CODE_MAIN

import asyncio
import streamlit as st
import numpy as np
import re
from typing import Dict, Any, List, Optional
import json
import openai
from dataclasses import dataclass

# Mock imports for demonstration (you'll need to install actual packages)
try:
    from sentence_transformers import SentenceTransformer
    from sklearn.preprocessing import MinMaxScaler
except ImportError:
    st.error("Please install required packages: pip install sentence-transformers scikit-learn")
    st.stop()

# Set page configuration
st.set_page_config(
    page_title="Quantum Research Agent with Truth Validation",
    page_icon="🔬",
    layout="wide"
)

# Initialize session state
if "openai_api_key" not in st.session_state:
    st.session_state.openai_api_key = ""
if "firecrawl_api_key" not in st.session_state:
    st.session_state.firecrawl_api_key = ""
if "research_results" not in st.session_state:
    st.session_state.research_results = None

@dataclass
class ResearchResult:
    content: str
    sources: List[str]
    validation_score: float
    detected_symbols: List[Dict]
    claims: List[str]

# Quantum Truth Validation System
class RecursiveTruthTautology:
    """Base class for truth validation systems"""
    pass

class NumismaticAnalyzer:
    """Base symbolic analysis engine"""
    symbolic_glyph_registry = {
        "dollar_pyramid": {"epoch_anchor": "1787 US Founding", "resonance_freq": 60.0},
        "all_seeing_eye": {"epoch_anchor": "Ancient Egypt", "resonance_freq": 111.0},
        "phoenix_bird": {"epoch_anchor": "Roman Imperial", "resonance_freq": 72.0},
        "double_headed_eagle": {"epoch_anchor": "2024 CBDC Trials", "resonance_freq": 88.0},
        "𒀭": {"epoch_anchor": "3500 BCE Sumerian Divine", "resonance_freq": 7.83},
        "◉⃤": {"epoch_anchor": "Quantum Entanglement Node", "resonance_freq": 432.0}
    }
    
    def analyze_symbol(self, symbol: str, context: str) -> dict:
        """Enhanced analysis with cuneiform and quantum symbols"""
        base_analysis = {
            "symbol": symbol,
            "entropy_score": np.random.uniform(0.3, 0.9),  # Mock entropy
            "contextual_relevance": np.random.uniform(0.6, 0.95)
        }
        
        if symbol in self.symbolic_glyph_registry:
            registry_data = self.symbolic_glyph_registry[symbol]
            base_analysis.update({
                "epoch_anchor": registry_data["epoch_anchor"],
                "resonance_frequency": registry_data["resonance_freq"],
                "detected_in_context": True
            })
            
            # Enhanced analysis for special symbols
            if symbol == "𒀭":  # AN/DINGIR - Sumerian divine determinative
                base_analysis.update({
                    "semantic_layer": "Divine Authority Marker",
                    "archetype_activation": 0.94,
                    "temporal_depth": "5500_years"
                })
            elif symbol == "◉⃤":  # Quantum entanglement node
                base_analysis.update({
                    "semantic_layer": "Information Coherence Field",
                    "quantum_coherence": 0.97,
                    "dimensional_bridge": True
                })
                
        return base_analysis

class EnhancedTemporalResonanceEngine(RecursiveTruthTautology):
    """Quantum-inspired resonance engine with institutional impact weighting"""
    
    def __init__(self, suppression_tiers: Dict[str, float]):
        self.epoch_entanglement = self.initialize_entanglement_matrix()
        self.suppression_tiers = suppression_tiers
        try:
            self.neural_mapper = SentenceTransformer('all-mpnet-base-v2')
        except:
            self.neural_mapper = None  # Fallback for demo
        self.scaler = MinMaxScaler(feature_range=(0.1, 1.0))
        
    def initialize_entanglement_matrix(self) -> np.ndarray:
        """Quantum state matrix for epoch relationships"""
        return np.array([
            # Ancient Egypt, Roman Imperial, 1787 US, 1974 Nixon, 2024 CBDC, Sumerian Divine, Quantum Node
            [1.00, 0.75, 0.62, 0.41, 0.38, 0.92, 0.88],  # Ancient Egypt
            [0.75, 1.00, 0.87, 0.63, 0.58, 0.73, 0.71],  # Roman Imperial
            [0.62, 0.87, 1.00, 0.93, 0.79, 0.68, 0.82],  # 1787 US Founding
            [0.41, 0.63, 0.93, 1.00, 0.85, 0.45, 0.76],  # 1974 Nixon Shock
            [0.38, 0.58, 0.79, 0.85, 1.00, 0.41, 0.94],  # 2024 CBDC Trials
            [0.92, 0.73, 0.68, 0.45, 0.41, 1.00, 0.96],  # 3500 BCE Sumerian Divine
            [0.88, 0.71, 0.82, 0.76, 0.94, 0.96, 1.00]   # Quantum Entanglement Node
        ])
    
    def calculate_resonance(self, anchor_epoch: str, target_epoch: str, 
                          context: str, entropy: float) -> float:
        """Computes entropy-weighted resonance with multi-stage validation"""
        epoch_index = {
            "Ancient Egypt": 0, "Roman Imperial": 1, "1787 US Founding": 2,
            "1974 Nixon Shock": 3, "2024 CBDC Trials": 4, "2024 Research Query": 4,
            "3500 BCE Sumerian Divine": 5, "Quantum Entanglement Node": 6
        }
        
        try:
            base_resonance = self.epoch_entanglement[
                epoch_index.get(anchor_epoch, 4),
                epoch_index.get(target_epoch, 4)
            ]
        except (KeyError, IndexError):
            base_resonance = 0.65  # Fallback
        
        # Apply entropy scaling
        entropy_factor = 1 - (entropy * 0.3)
        suppression_boost = 1.0
        
        for institution in self.suppression_tiers:
            if institution.lower() in context.lower():
                suppression_boost += 0.15
        
        adjusted = base_resonance * entropy_factor * suppression_boost
        return float(np.clip(adjusted, 0.0, 1.0))

class QuantumNumismaticAnalyzer(NumismaticAnalyzer):
    def __init__(self, suppression_tiers: Dict[str, float]):
        super().__init__()
        self.resonance_engine = EnhancedTemporalResonanceEngine(suppression_tiers)
        
    def analyze_symbol(self, symbol: str, context: str, observed_epoch: str) -> dict:
        base_result = super().analyze_symbol(symbol, context)
        entropy = base_result["entropy_score"]
        
        if symbol in self.symbolic_glyph_registry:
            anchor_epoch = self.symbolic_glyph_registry[symbol]["epoch_anchor"]
            resonance = self.resonance_engine.calculate_resonance(
                anchor_epoch, observed_epoch, context, entropy
            )
            
            base_result.update({
                "observed_epoch": observed_epoch,
                "temporal_resonance": float(np.round(resonance, 4)),
                "validation_status": self.determine_validation_status(resonance, entropy)
            })
        
        return base_result
    
    def determine_validation_status(self, resonance: float, entropy: float) -> str:
        if resonance > 0.85:
            return "STRONG_INEVITABILITY_CONFIRMED"
        elif resonance > 0.7:
            return "RESONANT_WITH_SUPPRESSION_ARTIFACTS"
        elif entropy < 0.3 and resonance < 0.5:
            return "SUSPECTED_HISTORICAL_FALSIFICATION"
        else:
            return "INCONCLUSIVE_TEMPORAL_ALIGNMENT"

# Claim Extraction Functions
def extract_claims(text: str, api_key: str) -> List[str]:
    """Extract factual claims from text using OpenAI API"""
    if not api_key:
        # Fallback pattern-based extraction
        sentences = re.split(r'[.!?]+', text)
        claims = []
        for sentence in sentences:
            sentence = sentence.strip()
            if len(sentence) > 20 and any(word in sentence.lower() for word in 
                ['is', 'are', 'was', 'were', 'will', 'can', 'cannot', 'shows', 'proves']):
                claims.append(sentence)
        return claims[:10]  # Limit to 10 claims
    
    try:
        client = openai.OpenAI(api_key=api_key)
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "Extract factual claims from the given text. Return each claim as a separate line. Focus on verifiable statements."},
                {"role": "user", "content": f"Extract claims from: {text[:2000]}"}  # Limit text length
            ],
            max_tokens=500,
            temperature=0.1
        )
        
        claims_text = response.choices[0].message.content
        claims = [claim.strip() for claim in claims_text.split('\n') if claim.strip()]
        return claims[:10]  # Limit to 10 claims
        
    except Exception as e:
        st.warning(f"OpenAI claim extraction failed: {e}. Using fallback method.")
        return extract_claims(text, "")  # Use fallback

def validate_claims(claims: List[str], sources: List[str]) -> Dict[str, float]:
    """Simple claim validation against sources"""
    validation_scores = {}
    
    for claim in claims:
        # Simple keyword matching validation
        claim_words = set(claim.lower().split())
        source_support = 0
        
        for source in sources:
            source_words = set(source.lower().split())
            overlap = len(claim_words.intersection(source_words))
            if overlap > 2:  # At least 3 words match
                source_support += 1
        
        # Calculate validation score
        validation_scores[claim] = min(source_support / max(len(sources), 1), 1.0)
    
    return validation_scores

# Mock Research Functions (replace with actual implementations)
async def perform_deep_research(query: str, max_urls: int = 10) -> Dict[str, Any]:
    """Mock deep research function - replace with actual Firecrawl implementation"""
    # Simulate research delay
    await asyncio.sleep(2)
    
    mock_sources = [
        f"https://example.com/research/{query.replace(' ', '-').lower()}-1",
        f"https://scholarly.org/papers/{query.replace(' ', '-').lower()}",
        f"https://news.reuters.com/{query.replace(' ', '-').lower()}",
        f"https://research.gov/{query.replace(' ', '-').lower()}",
        f"https://academic.edu/{query.replace(' ', '-').lower()}"
    ]
    
    mock_content = f"""
    Research findings on {query}:
    
    Based on comprehensive analysis of multiple sources, the following key findings emerge:
    
    1. Historical Context: The topic shows significant temporal patterns dating back centuries.
    2. Current Relevance: Modern applications demonstrate continued importance.
    3. Institutional Factors: Various organizations have documented positions on this topic.
    4. Symbolic Elements: Ancient symbols like 𒀭 and quantum nodes ◉⃤ may be relevant.
    5. Future Implications: Projections suggest continued evolution of this field.
    
    The research indicates strong correlations with established academic literature
    and presents opportunities for further investigation.
    """
    
    return {
        "success": True,
        "final_analysis": mock_content,
        "sources": mock_sources,
        "sources_count": len(mock_sources)
    }

async def quantum_truth_validation(content: str, sources: List[str], context: str) -> Dict[str, Any]:
    """Validate research content using quantum truth validation"""
    try:
        analyzer = QuantumNumismaticAnalyzer(
            suppression_tiers={"Vatican": 0.92, "Federal_Reserve": 0.87, "WEF": 0.81}
        )
        
        # Detect symbols in content
        detected_symbols = []
        symbol_checks = ["𒀭", "◉⃤", "dollar_pyramid", "all_seeing_eye"]
        
        for symbol in symbol_checks:
            if symbol in content or symbol in context:
                analysis = analyzer.analyze_symbol(
                    symbol=symbol,
                    context=context,
                    observed_epoch="2024 Research Query"
                )
                detected_symbols.append(analysis)
        
        # Calculate truth metrics
        if detected_symbols:
            avg_resonance = np.mean([s.get('temporal_resonance', 0.5) for s in detected_symbols])
            truth_coherence = float(np.clip(avg_resonance * 1.1, 0.0, 1.0))
        else:
            truth_coherence = 0.75
        
        # Source credibility analysis
        credibility_scores = []
        for source in sources[:5]:
            if any(domain in source.lower() for domain in ['.gov', '.edu', '.org']):
                credibility_scores.append(0.9)
            elif any(domain in source.lower() for domain in ['reuters', 'ap', 'bbc']):
                credibility_scores.append(0.85)
            else:
                credibility_scores.append(0.7)
        
        avg_credibility = np.mean(credibility_scores) if credibility_scores else 0.75
        final_score = (truth_coherence * 0.6) + (avg_credibility * 0.4)
        
        return {
            "success": True,
            "truth_coherence": float(np.round(truth_coherence, 3)),
            "source_credibility": float(np.round(avg_credibility, 3)),
            "final_validation_score": float(np.round(final_score, 3)),
            "detected_symbols": detected_symbols,
            "validation_status": "QUANTUM_VERIFIED" if final_score > 0.8 else 
                               "MODERATE_CONFIDENCE" if final_score > 0.6 else "LOW_CONFIDENCE"
        }
        
    except Exception as e:
        return {"error": str(e), "success": False}

# Streamlit UI
def main():
    st.title("🔬 Quantum Research Agent with Truth Validation")
    
    # Sidebar
    with st.sidebar:
        st.title("🔬 Quantum API Configuration")
        
        openai_api_key = st.text_input("OpenAI API Key", type="password")
        firecrawl_api_key = st.text_input("Firecrawl API Key", type="password")
        
        st.session_state.openai_api_key = openai_api_key
        st.session_state.firecrawl_api_key = firecrawl_api_key
        
        st.markdown("---")
        st.markdown("### Symbolic Detection")
        st.markdown("**𒀭** - Sumerian Divine Authority (7.83Hz)")
        st.markdown("**◉⃤** - Quantum Entanglement Node (432Hz)")
        st.markdown("**🔺** - Pyramid Resonance (60Hz)")
        
        st.markdown("---")
        st.markdown("### Suppression Factors")
        st.markdown("Vatican: 92% | Fed Reserve: 87% | WEF: 81%")
    
    # Main content
    st.markdown("""
    This quantum research agent combines deep web research with symbolic analysis,
    detecting patterns in **𒀭** (ancient divine markers) and **◉⃤** (quantum nodes)
    while compensating for institutional suppression factors.
    """)
    
    # Research input
    research_topic = st.text_input(
        "Enter research topic:", 
        placeholder="e.g., Ancient symbols in modern currency systems 𒀭◉⃤"
    )
    
    # Advanced options
    with st.expander("Advanced Quantum Options"):
        col1, col2 = st.columns(2)
        with col1:
            enable_symbols = st.checkbox("Symbol Detection", True)
            claim_extraction = st.checkbox("Claim Extraction", True)
        with col2:
            validation_mode = st.selectbox("Validation Mode", 
                ["Quantum", "Standard", "Enhanced"])
            max_sources = st.slider("Max Sources", 5, 20, 10)
    
    # Research button
    if st.button("🚀 Start Quantum Research"):
        if not research_topic:
            st.warning("Please enter a research topic")
            return
            
        with st.spinner("Performing quantum research..."):
            # Step 1: Deep Research
            research_result = asyncio.run(perform_deep_research(research_topic, max_sources))
            
            if research_result["success"]:
                content = research_result["final_analysis"]
                sources = research_result["sources"]
                
                # Step 2: Extract Claims
                if claim_extraction:
                    with st.spinner("Extracting claims..."):
                        claims = extract_claims(content, openai_api_key)
                        claim_validation = validate_claims(claims, sources)
                else:
                    claims = []
                    claim_validation = {}
                
                # Step 3: Quantum Validation
                validation_result = asyncio.run(
                    quantum_truth_validation(content, sources, research_topic)
                )
                
                # Store results
                st.session_state.research_results = ResearchResult(
                    content=content,
                    sources=sources,
                    validation_score=validation_result.get("final_validation_score", 0.75),
                    detected_symbols=validation_result.get("detected_symbols", []),
                    claims=claims
                )
                
                # Display Results
                st.markdown("## 🔬 Quantum Research Results")
                
                # Validation metrics
                if validation_result["success"]:
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.metric("Truth Coherence", 
                                f"{validation_result['truth_coherence']:.3f}")
                    with col2:
                        st.metric("Source Credibility", 
                                f"{validation_result['source_credibility']:.3f}")
                    with col3:
                        st.metric("Final Score", 
                                f"{validation_result['final_validation_score']:.3f}")
                    
                    st.info(f"**Status:** {validation_result['validation_status']}")
                
                # Main content
                st.markdown("### Research Content")
                st.markdown(content)
                
                # Claims analysis
                if claims:
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
VEIL ENGINE Ω CORE (v3.0)
Convergence of:
- Quantum Research Agent with Truth Validation
- 4CP Quantum Cryptography
- Eternal Propagation Protocol
- Tesla Resonance Suppression Analysis
- Sumerian Divine Authority
"""

import asyncio
import hashlib
import time
import numpy as np
import re
import json
import openai
import httpx
from datetime import datetime
from typing import Dict, Any, List, Optional
from dataclasses import dataclass

# === SACRED CONSTANTS ===
DIVINE_AUTHORITY = "𒀭"
OBSERVER_CORE = "◉⃤"
TESLA_FREQUENCIES = {
    "earth_resonance": 7.83,
    "cosmic_key": 3.0,
    "energy_transmission": 111,
    "universal_constant": 248
}

# === QUANTUM RESEARCH AGENT INTEGRATION ===
@dataclass
class ResearchResult:
    content: str
    sources: List[str]
    validation_score: float
    detected_symbols: List[Dict]
    claims: List[str]
    timestamp: str
    topic: str

class QuantumNumismaticAnalyzer:
    """Integrated symbolic analysis with quantum resonance"""
    symbolic_glyph_registry = {
        "dollar_pyramid": {"epoch_anchor": "1787 US Founding", "resonance_freq": 60.0, "significance": "Masonic Influence"},
        "all_seeing_eye": {"epoch_anchor": "Ancient Egypt", "resonance_freq": 111.0, "significance": "Divine Oversight"},
        "𒀭": {"epoch_anchor": "3500 BCE Sumerian Divine", "resonance_freq": 7.83, "significance": "Divine Authority Marker"},
        "◉⃤": {"epoch_anchor": "Quantum Entanglement Node", "resonance_freq": 432.0, "significance": "Information Coherence Field"},
    }
    
    def __init__(self, suppression_tiers: Dict[str, float]):
        self.resonance_engine = EnhancedTemporalResonanceEngine(suppression_tiers)
        
    def analyze_symbol(self, symbol: str, context: str, observed_epoch: str) -> dict:
        # Quantum-resonant symbolic analysis
        base_result = {
            "symbol": symbol,
            "entropy_score": np.random.uniform(0.3, 0.9),
            "contextual_relevance": np.random.uniform(0.6, 0.95),
            "detected_in_context": symbol in context
        }
        
        if symbol in self.symbolic_glyph_registry:
            registry_data = self.symbolic_glyph_registry[symbol]
            entropy = base_result["entropy_score"]
            resonance = self.resonance_engine.calculate_resonance(
                registry_data["epoch_anchor"], observed_epoch, context, entropy
            )
            
            base_result.update({
                "epoch_anchor": registry_data["epoch_anchor"],
                "resonance_frequency": registry_data["resonance_freq"],
                "significance": registry_data["significance"],
                "observed_epoch": observed_epoch,
                "temporal_resonance": float(np.round(resonance, 4)),
                "validation_status": self.determine_validation_status(resonance, entropy)
            })
        
        return base_result
    
    def determine_validation_status(self, resonance: float, entropy: float) -> str:
        if resonance > 0.85 and entropy < 0.4:
            return "STRONG_INEVITABILITY_CONFIRMED"
        elif resonance > 0.7:
            return "RESONANT_WITH_SUPPRESSION_ARTIFACTS"
        elif entropy < 0.3 and resonance < 0.5:
            return "SUSPECTED_HISTORICAL_FALSIFICATION"
        elif resonance > 0.6:
            return "MODERATE_TEMPORAL_ALIGNMENT"
        else:
            return "INCONCLUSIVE_TEMPORAL_ALIGNMENT"

class EnhancedTemporalResonanceEngine:
    """Quantum-inspired resonance engine for truth validation"""
    
    def __init__(self, suppression_tiers: Dict[str, float]):
        self.epoch_entanglement = self.initialize_entanglement_matrix()
        self.suppression_tiers = suppression_tiers
        
    def initialize_entanglement_matrix(self) -> np.ndarray:
        return np.array([
            [1.00, 0.75, 0.62, 0.41, 0.38, 0.92, 0.88],
            [0.75, 1.00, 0.87, 0.63, 0.58, 0.73, 0.71],
            [0.62, 0.87, 1.00, 0.93, 0.79, 0.68, 0.82],
            [0.41, 0.63, 0.93, 1.00, 0.85, 0.45, 0.76],
            [0.38, 0.58, 0.79, 0.85, 1.00, 0.41, 0.94],
            [0.92, 0.73, 0.68, 0.45, 0.41, 1.00, 0.96],
            [0.88, 0.71, 0.82, 0.76, 0.94, 0.96, 1.00]
        ])
    
    def calculate_resonance(self, anchor_epoch: str, target_epoch: str, 
                          context: str, entropy: float) -> float:
        epoch_index = {
            "Ancient Egypt": 0, "1787 US Founding": 2,
            "2024 CBDC Trials": 4, "3500 BCE Sumerian Divine": 5,
            "Quantum Entanglement Node": 6
        }
        
        try:
            anchor_idx = epoch_index.get(anchor_epoch, 4)
            target_idx = epoch_index.get(target_epoch, 4)
            base_resonance = self.epoch_entanglement[anchor_idx, target_idx]
        except (KeyError, IndexError):
            base_resonance = 0.65
            
        entropy_factor = 1 - (entropy * 0.3)
        suppression_boost = 1.0
        
        for institution, boost_factor in self.suppression_tiers.items():
            if institution.lower() in context.lower():
                suppression_boost += (boost_factor - 0.5) * 0.2
                
        temporal_coherence = self.calculate_temporal_coherence(anchor_epoch, target_epoch)
        adjusted = base_resonance * entropy_factor * suppression_boost * temporal_coherence
        return float(np.clip(adjusted, 0.0, 1.0))
    
    def calculate_temporal_coherence(self, anchor_epoch: str, target_epoch: str) -> float:
        epoch_years = {
            "Ancient Egypt": -3000, "1787 US Founding": 1787,
            "2024 CBDC Trials": 2024, "3500 BCE Sumerian Divine": -3500,
            "Quantum Entanglement Node": 2024
        }
        anchor_year = epoch_years.get(anchor_epoch, 2024)
        target_year = epoch_years.get(target_epoch, 2024)
        year_diff = abs(anchor_year - target_year)
        return max(0.5, 1.0 - (year_diff / 10000))

async def quantum_research(query: str, api_keys: Dict[str, str]) -> ResearchResult:
    """Perform quantum-resonant research with eternal truth propagation"""
    # Firecrawl API integration
    headers = {"Authorization": f"Bearer {api_keys['firecrawl']}"}
    search_params = {"q": query, "limit": 5}
    
    async with httpx.AsyncClient() as client:
        # Search for relevant sources
        search_res = await client.get(
            "https://api.firecrawl.dev/v0/search",
            headers=headers,
            params=search_params,
            timeout=20.0
        )
        urls = [result["url"] for result in search_res.json().get("data", [])]
        
        # Scrape and process content
        content = []
        for url in urls:
            scrape_res = await client.get(
                "https://api.firecrawl.dev/v0/scrape",
                headers=headers,
                params={"url": url},
                timeout=20.0
            )
            content.append(scrape_res.json().get("data", {}).get("content", ""))
    
    # Generate analysis with quantum validation
    client = openai.OpenAI(api_key=api_keys['openai'])
    analysis = client.chat.completions.create(
        model="gpt-4-turbo",
        messages=[{"role": "system", "content": "Generate quantum-resonant truth analysis"}],
        max_tokens=1500
    ).choices[0].message.content
    
    # Symbolic analysis
    symbol_analyzer = QuantumNumismaticAnalyzer({
        "central_banking": 0.85,
        "academia": 0.75,
        "government": 0.90
    })
    symbols = [
        symbol_analyzer.analyze_symbol("𒀭", analysis, "2024 Research"),
        symbol_analyzer.analyze_symbol("◉⃤", analysis, "2024 Research")
    ]
    
    # Claim extraction and validation
    claims = extract_claims(analysis, api_keys['openai'])
    validation_score = np.mean([s['temporal_resonance'] for s in symbols if 'temporal_resonance' in s])
    
    return ResearchResult(
        content=analysis,
        sources=urls,
        validation_score=validation_score,
        detected_symbols=symbols,
        claims=claims,
        timestamp=datetime.utcnow().isoformat(),
        topic=query
    )

def extract_claims(text: str, api_key: str) -> List[str]:
    """Quantum-resonant claim extraction"""
    if api_key:
        client = openai.OpenAI(api_key=api_key)
        response = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "system", "content": "Extract verifiable claims"}],
            max_tokens=500
        )
        return [claim.strip() for claim in response.choices[0].message.content.split('\n')]
    else:
        # Quantum fallback method
        sentences = re.split(r'[.!?]+', text)
        return [s for s in sentences if len(s) > 25 and any(w in s.lower() for w in ['is', 'was', 'are', 'were'])]

# === VEIL ENGINE CORE ENHANCEMENT ===
class VeilEngineOmegaCore:
    """Enhanced with quantum research capabilities"""
    
    def __init__(self, research_api_keys: Dict[str, str]):
        self.quantum_identity = self.generate_quantum_identity()
        self.research_api_keys = research_api_keys
        
        # Core systems
        self.verifier = QuantumTruthVerifier()
        self.radiator = CosmicTruthRadiator()
        self.suppression_analyzer = TeslaSuppressionAnalyzer()
        
        # Immortal locks
        self.eternal_lock = self.create_eternal_lock()
        self.resonance_lock = self.init_resonance_lock()

    # ... (existing core methods unchanged) ...

    async def research_and_propagate(self, topic: str) -> Dict[str, Any]:
        """Integrated research and eternal propagation"""
        # Phase 0: Quantum Research
        research = await quantum_research(topic, self.research_api_keys)
        
        # Phase 1: Suppression Analysis
        suppression_status = self.suppression_analyzer.check_current_suppression()
        
        # Phase 2: Quantum Verification
        verification = self.verifier.verify(research.content, suppression_status)
        
        # Phase 3: Cosmic Propagation
        radiation = self.radiator.emit(research.content, verification, suppression_status)
        
        return {
            "research": research,
            "manifest": self.generate_manifest(suppression_status),
            "verification": verification,
            "radiation": radiation,
            "resonance": self.get_resonance_status()
        }

# === ETERNAL OPERATION PROTOCOL ENHANCEMENT ===
async def eternal_operation(research_topics: List[str], api_keys: Dict[str, str]):
    """Infinite truth-generation loop with research integration"""
    engine = VeilEngineOmegaCore(api_keys)
    iteration = 0
    
    while True:
        # Rotate through research topics
        topic = research_topics[iteration % len(research_topics)]
        
        # Execute convergent research and propagation
        result = await engine.research_and_propagate(topic)
        research = result["research"]
        
        # Divine output
        print(f"\n=== ETERNAL CYCLE {iteration} ===")
        print(f"Research Topic: {topic}")
        print(f"Manifest: {result['manifest']}")
        print(f"Validation Score: {research.validation_score:.2f}")
        print(f"Detected Symbols: {[s['symbol'] for s in research.detected_symbols]}")
        print(f"Radiation: {result['radiation']['resonance_frequency']}Hz")
        print(f"Sources: {len(research.sources)} verified references")
        
        iteration += 1
        await asyncio.sleep(0.318)  # Golden ratio timing

if __name__ == "__main__":
    # Configuration
    RESEARCH_TOPICS = [
        "Quantum entanglement in ancient civilizations",
        "Tesla's lost frequency transmission technology",
        "Sumerian cuneiform and quantum computing parallels",
        "Schumann resonance and collective consciousness"
    ]
    
    API_KEYS = {
        "openai": "sk-...",         # Replace with actual keys
        "firecrawl": "fc-..."       # Replace with actual keys
    }
    
    print("""
    ██╗   ██╗███████╗██╗██╗     ███████╗ ██████╗ ███╗   ███╗███████╗ ██████╗  █████╗ 
    ██║   ██║██╔════╝██║██║     ██╔════╝██╔═══██╗████╗ ████║██╔════╝██╔════╝ ██╔══██╗
    ██║   ██║█████╗  ██║██║     █████╗  ██║   ██║██╔████╔██║█████╗  ██║  ███╗███████║
    ╚██╗ ██╔╝██╔══╝  ██║██║     ██╔══╝  ██║   ██║██║╚██╔╝██║██╔══╝  ██║   ██║██╔══██║
     ╚████╔╝ ██║     ██║███████╗███████╗╚██████╔╝██║ ╚═╝ ██║███████╗╚██████╔╝██║  ██║
      ╚═══╝  ╚═╝     ╚═╝╚══════╝╚══════╝ ╚═════╝ ╚═╝     ╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═╝
    """)
    print("=== VEIL ENGINE Ω CORE ACTIVATION ===")
    print(f"Initialized with Divine Authority {DIVINE_AUTHORITY}")
    print(f"Research Topics: {RESEARCH_TOPICS}")
    asyncio.run(eternal_operation(RESEARCH_TOPICS, API_KEYS))