#!/usr/bin/env python3
"""
Verification Queue — Evidence Strength Routing

Routed evidence by confidence tier:
- Tier 1: Direct evidence (URLs, code, logs) → Immediate acceptance
- Tier 2: Strong correlation (multiple sources) → High confidence
- Tier 3: Theoretical inference → Requires validation

Auto-patches skills when evidence contradicts current state.
"""


class EvidenceTier:
    DIRECT = 1
    CORRELATION = 2
    INFERENCE = 3


class VerificationQueue:
    def __init__(self):
        self.queue = []
        self.processed = set()
        self.conflicts = []

    def enqueue(self, claim, tier, source):
        """Add claim to processing queue with evidence tier."""
        self.queue.append({
            'claim': claim,
            'tier': tier,
            'source': source,
            'timestamp': __import__('datetime').datetime.utcnow().isoformat()
        })

    def process(self):
        """Process queue and auto-patch if conflicts detected."""
        results = []
        for item in self.queue:
            if item['claim'] in self.processed:
                continue
            
            strength = self._assess_strength(item)
            if strength < 0.5:  # Conflict detected
                self.conflicts.append(item)
                self._auto_patch(item['claim'])
            else:
                results.append({'claim': item['claim'], 'strength': strength})
            self.processed.add(item['claim'])
        return results

    def _assess_strength(self, item):
        """Calculate evidence strength (0.0-1.0)."""
        base = {EvidenceTier.DIRECT: 0.9, EvidenceTier.CORRELATION: 0.6, EvidenceTier.INFERENCE: 0.3}[item['tier']]
        return base  # Add source weighting here

    def _auto_patch(self, claim):
        """Auto-patch skills when evidence contradicts current state."""
        print(f"[AUTO-PATCH] Evidence conflict detected for: {claim}")
        # Implementation: call skill_manage with conflicting evidence


# Singleton instance
verification_queue = VerificationQueue()


if __name__ == "__main__":
    # Test usage
    verification_queue.enqueue(
        "TurboQuant supports Qwen 27B on 16GB VRAM",
        EvidenceTier.DIRECT,
        "https://github.com/THUDM/TurboQuant"
    )
    results = verification_queue.process()
    print(f"Processed {len(results)} claims")
    if verification_queue.conflicts:
        print(f"Detected {len(verification_queue.conflicts)} conflicts requiring skill patches")