"""Assistance orchestrator.

The piece that turns raw user intent + collected context into a well-formed
request for the LLM adapter, then turns the adapter response into the
final terminal output the user sees.

Key responsibilities:
- Coordinate context collector, memory (Profile 0 via recall with activation_context),
  rule-based risk classifier, and LLMAdapter.
- Wire memory influence into every assistance cycle and `--explain-context`.
- Own the `cya retrospect` flow that feeds higher-order memory back into the system.
- Keep the CLI surface thin.

**Memory note (Profile 0):** This module is the primary consumer of the
stable memory ports defined in src/cya/memory/__init__.py. All calls use
activation_context derived from the current working directory + git root.
See MemoryVision.md "Profile 0 Baseline" and CYA-WP-0005 for the evolution
path to Profiles 1–3.

See workplan CYA-WP-0001 (core) + CYA-WP-0003 (memory wiring + retrospect)
+ CYA-WP-0005 (profile formalization).
"""

from __future__ import annotations

from pathlib import Path

import typer
from rich.console import Console
from rich.panel import Panel

from cya.context.collector import collect, render_explanation
from cya.memory import (
    recall_preferences,
    remember_retrospection_outcome,
    KIND_RETROSPECTION,
    KIND_INTERACTION_GOAL,
    KIND_REFLECTION,
)
from cya.memory.reflections import (
    REFLECTION_CAPTURE_PROMPTS,
    collect_lessons_from_answers,
    compact_reflections,
    find_duplicate_reflection_groups,
    format_reflection_surfacing,
    preview_lessons,
    save_reflection_lessons,
    session_provenance,
)
from cya.safety.risk import classify, get_user_confirmation
from cya.config import bound_session_turns
from cya.llm.adapter import AssistanceRequest
from cya.llm.factory import get_adapter


console = Console()


def handle_request(
    user_request: str,
    *,
    explain_context: bool = False,
    dry_run: bool = False,
    offline: bool = False,
    session_turns: list[dict[str, str]] | None = None,
) -> None:
    """Primary orchestrator entry point.

    This is what the CLI (and future tests / other front-ends) should call.
    It coordinates the full current flow:
        context → safety (with mandatory confirmation) → LLMAdapter → render
    """
    # 1. Context (always cheap; needed for safety "affected" and for the adapter)
    try:
        envelope = collect(".")
    except Exception:
        envelope = None

    if explain_context and envelope:
        try:
            explanation = render_explanation(envelope)
            console.print(
                Panel(
                    explanation,
                    title="Context Envelope (T02)",
                    border_style="green",
                    padding=(1, 1),
                )
            )
        except Exception as exc:
            console.print(f"[red]Context explanation error: {exc}[/red]")

    # T03 (memory wiring): consult after context (so safety can see it in future T04 0002),
    # before risk/LLM. Real T02 prefs now available; graceful.
    # T03 (0003): pass activation_context so directory/project-bound memory is automatically
    # activated based on cwd + git root.
    memory = {}
    try:
        act_ctx = {"cwd": str(Path.cwd())}
        if envelope and getattr(envelope, "git", None):
            git_info = envelope.git or {}
            if git_info.get("workdir"):
                act_ctx["git_root"] = git_info["workdir"]
        memory = recall_preferences(
            ".",
            activation_context=act_ctx,
            kinds=[KIND_REFLECTION, KIND_RETROSPECTION, KIND_INTERACTION_GOAL, "preference"],
        )
    except Exception:
        memory = {"error": "recall failed (graceful degradation)"}

    if explain_context and memory.get("items"):
        try:
            prov = memory.get("provenance", [{}])[0]
            sample = ", ".join(i.get("key", "?") for i in memory.get("items", [])[:3])
            act_note = ""
            if prov.get("activation_context"):
                act_note = f" | ctx: {prov['activation_context']}"
            body = (
                f"Phase: {memory.get('phase')} | {len(memory.get('items', []))} items | "
                f"{prov.get('source', 'local')}{act_note}\n"
                f"Sample activated: {sample}"
            )
            refl_line = format_reflection_surfacing(memory, for_explain=True)
            if refl_line:
                body += f"\n\n[cyan]Reflections:[/cyan] {refl_line}"
            console.print(
                Panel(
                    body,
                    title="Memory Activated (T03)",
                    border_style="blue",
                    padding=(0, 1),
                )
            )
        except Exception:
            pass

    # 2. Risk classification + mandatory confirmation (T03 safety; T04 memory signals)
    assessment = classify(user_request, envelope, memory=memory)

    if assessment.requires_confirmation:
        from rich.table import Table

        table = Table(
            title=f"Risk Assessment — {assessment.level.value.upper()}",
            show_header=False,
            border_style="red",
        )
        table.add_row("Rationale", assessment.rationale)
        if assessment.preview:
            table.add_row("Preview", assessment.preview)
        if assessment.affected_summary:
            table.add_row("Would affect", assessment.affected_summary)
        table.add_row("Rules", ", ".join(assessment.rules_triggered[:3]))
        console.print(table)

        if not get_user_confirmation(assessment):
            console.print("[yellow]Action cancelled by user. No changes made.[/yellow]")
            return

    if dry_run:
        console.print("[green]--dry-run acknowledged.[/green] No side-effects.")
        return

    # 3. Call through the single LLMAdapter boundary (T04 / CYA-WP-0008)
    adapter = get_adapter(offline=offline)
    ctx = (envelope.to_dict() if envelope else {}) or {}
    ctx["memory"] = memory  # T03: memory now in context passed to LLM (for personalization + explain)
    if session_turns:
        ctx["session_turns"] = bound_session_turns(session_turns)
    llm_request = AssistanceRequest(
        user_request=user_request,
        context=ctx,
    )
    llm_response = adapter.complete(llm_request)

    # 4. Render final user-facing artifact (T06 responsibility; T03 memory surface)
    mem_line = ""
    if memory.get("items"):
        mem_line = f"\n[dim]Memory activated: {len(memory.get('items', []))} items (phase {memory.get('phase')})[/dim]"
        refl_line = format_reflection_surfacing(memory, for_explain=False)
        if refl_line:
            mem_line += f"\n[cyan]{refl_line}[/cyan]"

    console.print(
        Panel(
            f"[bold]Suggestion:[/bold]\n{llm_response.suggestion}\n\n"
            f"[dim]{llm_response.explanation}\n"
            f"Rationale: {llm_response.rationale}{mem_line}[/dim]",
            title="LLM Response (via T04 seam)",
            border_style="magenta",
            padding=(1, 1),
        )
    )

    console.print(
        "[green]✓[/green] Request processed by orchestrator (T02+T03+T04 coordinated by T06)."
    )


def run_retrospection(scope: str = ".", limit: int = 8) -> None:
    """Guided retrospection session (T04 of CYA-WP-0003).

    Helps the user review recent memory usage in the given scope,
    reflect, and record new interaction goals or preferences.
    These are stored using the retrospection-aware memory helper.
    """
    console.print(
        Panel(
            "[bold cyan]Retrospection Session[/bold cyan]\n\n"
            f"Scope: [green]{scope}[/green]\n"
            "We will look at recent memory items and help you reflect.\n"
            "Your answers will be stored as durable retrospection memory.",
            title="cya retrospect",
            border_style="magenta",
            padding=(1, 2),
        )
    )

    # Recall recent items, with bias toward retrospection kinds if present
    try:
        recent = recall_preferences(
            scope,
            limit=limit,
            kinds=[KIND_RETROSPECTION, KIND_INTERACTION_GOAL, "preference"],
        )
        items = recent.get("items", [])
    except Exception as e:
        console.print(f"[red]Could not load memory: {e}[/red]")
        return

    if not items:
        console.print(
            "[yellow]No memory items found in this scope yet.[/yellow]\n"
            "You can create some with normal usage or explicit remembers."
        )
        return

    console.print(
        Panel(
            "\n".join(
                f"• [bold]{item.get('key')}[/bold]: {item.get('value')}"
                for item in items[:5]
            ),
            title=f"Recent Memory in {scope} (showing up to 5)",
            border_style="blue",
        )
    )

    # Simple guided reflection
    console.print("\n[bold]Reflection time[/bold]")

    what_worked = typer.prompt(
        "What worked well in recent assistance? (short answer or 'skip')",
        default="",
        show_default=False,
    )
    if what_worked and what_worked.lower() not in ("skip", "s", ""):
        remember_retrospection_outcome(
            "what_worked", what_worked, scope=scope
        )
        console.print("[green]Recorded.[/green]")

    what_to_change = typer.prompt(
        "What should change in future interactions? (e.g. 'be more concise', 'always show alternatives')",
        default="",
        show_default=False,
    )
    if what_to_change and what_to_change.lower() not in ("skip", "s", ""):
        remember_retrospection_outcome(
            "interaction_goal", what_to_change, scope=scope
        )
        console.print("[green]Recorded as interaction goal.[/green]")

    safety_note = typer.prompt(
        "Any standing safety or preference rules for this project? (optional)",
        default="",
        show_default=False,
    )
    if safety_note and safety_note.lower() not in ("skip", "s", ""):
        remember_retrospection_outcome(
            "safety_preference", safety_note, scope=scope
        )
        console.print("[green]Recorded as safety preference.[/green]")

    _capture_reflection_lessons(scope)
    _offer_reflection_compaction(scope)

    console.print(
        Panel(
            "Thank you. Your reflections have been stored as retrospection memory.\n"
            "They will be preferentially activated in future sessions in this scope.\n\n"
            "You can review them anytime with:\n"
            f"  [bold]cya memory reflections --scope {scope}[/bold]\n"
            f"  [bold]cya --explain-context \"...\"[/bold]  (in this directory)\n"
            f"  or inspect the JSON files in [cyan]~/.config/cya/memory/[/cyan]",
            title="Retrospection Complete",
            border_style="green",
            padding=(1, 2),
        )
    )


def _capture_reflection_lessons(scope: str) -> None:
    """Profile 1: guided verbal lesson capture with preview and confirmation."""
    want = typer.prompt(
        "Capture 1–3 verbal lessons from this session? (y/n)",
        default="n",
        show_default=False,
    )
    if not want or want.strip().lower() in ("n", "no", "skip", "s", ""):
        return

    answers: dict[str, str] = {}
    for prompt_key, label in REFLECTION_CAPTURE_PROMPTS:
        answers[prompt_key] = typer.prompt(label, default="", show_default=False)

    lessons = collect_lessons_from_answers(answers)
    if not lessons:
        console.print("[yellow]No lessons captured — nothing stored.[/yellow]")
        return

    console.print(
        Panel(
            preview_lessons(lessons),
            title="Preview — verbal lessons to save",
            border_style="cyan",
        )
    )
    confirm = typer.prompt("Save these lessons? (y/n)", default="n", show_default=False)
    if confirm.strip().lower() not in ("y", "yes"):
        console.print("[yellow]Lessons discarded — nothing stored.[/yellow]")
        return

    count = save_reflection_lessons(
        lessons,
        scope,
        provenance=session_provenance(scope),
    )
    console.print(f"[green]Saved {count} verbal reflection(s) (Profile 1).[/green]")


def _offer_reflection_compaction(scope: str) -> None:
    """Offer explicit merge of near-duplicate reflections in this scope."""
    groups = find_duplicate_reflection_groups(scope)
    if not groups:
        return

    console.print(
        Panel(
            f"Found {len(groups)} group(s) of similar reflections in scope [green]{scope}[/green].\n"
            "Compaction is opt-in — nothing is deleted without your confirmation.",
            title="Reflection compaction available",
            border_style="yellow",
        )
    )
    do_compact = typer.prompt("Review and compact duplicates? (y/n)", default="n", show_default=False)
    if do_compact.strip().lower() not in ("y", "yes"):
        return

    for group in groups:
        console.print("\n[bold]Similar reflections:[/bold]")
        for item in group:
            console.print(f"  • {item.get('key')}: {item.get('value')}")

        keep = typer.prompt(
            "Key to keep (or 'skip' this group)",
            default=group[0].get("key", ""),
            show_default=True,
        )
        if not keep or keep.strip().lower() in ("skip", "s"):
            continue

        remove_keys = [i.get("key") for i in group if i.get("key") != keep]
        merge = typer.prompt(
            "Merged text (Enter to keep existing value of kept key)",
            default="",
            show_default=False,
        )
        result = compact_reflections(
            scope,
            keep_key=keep,
            remove_keys=[k for k in remove_keys if k],
            merged_value=merge.strip() or None,
        )
        console.print(
            f"[green]Compacted: kept {result['kept']}, removed {len(result['removed'])}[/green]"
        )


__all__ = ["handle_request", "run_retrospection"]