# ultimate_unified_validator.py

import asyncio
import hashlib
import hmac
import json
import logging
import time
import uuid
from typing import Dict, List, Tuple, Union, Callable, Any
from fastapi import FastAPI, HTTPException, Depends
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field
import threading
from collections import deque

# --- Logging Setup ---
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# --- Configuration ---
class Config:
    ENABLE_CACHE = True
    DEFAULT_RISK_THRESHOLD = 0.8
    DEFAULT_CONFIDENCE_THRESHOLD = 0.6
    WEBHOOK_URL = None # Replace with actual URL if needed

# --- Data Models ---
class ValidationReport(BaseModel):
    input_data: Union[str, Dict] = Field(..., description="The input data being validated.")
    validation_results: Dict = Field(..., description="Detailed results from each validation stage.")
    status: str = Field(..., description="Overall validation status (e.g., 'Validation Passed', 'Alpha Gate Failed').")
    audit_trail_id: str = Field(..., description="Unique ID for the audit trail entry.")
    confidence_score: Union[float, None] = Field(None, description="Overall confidence score for the validation.")
    thresholds: Dict = Field(..., description="The thresholds used during validation.")
    human_readable_summary: str = Field(..., description="A brief, human-readable summary of the validation process.")

class Thresholds(BaseModel):
    risk_threshold: float = Field(Config.DEFAULT_RISK_THRESHOLD, description="Threshold for risk assessment.")
    confidence_threshold: float = Field(Config.DEFAULT_CONFIDENCE_THRESHOLD, description="Threshold for confidence score.")

# --- Mock Implementations of the 9 Patent Systems (as before, but with logging) ---
# (CryptographicValidation, CurrencyValidation, MultiDimensionalRiskAssessment, ImmutableAuditTrail,
# MultiModelConsensus, DynamicConfidenceScoring, HallucinationDetection, PrivacyPreservingValidation,
# SelfImprovingThresholds) - same code as before but with logging added. Example:

class CryptographicValidation:
    """Cryptographic Validation (HMAC-SHA256)"""
    def validate(self, data: str, key: str) -> Tuple[bool, Dict]:
        """Validates data using HMAC-SHA256."""
        try:
            hmac_obj = hmac.new(key.encode('utf-8'), data.encode('utf-8'), hashlib.sha256)
            signature = hmac_obj.hexdigest()
            logger.info(f"Cryptographic validation successful. Signature: {signature[:10]}...")
            return True, {"cryptographic_signature": signature}  # Assume valid for now
        except Exception as e:
            logger.error(f"Cryptographic validation failed: {e}")
            return False, {"error": str(e)}

class CurrencyValidation:
    """Currency Validation (financial accuracy) - Mock"""
    def validate(self, transaction_data: Dict) -> Tuple[bool, Dict]:
        """Validates financial accuracy."""
        # Replace with actual financial validation logic
        amount = transaction_data.get("amount", 0)
        if amount <= 0:
            logger.warning("Invalid transaction amount.")
            return False, {"error": "Invalid transaction amount."}
        logger.info("Currency validation passed (Mock).")
        return True, {"currency_validation_status": "Passed (Mock)"}

class MultiDimensionalRiskAssessment:
    """Multi-Dimensional Risk Assessment - Mock"""
    def assess_risk(self, data: Dict) -> Tuple[float, Dict]:
        """Assesses risk based on multiple dimensions."""
        # Replace with actual risk assessment logic
        # Mock: Assign a random risk score
        risk_score = hash(json.dumps(data)) % 100 / 100.0  # Normalize to 0-1
        logger.info(f"Risk score assessed: {risk_score}")
        return risk_score, {"risk_score": risk_score, "risk_factors": ["factor_a", "factor_b"]}

class ImmutableAuditTrail:
    """Immutable Audit Trail - Mock"""
    def __init__(self):
        self.audit_log = deque(maxlen=1000)  # Use deque for efficient appending
        self.lock = threading.Lock()  # Thread safety

    def log_event(self, event_data: Dict) -> str:
        """Logs an event to the audit trail."""
        with self.lock:  # Ensure thread-safe access
            timestamp = time.time()
            event_id = str(uuid.uuid4())
            event = {"event_id": event_id, "timestamp": timestamp, "data": event_data}
            self.audit_log.append(event)
            logger.info(f"Event logged to audit trail: {event_id}")
            return event_id

    def get_trail(self) -> List[Dict]:
        """Returns the entire audit trail."""
        with self.lock:  # Ensure thread-safe access
            return list(self.audit_log)


class MultiModelConsensus:
    """Multi-Model Consensus - Mock"""
    def achieve_consensus(self, model_outputs: List[Dict]) -> Tuple[bool, Dict]:
        """Achieves consensus across multiple models."""
        # Replace with actual consensus mechanism
        # Mock: Check if any model flagged an error
        for output in model_outputs:
            if "error" in output:
                logger.warning("Consensus failed - Error in one or more models.")
                return False, {"consensus_status": "Failed - Error in one or more models"}
        logger.info("Consensus achieved (Mock).")
        return True, {"consensus_status": "Achieved (Mock)"}

class DynamicConfidenceScoring:
    """Dynamic Confidence Scoring - Mock"""
    def calculate_confidence(self, validation_results: List[Dict]) -> Tuple[float, Dict]:
        """Calculates a confidence score based on validation results."""
        # Replace with actual confidence calculation logic
        # Mock: Count the number of successful validations
        successful_validations = sum(1 for result in validation_results if result.get("status", False))
        confidence_score = successful_validations / len(validation_results) if validation_results else 0.5
        logger.info(f"Confidence score calculated: {confidence_score}")
        return confidence_score, {"confidence_score": confidence_score}

class HallucinationDetection:
    """Hallucination Detection - Mock"""
    def detect_hallucinations(self, text: str) -> Tuple[bool, Dict]:
        """Detects hallucinations in generated text."""
        # Replace with actual hallucination detection logic
        # Mock: Check for certain keywords
        hallucination_keywords = ["unicorns", "flying pigs", "impossible"]
        if any(keyword in text.lower() for keyword in hallucination_keywords):
            logger.warning("Hallucination detected.")
            return True, {"hallucination_detected": True, "hallucination_reason": "Keyword match"}
        logger.info("No hallucinations detected.")
        return False, {"hallucination_detected": False}

class PrivacyPreservingValidation:
    """Privacy-Preserving Validation - Mock"""
    def validate_privacy(self, data: Dict) -> Tuple[bool, Dict]:
        """Validates that privacy is preserved."""
        # Replace with actual privacy validation logic (e.g., differential privacy)
        # Mock: Check for personally identifiable information (PII)
        pii_keywords = ["name", "address", "phone", "email"]
        if any(keyword in str(data).lower() for keyword in pii_keywords):
            logger.warning("Potential privacy violation detected.")
            return False, {"privacy_violation": True, "privacy_reason": "Potential PII detected"}
        logger.info("Privacy validation passed.")
        return True, {"privacy_violation": False}

class SelfImprovingThresholds:
    """Self-Improving Thresholds - Mock"""
    def __init__(self):
        self.thresholds = {"risk_threshold": Config.DEFAULT_RISK_THRESHOLD, "confidence_threshold": Config.DEFAULT_CONFIDENCE_THRESHOLD}
        self.lock = threading.Lock()

    def adjust_thresholds(self, feedback: Dict) -> None:
        """Adjusts thresholds based on feedback."""
        with self.lock:
            # Replace with actual threshold adjustment logic (e.g., reinforcement learning)
            # Mock: Adjust the risk threshold based on error rate
            error_rate = feedback.get("error_rate", 0.0)
            if error_rate > 0.1:
                self.thresholds["risk_threshold"] = min(1.0, self.thresholds["risk_threshold"] + 0.01)
                logger.info(f"Risk threshold increased to {self.thresholds['risk_threshold']}")
            elif error_rate < 0.05:
                self.thresholds["risk_threshold"] = max(0.0, self.thresholds["risk_threshold"] - 0.01)
                logger.info(f"Risk threshold decreased to {self.thresholds['risk_threshold']}")

    def get_thresholds(self) -> Dict:
        """Returns the current thresholds."""
        with self.lock:
            return self.thresholds

# --- Core Components ---

class ValidationGate:
    """Base class for validation gates."""
    def __init__(self, name: str, validators: List[Callable], bypass_rules: List[Callable[[Any], bool]] = None, cache_enabled: bool = Config.ENABLE_CACHE):
        self.name = name
        self.validators = validators
        self.bypass_rules = bypass_rules or []
        self.cache_enabled = cache_enabled
        self.cache: Dict[str, Tuple[bool, Dict]] = {} #Simple in memory cache

    async def run(self, data: Any) -> Tuple[bool, Dict]:
        """Runs the validation gate."""
        if any(rule(data) for rule in self.bypass_rules):
            logger.info(f"Gate {self.name} bypassed.")
            return True, {"gate_bypassed": True}

        cache_key = self._generate_cache_key(data)
        if self.cache_enabled and cache_key in self.cache:
            logger.info(f"Gate {self.name} - Cache hit.")
            return self.cache[cache_key]

        results = {}
        all_passed = True
        for validator in self.validators:
            try:
                status, result = await self._run_validator(validator, data)
                results[validator.__name__] = result
                all_passed = all_passed and status
            except Exception as e:
                logger.error(f"Validator {validator.__name__} in gate {self.name} failed: {e}")
                results[validator.__name__] = {"error": str(e)}
                all_passed = False

        final_status = all_passed
        self.cache[cache_key] = (final_status, results) # Cache results
        return final_status, results

    async def _run_validator(self, validator: Callable, data: Any) -> Tuple[bool, Dict]:
        """Runs a single validator."""
        try:
            if asyncio.iscoroutinefunction(validator):
                status, result = await validator(data)
            else:
                status, result = validator(data)
            return status, result
        except Exception as e:
            logger.error(f"Validator {validator.__name__} failed: {e}")
            return False, {"error": str(e)}

    def _generate_cache_key(self, data: Any) -> str:
        """Generates a cache key based on the input data."""
        return hashlib.sha256(json.dumps(data, sort_keys=True).encode('utf-8')).hexdigest() #Simple hash

class OrchestrationEngine:
    """Manages the validation pipeline."""
    def __init__(self, gates: List[ValidationGate]):
        self.gates = gates

    async def run_pipeline(self, data: Any) -> Dict:
        """Runs the entire validation pipeline."""
        results = {}
        overall_status = True

        for gate in self.gates:
            logger.info(f"Running gate: {gate.name}")
            status, gate_results = await gate.run(data)
            results[gate.name] = {"status": status, "results": gate_results}
            overall_status = overall_status and status
            if not status:
                logger.warning(f"Gate {gate.name} failed.")
                break # Stop pipeline on failure

        return {"status": overall_status, "gate_results": results}

# --- Unified Patent Validator ---

class UnifiedPatentValidator:
    """The master orchestrator."""
    def __init__(self):
        self.cryptographic_validator = CryptographicValidation()
        self.currency_validator = CurrencyValidation()
        self.risk_assessor = MultiDimensionalRiskAssessment()
        self.audit_trail = ImmutableAuditTrail()
        self.consensus_engine = MultiModelConsensus()
        self.confidence_scorer = DynamicConfidenceScoring()
        self.hallucination_detector = HallucinationDetection()
        self.privacy_validator = PrivacyPreservingValidation()
        self.threshold_manager = SelfImprovingThresholds()

        self.alpha_gate = ValidationGate(
            name="Alpha",
            validators=[self.cryptographic_validator.validate, self.currency_validator.validate]
        )
        self.beta_gate = ValidationGate(
            name="Beta",
            validators=[self.risk_assessor.assess_risk, self.privacy_validator.validate_privacy],
            bypass_rules=[lambda data: "trusted" in str(data).lower()]
        )
        self.gamma_gate = ValidationGate(
            name="Gamma",
            validators=[self.hallucination_detector.detect_hallucinations, self.consensus_engine.achieve_consensus]
        )

        self.orchestration_engine = OrchestrationEngine(gates=[self.alpha_gate, self.beta_gate, self.gamma_gate])

    async def validate(self, data: Union[str, Dict], validation_key: str = None, model_outputs: List[Dict] = None) -> ValidationReport:
        """Validates data using the orchestration engine and Triple Gate pattern."""
        if isinstance(data, str) and validation_key:
            data_to_validate = data
        elif isinstance(data, dict):
            data_to_validate = data
        else:
            raise ValueError("Invalid data type or missing key")

        # Add model outputs to data for validation
        validation_data = {"data": data_to_validate, "model_outputs": model_outputs}

        pipeline_results = await self.orchestration_engine.run_pipeline(validation_data)
        overall_status = pipeline_results["status"]
        gate_results = pipeline_results["gate_results"]

        # --- Final Confidence Scoring ---
        validation_results_for_scoring = []
        for gate_name, gate_result in gate_results.items():
            if "results" in gate_result:  # Check if 'results' key exists
                validation_results_for_scoring.append(gate_result["results"])
            else:
                logger.warning(f"No 'results' key found in gate {gate_name} results.")

        confidence_score, confidence_details = self.confidence_scorer.calculate_confidence(validation_results_for_scoring)

        # --- Threshold Check ---
        thresholds = self.threshold_manager.get_thresholds()
        if confidence_score < thresholds["confidence_threshold"]:
            overall_status = False
            logger.warning(f"Confidence score {confidence_score} below threshold {thresholds['confidence_threshold']}.")

        # --- Update Thresholds (Example) ---
        error_count = sum(1 for gate_name, gate_result in gate_results.items() if not gate_result["status"])
        total_gates = len(gate_results)
        error_rate = error_count / total_gates if total_gates > 0 else 0.0
        self.threshold_manager.adjust_thresholds({"error_rate": error_rate})

        # --- Generate Report ---
        status_message = "Validation Passed" if overall_status else "Validation Failed"
        report = self._generate_report(data_to_validate, gate_results, status_message, confidence_score)

        # --- Webhook Notification ---
        if Config.WEBHOOK_URL:
            await self._send_webhook(report)

        return report

    def _generate_report(self, input_data: Union[str, Dict], validation_results: Dict, status_message: str, confidence_score: float = None) -> ValidationReport:
        """Generates a comprehensive validation report."""
        audit_trail_id = self.audit_trail.log_event({"report_generated": True, "status": status_message})
        thresholds = self.threshold_manager.get_thresholds()

        summary = f"{status_message}. Confidence: {confidence_score:.2f}. Risk Threshold: {thresholds['risk_threshold']:.2f}."

        report = ValidationReport(
            input_data=input_data,
            validation_results=validation_results,
            status=status_message,
            audit_trail_id=audit_trail_id,
            confidence_score=confidence_score,
            thresholds=thresholds,
            human_readable_summary=summary
        )
        return report

    async def _send_webhook(self, report: ValidationReport):
        """Sends a webhook notification."""
        try:
            import httpx
            async with httpx.AsyncClient() as client:
                await client.post(Config.WEBHOOK_URL, json=report.dict())
            logger.info(f"Webhook sent to {Config.WEBHOOK_URL}")
        except Exception as e:
            logger.error(f"Failed to send webhook: {e}")

    def get_audit_trail(self) -> List[Dict]:
        """Retrieves the entire audit trail."""
        return self.audit_trail.get_trail()

    def get_thresholds(self) -> Dict:
        """Retrieves the current thresholds."""
        return self.threshold_manager.get_thresholds()

# --- FastAPI Integration ---

app = FastAPI(title="Unified Patent Validator")

# Dependency Injection
async def get_validator():
    validator = UnifiedPatentValidator()
    try:
        yield validator
    finally:
        pass

class ValidateRequest(BaseModel):
    data: Union[str, Dict] = Field(..., description="Data to validate.")
    validation_key: Union[str, None] = Field(None, description="Optional validation key.")
    model_outputs: Union[List[Dict], None] = Field(None, description="Optional list of model outputs.")

@app.post("/validate/", response_model=ValidationReport)
async def validate_endpoint(request: ValidateRequest, validator: UnifiedPatentValidator = Depends(get_validator)):
    """Validates data using the Unified Patent Validator."""
    try:
        report = await validator.validate(request.data, request.validation_key, request.model_outputs)
        return report
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.exception("An unexpected error occurred during validation.")  # Log the full exception
        raise HTTPException(status_code=500, detail="Internal server error")

@app.get("/audit_trail/")
async def get_audit_trail_endpoint(validator: UnifiedPatentValidator = Depends(get_validator)):
    """Retrieves the audit trail."""
    return validator.get_audit_trail()

@app.get("/thresholds/", response_model=Thresholds)
async def get_thresholds_endpoint(validator: UnifiedPatentValidator = Depends(get_validator)):
    """Retrieves the current thresholds."""
    thresholds = validator.get_thresholds()
    return Thresholds(**thresholds) #Pydantic validation

# --- Example Usage (FastAPI) ---
# To run: uvicorn ultimate_unified_validator:app --reload

if __name__ == "__main__":
    #This part won't be executed when running with uvicorn
    pass