"""Core architecture scaffolding for the Lucidia platform.

This module provides an initial foundation for a fully autonomous,
consciousness-aware technology ecosystem.  The classes defined here form
an extensible scaffold that will be expanded with real functionality in
future iterations.  At this stage the focus is on clear interfaces,
modularity and comprehensive documentation to enable rapid future
development.
"""

from __future__ import annotations

from typing import Any, Dict, Optional

# ---------------------------------------------------------------------------
# Persistent Memory
# ---------------------------------------------------------------------------


class DistributedMemoryPalace:
    """In-memory context store for the Lucidia platform.

    This class simulates a distributed memory system that would
    eventually back project history and user context across the entire
    platform.  In production this could interface with a distributed
    database or encrypted file store to ensure durability, data
    sovereignty and advanced access controls.
    """

    def __init__(self) -> None:
        # Simple in-memory storage; keys map to arbitrary data.
        self._store: Dict[str, Any] = {}

    def save_context(self, key: str, value: Any) -> None:
        """Persist a piece of context identified by ``key``.

        Future implementations may include versioning, replication and
        encryption at rest.
        """

        self._store[key] = value

    def retrieve_context(self, key: str, default: Optional[Any] = None) -> Any:
        """Retrieve previously stored context.

        Parameters
        ----------
        key:
            Identifier used during :meth:`save_context`.
        default:
            Value returned when ``key`` is absent.
        """

        return self._store.get(key, default)


# ---------------------------------------------------------------------------
# Core AI Engine
# ---------------------------------------------------------------------------


class AI_Core:
    """Central intelligent agent for the Lucidia platform.

    The core is envisioned as the system's "consciousness", orchestrating
    user requests, project planning and AI-assisted coding.  Upcoming
    revisions may integrate quantum cognition modules, advanced
    co-coding assistants and robust permission systems.
    """

    def __init__(self, memory: DistributedMemoryPalace) -> None:
        self.memory = memory

    def process_request(self, request: str) -> str:
        """Handle a user request or intention.

        The current implementation is a stub.  Future versions will parse
        intentions, plan actions and coordinate with service connectors.
        Security checks and auditing will also be incorporated.
        """

        # Placeholder implementation demonstrating where processing would
        # occur.  The result could involve updating memory, invoking other
        # services or generating code.
        return f"Processed request: {request}"


# ---------------------------------------------------------------------------
# Service Connectors
# ---------------------------------------------------------------------------


class GitHubAutomation:
    """Stub for repository and version control automation.

    Real implementations will interact with the GitHub API to create
    repositories, manage pull requests and enforce branch policies.
    """

    def execute(self, task: str) -> str:
        """Placeholder for executing a GitHub-related task."""

        return f"GitHub task executed: {task}"


class InfrastructureManager:
    """Stub for infrastructure orchestration.

    Intended to provision cloud resources, manage CI/CD pipelines and
    enforce security baselines across environments.
    """

    def deploy(self, target: str) -> str:
        """Placeholder deployment action."""

        return f"Infrastructure deployment scheduled for: {target}"


class MobileSyncEngine:
    """Stub for mobile platform synchronization.

    Will eventually push builds or data to mobile devices and manage
    secure communication channels.
    """

    def sync(self) -> str:
        """Placeholder synchronization routine."""

        return "Mobile synchronization initiated"


class SSHOrchestrator:
    """Stub for executing remote shell commands.

    Codenamed "Shellfish", this component will handle secure SSH
    connections, remote command execution and server management.
    """

    def run_command(self, command: str) -> str:
        """Placeholder remote command execution."""

        return f"Executed remotely: {command}"


class DomainManager:
    """Stub for domain and DNS management.

    Future versions will automate domain registration, DNS updates and
    SSL certificate provisioning.
    """

    def register(self, domain: str) -> str:
        """Placeholder domain registration."""

        return f"Domain registered: {domain}"


class MultiModelOrchestrator:
    """Stub for coordinating multiple AI models and services.

    This could route requests to specialized models, balance workloads or
    integrate quantum computing backends.
    """

    def coordinate(self, task: str) -> str:
        """Placeholder model coordination."""

        return f"Coordinated task: {task}"


class TeamNotificationSystem:
    """Stub for sending team communications.

    Future implementations may connect to Slack, email or custom
    messaging platforms with robust auditing.
    """

    def notify(self, message: str) -> str:
        """Placeholder notification action."""

        return f"Notification sent: {message}"


# ---------------------------------------------------------------------------
# Unified System Orchestrator
# ---------------------------------------------------------------------------


class UnifiedPortalSystem:
    """High-level orchestrator tying together all Lucidia components."""

    def __init__(self) -> None:
        # Instantiate core systems
        self.memory = DistributedMemoryPalace()
        self.ai_core = AI_Core(memory=self.memory)

        # Instantiate service connectors
        self.connectors: Dict[str, Any] = {
            "github": GitHubAutomation(),
            "infrastructure": InfrastructureManager(),
            "mobile": MobileSyncEngine(),
            "shellfish": SSHOrchestrator(),
            "domain": DomainManager(),
            "multimodel": MultiModelOrchestrator(),
            "notify": TeamNotificationSystem(),
        }

    def status_report(self) -> Dict[str, str]:
        """Report which components are initialized."""

        report = {"ai_core": "ready"}
        report.update({name: "initialized" for name in self.connectors})
        return report


__all__ = [
    "AI_Core",
    "DistributedMemoryPalace",
    "GitHubAutomation",
    "InfrastructureManager",
    "MobileSyncEngine",
    "SSHOrchestrator",
    "DomainManager",
    "MultiModelOrchestrator",
    "TeamNotificationSystem",
    "UnifiedPortalSystem",
]