import asyncio
import re
import hashlib
from datetime import datetime
from aiva.db_connector import memory  # Assuming this exists

# Utility Functions (Can be moved to a separate module)
def calculate_score(checks):
    """Calculates an overall score based on boolean checks."""
    if not checks:
        return 0.0
    return sum(checks.values()) / len(checks)

def log_decision(gate_name, worker_id, passed, reason, memory):
    """Logs a validation decision."""
    log_entry = {
        "timestamp": datetime.utcnow().isoformat(),
        "gate": gate_name,
        "worker_id": worker_id,
        "passed": passed,
        "reason": reason
    }
    try:
        memory.log_audit(f"{gate_name}_validation", worker_id, log_entry)
    except Exception as e:
        print(f"[WARNING] Audit logging failed for {gate_name}: {e}")


# Gate Implementations
class GateAlpha:  # Input Validity
    def __init__(self, memory):
        self.memory = memory

    async def validate(self, source_data, worker_id, metadata):
        """Validates source data quality."""
        checks = {}

        # Check 1: Data completeness (example)
        checks["data_completeness"] = source_data is not None and len(source_data) > 0

        # Check 2: Data type validation (example - assuming source_data is a string)
        checks["data_type_valid"] = isinstance(source_data, str)

        score = calculate_score(checks)
        passed = score >= 0.8  # Threshold

        reason = "Source data is valid." if passed else "Source data is invalid."
        log_decision("GateAlpha", worker_id, passed, reason, self.memory)

        return passed, score, checks

class GateBeta:  # Output Quality
    def __init__(self, memory):
        self.memory = memory

    async def validate(self, output, expected_format, worker_id, metadata):
        """Checks accuracy and completeness of the output."""
        checks = {}

        # Check 1: Format compliance (example - assuming JSON output)
        checks["format_compliance"] = isinstance(output, dict)  # or a more specific JSON check

        # Check 2: Completeness (example - check for required fields)
        required_fields = expected_format.get("required_fields", [])
        checks["completeness"] = all(field in output for field in required_fields)

        score = calculate_score(checks)
        passed = score >= 0.8  # Threshold

        reason = "Output is valid." if passed else "Output is invalid."
        log_decision("GateBeta", worker_id, passed, reason, self.memory)

        return passed, score, checks

class GateGamma:  # Insight Purity
    def __init__(self, memory):
        self.memory = memory

    async def validate(self, output, task_description, worker_id, metadata):
        """Detects hallucinations in the output."""
        checks = {}

        # Check 1: Hallucination Detection (Placeholder - needs sophisticated logic)
        # This is a SIMPLIFIED example; real-world would use NLP techniques.
        checks["no_hallucinations"] = "nonsense" not in output.lower() and "i don't know" not in output.lower()

        # Check 2: Relevance to Task (Placeholder - needs sophisticated logic)
        # This checks if the output contains keywords from the task description
        task_keywords = task_description.lower().split()
        output_keywords = output.lower().split()
        checks["relevance"] = any(keyword in output_keywords for keyword in task_keywords)

        score = calculate_score(checks)
        passed = score >= 0.7  # Threshold

        reason = "No hallucinations detected." if passed else "Possible hallucinations detected."
        log_decision("GateGamma", worker_id, passed, reason, self.memory)

        return passed, score, checks

class GateDelta:  # Memory Integration
    def __init__(self, memory):
        self.memory = memory

    async def validate(self, data_to_store, worker_id, metadata):
        """Validates storage operations to memory."""
        checks = {}

        # Check 1: Successful Storage (Placeholder - Assuming a successful storage function)
        try:
            self.memory.store_data("temp_key", data_to_store)  # Example storage
            checks["storage_successful"] = True
        except Exception:
            checks["storage_successful"] = False

        # Check 2: Data Integrity (Placeholder - Simple Hash Check)
        original_hash = hashlib.sha256(str(data_to_store).encode()).hexdigest()
        retrieved_data = self.memory.retrieve_data("temp_key") #Assumed retrieve function
        if retrieved_data:
            retrieved_hash = hashlib.sha256(str(retrieved_data).encode()).hexdigest()
            checks["data_integrity"] = original_hash == retrieved_hash
        else:
            checks["data_integrity"] = False

        score = calculate_score(checks)
        passed = score >= 0.9  # Threshold

        reason = "Memory integration successful." if passed else "Memory integration failed."
        log_decision("GateDelta", worker_id, passed, reason, self.memory)

        return passed, score, checks

class GateEpsilon:  # Strategy Alignment
    def __init__(self, memory):
        self.memory = memory

    async def validate(self, output, revenue_pathway, worker_id, metadata):
        """Ensures output aligns with revenue pathway."""
        checks = {}

        # Check 1: Pathway Relevance (Placeholder - Needs business logic)
        # This example checks for keywords related to the pathway in the output
        pathway_keywords = revenue_pathway.get("keywords", [])
        output_keywords = output.lower().split()
        checks["pathway_relevance"] = any(keyword in output_keywords for keyword in pathway_keywords)

        # Check 2: Risk Assessment (Placeholder - Needs risk assessment logic)
        # Simple example: avoids mentioning "risk" or "loss"
        checks["no_negative_risk"] = "risk" not in output.lower() and "loss" not in output.lower()

        score = calculate_score(checks)
        passed = score >= 0.7  # Threshold

        reason = "Strategy alignment confirmed." if passed else "Strategy alignment failed."
        log_decision("GateEpsilon", worker_id, passed, reason, self.memory)

        return passed, score, checks

class GateZeta:  # Budget Compliance
    def __init__(self, memory):
        self.memory = memory

    async def validate(self, resource_usage, budget_limit, worker_id, metadata):
        """Monitors resource usage and ensures budget compliance."""
        checks = {}

        # Check 1: Usage within Limit
        checks["within_budget"] = resource_usage <= budget_limit

        # Check 2: Alert on Near Limit (Optional)
        near_limit_threshold = 0.9 * budget_limit
        checks["near_limit_alert"] = resource_usage <= near_limit_threshold

        score = calculate_score(checks)
        passed = score >= 0.8  # Threshold

        reason = "Budget compliance confirmed." if passed else "Budget compliance failed."
        log_decision("GateZeta", worker_id, passed, reason, self.memory)

        return passed, score, checks


# Orchestrator Class
class SixGateValidator:
    def __init__(self, memory):
        self.memory = memory
        self.gate_alpha = GateAlpha(memory)
        self.gate_beta = GateBeta(memory)
        self.gate_gamma = GateGamma(memory)
        self.gate_delta = GateDelta(memory)
        self.gate_epsilon = GateEpsilon(memory)
        self.gate_zeta = GateZeta(memory)

    async def validate_worker_output(self, source_data, output, task_description, worker_id, metadata, expected_format, revenue_pathway, resource_usage, budget_limit):
        """Runs the complete 6-gate validation suite asynchronously."""

        # Gate Alpha: Input Validity
        alpha_passed, alpha_score, alpha_checks = await self.gate_alpha.validate(source_data, worker_id, metadata)
        if not alpha_passed:
            return self._create_report(worker_id, False, 0.0, {
                "alpha": {"score": alpha_score, "checks": alpha_checks, "passed": alpha_passed},
                "beta": {"score": 0.0, "checks": {}, "passed": False},
                "gamma": {"score": 0.0, "checks": {}, "passed": False},
                "delta": {"score": 0.0, "checks": {}, "passed": False},
                "epsilon": {"score": 0.0, "checks": {}, "passed": False},
                "zeta": {"score": 0.0, "checks": {}, "passed": False}
            })

        # Gate Beta: Output Quality
        beta_passed, beta_score, beta_checks = await self.gate_beta.validate(output, expected_format, worker_id, metadata)
        if not beta_passed:
            return self._create_report(worker_id, False, 0.0, {
                "alpha": {"score": alpha_score, "checks": alpha_checks, "passed": alpha_passed},
                "beta": {"score": beta_score, "checks": beta_checks, "passed": beta_passed},
                "gamma": {"score": 0.0, "checks": {}, "passed": False},
                "delta": {"score": 0.0, "checks": {}, "passed": False},
                "epsilon": {"score": 0.0, "checks": {}, "passed": False},
                "zeta": {"score": 0.0, "checks": {}, "passed": False}
            })

        # Gate Gamma: Insight Purity
        gamma_passed, gamma_score, gamma_checks = await self.gate_gamma.validate(output, task_description, worker_id, metadata)
        if not gamma_passed:
            return self._create_report(worker_id, False, 0.0, {
                "alpha": {"score": alpha_score, "checks": alpha_checks, "passed": alpha_passed},
                "beta": {"score": beta_score, "checks": beta_checks, "passed": beta_passed},
                "gamma": {"score": gamma_score, "checks": gamma_checks, "passed": gamma_passed},
                "delta": {"score": 0.0, "checks": {}, "passed": False},
                "epsilon": {"score": 0.0, "checks": {}, "passed": False},
                "zeta": {"score": 0.0, "checks": {}, "passed": False}
            })

        # Gate Delta: Memory Integration
        delta_passed, delta_score, delta_checks = await self.gate_delta.validate(output, worker_id, metadata)
        if not delta_passed:
            return self._create_report(worker_id, False, 0.0, {
                "alpha": {"score": alpha_score, "checks": alpha_checks, "passed": alpha_passed},
                "beta": {"score": beta_score, "checks": beta_checks, "passed": beta_passed},
                "gamma": {"score": gamma_score, "checks": gamma_checks, "passed": gamma_passed},
                "delta": {"score": delta_score, "checks": delta_checks, "passed": delta_passed},
                "epsilon": {"score": 0.0, "checks": {}, "passed": False},
                "zeta": {"score": 0.0, "checks": {}, "passed": False}
            })

        # Gate Epsilon: Strategy Alignment
        epsilon_passed, epsilon_score, epsilon_checks = await self.gate_epsilon.validate(output, revenue_pathway, worker_id, metadata)
        if not epsilon_passed:
            return self._create_report(worker_id, False, 0.0, {
                "alpha": {"score": alpha_score, "checks": alpha_checks, "passed": alpha_passed},
                "beta": {"score": beta_score, "checks": beta_checks, "passed": beta_passed},
                "gamma": {"score": gamma_score, "checks": gamma_checks, "passed": gamma_passed},
                "delta": {"score": delta_score, "checks": delta_checks, "passed": delta_passed},
                "epsilon": {"score": epsilon_score, "checks": epsilon_checks, "passed": epsilon_passed},
                "zeta": {"score": 0.0, "checks": {}, "passed": False}
            })

        # Gate Zeta: Budget Compliance
        zeta_passed, zeta_score, zeta_checks = await self.gate_zeta.validate(resource_usage, budget_limit, worker_id, metadata)


        overall_score = (alpha_score + beta_score + gamma_score + delta_score + epsilon_score + zeta_score) / 6
        passed = overall_score >= 0.8

        report = self._create_report(worker_id, passed, overall_score, {
            "alpha": {"score": alpha_score, "checks": alpha_checks, "passed": alpha_passed},
            "beta": {"score": beta_score, "checks": beta_checks, "passed": beta_passed},
            "gamma": {"score": gamma_score, "checks": gamma_checks, "passed": gamma_passed},
            "delta": {"score": delta_score, "checks": delta_checks, "passed": delta_passed},
            "epsilon": {"score": epsilon_score, "checks": epsilon_checks, "passed": epsilon_passed},
            "zeta": {"score": zeta_score, "checks": zeta_checks, "passed": zeta_passed}
        })

        # Log to immutable audit trail (Patent P4)
        try:
            self.memory.log_audit("six_gate_validation", worker_id, report)
        except Exception as e:
            print(f"[WARNING] Audit logging failed: {e}")

        # Update worker trust (Patent P9)
        try:
            self.memory.update_worker_trust(worker_id, passed)
        except Exception as e:
            print(f"[WARNING] Trust update failed: {e}")

        return report

    def _create_report(self, worker_id, passed, overall_score, gates):
        """Helper function to create a standardized report."""
        return {
            "timestamp": datetime.utcnow().isoformat(),
            "worker_id": worker_id,
            "passed": passed,
            "overall_score": overall_score,
            "gates": gates
        }

# Example Usage (assuming memory is initialized)
# memory = ... # Initialize your memory store
# validator = SixGateValidator(memory)
#
# async def main():
#     report = await validator.validate_worker_output(
#         source_data="Valid source data",
#         output={"result": "Valid output"},
#         task_description="Summarize the article.",
#         worker_id="worker123",
#         metadata={"output_hash": "some_hash"},
#         expected_format={"required_fields": ["result"]},
#         revenue_pathway={"keywords": ["summary", "article"]},
#         resource_usage=50,
#         budget_limit=100
#     )
#     print(report)
#
# if __name__ == "__main__":
#     asyncio.run(main())