shinkan-jinkendo/backend/ai_prompt_planning_preview.py

"""
Admin-Vorschau: Platzhalter für Planungs-Prompts (Progressionsgraph, Pfad-QS, Suggest).

Nutzt repräsentative Beispieldaten + echte Katalog-Auszüge aus der DB.
"""
from __future__ import annotations

import json
from typing import Any, Dict, List, Mapping, Optional

from pydantic import BaseModel, Field

from planning_exercise_semantics import brief_to_summary_dict, build_semantic_brief
from planning_intent_context import build_planning_intent_context

PLANNING_PROMPT_SLUGS = frozenset(
    {
        "planning_progression_start_target",
        "planning_progression_goal_analysis",
        "planning_progression_roadmap",
        "planning_progression_stage_spec",
        "planning_exercise_query_semantics",
        "planning_exercise_path_qa",
        "planning_exercise_search_intent",
        "planning_exercise_search_rank",
        "planning_exercise_expectation_profile",
    }
)


class PlanningPromptPreviewInput(BaseModel):
    goal_query: str = Field(
        default="Mae Geri vom Grundschritt bis zur kontrollierten Kumite-Nähe",
        max_length=2000,
    )
    user_notes: str = Field(default="Fokus Breitensport, ohne Wettkampfdruck.", max_length=2000)
    max_steps: int = Field(default=5, ge=2, le=10)
    search_query: Optional[str] = Field(default=None, max_length=2000)


def is_planning_prompt_slug(slug: str) -> bool:
    return (slug or "").strip().lower() in PLANNING_PROMPT_SLUGS


def _compact_json(obj: Any) -> str:
    return json.dumps(obj, ensure_ascii=False, separators=(",", ":"))


def _sample_goal_analysis() -> Dict[str, Any]:
    return {
        "primary_topic": "Mae Geri",
        "start_assumption": "Grundstellung und einfache Frontkick-Bewegung bekannt",
        "target_state": "Kontrollierter Mae Geri in Kumite-Nähe mit Hüftöffnung",
        "success_criteria": [
            "Hüfte öffnet vor dem Kick",
            "Ballen trifft Zielzone",
            "Rückzug ohne Balanceverlust",
        ],
        "constraints": {
            "partner_required": False,
            "excluded_themes": ["reine Kraft ohne Technikbezug"],
            "trainer_notes": "Breitensport, kein Wettkampf",
        },
    }


def _sample_major_steps(max_steps: int) -> List[Dict[str, Any]]:
    phases = ["einstieg", "grundlage", "vertiefung", "anwendung", "perfektion"]
    titles = [
        "Grundstellung und Mae Geri Einstieg",
        "Hüftöffnung und Ballen-Fokus",
        "Koordination und Rückzug",
        "Anwendung in Partnerübung",
        "Qualität unter leichtem Druck",
    ]
    out: List[Dict[str, Any]] = []
    for i in range(max_steps):
        out.append(
            {
                "index": i,
                "phase": phases[min(i, len(phases) - 1)],
                "title": titles[min(i, len(titles) - 1)],
                "learning_goal": titles[min(i, len(titles) - 1)],
            }
        )
    return out


def _sample_path_steps() -> List[Dict[str, Any]]:
    return [
        {
            "index": 1,
            "exercise_id": 101,
            "title": "Mae Geri — Stand und Hüftöffnung",
            "goal": "Frontkick mit geöffneter Hüfte aus Grundstellung",
            "is_bridge": False,
            "is_ai_proposal": False,
            "reasons": ["Stufen-Gate: Grundlagen"],
        },
        {
            "index": 2,
            "exercise_id": 102,
            "title": "Mae Geri — Ballen und Rückzug",
            "goal": "Präziser Ballentreffer mit kontrolliertem Rückzug",
            "is_bridge": False,
            "is_ai_proposal": False,
            "reasons": ["Nachfolger im Graph"],
        },
    ]


def _sample_planning_context() -> Dict[str, Any]:
    return {
        "scope": "progression_path",
        "goal_query": "Mae Geri vom Grundschritt bis zur Kumite-Nähe",
        "stage_index": 1,
        "learning_goal": "Hüftöffnung und Ballen-Fokus",
    }


def _sample_target_profile() -> Dict[str, Any]:
    return {
        "primary_focus": "Kihon",
        "training_type": "Breitensport",
        "skill_expectations": ["Geri Waza", "Koordination"],
    }


def _sample_candidates() -> List[Dict[str, Any]]:
    return [
        {
            "exercise_id": 101,
            "title": "Mae Geri — Stand und Hüftöffnung",
            "summary": "Frontkick mit Hüftöffnung",
            "skill_names": ["Geri Waza"],
            "score_hint": 0.82,
        },
        {
            "exercise_id": 102,
            "title": "Mae Geri — Ballen und Rückzug",
            "summary": "Ballentreffer mit Rückzug",
            "skill_names": ["Geri Waza", "Koordination"],
            "score_hint": 0.76,
        },
    ]


def _load_catalog_variables(cur) -> Dict[str, str]:
    from planning_exercise_intent import (
        _load_compact_catalog,
        _load_skills_catalog_compact,
    )

    return {
        "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")),
    }


def resolve_planning_prompt_preview_variables(
    cur,
    slug: str,
    body: PlanningPromptPreviewInput,
) -> Dict[str, str]:
    """Mustache-Variablen für Planungs-Prompt-Vorschau im Admin."""
    s = (slug or "").strip().lower()
    if s not in PLANNING_PROMPT_SLUGS:
        raise ValueError(f"Kein Planungs-Prompt-Slug: {slug!r}")

    goal_query = (body.goal_query or "").strip() or "Mae Geri Progression"
    search_query = (body.search_query or "").strip() or goal_query
    max_steps = int(body.max_steps)
    brief = build_semantic_brief(goal_query)
    brief_json = _compact_json(brief_to_summary_dict(brief))
    goal_analysis = _sample_goal_analysis()
    major_steps = _sample_major_steps(max_steps)
    intent_ctx = build_planning_intent_context(
        goal_query=goal_query,
        goal_analysis=goal_analysis,
        semantic_brief=brief,
        extra_context=(body.user_notes or "").strip() or None,
    )
    intent_ctx_json = _compact_json(intent_ctx.to_api_dict())
    ctx = _sample_planning_context()
    target = _sample_target_profile()
    catalogs = _load_catalog_variables(cur)

    if s == "planning_progression_start_target":
        return {
            "goal_query": goal_query,
            "semantic_brief_json": brief_json,
            "user_notes": (body.user_notes or "").strip(),
        }

    if s == "planning_progression_goal_analysis":
        return {
            "goal_query": goal_query,
            "semantic_brief_json": brief_json,
        }

    if s == "planning_progression_roadmap":
        return {
            "goal_query": goal_query,
            "goal_analysis_json": _compact_json(goal_analysis),
            "semantic_brief_json": brief_json,
            "max_steps": str(max_steps),
        }

    if s == "planning_progression_stage_spec":
        return {
            "goal_query": goal_query,
            "goal_analysis_json": _compact_json(goal_analysis),
            "major_steps_json": _compact_json(major_steps),
            "intent_context_json": intent_ctx_json,
            "semantic_brief_json": brief_json,
        }

    if s == "planning_exercise_query_semantics":
        return {
            "search_query": search_query,
            "semantic_brief_json": brief_json,
        }

    if s == "planning_exercise_path_qa":
        return {
            "goal_query": goal_query,
            "semantic_brief_json": brief_json,
            "steps_json": _compact_json(_sample_path_steps()),
            "gaps_json": _compact_json([]),
            "bridge_inserts_json": _compact_json([]),
        }

    if s == "planning_exercise_search_intent":
        return {
            "search_query": search_query,
            "heuristic_intent": "progression_next",
            "scenario_hint": "preset_next",
            "planning_context_json": _compact_json(ctx),
            "target_profile_json": _compact_json(target),
            **catalogs,
        }

    if s == "planning_exercise_search_rank":
        return {
            "search_query": search_query,
            "intent": "progression_next",
            "planning_context_json": _compact_json(ctx),
            "target_profile_json": _compact_json(target),
            "candidates_json": _compact_json(_sample_candidates()),
            "result_limit": "5",
        }

    if s == "planning_exercise_expectation_profile":
        return {
            "heuristic_intent": "suggest_next",
            "planning_context_json": _compact_json(ctx),
            "target_profile_json": _compact_json(target),
            **{k: v for k, v in catalogs.items() if k != "style_directions_catalog_json"},
        }

    raise ValueError(f"Planungs-Prompt-Slug nicht implementiert: {slug!r}")


__all__ = [
    "PLANNING_PROMPT_SLUGS",
    "PlanningPromptPreviewInput",
    "is_planning_prompt_slug",
    "resolve_planning_prompt_preview_variables",
]