out, lib_state], outputs=[lib_state, p_msg])
+
+        # ── 05 Layoutbot ──────────────────────────────────────────────────────
+        with gr.Tab("05 · Layoutbot — Export"):
+            gr.HTML('<div class="card"><div class="badge">Export Organ — Layoutbot</div><p>Six destination formats: High Court, FCA, ICO, Parliament, Public Disclosure, Media Briefing. All outputs watermarked Ms. Nala | Injectiv8 | Dwayne Anthony Brian Galloway.</p></div>')
+            l_dest = gr.Dropdown(
+                label="Output Destination",
+                choices=["High Court (CPR-compliant Particulars of Claim)","FCA Regulatory Complaint","ICO Data Protection Notice","Parliamentary Evidence Submission","Public Interest Disclosure","Media Briefing Kit"],
+                value="High Court (CPR-compliant Particulars of Claim)"
+            )
+            l_case     = gr.Textbox(label="Case / Matter Name", placeholder="e.g. Galloway v DBL-Max Limited & ORS")
+            l_findings = gr.Textbox(label="Key Findings to Format", lines=6, placeholder="Paste your Introbot / RHB 2.0 / Phoenix Badger output here, or describe the key facts and breaches")
+            gr.HTML('<div class="pnote">📄 optional supporting document</div>')
+            l_pdf  = gr.File(label="Upload PDF", file_types=[".pdf"], type="filepath")
+            l_btn  = gr.Button("Generate Document →", variant="primary")
+            l_out  = gr.Textbox(label="Formatted Output", lines=20, elem_classes=["out"])
+            with gr.Row():
+                l_title = gr.Textbox(label="Save as (title) — blank = auto", scale=4)
+                l_save  = gr.Button("💾 Save", variant="secondary", scale=1)
+            l_msg = gr.Markdown("")
+            l_btn.click(run_layoutbot, inputs=[l_dest, l_case, l_findings, l_pdf], outputs=l_out)
+            l_save.click(save_layout, inputs=[l_title, l_out, lib_state], outputs=[lib_state, l_msg])
+
+        # ── 06 Library ───────────────────────────────────────────────────────��
+        with gr.Tab("📚 Fact Ledger Library"):
+            gr.HTML('<div class="card"><div class="badge">Session Document Library</div><p>All outputs saved this session across all five organs. View, download as .txt, or delete.</p></div>')
+            lib_ref_btn = gr.Button("🔄 Refresh", variant="secondary")
+            lib_dd      = gr.Dropdown(label="Saved Documents", choices=lib_choices(load_library()), value=None, interactive=True)
+            lib_viewer  = gr.Textbox(label="Content", lines=20, elem_classes=["out"], interactive=False)
+            with gr.Row():
+                lib_dl_btn  = gr.Button("⬇️ Download .txt", variant="secondary")
+                lib_del_btn = gr.Button("🗑️ Delete", variant="secondary")
+            lib_dl_file = gr.File(label="Your file", visible=False)
+            lib_msg     = gr.Markdown("")
+
+            lib_ref_btn.click(lib_refresh,  [lib_state],          [lib_dd, lib_msg])
+            lib_dd.change(lib_view,         [lib_dd, lib_state],   lib_viewer)
+            lib_dl_btn.click(lib_download,  [lib_dd, lib_state],   [lib_dl_file, lib_msg])
+            lib_del_btn.click(lib_delete,   [lib_dd, lib_state],   [lib_state, lib_dd, lib_msg])
+            lib_state.change(lib_sync,      [lib_state],           lib_dd)
+
+    gr.HTML("""
+    <div class="foot">
+        THE METABOLIC PHOENIX FRAMEWORK &nbsp;·&nbsp; Injectiv8 &nbsp;·&nbsp; Ms. Nala
+        &nbsp;·&nbsp; © Dwayne Anthony Brian Galloway 2026 &nbsp;·&nbsp; All Rights Reserved
+        &nbsp;·&nbsp; Llama-3.3-70B via HF Inference API
+    </div>
+    """)
+
+if __name__ == "__main__":
+    demo.launch()