"""
Preset „Nächste aus Kontext“: LLM leitet Erwartungsprofil aus Planungskontext ab.

Prompt: planning_exercise_expectation_profile (Migration 074)
"""
from __future__ import annotations

import logging
from typing import Any, Dict, Mapping, Optional, Tuple

from planning_exercise_intent import (
    PlanningQueryIntentParsed,
    _compact_json,
    _load_compact_catalog,
    _load_skills_catalog_compact,
    parse_planning_query_intent_response,
)
from ai_prompt_runtime import AiPromptUnavailableError, load_and_render_ai_prompt
from openrouter_chat import (
    effective_openrouter_model_for_prompt_row,
    normalize_openrouter_env,
    openrouter_chat_completion,
)

_logger = logging.getLogger("shinkan.planning_exercise_expectation")


def try_build_planning_expectation_from_context(
    cur,
    *,
    heuristic_intent: str,
    context_summary: Mapping[str, Any],
    target_profile_summary: Mapping[str, Any],
) -> Tuple[Optional[PlanningQueryIntentParsed], bool]:
    """
    LLM-Erwartungsprofil für preset_next / leere Anfrage mit Planungsbezug.
    Returns (parsed overlay, applied).
    """
    api_key, _ = normalize_openrouter_env()
    if not api_key:
        return None, False

    variables = {
        "heuristic_intent": heuristic_intent or "suggest_next",
        "planning_context_json": _compact_json(dict(context_summary or {})),
        "target_profile_json": _compact_json(dict(target_profile_summary or {})),
        "skills_catalog_json": _compact_json(_load_skills_catalog_compact(cur)),
        "focus_areas_catalog_json": _compact_json(_load_compact_catalog(cur, "focus_areas", "id")),
        "training_types_catalog_json": _compact_json(_load_compact_catalog(cur, "training_types", "id")),
        "style_directions_catalog_json": _compact_json(_load_compact_catalog(cur, "style_directions", "id")),
        "target_groups_catalog_json": _compact_json(_load_compact_catalog(cur, "target_groups", "id")),
    }

    try:
        prow, rendered = load_and_render_ai_prompt(cur, "planning_exercise_expectation_profile", variables)
        model = effective_openrouter_model_for_prompt_row(prow)
        raw = openrouter_chat_completion(api_key=api_key, model=model, user_content=rendered.text)
        parsed = parse_planning_query_intent_response(raw)
        if parsed.scenario not in ("preset_next", "continue_plan", "free_search"):
            parsed = parsed.model_copy(update={"scenario": "preset_next"})
        return parsed, True
    except AiPromptUnavailableError:
        return None, False
    except Exception as exc:
        _logger.warning("Planungs-Erwartungsprofil-LLM fehlgeschlagen: %s", exc)
        return None, False


__all__ = ["try_build_planning_expectation_from_context"]