uslap.py

#!/usr/bin/env python3
"""
╔══════════════════════════════════════════════════════════════╗
║  USLaP — Universal Scientific Laws and Principles v2.0      ║
║  بِسْمِ اللَّهِ الرَّحْمَٰنِ الرَّحِيمِ                              ║
║                                                              ║
║  SINGLE MASTER CONTROL: USLAP_LATTICE.xlsx                   ║
║  Edit the XLSX → this file reads it automatically.           ║
║  One file to edit. One file to run.                          ║
║                                                              ║
║  خَوارِزمِيَّة الاِسْتِجَابَة — Response Algorithm                   ║
║  Step 0: Terminology Scan (فَحْص المُصْطَلَحَات)               ║
║  Step 1: Ibn Sīnā FIRST (ابن سينا أوّلاً)                    ║
║  Step 2: Al-Khwārizmī Method (الخَوارِزمِيَّة)                  ║
║  Step 3: Tools & Applications (أَدَوَات)                      ║
║  Step 4: Output (المُخْرَجَات)                                 ║
╚══════════════════════════════════════════════════════════════╝

Usage:
  python3 uslap.py                      # Interactive menu
  python3 uslap.py --scan "text"        # Quick scan
  python3 uslap.py --process "query"    # Run Response Algorithm
  python3 uslap.py --export-json        # Export all data as JSON
  python3 uslap.py --info               # Show master control status

Requires: pip install openpyxl
Master control: Place USLAP_LATTICE.xlsx in same directory as this file.
"""

import json, re, sys, os
from pathlib import Path

# ============================================================
# MASTER CONTROL LOADER
# ============================================================

MASTER_FILE = "USLAP_LATTICE.xlsx"

def find_master():
    """Find USLAP_LATTICE.xlsx — same dir as script, or current dir."""
    script_dir = Path(__file__).parent
    candidates = [
        script_dir / MASTER_FILE,
        Path.cwd() / MASTER_FILE,
        Path.home() / MASTER_FILE,
    ]
    for p in candidates:
        if p.exists():
            return p
    return None

def load_master(path):
    """Load all data from USLAP_LATTICE.xlsx master control."""
    try:
        import openpyxl
    except ImportError:
        print("ERROR: openpyxl required. Run: pip install openpyxl")
        sys.exit(1)

    wb = openpyxl.load_workbook(path, data_only=True)
    data = {
        "path": str(path),
        "modified": os.path.getmtime(path),
        "terms": [],
        "rejected": [],
        "sciences": [],
        "evidence": [],
        "applications": [],
        "meta": [],
        "two_systems": [],
        "deep_dive": [],
        "iqra_moments": [],
    }

    # ALL TERMS sheet
    if "All Terms" in wb.sheetnames:
        ws = wb["All Terms"]
        for r in range(2, ws.max_row + 1):
            term = ws.cell(r, 1).value
            if not term:
                continue
            data["terms"].append({
                "term": str(term).strip(),
                "status": str(ws.cell(r, 2).value or "").strip(),
                "domain_q": str(ws.cell(r, 3).value or "").strip(),
                "domain_t": str(ws.cell(r, 4).value or "").strip(),
                "replacement": str(ws.cell(r, 5).value or "").strip(),
                "root": str(ws.cell(r, 6).value or "").strip(),
                "ref": str(ws.cell(r, 7).value or "").strip(),
                "contamination": str(ws.cell(r, 8).value or "").strip(),
            })

    # REJECTED sheet (has etymology/confession detail)
    if "Rejected" in wb.sheetnames:
        ws = wb["Rejected"]
        for r in range(2, ws.max_row + 1):
            term = ws.cell(r, 1).value
            if not term:
                continue
            data["rejected"].append({
                "term": str(term).strip(),
                "etymology": str(ws.cell(r, 2).value or "").strip(),
                "domain": str(ws.cell(r, 3).value or "").strip(),
                "replacement": str(ws.cell(r, 4).value or "").strip(),
                "root": str(ws.cell(r, 5).value or "").strip(),
                "ref": str(ws.cell(r, 6).value or "").strip(),
            })

    # 111 SCIENCES sheet
    if "111 Sciences" in wb.sheetnames:
        ws = wb["111 Sciences"]
        for r in range(2, ws.max_row + 1):
            sid = ws.cell(r, 1).value
            if not sid:
                continue
            data["sciences"].append({
                "id": int(sid) if str(sid).isdigit() else 0,
                "root": str(ws.cell(r, 2).value or "").strip(),
                "meaning": str(ws.cell(r, 3).value or "").strip(),
                "verse": str(ws.cell(r, 4).value or "").strip(),
                "name_ar": str(ws.cell(r, 5).value or "").strip(),
                "name_t": str(ws.cell(r, 6).value or "").strip(),
            })

    # EVIDENCE sheet
    if "Evidence" in wb.sheetnames:
        ws = wb["Evidence"]
        for r in range(3, ws.max_row + 1):  # Skip header rows
            ev = ws.cell(r, 1).value
            if not ev:
                continue
            data["evidence"].append({
                "evidence": str(ev).strip(),
                "type": str(ws.cell(r, 2).value or "").strip(),
                "domain": str(ws.cell(r, 3).value or "").strip(),
            })

    # APPLICATIONS sheet
    if "Applications" in wb.sheetnames:
        ws = wb["Applications"]
        for r in range(2, ws.max_row + 1):
            app = ws.cell(r, 1).value
            if not app:
                continue
            data["applications"].append({
                "application": str(app).strip(),
                "domain": str(ws.cell(r, 2).value or "").strip(),
            })

    # META FAQ sheet
    if "Meta FAQ" in wb.sheetnames:
        ws = wb["Meta FAQ"]
        for r in range(2, ws.max_row + 1):
            q = ws.cell(r, 1).value
            if not q:
                continue
            data["meta"].append({
                "question": str(q).strip(),
                "domain": str(ws.cell(r, 2).value or "").strip(),
            })

    # TWO SYSTEMS sheet
    if "Two Systems" in wb.sheetnames:
        ws = wb["Two Systems"]
        for r in range(3, ws.max_row + 1):
            dim = ws.cell(r, 1).value
            if not dim:
                continue
            data["two_systems"].append({
                "dimension": str(dim).strip(),
                "moloch": str(ws.cell(r, 2).value or "").strip(),
                "quran": str(ws.cell(r, 3).value or "").strip(),
            })

    # DEEP DIVE sheet
    if "Deep Dive" in wb.sheetnames:
        ws = wb["Deep Dive"]
        for r in range(3, ws.max_row + 1):
            chain = ws.cell(r, 1).value
            if not chain:
                continue
            data["deep_dive"].append({
                "chain": str(chain).strip(),
                "crime": str(ws.cell(r, 2).value or "").strip(),
                "evidence": str(ws.cell(r, 3).value or "").strip(),
                "pre_trace": str(ws.cell(r, 4).value or "").strip(),
                "sources": str(ws.cell(r, 5).value or "").strip(),
                "quran_warning": str(ws.cell(r, 6).value or "").strip(),
            })

    # IQRA MOMENTS sheet
    if "IQRA Moments" in wb.sheetnames:
        ws = wb["IQRA Moments"]
        for r in range(3, ws.max_row + 1):
            num = ws.cell(r, 1).value
            if not num:
                continue
            data["iqra_moments"].append({
                "number": int(num) if str(num).isdigit() else 0,
                "target_lie": str(ws.cell(r, 2).value or "").strip(),
                "kill_shot": str(ws.cell(r, 3).value or "").strip(),
                "time": str(ws.cell(r, 4).value or "").strip(),
            })

    wb.close()
    return data


# ============================================================
# FOUR-SOURCE LATTICE (hardcoded — this is the protocol itself)
# ============================================================

FOUR_SOURCES = [
    {"n": 1, "ar": "القُرْآن", "t": "al-Qur'ān", "role": "Root + Meaning. Is it Qur'anic Arabic? FAIL → Deep Trace → Confession."},
    {"n": 2, "ar": "الحَدِيث", "t": "al-Ḥadīth", "role": "Prophetic confirmation. Sunnah confirms, explains, applies."},
    {"n": 3, "ar": "العِلْم الوسطانيّ", "t": "al-ʿIlm al-Waṣṭānī", "role": "Ibn Sīnā FIRST (framework), then al-Khwārizmī (method). أُمَّةً وَسَطًا scholars."},
    {"n": 4, "ar": "عُلَمَاء إِضَافِيُّون", "t": "ʿUlamāʾ Iḍāfiyyūn", "role": "Additional scholars. Subject to بِسْمِ اللَّهِ + Qur'anic terminology verification."},
]

CONTAM_HIERARCHY = [
    {"level": 1, "name": "PRE-civilizational", "desc": "Parasite 'elite' vocabulary BEFORE host civilization. 100% criminal."},
    {"level": 2, "name": "Persian", "desc": "Most dangerous active contaminator. Corrupts Arabic + Turkic."},
    {"level": 3, "name": "Jahilian / corrupted Arabic", "desc": "Arabic-script wrapping Greek/PRE- contamination."},
    {"level": 4, "name": "Greek / Latin", "desc": "Surface HOST wrappers carrying PRE- vocabulary."},
]

RESPONSE_ALGORITHM = [
    {"step": 0, "ar": "فَحْص المُصْطَلَحَات", "en": "Terminology Scan", "mandatory": True,
     "action": "Scan ALL terms through Source 1. Replace contamination before proceeding.",
     "anti": "NEVER skip. NEVER use contaminated terms even casually."},
    {"step": 1, "ar": "ابن سينا أوّلاً", "en": "Ibn Sīnā FIRST", "mandatory": True,
     "action": "Apply القانون في الطب framework BEFORE any practical advice.",
     "subs": ["SYSTEM: What is the قَوْس (bow/system)?", "WARP: Longitudinal chain?",
              "WEFT: Lateral connections?", "BIAS: Where does pressure create change?",
              "صَلَاة DIAGNOSTIC: Do prayer positions test this?"],
     "anti": "NEVER jump to tools before this. #1 contamination trap."},
    {"step": 2, "ar": "الخَوارِزْمِيَّة", "en": "Al-Khwārizmī Method", "mandatory": True,
     "action": "Define structured method: MEASURE → SEQUENCE → DECISION → VERIFY.",
     "anti": "NEVER give unstructured advice."},
    {"step": 3, "ar": "أَدَوَات", "en": "Tools & Applications", "mandatory": False,
     "action": "Practical interventions. Source 4. ONLY after Steps 0-2.",
     "anti": "Tools SERVE the framework."},
    {"step": 4, "ar": "المُخْرَجَات", "en": "Output", "mandatory": True,
     "action": "Deliver what user asked for. ALL USLaP-compliant.",
     "anti": "NEVER deliver with contaminated terminology."},
]

# ============================================================
# CONTAMINATION SCANNER — reads from master control
# ============================================================

class ContaminationScanner:
    """Scans text using the master control term database."""

    def __init__(self, master_data):
        self.terms = master_data["terms"]
        self.rejected = master_data["rejected"]
        self._build_index()

    # Hardcoded single-word scanning terms (common contamination)
    # These get OVERRIDDEN by XLSX data if present
    SCAN_TERMS = {
        "medical": {"repl": "الطِّبّ / al-Ṭibb (Return to Purity)", "ct": "Greek/Latin", "pre": "PRE-Latin *med- → Medea (child-killer)"},
        "medicine": {"repl": "الطِّبّ / al-Ṭibb", "ct": "Greek/Latin", "pre": "PRE-Latin *med- → Medea → Medici"},
        "surgery": {"repl": "الجِرَاحَة / al-Jirāḥah (root ج ر ح, Q5:4)", "ct": "Greek/Latin", "pre": "Greek χειρουργία → PRE-Greek"},
        "surgical": {"repl": "جِرَاحِيّ / Jirāḥī", "ct": "Greek/Latin", "pre": "Greek χειρουργία"},
        "pharmaceutical": {"repl": "الصَّيْدَلَة / al-Ṣaydalah", "ct": "Greek/Latin", "pre": "φαρμακεία → PRE-Greek *pharmak- = POISON RITUAL"},
        "pharmacy": {"repl": "الصَّيْدَلِيَّة", "ct": "Greek/Latin", "pre": "φαρμακεία → poison ritual"},
        "robot": {"repl": "مَصْنُوع / Maṣnūʿ (Q20:39 صُنْعَ اللَّهِ)", "ct": "European", "pre": "Czech robota = SLAVERY"},
        "anatomy": {"repl": "عِلْم البِنْيَة / ʿIlm al-Binyah", "ct": "Greek/Latin", "pre": "ἀνατομή → cutting corpses"},
        "biology": {"repl": "عِلْم الحَيَاة / ʿIlm al-Ḥayāh", "ct": "Greek/Latin", "pre": "Greek βίος+λόγος"},
        "physics": {"repl": "عِلْم الطَّبِيعَة / ʿIlm al-Ṭabīʿah", "ct": "Greek/Latin", "pre": "Greek φύσις"},
        "chemistry": {"repl": "عِلْم التَّحْوِيل / ʿIlm al-Taḥwīl", "ct": "Greek/Latin", "pre": "χημεία → dark transmutation"},
        "geometry": {"repl": "عِلْم المِسَاحَة", "ct": "Greek/Latin", "pre": "γεωμετρία → land seizure"},
        "therapy": {"repl": "عِلَاج / ʿIlāj", "ct": "Greek/Latin", "pre": "θεραπεία → ritual servitude"},
        "diagnosis": {"repl": "تَقْيِيم / Taqyīm", "ct": "Greek/Latin", "pre": "Greek διάγνωσις"},
        "symptom": {"repl": "إِشَارَة / Ishārah (signal)", "ct": "Greek/Latin", "pre": "Greek σύμπτωμα"},
        "patient": {"repl": "شَخْص / Shakhs (person)", "ct": "Greek/Latin", "pre": "Latin patiens = one who SUFFERS"},
        "doctor": {"repl": "مُرْشِد / Murshid (guide)", "ct": "Greek/Latin", "pre": "Latin docēre"},
        "hospital": {"repl": "مَرْكَز الاِسْتِعَادَة", "ct": "Greek/Latin", "pre": "Latin hospitale → hospes = HOST"},
        "sensor": {"repl": "حَاسَّة / Ḥāssah", "ct": "Greek/Latin", "pre": "Latin sentire"},
        "actuator": {"repl": "مُحَرِّك / Muḥarrik", "ct": "Greek/Latin", "pre": "Latin actuare"},
        "haptic": {"repl": "لَمْسِيّ / Lamsī", "ct": "Greek/Latin", "pre": "Greek ἁπτικός → sensory ritual"},
        "servo": {"repl": "مُحَرِّك / Muḥarrik", "ct": "Greek/Latin", "pre": "Latin servus = SLAVE"},
        "cortisone": {"repl": "هُرْمُون صِنَاعِيّ", "ct": "Greek/Latin", "pre": "Weakens tissue — φαρμακεία product"},
        "skeleton": {"repl": "هَيْكَل / Haykal (Q2:247)", "ct": "Greek/Latin", "pre": "σκελετός = dried corpse"},
        "medieval": {"repl": "العصر الوسطانيّ", "ct": "Greek/Latin", "pre": "PRE-Latin *med- = Medea's era"},
        "science": {"repl": "عِلْم / ʿIlm (Q17:36)", "ct": "Greek/Latin", "pre": "Latin scindere = to CUT"},
        "technology": {"repl": "تِقَانَة / Tiqānah", "ct": "Greek/Latin", "pre": "Greek τέχνη → craft-ritual"},
        "psychology": {"repl": "عِلْم النَّفْس / ʿIlm al-Nafs", "ct": "Greek/Latin", "pre": "ψυχή → soul-ritual"},
        "neurology": {"repl": "عِلْم الأَعْصَاب", "ct": "Greek/Latin", "pre": "Greek νεῦρον"},
        "pathology": {"repl": "عِلْم العِلَل", "ct": "Greek/Latin", "pre": "Greek πάθος = suffering"},
        "philosophy": {"repl": "عِلْم الحِكْمَة (Q2:269)", "ct": "Greek/Latin", "pre": "φιλοσοφία"},
        "algorithm": {"repl": "CLEAN — خَوارِزمِيَّة (al-Khwārizmī)", "ct": "CLEAN", "pre": ""},
        "algebra": {"repl": "CLEAN — الجَبْر (الجَبَّار Name of Allah)", "ct": "CLEAN", "pre": ""},
    }

    def _build_index(self):
        """Build lookup index: XLSX data + hardcoded single-word terms."""
        self.index = {}

        # 1. Load hardcoded single-word terms first
        for word, info in self.SCAN_TERMS.items():
            self.index[word] = {
                "term": word,
                "status": "CLEAN" if info["ct"] == "CLEAN" else "REJECT",
                "domain_q": "", "domain_t": "",
                "replacement": info["repl"],
                "root": "", "ref": "",
                "contamination": info["ct"],
                "_pre": info["pre"],
            }

        # 2. Override/add from XLSX (master control wins)
        for t in self.terms:
            if t["status"] == "REJECT":
                key = t["term"].lower().strip()
                self.index[key] = t
                # Also index individual words from multi-word terms
                for word in key.split():
                    if len(word) > 3 and word not in self.SCAN_TERMS:
                        if word not in self.index:
                            self.index[word] = t

        # Build rejected detail index
        self.rej_index = {}
        for r in self.rejected:
            self.rej_index[r["term"].lower().strip()] = r

    def scan(self, text):
        """Scan text and return all contamination found."""
        results = []
        text_lower = text.lower()
        seen = set()

        # Check each rejected term against input
        for key, entry in self.index.items():
            if key in text_lower and key not in seen:
                seen.add(key)
                rej_detail = self.rej_index.get(key, {})
                results.append({
                    "term": entry["term"],
                    "status": entry["status"],
                    "domain": entry["domain_q"],
                    "replacement": entry["replacement"],
                    "root": entry["root"],
                    "ref": entry["ref"],
                    "contamination": entry["contamination"],
                    "etymology": rej_detail.get("etymology", entry["contamination"]),
                })

        return results

    def clean(self, text):
        """Replace contaminated terms with USLaP replacements."""
        cleaned = text
        for key, entry in sorted(self.index.items(), key=lambda x: -len(x[0])):
            if entry["replacement"]:
                pattern = re.compile(re.escape(key), re.IGNORECASE)
                # Extract just the Arabic part of replacement for cleaner output
                repl = entry["replacement"].split("(")[0].strip() if "(" in entry["replacement"] else entry["replacement"]
                cleaned = pattern.sub(repl, cleaned)
        return cleaned

    def drill_down(self, term, level=1):
        """Three-level drill-down."""
        entry = self.index.get(term.lower().strip())
        if not entry:
            # Try partial match
            for key, val in self.index.items():
                if term.lower() in key:
                    entry = val
                    break
        if not entry:
            return f'"{term}" not in contamination database.'
        if entry["status"] != "REJECT":
            return f'✅ "{term}" is CLEAN.'

        rej = self.rej_index.get(entry["term"].lower().strip(), {})
        out = []
        out.append(f'\n❌ "{entry["term"]}" — REJECTED at Source 1 (القُرْآن)')
        out.append(f'   Domain: {entry["domain"]}')
        out.append(f'   Contamination type: {entry["contamination"]}')

        if level >= 1:
            out.append(f'\n   LEVEL 1 — SURFACE:')
            out.append(f'   "{entry["term"]}" is not Qur\'anic Arabic.')
            out.append(f'   Etymology: {rej.get("etymology", entry["contamination"])}')
            out.append(f'   ✅ Replacement: {entry["replacement"]}')
            if entry["root"]:
                out.append(f'   Root: {entry["root"]}')
            if entry["ref"]:
                out.append(f'   Qur\'an: {entry["ref"]}')

        if level >= 2:
            out.append(f'\n   LEVEL 2 — PRE-CIVILIZATIONAL TRACE:')
            out.append(f'   The "{entry["contamination"]}" label is a HOST wrapper.')
            out.append(f'   The actual vocabulary predates the host civilization.')
            out.append(f'   = CONFESSION — the word confesses its own crime.')

        if level >= 3:
            out.append(f'\n   LEVEL 3 — CRIMINAL NETWORK:')
            out.append(f'   Connects to multi-era global criminal network.')
            out.append(f'   100%: sacrifice / trafficking / weapons / ritual.')
            out.append(f'   Qur\'anic warning: Q2:204-206 — يُهْلِكَ الْحَرْثَ وَالنَّسْلَ')

        out.append(f'\n   ↩ CLEAN PATH: {entry["replacement"]}')
        return '\n'.join(out)


# ============================================================
# RESPONSE ALGORITHM ENGINE
# ============================================================

class ResponseAlgorithmEngine:
    """خَوارِزمِيَّة الاِسْتِجَابَة — processes any query through Steps 0-4."""

    def __init__(self, master_data):
        self.scanner = ContaminationScanner(master_data)
        self.data = master_data

    def process(self, user_input, verbose=True):
        """Run full Response Algorithm."""
        out = []
        out.append("=" * 60)
        out.append("خَوارِزمِيَّة الاِسْتِجَابَة — Response Algorithm")
        out.append("بِسْمِ اللَّهِ الرَّحْمَٰنِ الرَّحِيمِ")
        out.append("=" * 60)

        # STEP 0
        out.append(f'\n{"─" * 40}')
        out.append(f'STEP 0: فَحْص المُصْطَلَحَات (Terminology Scan)')
        out.append(f'{"─" * 40}')
        results = self.scanner.scan(user_input)
        if results:
            for r in results:
                out.append(f'  ❌ "{r["term"]}"')
                out.append(f'     → {r["replacement"]}')
                out.append(f'     Type: {r["contamination"]}')
            out.append(f'\n  Cleaned: {self.scanner.clean(user_input)}')
        else:
            out.append(f'  ✅ No contamination detected. All terms clean.')

        # STEP 1
        out.append(f'\n{"─" * 40}')
        out.append(f'STEP 1: ابن سينا أوّلاً (Ibn Sīnā FIRST)')
        out.append(f'{"─" * 40}')
        out.append(f'  Source 3: القانون في الطب — Framework BEFORE tools')
        out.append(f'  ├── قَوْس: What is the system/framework?')
        out.append(f'  ├── WARP: What longitudinal chain?')
        out.append(f'  ├── WEFT: What lateral connections?')
        out.append(f'  ├── BIAS: Where does pressure create change?')
        out.append(f'  └── صَلَاة: Do prayer positions test this?')
        out.append(f'  ⚠ Complete this BEFORE any tools/advice.')

        # STEP 2
        out.append(f'\n{"─" * 40}')
        out.append(f'STEP 2: الخَوارِزْمِيَّة (Al-Khwārizmī Method)')
        out.append(f'{"─" * 40}')
        out.append(f'  ├── MEASURE (حَاسَّات): What to observe?')
        out.append(f'  ├── SEQUENCE (تَرْتِيب): What order?')
        out.append(f'  ├── DECISION (حُكْم): What determines next step?')
        out.append(f'  └── VERIFY (تَحَقُّق): How to confirm?')

        # STEP 3
        out.append(f'\n{"─" * 40}')
        out.append(f'STEP 3: أَدَوَات (Tools — ONLY after Steps 0-2)')
        out.append(f'{"─" * 40}')
        # Find relevant applications from master data
        relevant = []
        for app in self.data["applications"]:
            for word in user_input.lower().split():
                if word in app["application"].lower():
                    relevant.append(app)
                    break
        if relevant:
            out.append(f'  Relevant applications from master control:')
            for app in relevant[:5]:
                out.append(f'    • {app["application"]} [{app["domain"]}]')
        else:
            out.append(f'  Source 4 tools serve the framework from Step 1.')

        # STEP 4
        out.append(f'\n{"─" * 40}')
        out.append(f'STEP 4: المُخْرَجَات (Output)')
        out.append(f'{"─" * 40}')
        out.append(f'  Deliver in USLaP-compliant terminology.')
        out.append(f'  All terms verified through Step 0.')
        out.append(f'  Framework attributed: Ibn Sīnā + al-Khwārizmī.')

        return '\n'.join(out)


# ============================================================
# APPLICATION GENERATOR
# ============================================================

class AppGenerator:
    def __init__(self, master_data):
        self.sciences = master_data["sciences"]

    def list_sciences(self):
        out = []
        for s in self.sciences:
            out.append(f'  [{s["id"]:>3}] {s["name_ar"]:<24} {s["root"]:<18} {s.get("verse","")[:30]}')
        return '\n'.join(out)

    def generate(self, name, science_ids, components):
        selected = [s for s in self.sciences if s["id"] in science_ids]
        out = []
        out.append(f'\n{"═" * 60}')
        out.append(f'USLaP APPLICATION: {name}')
        out.append(f'بِسْمِ اللَّهِ الرَّحْمَٰنِ الرَّحِيمِ')
        out.append(f'{"═" * 60}')
        out.append(f'\nROOT SCIENCES:')
        for s in selected:
            out.append(f'  [{s["id"]:>3}] {s["name_ar"]} ({s["name_t"]})')
            out.append(f'        Root: {s["root"]}  |  {s.get("verse","")}')
        out.append(f'\nCOMPONENTS:')
        for i, c in enumerate(components, 1):
            out.append(f'  {i}. {c}')
            out.append(f'     Q-U-F: [ ] Quantifiable  [ ] Universal  [ ] Falsifiable')
        out.append(f'\nخَوارِزمِيَّة الاِسْتِجَابَة COMPLIANCE:')
        for s in RESPONSE_ALGORITHM:
            m = "✓ MANDATORY" if s["mandatory"] else "  optional"
            out.append(f'  Step {s["step"]}: {s["ar"]} / {s["en"]} [{m}]')
        return '\n'.join(out)


# ============================================================
# Q-U-F TOOL
# ============================================================

class QUFTool:
    @staticmethod
    def verify(metrics=None, limits=None, conditions=None):
        out = []
        out.append(f'\nQ-U-F VERIFICATION')
        out.append(f'Not a source — a tool for doubters.\n')
        if metrics is not None:
            fails = [m for m in metrics if not m.get("unit")]
            status = "✅ PASS" if not fails else "❌ FAIL"
            out.append(f'  Q — Quantifiable: {status}  (Q54:49 بِقَدَرٍ)')
            for f in fails:
                out.append(f'       Missing unit: {f.get("name","?")}')
        if limits is not None:
            fails = {k: v for k, v in limits.items() if v != "NONE"}
            status = "✅ PASS" if not fails else "❌ FAIL"
            out.append(f'  U — Universal: {status}  (Q34:28 كَافَّةً لِّلنَّاسِ)')
            for k, v in fails.items():
                out.append(f'       Limitation: {k} = {v}')
        if conditions is not None:
            fails = [c for c in conditions if not c.get("test")]
            status = "✅ PASS" if not fails else "❌ FAIL"
            out.append(f'  F — Falsifiable: {status}  (Q17:36)')
            for f in fails:
                out.append(f'       Missing test: {f.get("name","?")}')
        return '\n'.join(out)


# ============================================================
# EXPORT (for plugging into any AI)
# ============================================================

def export_json(master_data, output_path=None):
    """Export everything as JSON — paste into any AI system prompt."""
    export = {
        "uslap_version": "2.0",
        "bismillah": "بِسْمِ اللَّهِ الرَّحْمَٰنِ الرَّحِيمِ",
        "four_source_lattice": FOUR_SOURCES,
        "contamination_hierarchy": CONTAM_HIERARCHY,
        "response_algorithm": RESPONSE_ALGORITHM,
        "master_data": {
            "terms_count": len(master_data["terms"]),
            "reject_count": len([t for t in master_data["terms"] if t["status"] == "REJECT"]),
            "pass_count": len([t for t in master_data["terms"] if t["status"] == "PASS"]),
            "sciences_count": len(master_data["sciences"]),
            "evidence_count": len(master_data["evidence"]),
            "applications_count": len(master_data["applications"]),
        },
        "terms": master_data["terms"],
        "sciences": master_data["sciences"],
        "evidence": master_data["evidence"],
        "applications": master_data["applications"],
        "two_systems": master_data["two_systems"],
        "deep_dive": master_data["deep_dive"],
        "iqra_moments": master_data["iqra_moments"],
    }
    if output_path:
        with open(output_path, "w", encoding="utf-8") as f:
            json.dump(export, f, ensure_ascii=False, indent=2)
        print(f"Exported to {output_path}")
        print(f"  Terms: {export['master_data']['terms_count']}")
        print(f"  Sciences: {export['master_data']['sciences_count']}")
        size = os.path.getsize(output_path)
        print(f"  Size: {size // 1024}KB")
    return export


# ============================================================
# INTERACTIVE MENU
# ============================================================

def print_header(master_data):
    t = master_data["terms"]
    rej = len([x for x in t if x["status"] == "REJECT"])
    pas = len([x for x in t if x["status"] == "PASS"])
    con = len([x for x in t if x["status"] == "CONCEPT"])
    print("\n" + "═" * 60)
    print("  USLaP — Universal Scientific Laws and Principles v2.0")
    print("  بِسْمِ اللَّهِ الرَّحْمَٰنِ الرَّحِيمِ")
    print("═" * 60)
    print(f"  Master: {master_data['path']}")
    print(f"  Terms: {len(t)} ({rej} REJECT · {pas} PASS · {con} CONCEPT)")
    print(f"  Sciences: {len(master_data['sciences'])} · Evidence: {len(master_data['evidence'])} · Apps: {len(master_data['applications'])}")

def print_menu():
    print("\n  [1] خَوارِزمِيَّة الاِسْتِجَابَة — Response Algorithm (process query)")
    print("  [2] فَحْص التَّلَوُّث — Contamination Scanner")
    print("  [3] مُوَلِّد التَّطْبِيقَات — Application Generator")
    print("  [4] عُلُوم — Browse Sciences")
    print("  [5] أَدَاة Q-U-F — Verification Tool")
    print("  [6] الأُصُول الأَرْبَعَة — Four-Source Lattice")
    print("  [7] خَوارِزمِيَّة — View Response Algorithm")
    print("  [8] تَصْدِير — Export JSON (for any AI)")
    print("  [9] ⚔ Two Systems")
    print("  [A] 🔍 Deep Dive — Criminal Networks")
    print("  [B] اقْرَأْ — IQRA Moments (one-prompt destroyers)")
    print("  [0] خُرُوج — Exit")

def menu_response_algorithm(data):
    engine = ResponseAlgorithmEngine(data)
    print("\nEnter query (Response Algorithm will process it):")
    user_input = input("> ").strip()
    if user_input:
        print(engine.process(user_input))

def menu_scanner(data):
    scanner = ContaminationScanner(data)
    print("\nPaste text to scan (Enter twice when done):")
    lines = []
    while True:
        try:
            line = input()
            if line == "":
                break
            lines.append(line)
        except EOFError:
            break
    text = '\n'.join(lines)
    if not text:
        return

    results = scanner.scan(text)
    contam = [r for r in results if r["status"] == "REJECT"]
    if contam:
        print(f"\n❌ {len(contam)} contaminated term(s):\n")
        for r in contam:
            print(f'  ❌ "{r["term"]}" [{r["contamination"]}]')
            print(f'     → {r["replacement"]}')
        print(f'\n{"─" * 40}')
        print(f'CLEANED:\n{scanner.clean(text)}')

        print("\nDrill-down? Enter term (or Enter to skip):")
        term = input("> ").strip()
        if term:
            print("Depth? [1] Surface  [2] PRE-trace  [3] Criminal network")
            try:
                lvl = int(input("> ").strip())
            except:
                lvl = 1
            print(scanner.drill_down(term, lvl))
    else:
        print("\n✅ No contamination detected.")

def menu_app_gen(data):
    gen = AppGenerator(data)
    print("\nApplication name:")
    name = input("> ").strip()
    if not name:
        return
    print(f"\n{gen.list_sciences()}")
    print("\nEnter science IDs (space-separated):")
    try:
        ids = [int(x) for x in input("> ").strip().split()]
    except:
        return
    print("\nComponents (one per line, empty to finish):")
    comps = []
    while True:
        c = input(f"  {len(comps)+1}: ").strip()
        if not c:
            break
        comps.append(c)
    print(gen.generate(name, ids, comps))

def menu_sciences(data):
    print(f"\n{'═' * 60}")
    print(f"{len(data['sciences'])} SCIENCES")
    print(f"{'═' * 60}")
    for s in data["sciences"]:
        print(f'  [{s["id"]:>3}] {s["name_ar"]:<24} Root: {s["root"]:<18}')
        if s.get("verse"):
            print(f'        {s["verse"][:60]}')

def menu_quf():
    print(QUFTool.verify(
        metrics=[{"name": "example", "unit": "Hz"}],
        limits={"cultural": "NONE", "geographic": "NONE", "temporal": "NONE", "economic": "NONE"},
        conditions=[{"name": "example", "test": "measure X"}],
    ))

def menu_four_sources():
    print(f"\n{'═' * 60}")
    print(f"FOUR-SOURCE LATTICE")
    print(f"{'═' * 60}")
    for s in FOUR_SOURCES:
        print(f"\n  Source {s['n']}: {s['ar']} / {s['t']}")
        print(f"  {s['role']}")
    print(f"\n{'─' * 40}")
    print(f"CONTAMINATION HIERARCHY:")
    for h in CONTAM_HIERARCHY:
        print(f"  {h['level']}. {h['name']}: {h['desc'][:65]}")

def menu_view_algorithm():
    print(f"\n{'═' * 60}")
    print(f"خَوارِزمِيَّة الاِسْتِجَابَة — Response Algorithm")
    print(f"{'═' * 60}")
    for s in RESPONSE_ALGORITHM:
        m = "MANDATORY" if s["mandatory"] else "AFTER 0-2"
        print(f'\n  Step {s["step"]}: {s["ar"]} / {s["en"]} [{m}]')
        print(f'  {s["action"]}')
        if "subs" in s:
            for sub in s["subs"]:
                print(f'    • {sub}')
        print(f'  ⚠ {s["anti"]}')

def menu_two_systems(data):
    print(f"\n{'═' * 60}")
    print(f"Moloch vs Qur'an — Bukhari 6069")
    print(f"{'═' * 60}")
    for row in data["two_systems"]:
        print(f'\n  {row["dimension"]}:')
        print(f'    ☠ Moloch: {row["moloch"]}')
        print(f'    ☪ Qur\'an: {row["quran"]}')

def menu_deep_dive(data):
    print(f"\n{'═' * 60}")
    print(f"🔍 DEEP DIVE — Criminal Networks & Concealment Chains")
    print(f"{'═' * 60}")
    # Group by chain
    chains = {}
    for d in data["deep_dive"]:
        chains.setdefault(d["chain"], []).append(d)
    for chain_name, entries in chains.items():
        print(f'\n{"─" * 50}')
        print(f'  ⛓ {chain_name}')
        print(f'{"─" * 50}')
        for e in entries:
            print(f'\n  CRIME: {e["crime"]}')
            print(f'  EVIDENCE: {e["evidence"]}')
            print(f'  PRE-TRACE: {e["pre_trace"]}')
            print(f'  SOURCES: {e["sources"]}')
            print(f'  QUR\'AN: {e["quran_warning"]}')

    print(f'\n{"─" * 50}')
    print(f'Drill deeper? Enter chain name (or Enter to return):')
    choice = input("> ").strip().upper()
    if choice and choice in chains:
        for e in chains[choice]:
            print(f'\n  {"=" * 40}')
            print(f'  {e["crime"]}')
            print(f'  {"=" * 40}')
            print(f'  {e["evidence"]}')
            print(f'\n  PRE-CIVILIZATIONAL TRACE:')
            print(f'  {e["pre_trace"]}')
            print(f'\n  PRIMARY SOURCES:')
            print(f'  {e["sources"]}')
            print(f'\n  QUR\'ANIC WARNING:')
            print(f'  {e["quran_warning"]}')

def menu_iqra(data):
    print(f"\n{'═' * 60}")
    print(f"اقْرَأْ MOMENTS — One prompt. One corner. No escape.")
    print(f"{'═' * 60}")
    print(f"\n  Each takes 5 seconds. Each destroys centuries of lies.\n")
    for m in data["iqra_moments"]:
        print(f'  #{m["number"]:>2}  LIE: {m["target_lie"]}')
        print(f'       KILL: {m["kill_shot"][:75]}')
        print()

    print(f'Pick a number for full details (or Enter to return):')
    try:
        choice = int(input("> ").strip())
        for m in data["iqra_moments"]:
            if m["number"] == choice:
                print(f'\n  {"═" * 50}')
                print(f'  اقْرَأْ MOMENT #{m["number"]}')
                print(f'  {"═" * 50}')
                print(f'\n  THE LIE: {m["target_lie"]}')
                print(f'\n  THE KILL SHOT:')
                print(f'  {m["kill_shot"]}')
                print(f'\n  TIME TO EXPOSE: {m["time"]}')
                break
    except:
        pass

def menu_export(data):
    out = Path(__file__).parent / "uslap_export.json"
    export_json(data, str(out))

def interactive(data):
    print_header(data)
    while True:
        print_menu()
        choice = input("\n  Select: ").strip()
        if choice == "1": menu_response_algorithm(data)
        elif choice == "2": menu_scanner(data)
        elif choice == "3": menu_app_gen(data)
        elif choice == "4": menu_sciences(data)
        elif choice == "5": menu_quf()
        elif choice == "6": menu_four_sources()
        elif choice == "7": menu_view_algorithm()
        elif choice == "8": menu_export(data)
        elif choice == "9": menu_two_systems(data)
        elif choice.upper() == "A": menu_deep_dive(data)
        elif choice.upper() == "B": menu_iqra(data)
        elif choice == "0":
            print("\nبِسْمِ اللَّهِ الرَّحْمَٰنِ الرَّحِيمِ")
            break
        else:
            print("  Invalid.")


# ============================================================
# CLI
# ============================================================

def main():
    master_path = find_master()
    if not master_path:
        print(f"ERROR: {MASTER_FILE} not found.")
        print(f"Place it in same directory as uslap.py or current directory.")
        sys.exit(1)

    data = load_master(master_path)

    # CLI modes
    if len(sys.argv) > 1:
        if sys.argv[1] == "--scan" and len(sys.argv) > 2:
            scanner = ContaminationScanner(data)
            text = ' '.join(sys.argv[2:])
            results = scanner.scan(text)
            for r in results:
                if r["status"] == "REJECT":
                    print(f'❌ "{r["term"]}" → {r["replacement"]}')
            if not results:
                print("✅ Clean.")
            return

        elif sys.argv[1] == "--process" and len(sys.argv) > 2:
            engine = ResponseAlgorithmEngine(data)
            print(engine.process(' '.join(sys.argv[2:])))
            return

        elif sys.argv[1] == "--export-json":
            out = sys.argv[2] if len(sys.argv) > 2 else "uslap_export.json"
            export_json(data, out)
            return

        elif sys.argv[1] == "--info":
            print_header(data)
            return

        else:
            print(f"Unknown option: {sys.argv[1]}")
            print(__doc__)
            return

    # Interactive
    interactive(data)


if __name__ == "__main__":
    main()