"""
Causal Lab v2 — Bibliothèque de graphes causaux + analyse empirique.

Sources d'événements : market_events (toutes catégories).
Templates de graphes  : causal_graph_templates (seed automatique au démarrage).
IA                    : GPT-4o pour recommandation de template + coefficients.

Endpoints :
  GET  /api/causal-lab/templates               — liste des templates
  GET  /api/causal-lab/template/{id}           — détail d'un template
  PUT  /api/causal-lab/template/{id}           — mettre à jour les coefficients
  GET  /api/causal-lab/market-events           — événements analysables
  POST /api/causal-lab/recommend               — GPT-4o recommande un template
  POST /api/causal-lab/analyze                 — analyse (prédiction + réel)
  GET  /api/causal-lab/analyses                — historique des analyses
  GET  /api/causal-lab/calibration             — stats de calibration par template
"""
import json
import logging
import math
from datetime import datetime, timedelta
from typing import Optional
from fastapi import APIRouter, HTTPException, Query
from pydantic import BaseModel

router = APIRouter()
logger = logging.getLogger(__name__)


# ── Initialisation DB (appelée depuis startup) ────────────────────────────────

def _init(conn):
    from services.causal_graphs import init_tables, seed_templates
    init_tables(conn)
    seed_templates(conn)


# ── Récupération des prix autour d'un événement ───────────────────────────────

def _fetch_prices(event_date_str: str, instruments: list[str], lag_days: int = 0) -> dict:
    """
    Télécharge les prix autour de l'événement pour chaque instrument demandé.
    Retourne dict { instrument: [{"t": iso, "c": float}, ...] }
    Si lag_days > 0, force le mode journalier (la fenêtre doit couvrir N jours après l'event).
    """
    YFINANCE_MAP = {
        "EURUSD": "EURUSD=X",
        "GBPUSD": "GBPUSD=X",
        "USDJPY": "USDJPY=X",
        "USDCHF": "USDCHF=X",
        "AUDUSD": "AUDUSD=X",
        "NZDUSD": "NZDUSD=X",
        "USDCAD": "USDCAD=X",
        "XAUUSD": "GC=F",
        "XAGUSD": "SI=F",
        "SP500":  "^GSPC",
        "NASDAQ": "^IXIC",
        "DAX":    "^GDAXI",
        "FTSE":   "^FTSE",
        "BRENT":  "BZ=F",
        "WTI":    "CL=F",
        "US2Y":   "US2YT=RR",
        "US10Y":  "^TNX",
        "EU10Y":  "GE10YT=RR",
        "US30Y":  "^TYX",
        "DXY":    "DX-Y.NYB",
    }

    out: dict = {"mode": "none"}
    for inst in instruments + ["US2Y", "US10Y", "EU10Y"]:
        out[inst] = []

    try:
        import yfinance as yf
        event_dt = datetime.strptime(event_date_str[:10], "%Y-%m-%d")
        days_ago = (datetime.utcnow() - event_dt).days

        # Intraday 5min (< 55 jours) sauf si lag_days > 0 (besoin fenêtre journalière étendue)
        use_intraday = days_ago < 55 and lag_days == 0

        for inst in instruments:
            sym = YFINANCE_MAP.get(inst)
            if not sym:
                continue
            try:
                if use_intraday:
                    start = (event_dt - timedelta(days=1)).strftime("%Y-%m-%d")
                    end   = (event_dt + timedelta(days=2)).strftime("%Y-%m-%d")
                    df    = yf.download(sym, start=start, end=end,
                                        interval="5m", progress=False, auto_adjust=True)
                    if df is not None and len(df) > 0:
                        if hasattr(df.columns, "levels"):
                            df.columns = df.columns.get_level_values(0)
                        day_df = df[df.index.date == event_dt.date()]
                        rows = [
                            {"t": idx.isoformat(), "c": round(float(row["Close"]), 5)}
                            for idx, row in day_df.iterrows()
                            if not math.isnan(float(row["Close"]))
                        ]
                        if len(rows) >= 6:
                            out[inst] = rows
                            out["mode"] = "intraday_5m"
                            continue

                # Fallback journalier (ou mode forcé si lag_days > 0)
                start = (event_dt - timedelta(days=5)).strftime("%Y-%m-%d")
                end   = (event_dt + timedelta(days=max(5, lag_days + 3))).strftime("%Y-%m-%d")
                df    = yf.download(sym, start=start, end=end,
                                    interval="1d", progress=False, auto_adjust=True)
                if df is not None and len(df) > 0:
                    if hasattr(df.columns, "levels"):
                        df.columns = df.columns.get_level_values(0)
                    out[inst] = [
                        {"t": str(idx.date()), "c": round(float(row["Close"]), 5)}
                        for idx, row in df.iterrows()
                        if float(row["Close"]) == float(row["Close"])
                    ]
                    if out["mode"] == "none":
                        out["mode"] = "daily"
            except Exception as e:
                logger.debug(f"[causal_lab] price {sym}: {e}")

        # Taux (toujours en journalier)
        start = (event_dt - timedelta(days=4)).strftime("%Y-%m-%d")
        end   = (event_dt + timedelta(days=4)).strftime("%Y-%m-%d")
        for key in ["US2Y", "US10Y", "EU10Y"]:
            sym = YFINANCE_MAP.get(key)
            if not sym:
                continue
            try:
                df = yf.download(sym, start=start, end=end,
                                 interval="1d", progress=False, auto_adjust=True)
                if df is None or len(df) == 0:
                    continue
                if hasattr(df.columns, "levels"):
                    df.columns = df.columns.get_level_values(0)
                out[key] = [
                    {"t": str(idx.date()), "c": round(float(row["Close"]), 3)}
                    for idx, row in df.iterrows()
                    if float(row["Close"]) == float(row["Close"])
                ]
            except Exception as e:
                logger.debug(f"[causal_lab] yield {sym}: {e}")

    except Exception as e:
        logger.error(f"[causal_lab] _fetch_prices: {e}")

    return out


def _drift_metrics(prices: dict, event_date_str: str, inst: str, lag_min: int = 0, lag_days: int = 0) -> dict:
    series = prices.get(inst, [])
    mode   = prices.get("mode", "none")
    edate  = event_date_str[:10]

    empty = {"pre_pips": None, "post_pips": None, "drift_ratio": None, "leak": "unknown", "lag_min": lag_min, "lag_days": lag_days}
    if not series:
        return empty

    mult = 10000 if inst in ("EURUSD",) else 10
    if mode == "intraday_5m":
        n = len(series)
        if n < 8:
            return empty
        # Décale le mid en avant selon le lag (chaque barre = 5 min)
        lag_bars = max(0, round(lag_min / 5))
        mid = min(n // 2 + lag_bars, n - 2)
        pre_pips  = round((series[mid - 1]["c"] - series[0]["c"]) * mult)
        post_pips = round((series[-1]["c"] - series[mid]["c"]) * mult)
    else:
        # En mode journalier, lag_days décale la date de référence "post"
        if lag_days > 0:
            post_date = (datetime.strptime(edate, "%Y-%m-%d") + timedelta(days=lag_days)).strftime("%Y-%m-%d")
            pre  = [b for b in series if b["t"] <= edate]
            post = [b for b in series if b["t"] >= post_date]
        else:
            pre  = [b for b in series if b["t"] <  edate]
            post = [b for b in series if b["t"] == edate]
        if not pre or not post:
            return empty
        pre_pips  = None
        post_pips = round((post[0]["c"] - pre[-1]["c"]) * mult)

    ratio: Optional[float] = None
    if pre_pips is not None and post_pips and post_pips != 0:
        ratio = round(pre_pips / post_pips, 3)

    leak = "unknown"
    if ratio is not None:
        ar = abs(ratio)
        leak = "high" if ar > 0.5 else "medium" if ar > 0.25 else "low"

    return {"pre_pips": pre_pips, "post_pips": post_pips, "drift_ratio": ratio, "leak": leak}


def _yield_delta(series: list, event_date: str) -> Optional[float]:
    if not series:
        return None
    pre  = [b for b in series if b["t"] <  event_date]
    post = [b for b in series if b["t"] >= event_date]
    if not pre or not post:
        return None
    return round(post[0]["c"] - pre[-1]["c"], 3)


def _activation_score(node_values: dict, actual_moves: dict, template_graph: dict) -> dict:
    """
    Compare les sorties du graphe (outputs) aux mouvements réels.
    Retourne {score, nodes: {node_id: {pred, act, status}}}
    """
    output_nodes = [n for n in template_graph.get("nodes", []) if n.get("type") == "output"]
    nodes_detail: dict = {}
    correct = 0
    evaluated = 0

    for node in output_nodes:
        nid  = node["id"]
        inst = node.get("instrument", "")
        pred = node_values.get(nid)
        act  = actual_moves.get(inst)

        if pred is None or act is None:
            status = "unknown"
        elif abs(pred) < 2 and abs(act) < 2:
            status = "neutral"
        elif (pred > 0) == (act > 0):
            status = "correct"
            correct += 1
            evaluated += 1
        else:
            status = "wrong"
            evaluated += 1

        nodes_detail[nid] = {
            "pred":   round(pred, 2) if pred is not None else None,
            "act":    round(act,  2) if act  is not None else None,
            "status": status,
            "label":  node.get("label", nid),
        }

    score = round(correct / evaluated, 2) if evaluated else None
    return {"score": score, "nodes": nodes_detail, "correct": correct, "total": evaluated}


# ── Helpers GPT-4o ─────────────────────────────────────────────────────────────

def _gpt4o_recommend(event: dict, templates: list) -> dict:
    """Appelle GPT-4o pour recommander un template + coefficients."""
    try:
        from services.database import get_config
        import openai
        key = get_config("openai_api_key") or ""
        if not key:
            return {"error": "GPT-4o non configuré (clé OpenAI manquante)"}

        client = openai.OpenAI(api_key=key)

        template_summary = [
            {
                "id":          t["id"],
                "name":        t["name"],
                "category":    t["category"],
                "sub_type":    t.get("sub_type", ""),
                "instruments": t.get("instruments", []),
                "description": t.get("description", ""),
            }
            for t in templates
        ]

        system_prompt = (
            "You are a macro market analyst and causal graph specialist for the OpenFin platform. "
            "Given a market event and a library of causal graph templates, recommend the most appropriate template "
            "and suggest any coefficient adjustments. Respond with a JSON object only.\n"
            "IMPORTANT: Only recommend a template if it is structurally compatible with the event type "
            "(same causal mechanism — e.g. do NOT recommend an ECB rate decision template for a sentiment survey). "
            "If no template fits well (structural mismatch, confidence < 0.6), set template_id to null "
            "and explain in 'notes' what kind of template would be needed."
        )

        user_prompt = f"""Market event to analyze:
- Name: {event.get('name')}
- Category: {event.get('category')}
- Sub-type: {event.get('sub_type', '')}
- Description: {event.get('description', '')}
- Market impact: {event.get('market_impact', '')}
- Affected assets: {event.get('affected_assets', '')}
- Impact score: {event.get('impact_score', '')}
- Surprise (%): {event.get('surprise_pct', 'N/A')}
- Expected: {event.get('expected_value', 'N/A')}
- Actual: {event.get('actual_value', 'N/A')}

Available templates:
{json.dumps(template_summary, ensure_ascii=False, indent=2)}

Respond with this exact JSON structure:
{{
  "template_id": <int or null>,
  "template_name": "<name>",
  "confidence": <0.0-1.0>,
  "rationale": "<2-3 sentences explaining why this template fits>",
  "coefficient_suggestions": {{
    "<coef_key>": <suggested_value>
  }},
  "instrument_focus": ["<primary instrument>"],
  "alternative_template_id": <int or null>,
  "notes": "<any additional context>"
}}"""

        resp = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user",   "content": user_prompt},
            ],
            response_format={"type": "json_object"},
            temperature=0.3,
            max_tokens=600,
        )
        raw = resp.choices[0].message.content or "{}"
        return json.loads(raw)

    except Exception as e:
        logger.error(f"[causal_lab] GPT-4o recommend: {e}")
        return {"error": str(e)}


# ── Schémas Pydantic ──────────────────────────────────────────────────────────

class UpdateCoefRequest(BaseModel):
    coefficients: dict  # {coef_key: float}


class CreateTemplateRequest(BaseModel):
    name: str
    category: str
    sub_type: str = ""
    description: str = ""
    instruments: list = ["EURUSD"]
    graph_json: dict
    ai_rationale: str = ""


class RecommendRequest(BaseModel):
    market_event_id: int


class InstantiateRequest(BaseModel):
    market_event_id: int
    template_id: int


class AnalyzeRequest(BaseModel):
    market_event_id: int
    template_id: int
    instrument: str = ""   # vide = analyser tous les instruments du template
    inputs: dict = {}
    coef_overrides: dict = {}


class CreateFromEventRequest(BaseModel):
    market_event_id: int


# ── Endpoints ─────────────────────────────────────────────────────────────────

@router.get("/api/causal-lab/debug")
def debug_causal():
    """Endpoint de diagnostic — vérifie que causal_graphs est bien chargé."""
    try:
        from services.database import get_conn
        from services.causal_graphs import BUILT_IN_TEMPLATES, init_tables, seed_templates
        conn = get_conn()
        init_tables(conn)
        seed_templates(conn)
        rows = conn.execute("SELECT id, name, graph_json FROM causal_graph_templates ORDER BY id").fetchall()
        templates_info = []
        for r in rows:
            g = json.loads(r["graph_json"] or "{}")
            edges = g.get("edges", [])
            templates_info.append({
                "id":   r["id"],
                "name": r["name"],
                "edges_count": len(edges),
                "edges_with_lag_days":  sum(1 for e in edges if (e.get("lag_days") or 0) > 0),
                "edges_with_lag_min":   sum(1 for e in edges if (e.get("lag_min")  or 0) > 0),
                "output_node_ids": [n["id"] for n in g.get("nodes", []) if n.get("type") in ("market_asset", "output")],
                "edges": [{"from": e.get("from"), "to": e.get("to"),
                           "lag_min": e.get("lag_min"), "lag_days": e.get("lag_days")} for e in edges],
            })
        conn.close()
        return {
            "built_in_templates": len(BUILT_IN_TEMPLATES),
            "db_templates": len(rows),
            "status": "ok",
            "templates": templates_info,
        }
    except Exception as e:
        import traceback
        return {"status": "error", "error": str(e), "trace": traceback.format_exc()}


@router.get("/api/causal-lab/templates")
def list_templates(category: str = Query("")):
    try:
        from services.database import get_conn
        from services.causal_graphs import get_templates
        conn = get_conn()
        _init(conn)
        data = get_templates(conn, category)
        conn.close()
        print(f"[causal_lab] list_templates → {len(data)} templates (category={category!r})", flush=True)
        return data
    except Exception as e:
        import traceback
        print(f"[causal_lab] list_templates ERROR: {e}\n{traceback.format_exc()}", flush=True)
        logger.error(f"[causal_lab] list_templates: {e}")
        raise HTTPException(500, str(e))


@router.post("/api/causal-lab/template")
def create_template(body: CreateTemplateRequest):
    """Crée un nouveau template utilisateur."""
    try:
        from services.database import get_conn
        from services.causal_graphs import init_tables, seed_templates
        conn = get_conn()
        init_tables(conn); seed_templates(conn)
        existing = conn.execute(
            "SELECT id FROM causal_graph_templates WHERE name = ?", (body.name,)
        ).fetchone()
        if existing:
            raise HTTPException(409, f"Un template nommé '{body.name}' existe déjà")
        cur = conn.execute("""
            INSERT INTO causal_graph_templates
                (name, category, sub_type, instruments, description, graph_json, ai_rationale, created_by)
            VALUES (?, ?, ?, ?, ?, ?, ?, 'user')
        """, (
            body.name, body.category, body.sub_type,
            json.dumps(body.instruments), body.description,
            json.dumps(body.graph_json), body.ai_rationale,
        ))
        conn.commit()
        new_id = cur.lastrowid
        conn.close()
        return {"id": new_id, "name": body.name}
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] create_template: {e}")
        raise HTTPException(500, str(e))


@router.patch("/api/causal-lab/template/{template_id}")
def patch_template(template_id: int, body: dict):
    """Met à jour un template existant en entier (graph_json, métadonnées, coefficients)."""
    try:
        from services.database import get_conn
        from services.causal_graphs import get_template
        conn = get_conn()
        tmpl = get_template(conn, template_id)
        if not tmpl:
            conn.close(); raise HTTPException(404, "Template introuvable")

        sets, params = [], []
        for field in ("name", "category", "sub_type", "description", "ai_rationale"):
            if field in body:
                sets.append(f"{field}=?"); params.append(body[field])
        if "instruments" in body:
            sets.append("instruments=?"); params.append(json.dumps(body["instruments"]))
        if "graph_json" in body:
            sets.append("graph_json=?"); params.append(json.dumps(body["graph_json"]))
        if "calibration_json" in body:
            sets.append("calibration_json=?"); params.append(json.dumps(body["calibration_json"]))

        if not sets:
            conn.close(); return {"ok": True}

        sets.append("updated_at=datetime('now')")
        params.append(template_id)
        conn.execute(f"UPDATE causal_graph_templates SET {', '.join(sets)} WHERE id=?", params)
        conn.commit(); conn.close()
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] patch_template {template_id}: {e}")
        raise HTTPException(500, str(e))


@router.delete("/api/causal-lab/template/{template_id}")
def delete_template(template_id: int):
    """Supprime un template (tous les templates peuvent être supprimés)."""
    try:
        from services.database import get_conn
        conn = get_conn()
        row = conn.execute(
            "SELECT id FROM causal_graph_templates WHERE id = ?", (template_id,)
        ).fetchone()
        if not row:
            conn.close(); raise HTTPException(404, "Template introuvable")
        conn.execute("DELETE FROM causal_graph_templates WHERE id = ?", (template_id,))
        conn.commit(); conn.close()
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] delete_template {template_id}: {e}")
        raise HTTPException(500, str(e))


@router.get("/api/causal-lab/template/{template_id}")
def get_template_detail(template_id: int):
    try:
        from services.database import get_conn
        from services.causal_graphs import get_template, init_tables, seed_templates
        conn = get_conn()
        init_tables(conn)
        seed_templates(conn)
        tmpl = get_template(conn, template_id)
        conn.close()
        if not tmpl:
            raise HTTPException(404, "Template introuvable")
        return tmpl
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] get_template {template_id}: {e}")
        raise HTTPException(500, str(e))


@router.put("/api/causal-lab/template/{template_id}")
def update_template(template_id: int, body: UpdateCoefRequest):
    try:
        from services.database import get_conn
        from services.causal_graphs import update_coefficients, init_tables, seed_templates
        conn = get_conn()
        init_tables(conn)
        seed_templates(conn)
        ok = update_coefficients(conn, template_id, body.coefficients)
        conn.close()
        if not ok:
            raise HTTPException(404, "Template introuvable")
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] update_template {template_id}: {e}")
        raise HTTPException(500, str(e))


@router.get("/api/causal-lab/data-sources")
def get_data_sources():
    """
    Retourne toutes les sources de données disponibles pour le mapping des nœuds observables :
    - prices       : tickers yfinance (market_watchlist + defaults)
    - macro_series : series_id distincts depuis economic_events
    - ff_events    : événements récurrents depuis ff_calendar
    """
    PRICE_DEFAULTS = [
        {"key": "EURUSD",  "label": "EUR/USD"},
        {"key": "XAUUSD",  "label": "Or (Gold)"},
        {"key": "SP500",   "label": "S&P 500"},
        {"key": "BRENT",   "label": "Brent Crude"},
        {"key": "US2Y",    "label": "US 2Y Yield"},
        {"key": "US10Y",   "label": "US 10Y Yield"},
        {"key": "EU10Y",   "label": "EU 10Y Yield"},
    ]
    try:
        from services.database import get_conn
        conn = get_conn()

        wl = conn.execute(
            "SELECT ticker, name FROM market_watchlist ORDER BY ticker"
        ).fetchall()
        known = {x["key"] for x in PRICE_DEFAULTS}
        extra = [
            {"key": r["ticker"], "label": r["name"] or r["ticker"]}
            for r in wl if r["ticker"] not in known
        ]

        macro = conn.execute(
            """SELECT DISTINCT series_id, event_name
               FROM economic_events
               WHERE series_id IS NOT NULL AND series_id != ''
               ORDER BY series_id LIMIT 200"""
        ).fetchall()

        ff = conn.execute(
            """SELECT event_name, currency, COUNT(*) as cnt
               FROM ff_calendar
               WHERE event_name IS NOT NULL AND event_name != ''
               GROUP BY event_name, currency
               HAVING cnt >= 2
               ORDER BY cnt DESC LIMIT 100"""
        ).fetchall()

        conn.close()
        return {
            "prices": PRICE_DEFAULTS + extra,
            "macro_series": [
                {"key": r["series_id"], "label": r["event_name"] or r["series_id"]}
                for r in macro
            ],
            "ff_events": [
                {"key": r["event_name"], "label": f"{r['event_name']} ({r['currency']})"}
                for r in ff
            ],
        }
    except Exception as e:
        logger.error(f"[causal_lab] data_sources: {e}")
        raise HTTPException(500, str(e))


@router.get("/api/causal-lab/market-events")
def list_market_events(
    limit: int    = Query(200, le=500),
    category: str = Query(""),
    q: str        = Query(""),
):
    """Tous les market_events analysables (avec ou sans analyse existante)."""
    try:
        from services.database import get_conn
        conn = get_conn()
        _init(conn)

        conditions = ["1=1"]
        if category:
            conditions.append(f"e.category = '{category}'")
        if q:
            safe_q = q.replace("'", "''")
            conditions.append(f"(e.name LIKE '%{safe_q}%' OR e.description LIKE '%{safe_q}%')")

        where = " AND ".join(conditions)
        rows = conn.execute(f"""
            SELECT e.id, e.name, e.category, e.sub_type,
                   e.start_date, e.end_date, e.level,
                   e.impact_score, e.market_impact, e.affected_assets,
                   e.actual_value, e.expected_value, e.surprise_pct,
                   a.id as analysis_id, a.template_id, a.activation_score,
                   a.instrument, a.analyzed_at
            FROM market_events e
            LEFT JOIN causal_event_analyses a ON a.market_event_id = e.id
            WHERE {where}
            ORDER BY e.start_date DESC
            LIMIT {limit}
        """).fetchall()
        conn.close()

        return [dict(r) for r in rows]

    except Exception as e:
        logger.error(f"[causal_lab] list_market_events: {e}")
        raise HTTPException(500, str(e))


@router.post("/api/causal-lab/recommend")
def recommend_template(body: RecommendRequest):
    """GPT-4o recommande le template le plus adapté à l'événement."""
    try:
        from services.database import get_conn
        from services.causal_graphs import get_templates, init_tables, seed_templates
        conn = get_conn()
        init_tables(conn)
        seed_templates(conn)

        ev_row = conn.execute(
            "SELECT * FROM market_events WHERE id = ?", (body.market_event_id,)
        ).fetchone()
        if not ev_row:
            conn.close()
            raise HTTPException(404, "market_event introuvable")

        event     = dict(ev_row)
        templates = get_templates(conn)
        conn.close()

        recommendation = _gpt4o_recommend(event, templates)
        return {"event": event, "recommendation": recommendation}

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] recommend: {e}")
        raise HTTPException(500, str(e))


@router.post("/api/causal-lab/instantiate")
def instantiate_template(body: InstantiateRequest):
    """
    GPT-4o-mini suggère les valeurs des nœuds user_input du template
    en fonction des spécificités de l'événement (surprise +/-, magnitude, catégorie…).
    Retourne { inputs: { node_id: value }, rationale: str }
    """
    try:
        from services.database import get_conn
        conn = get_conn()
        _init(conn)

        ev_row   = conn.execute("SELECT * FROM market_events WHERE id = ?",      (body.market_event_id,)).fetchone()
        tmpl_row = conn.execute("SELECT * FROM causal_graph_templates WHERE id = ?", (body.template_id,)).fetchone()
        conn.close()

        if not ev_row or not tmpl_row:
            raise HTTPException(404, "event ou template introuvable")

        event    = dict(ev_row)
        template = dict(tmpl_row)
        graph_json    = json.loads(template.get("graph_json") or "{}")
        input_mapping = graph_json.get("input_mapping", {})

        # Only user_input nodes need to be suggested (auto-sourced nodes are pulled from DB)
        user_nodes = {
            k: v for k, v in input_mapping.items()
            if not v.get("source") or v["source"] == "user_input"
        }

        if not user_nodes:
            return {"inputs": {}, "rationale": "Aucun nœud manuel — tout est auto-récupéré depuis la BD"}

        prompt = f"""Tu es un analyste financier. Tu dois configurer un graphe causal pour un événement de marché précis.

Événement : {event.get('name')}
Catégorie : {event.get('category')} / {event.get('sub_type', '')}
Date : {event.get('start_date')}
Description : {(event.get('description') or '')[:400]}
Score impact : {event.get('impact_score', 0.5)}
Valeur réelle : {event.get('actual_value')} | Valeur attendue : {event.get('expected_value')} | Surprise % : {event.get('surprise_pct')}

Template : {template.get('name')} (catégorie : {template.get('category')})

Nœuds à configurer (entrées manuelles) :
{json.dumps(user_nodes, indent=2, ensure_ascii=False)}

Règles :
- Surprise NÉGATIVE → valeurs négatives pour les nœuds de surprise
- Surprise POSITIVE → valeurs positives
- Si le nœud a un champ "range" (ex: [-3, 3]) : normalise OBLIGATOIREMENT la valeur dans ce range.
  Formule : valeur = clamp(surprise_pct / 100 * abs(range_max), range_min, range_max)
  Exemples : surprise_pct=-33.3%, range=[-3,3] → -0.333*3 = -1.0 | surprise_pct=-29.1%, range=[-3,3] → -0.873
  NE JAMAIS retourner la valeur brute surprise_pct (-33.3, -29.1…) — c'est une erreur de calibration.
- Si pas de "range" défini : retourne surprise_pct / 100 (forme fractionnelle, ex: -33.3% → -0.333)
- Magnitude proportionnelle à l'ampleur de la surprise dans la plage autorisée

Retourne UNIQUEMENT un JSON : {{ "node_id": valeur_numerique, ... }}
Inclure uniquement les nœuds listés ci-dessus."""

        from services.database import get_config as _get_cfg
        import openai as _openai
        _key = _get_cfg("openai_api_key") or ""
        if not _key:
            raise HTTPException(400, "Clé OpenAI manquante dans la configuration")
        client = _openai.OpenAI(api_key=_key)
        resp   = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"},
            temperature=0.2,
            max_tokens=400,
        )
        raw = resp.choices[0].message.content or "{}"
        suggested = json.loads(raw)

        # Validate: only keep known nodes with numeric values
        validated: dict = {}
        for k, v in suggested.items():
            if k in user_nodes:
                try:
                    validated[k] = float(v)
                except (TypeError, ValueError):
                    pass

        surprise = event.get("surprise_pct")
        rationale = (
            f"Surprise {'+' if (surprise or 0) >= 0 else ''}{surprise:.1f}%" if surprise is not None
            else "Calibration basée sur la description de l'événement"
        )
        return {"inputs": validated, "rationale": rationale}

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] instantiate: {e}")
        raise HTTPException(500, str(e))


@router.post("/api/causal-lab/analyze")
def analyze_event(body: AnalyzeRequest):
    """
    Analyse complète : évalue le template sur l'événement,
    compare aux mouvements réels, stocke le résultat.
    """
    try:
        from services.database import get_conn
        from services.causal_graphs import (
            get_template, evaluate_graph, update_calibration,
            init_tables, seed_templates,
        )
        conn = get_conn()
        init_tables(conn)
        seed_templates(conn)

        ev_row = conn.execute(
            "SELECT * FROM market_events WHERE id = ?", (body.market_event_id,)
        ).fetchone()
        if not ev_row:
            conn.close()
            raise HTTPException(404, "market_event introuvable")
        event = dict(ev_row)

        tmpl = get_template(conn, body.template_id)
        if not tmpl:
            conn.close()
            raise HTTPException(404, "Template introuvable")

        graph = tmpl["graph_json"]

        # Inputs de base depuis l'événement + mappings DB/yfinance
        inputs = {}
        mapping = graph.get("input_mapping", {})
        YFINANCE_MAP = {
            "EURUSD": "EURUSD=X", "XAUUSD": "GC=F", "SP500": "^GSPC",
            "BRENT": "BZ=F", "US2Y": "US2YT=RR", "US10Y": "^TNX", "EU10Y": "GE10YT=RR",
        }
        edate_str = event["start_date"][:10]
        for input_id, cfg in mapping.items():
            src   = cfg.get("source", "")
            key   = cfg.get("key", "")
            field = cfg.get("field", "close")

            if src == "market_watchlist" and key:
                try:
                    import yfinance as yf
                    from datetime import datetime as _dt, timedelta as _td
                    event_dt = _dt.strptime(edate_str, "%Y-%m-%d")
                    start = (event_dt - _td(days=5)).strftime("%Y-%m-%d")
                    end   = (event_dt + _td(days=2)).strftime("%Y-%m-%d")
                    sym   = YFINANCE_MAP.get(key, key)
                    df = yf.download(sym, start=start, end=end,
                                     interval="1d", progress=False, auto_adjust=True)
                    if df is not None and len(df) > 0:
                        if hasattr(df.columns, "levels"):
                            df.columns = df.columns.get_level_values(0)
                        rows_yf = [
                            (str(idx.date()), float(row["Close"]))
                            for idx, row in df.iterrows()
                            if float(row["Close"]) == float(row["Close"])
                        ]
                        if field == "change_pct" and len(rows_yf) >= 2:
                            pre = next((c for d, c in rows_yf if d < edate_str), None)
                            cur = next((c for d, c in rows_yf if d == edate_str), None)
                            if pre and cur and pre != 0:
                                inputs[input_id] = round((cur - pre) / pre * 100, 4)
                        else:
                            val = next((c for d, c in rows_yf if d == edate_str), None)
                            if val is None:
                                val = next((c for d, c in reversed(rows_yf) if d <= edate_str), None)
                            if val is not None:
                                inputs[input_id] = round(val, 5)
                except Exception as _e:
                    logger.debug(f"[causal_lab] market_watchlist fetch {key}: {_e}")

            elif src == "economic_events" and key:
                row_e = conn.execute(
                    """SELECT actual_value, forecast_value FROM economic_events
                       WHERE series_id = ? AND event_date <= ? AND actual_value IS NOT NULL
                       ORDER BY event_date DESC LIMIT 1""",
                    (key, edate_str)
                ).fetchone()
                if row_e:
                    if field == "surprise" and row_e["forecast_value"] is not None:
                        inputs[input_id] = round(
                            float(row_e["actual_value"]) - float(row_e["forecast_value"]), 4
                        )
                    else:
                        inputs[input_id] = float(row_e["actual_value"])

            elif src == "ff_calendar" and key:
                row_f = conn.execute(
                    """SELECT actual_value, forecast_value FROM ff_calendar
                       WHERE event_name = ? AND event_date <= ? AND actual_value IS NOT NULL
                       ORDER BY event_date DESC LIMIT 1""",
                    (key, edate_str)
                ).fetchone()
                if row_f and row_f["actual_value"]:
                    try:
                        actual = float(row_f["actual_value"])
                        if field == "surprise" and row_f["forecast_value"]:
                            inputs[input_id] = round(actual - float(row_f["forecast_value"]), 4)
                        else:
                            inputs[input_id] = actual
                    except (ValueError, TypeError):
                        pass

            # Sources legacy
            elif src == "surprise_bps" and event.get("surprise_pct") is not None:
                # Valeur brute en bps (ex: écart taux BCE en points de base)
                raw = float(event["surprise_pct"])
                node_range = cfg.get("range")
                if node_range and len(node_range) == 2:
                    lo, hi = float(node_range[0]), float(node_range[1])
                    inputs[input_id] = round(max(lo, min(hi, raw)), 4)
                else:
                    inputs[input_id] = round(raw, 4)
            elif src == "surprise" and event.get("surprise_pct") is not None:
                raw = float(event["surprise_pct"])
                if cfg.get("normalize"):
                    raw = raw / 100
                node_range = cfg.get("range")
                if node_range and len(node_range) == 2:
                    lo, hi = float(node_range[0]), float(node_range[1])
                    inputs[input_id] = round(max(lo, min(hi, raw)), 4)
                else:
                    inputs[input_id] = round(raw, 4)
            elif src == "impact_score_scaled" and event.get("impact_score") is not None:
                inputs[input_id] = float(event["impact_score"]) / 10.0
            elif src == "impact_score_pct" and event.get("impact_score") is not None:
                inputs[input_id] = float(event["impact_score"])
            elif src == "actual_value" and event.get("actual_value") is not None:
                inputs[input_id] = float(event["actual_value"])

        # Inputs manuels (saisie frontend — priorité sur auto-fetch)
        inputs.update(body.inputs)

        # Évaluation du graphe
        node_values = evaluate_graph(graph, inputs, body.coef_overrides or {})

        # Lag effectif : max sur toutes les arêtes du graphe
        # (filtre output_ids optionnel mais peu fiable si types de nœuds non standards)
        edges      = graph.get("edges", [])
        nodes_map  = {n["id"]: n for n in graph.get("nodes", [])}
        output_ids = {n["id"] for n in graph.get("nodes", []) if n.get("type") in ("market_asset", "output")}

        edges_to_output   = [e for e in edges if e.get("to") in output_ids]
        edges_for_lag     = edges_to_output if edges_to_output else edges  # fallback : toutes arêtes

        effective_lag = max(
            (e.get("lag_min", 0) or 0 for e in edges_for_lag),
            default=0,
        )
        effective_lag_days = max(
            (e.get("lag_days", 0) or 0 for e in edges_for_lag),
            default=0,
        )

        # Diagnostic : combien d'arêtes portent un lag_days > 0
        edges_with_lag_days = sum(1 for e in edges if (e.get("lag_days") or 0) > 0)
        lag_debug = {
            "output_ids":           list(output_ids),
            "edges_to_output":      len(edges_to_output),
            "edges_with_lag_days":  edges_with_lag_days,
            "used_all_edges":       not edges_to_output,
        }

        # Instruments : utiliser tous ceux du template si aucun spécifié
        if body.instrument:
            instruments = list({body.instrument} | set(tmpl.get("instruments", [body.instrument])))
        else:
            instruments = list(set(tmpl.get("instruments", []))) or ["EURUSD"]
        primary_inst = body.instrument or (instruments[0] if instruments else "EURUSD")

        prices = _fetch_prices(event["start_date"], instruments, lag_days=effective_lag_days)
        edate  = event["start_date"][:10]

        actual_moves: dict = {}
        drift_by_inst: dict = {}
        for inst in instruments:
            drift = _drift_metrics(prices, event["start_date"], inst, lag_min=effective_lag, lag_days=effective_lag_days)
            drift_by_inst[inst] = drift
            if drift.get("post_pips") is not None:
                actual_moves[inst] = drift["post_pips"]

        # Activation
        activation = _activation_score(node_values, actual_moves, graph)

        # Taux
        yields = {
            "us10y": _yield_delta(prices.get("US10Y", []), edate),
            "eu10y": _yield_delta(prices.get("EU10Y", []), edate),
            "us2y":  _yield_delta(prices.get("US2Y",  []), edate),
        }

        analyzed_at = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")

        result = {
            "event":              event,
            "template_id":        body.template_id,
            "template_name":      tmpl["name"],
            "instrument":         primary_inst,
            "instruments":        instruments,
            "inputs":             inputs,
            "node_values":        node_values,
            "actual_moves":       actual_moves,
            "yields":             yields,
            "activation":         activation,
            "drift":              drift_by_inst.get(primary_inst, {}),
            "drift_by_inst":      drift_by_inst,
            "prices_mode":        prices.get("mode", "none"),
            "effective_lag_min":  effective_lag,
            "effective_lag_days": effective_lag_days,
            "lag_debug":          lag_debug,
            "analyzed_at":        analyzed_at,
            "graph_json":         graph,   # structure complète pour visualisation frontend
        }

        # Persistance — UPSERT : one analysis per (market_event_id, template_id)
        existing = conn.execute(
            "SELECT id FROM causal_event_analyses WHERE market_event_id=? AND template_id=?",
            (body.market_event_id, body.template_id),
        ).fetchone()
        # Stocker tous les instruments analysés pour que la frise Instrument Analysis les retrouve
        all_instruments_csv = ",".join(sorted(drift_by_inst.keys())) or primary_inst

        if existing:
            conn.execute("""
                UPDATE causal_event_analyses
                SET instrument=?, inputs_json=?, override_params=?, prediction_json=?,
                    actual_json=?, activation_score=?, drift_json=?, analyzed_at=?
                WHERE market_event_id=? AND template_id=?
            """, (
                all_instruments_csv, json.dumps(inputs), json.dumps(body.coef_overrides),
                json.dumps(node_values), json.dumps(actual_moves), activation.get("score"),
                json.dumps(drift_by_inst), analyzed_at,
                body.market_event_id, body.template_id,
            ))
        else:
            conn.execute("""
                INSERT INTO causal_event_analyses
                    (market_event_id, template_id, instrument, inputs_json,
                     override_params, prediction_json, actual_json,
                     activation_score, drift_json, analyzed_at)
                VALUES (?,?,?,?,?,?,?,?,?,?)
            """, (
                body.market_event_id, body.template_id, all_instruments_csv,
                json.dumps(inputs), json.dumps(body.coef_overrides),
                json.dumps(node_values), json.dumps(actual_moves),
                activation.get("score"),
                json.dumps(drift_by_inst), analyzed_at,
            ))
        conn.commit()

        # Calibration
        update_calibration(conn, body.template_id, {
            "activation_score": activation.get("score"),
            "instrument":       primary_inst,
            "pred_pips":        node_values.get(primary_inst.lower(), 0) or
                                next((v for k, v in node_values.items()
                                      if primary_inst.lower() in k.lower()), 0),
            "actual_pips":      actual_moves.get(primary_inst),
            "analyzed_at":      analyzed_at,
        })
        conn.close()

        return result

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] analyze: {e}")
        raise HTTPException(500, str(e))


def _build_graph_json_from_spec(spec: dict) -> dict:
    """Construit un graph_json complet depuis le spec simplifié généré par GPT-4o."""
    input_node   = spec.get("input_node", {})
    intermediates = spec.get("intermediate_nodes", [])
    outputs       = spec.get("output_nodes", [])

    nodes: list = []
    edges: list = []
    coefficients: dict = {}
    input_mapping: dict = {}

    # Nœud d'entrée — centré en haut
    inp_id = input_node.get("id", "surprise")
    nodes.append({
        "id": inp_id, "label": input_node.get("label", "Surprise"),
        "type": "input", "x": 200, "y": 50,
        "unit": input_node.get("unit", "% vs consensus"),
    })
    input_mapping[inp_id] = {
        "source": "surprise",
        "unit":   input_node.get("unit", "% vs consensus"),
        "description": input_node.get("label", "Surprise"),
    }

    # Nœuds intermédiaires — étalés horizontalement au milieu
    n_inter = max(len(intermediates), 1)
    x_step_i = max(160, 340 // n_inter)
    for i, inter in enumerate(intermediates):
        x = 200 - (len(intermediates) - 1) * x_step_i // 2 + i * x_step_i
        inter_id  = inter["id"]
        coef_name = inter.get("coef_name", f"coef_{inter_id}")
        coef_val  = float(inter.get("coef_value", 2.0))
        coefficients[coef_name] = {
            "value": coef_val, "calibrated": False,
            "description": inter.get("coef_desc", coef_name),
        }
        nodes.append({
            "id": inter_id, "label": inter.get("label", inter_id),
            "type": "intermediate", "x": x, "y": 175,
            "formula": inp_id + " * {{" + coef_name + "}}",
        })
        edges.append({"from": inp_id, "to": inter_id,
                      "sign": "positive" if coef_val >= 0 else "negative", "style": "solid",
                      "lag_days": 3})

    # Nœuds de sortie — étalés horizontalement en bas
    inter_ids = [n["id"] for n in intermediates]
    n_out     = max(len(outputs), 1)
    x_step_o  = max(140, 320 // n_out)
    for i, out in enumerate(outputs):
        x = 200 - (len(outputs) - 1) * x_step_o // 2 + i * x_step_o
        out_id    = out["id"]
        coef_name = out.get("coef_name", f"coef_{out_id}")
        coef_val  = float(out.get("coef_value", -100.0))
        coefficients[coef_name] = {
            "value": coef_val, "calibrated": False,
            "description": out.get("coef_desc", coef_name),
        }
        src = out.get("source_intermediate", "")
        from_id = src if src in inter_ids else (intermediates[0]["id"] if intermediates else inp_id)
        nodes.append({
            "id": out_id, "label": out.get("label", out_id),
            "type": "market_asset", "x": x, "y": 310,
            "unit": "pips", "instrument": out.get("instrument", "EURUSD"),
            "formula": from_id + " * {{" + coef_name + "}}",
        })
        edges.append({"from": from_id, "to": out_id,
                      "sign": "positive" if coef_val >= 0 else "negative", "style": "solid",
                      "lag_days": 3})

    instruments = list({out.get("instrument", "EURUSD") for out in outputs})
    return {
        "nodes": nodes, "edges": edges,
        "coefficients": coefficients, "input_mapping": input_mapping,
        "instruments": instruments,
    }


def _run_auto_analysis(event: dict, template_id: int) -> dict:
    """
    Causal analysis for the auto_template pipeline.
    Returns {"ok": bool, "inputs": dict, "node_values": dict, "actual_moves": dict, "error": str}.
    """
    try:
        from services.database import get_conn
        from services.causal_graphs import get_template, evaluate_graph

        conn = get_conn()
        tmpl = get_template(conn, template_id)
        if not tmpl:
            conn.close()
            logger.warning(f"[auto_analysis] template #{template_id} introuvable")
            return {"ok": False, "error": f"template #{template_id} introuvable"}

        graph   = tmpl["graph_json"]
        inputs  = {}
        mapping = graph.get("input_mapping", {})
        edate_str = event["start_date"][:10]

        for input_id, cfg in mapping.items():
            src   = cfg.get("source", "")
            key   = cfg.get("key", "")
            field = cfg.get("field", "close")

            if src == "economic_events" and key:
                row_e = conn.execute(
                    """SELECT actual_value, forecast_value FROM economic_events
                       WHERE series_id = ? AND event_date <= ? AND actual_value IS NOT NULL
                       ORDER BY event_date DESC LIMIT 1""",
                    (key, edate_str),
                ).fetchone()
                if row_e:
                    if field == "surprise" and row_e["forecast_value"] is not None:
                        inputs[input_id] = round(float(row_e["actual_value"]) - float(row_e["forecast_value"]), 4)
                    else:
                        inputs[input_id] = float(row_e["actual_value"])

            elif src == "ff_calendar" and key:
                row_f = conn.execute(
                    """SELECT actual_value, forecast_value FROM ff_calendar
                       WHERE event_name = ? AND event_date <= ? AND actual_value IS NOT NULL
                       ORDER BY event_date DESC LIMIT 1""",
                    (key, edate_str),
                ).fetchone()
                if row_f and row_f["actual_value"]:
                    try:
                        actual = float(row_f["actual_value"])
                        if field == "surprise" and row_f["forecast_value"]:
                            inputs[input_id] = round(actual - float(row_f["forecast_value"]), 4)
                        else:
                            inputs[input_id] = actual
                    except (ValueError, TypeError):
                        pass

            elif src in ("surprise_bps", "surprise"):
                # Step 1: use stored surprise_pct
                sp = event.get("surprise_pct")
                # Step 2: compute from actual_value / expected_value
                if sp is None:
                    try:
                        act_s = str(event.get("actual_value") or "").replace(",", ".").strip()
                        exp_s = str(event.get("expected_value") or "").replace(",", ".").strip()
                        if act_s and exp_s:
                            act_f = float(act_s)
                            exp_f = float(exp_s)
                            sp = (act_f - exp_f) / abs(exp_f) * 100 if abs(exp_f) > 1e-9 else (act_f - exp_f)
                    except (ValueError, TypeError):
                        pass
                # Step 3: look up ff_calendar by currency + date
                if sp is None:
                    try:
                        currency = (event.get("sub_type") or "").upper()
                        if currency:
                            row_ff = conn.execute("""
                                SELECT actual_value, forecast_value FROM ff_calendar
                                WHERE currency = ? AND event_date <= ? AND actual_value IS NOT NULL
                                  AND forecast_value IS NOT NULL
                                ORDER BY event_date DESC LIMIT 1
                            """, (currency, edate_str)).fetchone()
                            if row_ff:
                                a_f = float(row_ff["actual_value"])
                                e_f = float(row_ff["forecast_value"])
                                sp = (a_f - e_f) / abs(e_f) * 100 if abs(e_f) > 1e-9 else (a_f - e_f)
                    except (ValueError, TypeError):
                        pass
                if sp is not None:
                    raw = float(sp)
                    # normalize:true in node cfg → divide by 100 (coefficients calibrated for fraction)
                    if cfg.get("normalize"):
                        raw = raw / 100
                    node_range = cfg.get("range")
                    if node_range and len(node_range) == 2:
                        lo, hi = float(node_range[0]), float(node_range[1])
                        inputs[input_id] = round(max(lo, min(hi, raw)), 4)
                    else:
                        inputs[input_id] = round(raw, 4)

            elif src == "impact_score_scaled" and event.get("impact_score") is not None:
                inputs[input_id] = float(event["impact_score"]) / 10.0

            elif src == "impact_score_pct" and event.get("impact_score") is not None:
                inputs[input_id] = float(event["impact_score"])

            elif src == "actual_value" and event.get("actual_value") is not None:
                inputs[input_id] = float(event["actual_value"])

        node_values = evaluate_graph(graph, inputs, {})

        instruments         = list(set(tmpl.get("instruments", []))) or ["EURUSD"]
        all_instruments_csv = ",".join(sorted(instruments)) or "EURUSD"
        analyzed_at         = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")
        event_id            = event["id"]

        # Effective lag from template edges (for price window and chip width)
        edges = graph.get("edges", [])
        effective_lag_days = max((e.get("lag_days", 0) or 0 for e in edges), default=0)
        if effective_lag_days == 0:
            effective_lag_days = 5  # Default: measure close-to-close impact over 5 trading days

        # Fetch actual prices to compute actual pips for scoring
        actual_moves: dict = {}
        drift_by_inst: dict = {}
        try:
            prices = _fetch_prices(event["start_date"], instruments, lag_days=effective_lag_days)
            for inst in instruments:
                drift = _drift_metrics(prices, event["start_date"], inst,
                                       lag_min=0, lag_days=effective_lag_days)
                drift_by_inst[inst] = drift
                if drift.get("post_pips") is not None:
                    actual_moves[inst] = drift["post_pips"]
        except Exception as _pe:
            logger.warning(f"[auto_analysis] price fetch failed for event #{event_id}: {_pe}")

        logger.info(
            f"[auto_analysis] event #{event_id} → tmpl #{template_id} | "
            f"surprise_pct={event.get('surprise_pct')} inputs={inputs} | "
            f"nodes={len(node_values)} | instruments={all_instruments_csv} | actual_moves={actual_moves}"
        )

        existing = conn.execute(
            "SELECT id FROM causal_event_analyses WHERE market_event_id=? AND template_id=?",
            (event_id, template_id),
        ).fetchone()

        if existing:
            conn.execute("""
                UPDATE causal_event_analyses
                SET instrument=?, inputs_json=?, override_params=?, prediction_json=?,
                    actual_json=?, activation_score=?, drift_json=?, analyzed_at=?
                WHERE market_event_id=? AND template_id=?
            """, (
                all_instruments_csv, json.dumps(inputs), json.dumps({}),
                json.dumps(node_values), json.dumps(actual_moves), None,
                json.dumps(drift_by_inst), analyzed_at,
                event_id, template_id,
            ))
        else:
            conn.execute("""
                INSERT INTO causal_event_analyses
                    (market_event_id, template_id, instrument, inputs_json,
                     override_params, prediction_json, actual_json,
                     activation_score, drift_json, analyzed_at)
                VALUES (?,?,?,?,?,?,?,?,?,?)
            """, (
                event_id, template_id, all_instruments_csv,
                json.dumps(inputs), json.dumps({}),
                json.dumps(node_values), json.dumps(actual_moves),
                None, json.dumps(drift_by_inst), analyzed_at,
            ))
        conn.commit()
        conn.close()
        logger.info(f"[auto_analysis] upserted event #{event_id}, tmpl #{template_id}, actual_moves={actual_moves}")
        return {
            "ok": True,
            "inputs": inputs,
            "node_values": node_values,
            "actual_moves": actual_moves,
            "instruments": instruments,
        }

    except Exception as e:
        logger.error(f"[auto_analysis] event #{event.get('id')}: {e}", exc_info=True)
        return {"ok": False, "error": str(e)}


def _classify_to_regime(event: dict) -> dict:
    """
    Classify a market event to one of the 12 closed-list regime slugs using GPT-4o.
    Never creates new templates. Falls back to UNCLASSIFIED_IMPACT if confidence < 0.4.
    Returns {"slug": str, "confidence": float, "rationale": str}
    """
    from services.causal_graphs import REGIME_SLUGS
    try:
        from services.database import get_config
        import openai
        key = get_config("openai_api_key") or ""
        if not key:
            return {"slug": "UNCLASSIFIED_IMPACT", "confidence": 0.0, "rationale": "OpenAI key not configured"}

        client = openai.OpenAI(api_key=key)

        regime_descriptions = {
            "MACRO_DATA_SURPRISE":         "Scheduled economic release vs consensus (CPI, NFP, GDP, PMI, retail sales, jobless claims, etc.)",
            "CENTRAL_BANK_DECISION":       "Any central bank rate decision, FOMC/ECB/BoJ/BoE statement, QE/QT announcement",
            "GROWTH_CORPORATE_SIGNAL":     "Corporate earnings, guidance, M&A, layoffs, analyst revisions, sector-wide activity signals",
            "GEOPOLITICAL_RISK_OFF":       "Military conflict, sanctions, coups, political crises, election shocks generating risk-off",
            "COMMODITY_SUPPLY_SHOCK":      "OPEC decision, pipeline disruption, sanctions on producer, WASDE crop report changing supply",
            "TRADE_POLICY_SHOCK":          "Tariff announcement, trade restriction, protectionist measure, export ban",
            "CREDIT_SYSTEMIC_EVENT":       "Bank failure, sovereign debt crisis, HY spread blowout, counterparty risk / financial contagion",
            "TECHNICAL_MOMENTUM_BREAKOUT": "MA cross, key level break, RSI/Bollinger extreme, 52-week high/low — price-driven with no macro trigger",
            "SENTIMENT_POSITIONING_EXTREME": "VIX spike extreme, COT crowded positioning, extreme put/call skew — contrarian setup",
            "COMMODITY_INVENTORY_REPORT":  "EIA crude / product weekly, API crude, DOE report — inventory vs consensus surprise",
            "INSTITUTIONAL_FLOW":          "COT repositioning, FX intervention, central bank buying/selling, end-of-month rebalancing",
            "UNCLASSIFIED_IMPACT":         "Use ONLY if none of the above clearly fits (low confidence fallback)",
        }

        regimes_text = "\n".join(
            f"- {slug}: {desc}" for slug, desc in regime_descriptions.items()
        )

        system_prompt = (
            "You are a macro market analyst. Classify the given market event into exactly one of the 12 regime slugs. "
            "Choose the slug that best matches the PRIMARY causal mechanism — not just the asset class. "
            "If genuinely uncertain, use UNCLASSIFIED_IMPACT. "
            "Respond with a JSON object only."
        )

        user_prompt = f"""Market event:
- Name: {event.get('name')}
- Category: {event.get('category')}
- Sub-type: {event.get('sub_type', '')}
- Description: {(event.get('description') or '')[:300]}
- Market impact: {event.get('market_impact', '')}
- Affected assets: {event.get('affected_assets', '')}

Regime slugs to choose from:
{regimes_text}

Respond with this exact JSON:
{{
  "slug": "<one of the 12 slugs above>",
  "confidence": <0.0-1.0>,
  "rationale": "<1-2 sentences explaining why this regime fits>"
}}"""

        resp = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user",   "content": user_prompt},
            ],
            response_format={"type": "json_object"},
            temperature=0.2,
            max_tokens=200,
        )
        result = json.loads(resp.choices[0].message.content or "{}")
        slug = result.get("slug", "UNCLASSIFIED_IMPACT")
        if slug not in REGIME_SLUGS:
            slug = "UNCLASSIFIED_IMPACT"
        confidence = float(result.get("confidence") or 0.0)
        if confidence < 0.4:
            slug = "UNCLASSIFIED_IMPACT"
        return {"slug": slug, "confidence": confidence, "rationale": result.get("rationale", "")}

    except Exception as e:
        logger.error(f"[classify_regime] {e}")
        return {"slug": "UNCLASSIFIED_IMPACT", "confidence": 0.0, "rationale": f"Error: {e}"}


def auto_assign_template(event_id: int) -> dict:
    """
    Classify a market event to one of 12 closed-list regime templates and assign it.
    Never creates new templates — falls back to UNCLASSIFIED_IMPACT if confidence < 0.4.
    Returns {"template_id": int, "action": "assigned", "name": str} or {"error": ...}
    """
    try:
        from services.database import get_conn
        from services.causal_graphs import get_templates, init_tables, seed_templates

        conn = get_conn()
        init_tables(conn)
        seed_templates(conn)

        ev_row = conn.execute("SELECT * FROM market_events WHERE id = ?", (event_id,)).fetchone()
        if not ev_row:
            conn.close()
            return {"error": f"Event {event_id} introuvable"}

        event = dict(ev_row)

        # Classify to one of the 12 regime slugs (never creates a new template)
        classification = _classify_to_regime(event)
        slug       = classification["slug"]
        confidence = classification["confidence"]
        rationale  = classification.get("rationale", "")

        # Look up the template by slug field (name matches slug label)
        templates = get_templates(conn)
        tmpl = next((t for t in templates if t.get("name") == _slug_to_name(slug)), None)

        # Fallback: try matching by category name if slug lookup fails
        if not tmpl:
            tmpl = next((t for t in templates if t.get("name") == "Unclassified Market Impact"), None)

        if not tmpl:
            conn.close()
            return {"error": f"Template for slug {slug} not found in DB — run seed_templates first"}

        tmpl_id = tmpl["id"]
        conn.execute("UPDATE market_events SET auto_template_id = ? WHERE id = ?", (tmpl_id, event_id))
        conn.commit()
        conn.close()

        logger.info(f"[auto_template] Event #{event_id} -> {slug} (conf={confidence:.2f}) template #{tmpl_id}")
        _run_auto_analysis(event, tmpl_id)
        return {"template_id": tmpl_id, "action": "assigned", "name": tmpl["name"],
                "slug": slug, "confidence": confidence, "rationale": rationale}

    except Exception as e:
        logger.error(f"[auto_template] event #{event_id}: {e}")
        return {"error": str(e)}


_SLUG_TO_NAME = {
    "MACRO_DATA_SURPRISE":          "Macro Data Surprise",
    "CENTRAL_BANK_DECISION":        "Central Bank Decision",
    "GROWTH_CORPORATE_SIGNAL":      "Growth & Corporate Signal",
    "GEOPOLITICAL_RISK_OFF":        "Geopolitical Risk-Off",
    "COMMODITY_SUPPLY_SHOCK":       "Commodity Supply Shock",
    "TRADE_POLICY_SHOCK":           "Trade Policy Shock",
    "CREDIT_SYSTEMIC_EVENT":        "Credit & Systemic Stress",
    "TECHNICAL_MOMENTUM_BREAKOUT":  "Technical Momentum Breakout",
    "SENTIMENT_POSITIONING_EXTREME":"Sentiment & Positioning Extreme",
    "COMMODITY_INVENTORY_REPORT":   "Commodity Inventory Report",
    "INSTITUTIONAL_FLOW":           "Institutional Flow & Repositioning",
    "UNCLASSIFIED_IMPACT":          "Unclassified Market Impact",
}

def _slug_to_name(slug: str) -> str:
    return _SLUG_TO_NAME.get(slug, "Unclassified Market Impact")


@router.post("/api/causal-lab/auto-analyze/refresh")
def refresh_auto_analyses():
    """
    Re-run _run_auto_analysis for all causal_event_analyses rows with empty actual_json.
    Returns detailed per-event debug info.
    """
    try:
        from services.database import get_conn
        conn = get_conn()

        # Also return a DB state summary for all analyses (not just empty ones)
        all_rows = conn.execute("""
            SELECT a.id AS cea_id, a.market_event_id, a.template_id,
                   a.actual_json, a.prediction_json,
                   e.name AS event_name, e.start_date
            FROM causal_event_analyses a
            LEFT JOIN market_events e ON e.id = a.market_event_id
            ORDER BY a.id DESC
        """).fetchall()

        db_state = []
        for r in all_rows:
            actual = r["actual_json"] or ""
            pred   = r["prediction_json"] or ""
            db_state.append({
                "cea_id":       r["cea_id"],
                "event_id":     r["market_event_id"],
                "event_name":   r["event_name"],
                "start_date":   r["start_date"],
                "template_id":  r["template_id"],
                "actual_empty": actual in (None, "", "{}", "null"),
                "pred_empty":   pred   in (None, "", "{}", "null"),
                "actual_keys":  list(json.loads(actual).keys()) if actual not in (None, "", "{}", "null") else [],
                "pred_keys":    list(json.loads(pred).keys())   if pred   not in (None, "", "{}", "null") else [],
            })

        _empty = (None, "", "{}", "null", "[]")
        rows = [r for r in all_rows if
                (r["actual_json"]     or "") in _empty or
                (r["prediction_json"] or "") in _empty]
        print(f"[refresh] {len(rows)} analyses with empty actual_json out of {len(all_rows)} total", flush=True)
        conn.close()

        details = []
        refreshed, failed = 0, 0
        for row in rows:
            detail: dict = {"cea_id": row["cea_id"], "event_id": row["market_event_id"],
                            "event_name": row["event_name"], "template_id": row["template_id"]}
            try:
                conn2 = get_conn()
                ev_row = conn2.execute(
                    "SELECT * FROM market_events WHERE id = ?", (row["market_event_id"],)
                ).fetchone()
                conn2.close()
                if not ev_row:
                    detail["result"] = "event_not_found"
                    details.append(detail)
                    continue
                ev = dict(ev_row)
                detail["surprise_pct"]    = ev.get("surprise_pct")
                detail["actual_value"]    = ev.get("actual_value")
                detail["expected_value"]  = ev.get("expected_value")
                detail["start_date"]      = ev.get("start_date")
                res = _run_auto_analysis(ev, row["template_id"])
                detail["result"]         = "ok" if res["ok"] else "failed"
                detail["inputs"]         = res.get("inputs", {})
                detail["node_keys"]      = list(res.get("node_values", {}).keys())
                detail["actual_moves"]   = res.get("actual_moves", {})
                detail["run_error"]      = res.get("error")
                if res["ok"]:
                    refreshed += 1
                else:
                    failed += 1
            except Exception as _e:
                logger.warning(f"[refresh_auto_analyses] cea#{row['cea_id']}: {_e}")
                detail["result"] = f"exception: {_e}"
                failed += 1
            details.append(detail)

        print(f"[refresh] done: {refreshed} refreshed, {failed} failed", flush=True)
        return {
            "refreshed":  refreshed,
            "failed":     failed,
            "total":      len(rows),
            "db_state":   db_state,
            "details":    details,
        }
    except Exception as e:
        logger.error(f"[refresh_auto_analyses] {e}")
        raise HTTPException(500, str(e))


@router.post("/api/causal-lab/create-from-event")
def create_template_from_event(body: CreateFromEventRequest):
    """GPT-4o génère et enregistre un template causal adapté à l'événement."""
    try:
        from services.database import get_conn, get_config
        from services.causal_graphs import init_tables
        import openai

        key = get_config("openai_api_key") or ""
        if not key:
            raise HTTPException(400, "Clé OpenAI manquante dans la configuration")

        conn = get_conn()
        init_tables(conn)

        ev_row = conn.execute(
            "SELECT * FROM market_events WHERE id = ?", (body.market_event_id,)
        ).fetchone()
        if not ev_row:
            conn.close()
            raise HTTPException(404, "Événement introuvable")
        event = dict(ev_row)

        prompt = f"""Tu es un analyste financier spécialisé en graphes causaux macro.
Crée un graphe causal pour l'événement suivant.

Événement :
- Nom : {event.get('name')}
- Catégorie : {event.get('category')} / {event.get('sub_type', '')}
- Description : {(event.get('description') or '')[:400]}
- Réel : {event.get('actual_value')} | Attendu : {event.get('expected_value')} | Surprise % : {event.get('surprise_pct')}

Retourne UNIQUEMENT ce JSON (sans commentaire ni markdown) :
{{
  "name": "<nom court, ex: 'CPI US — Taux/USD'>",
  "category": "macro_us",
  "description": "<1 phrase décrivant le mécanisme>",
  "input_node": {{
    "id": "<snake_case>",
    "label": "<label court>",
    "unit": "<unité>"
  }},
  "intermediate_nodes": [
    {{
      "id": "<snake_case>",
      "label": "<label>",
      "coef_name": "<coef_snake_case>",
      "coef_value": <float [-5,5]>,
      "coef_desc": "<description>"
    }}
  ],
  "output_nodes": [
    {{
      "id": "<instr_lower>_pip",
      "label": "<INSTRUMENT>",
      "instrument": "<EURUSD|XAUUSD|SP500|BRENT|US10Y>",
      "source_intermediate": "<id_nœud_intermédiaire>",
      "coef_name": "<coef_snake_case>",
      "coef_value": <pips/unité, valeur absolue 30-200, signe selon impact>,
      "coef_desc": "<description>"
    }}
  ]
}}

Règles : coef intermédiaire ∈ [-5,5] ; coef output en pips, |val| ∈ [30,200].
Surprise CPI US supérieur → taux ↑ → USD ↑ → EURUSD ↓ (coef négatif), XAU ↓, SP500 ↓."""

        client = openai.OpenAI(api_key=key)
        resp = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"},
            temperature=0.3,
            max_tokens=900,
        )
        spec = json.loads(resp.choices[0].message.content or "{}")

        graph_json  = _build_graph_json_from_spec(spec)
        category    = spec.get("category", "macro_us")
        instruments = graph_json.get("instruments", ["EURUSD"])

        cur = conn.execute("""
            INSERT INTO causal_graph_templates
                (name, category, sub_type, description, instruments, graph_json, heuristic_ver)
            VALUES (?, ?, ?, ?, ?, ?, 1)
        """, (
            spec.get("name", event.get("name", "Template IA")),
            category,
            event.get("sub_type", ""),
            spec.get("description", ""),
            json.dumps(instruments),
            json.dumps(graph_json),
        ))
        new_id = cur.lastrowid
        conn.commit()
        conn.close()

        return {
            "id":          new_id,
            "name":        spec.get("name", "Template IA"),
            "category":    category,
            "instruments": instruments,
            "graph_json":  graph_json,
        }

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] create-from-event: {e}")
        raise HTTPException(500, str(e))


@router.get("/api/causal-lab/analyses")
def list_analyses(
    template_id:      int = Query(0),
    market_event_id:  int = Query(0),
    instrument:       str = Query(""),
    limit:            int = Query(100, le=500),
):
    try:
        from services.database import get_conn
        conn = get_conn()
        _init(conn)

        conditions = ["1=1"]
        if template_id:
            conditions.append(f"a.template_id = {template_id}")
        if market_event_id:
            conditions.append(f"a.market_event_id = {market_event_id}")
        if instrument:
            conditions.append(f"a.instrument = '{instrument}'")
        where = " AND ".join(conditions)

        rows = conn.execute(f"""
            SELECT a.*, e.name as event_name, e.category, e.start_date,
                   t.name as template_name, t.graph_json as template_graph_json
            FROM causal_event_analyses a
            LEFT JOIN market_events e ON e.id = a.market_event_id
            LEFT JOIN causal_graph_templates t ON t.id = a.template_id
            WHERE {where}
            ORDER BY a.analyzed_at DESC
            LIMIT {limit}
        """).fetchall()
        conn.close()

        result = []
        for r in rows:
            d = dict(r)
            for f in ("inputs_json", "override_params", "prediction_json", "actual_json", "drift_json"):
                try:
                    d[f] = json.loads(d[f] or "{}")
                except Exception:
                    d[f] = {}
            try:
                d["graph_json"] = json.loads(d.get("template_graph_json") or "{}")
            except Exception:
                d["graph_json"] = {}
            result.append(d)
        return result

    except Exception as e:
        logger.error(f"[causal_lab] list_analyses: {e}")
        raise HTTPException(500, str(e))


@router.delete("/api/causal-lab/analyses/{analysis_id}")
def delete_analysis(analysis_id: int):
    try:
        from services.database import get_conn
        conn = get_conn()
        _init(conn)
        row = conn.execute("SELECT id FROM causal_event_analyses WHERE id=?", (analysis_id,)).fetchone()
        if not row:
            conn.close(); raise HTTPException(404, "Analyse introuvable")
        conn.execute("DELETE FROM causal_event_analyses WHERE id=?", (analysis_id,))
        conn.commit(); conn.close()
        return {"ok": True}
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] delete_analysis: {e}")
        raise HTTPException(500, str(e))


@router.get("/api/causal-lab/calibration")
def get_calibration():
    """Statistiques de calibration par template (avg_activation, avg_pred vs avg_actual)."""
    try:
        from services.database import get_conn
        conn = get_conn()
        _init(conn)

        rows = conn.execute("""
            SELECT t.id, t.name, t.category, t.sub_type,
                   t.calibration_json, t.heuristic_ver,
                   COUNT(a.id) as n_analyses,
                   AVG(a.activation_score) as avg_activation
            FROM causal_graph_templates t
            LEFT JOIN causal_event_analyses a ON a.template_id = t.id
            GROUP BY t.id
            ORDER BY t.category, t.name
        """).fetchall()
        conn.close()

        result = []
        for r in rows:
            d = dict(r)
            try:
                d["calibration"] = json.loads(d.pop("calibration_json") or "{}")
            except Exception:
                d["calibration"] = {}
            if d.get("avg_activation") is not None:
                d["avg_activation"] = round(d["avg_activation"], 3)
            result.append(d)
        return result

    except Exception as e:
        logger.error(f"[causal_lab] calibration: {e}")
        raise HTTPException(500, str(e))


@router.post("/api/causal-lab/template/{template_id}/generate-theory")
def generate_theory(template_id: int):
    """GPT-4o-mini génère absorption_days, decay_type et confidence pour le template parent."""
    try:
        from services.database import get_conn, get_config
        from services.causal_graphs import get_template

        conn = get_conn()
        _init(conn)
        tmpl = get_template(conn, template_id)
        if not tmpl:
            conn.close(); raise HTTPException(404, "Template introuvable")

        stats = conn.execute("""
            SELECT COUNT(*) as n, AVG(activation_score) as avg_act
            FROM causal_event_analyses WHERE template_id = ?
        """, (template_id,)).fetchone()
        conn.close()

        n_analyses = stats["n"] if stats else 0
        avg_act    = round((stats["avg_act"] or 0.0), 2) if stats else 0.0

        graph = tmpl.get("graph_json", {})
        coefs = {k: v.get("value") for k, v in graph.get("coefficients", {}).items()}

        key = get_config("openai_api_key") or ""
        if not key:
            raise HTTPException(400, "Clé OpenAI manquante dans la configuration")

        import openai
        prompt = (
            f'Tu es un expert en microstructure de marché et dynamique d\'absorption des chocs de prix.\n\n'
            f'Template causal : "{tmpl["name"]}"\n'
            f'Catégorie : {tmpl["category"]} / {tmpl.get("sub_type", "")}\n'
            f'Description : {tmpl.get("description", "")}\n'
            f'Instruments : {tmpl.get("instruments", [])}\n'
            f'Coefficients : {json.dumps(coefs, ensure_ascii=False)}\n'
            f'Analyses historiques : {n_analyses} (activation directionnelle moy. : {avg_act:.0%})\n\n'
            f'Propose les paramètres d\'absorption de l\'impact de marché :\n'
            f'- absorption_days : jours calendaires avant absorption à >90% (entier 1-60)\n'
            f'- decay_type : "step" (tout-ou-rien), "linear" (déclin linéaire), "exp" (exponentiel)\n'
            f'- confidence : confiance 0.0-1.0\n'
            f'- rationale : justification courte (max 120 chars)\n\n'
            f'Références : décisions taux→3-7j/exp ; CPI/NFP→2-5j/exp ; '
            f'géopolitique→5-21j/linear ; PMI secondaire→1-2j/step\n\n'
            f'JSON uniquement : {{"absorption_days": N, "decay_type": "...", "confidence": 0.X, "rationale": "..."}}'
        )

        client = openai.OpenAI(api_key=key)
        resp = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"},
            temperature=0.2,
            max_tokens=200,
        )
        raw = resp.choices[0].message.content or "{}"
        params = json.loads(raw)

        absorption_days = max(1, min(60, int(params.get("absorption_days", 7))))
        decay_type = params.get("decay_type", "exp")
        if decay_type not in ("step", "linear", "exp"):
            decay_type = "exp"
        confidence = round(max(0.0, min(1.0, float(params.get("confidence", 0.5)))), 2)
        rationale  = str(params.get("rationale", ""))[:150]

        conn2 = get_conn()
        existing_calib = dict(tmpl.get("calibration_json") or {})
        existing_calib.update({
            "absorption_days": absorption_days,
            "decay_type": decay_type,
            "confidence": confidence,
            "theory_rationale": rationale,
            "theory_generated_at": datetime.utcnow().isoformat() + "Z",
        })
        conn2.execute(
            "UPDATE causal_graph_templates SET calibration_json=?, updated_at=datetime('now') WHERE id=?",
            (json.dumps(existing_calib), template_id),
        )
        conn2.commit()
        conn2.close()

        return {
            "template_id": template_id,
            "absorption_days": absorption_days,
            "decay_type": decay_type,
            "confidence": confidence,
            "rationale": rationale,
        }

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] generate_theory {template_id}: {e}")
        raise HTTPException(500, str(e))


# ── AI graph modifier & grammar compiler ─────────────────────────────────────

class AiModifyRequest(BaseModel):
    prompt: str
    current_graph: dict = {}   # client can pass current in-memory state


GRAMMAR_SPEC = """\
Grammar for causal graph patch operations (one operation per line, # = comment):

NODE OPERATIONS
+node id=<id> label="<text>" type=<macro_event|observable|latent|market_asset> [x=<int>] [y=<int>] [instrument=<str>] [formula=<expr>] [unit=<str>]
~node id=<id> [label="<text>"] [type=...] [x=<int>] [y=<int>] [instrument=<str>]
-node id=<id>

EDGE OPERATIONS  (sign: + positive  - negative  = neutral)
+edge from=<id> to=<id> [sign=+|-|=] [s=1|2|3] [style=solid|dashed] [label="<text>"] [lag=<int>] [decay=<float|null>]
~edge from=<id> to=<id> [sign=...] [s=...] [label="<text>"] [lag=<int>] [decay=<float|null>]
-edge from=<id> to=<id>

COEFFICIENT OPERATIONS
+coef key=<str> value=<float> [desc="<text>"]
~coef key=<str> [value=<float>] [desc="<text>"]
-coef key=<str>

INPUT MAPPING
~input node=<id> source=<surprise|actual_value|user_input|impact_score_scaled> [key=<str>]
-input node=<id>

GRAPH METADATA
~instruments EURUSD,SP500,...

Vocabulary:
- macro_event: source event node (NFP surprise, etc.)
- observable: directly measurable market variable (OIS rate, US 2Y yield, spread)
- latent: hidden variable (risk aversion, growth expectations, sentiment)
- market_asset: traded instrument (EUR/USD, S&P 500 — final output nodes)
- s=1 weak / s=2 medium (default) / s=3 strong causal link
- style=dashed for indirect/uncertain channels
- lag: trading days before effect onset; decay: half-life in days (null = permanent shift)
"""


class AiToGrammarRequest(BaseModel):
    prompt: str
    current_graph: dict = {}


@router.post("/api/causal-lab/template/{template_id}/ai-to-grammar")
def ai_to_grammar(template_id: int, body: AiToGrammarRequest):
    """
    Convert a natural-language modification request into graph patch grammar.
    Returns only the grammar lines — user reviews before applying.
    """
    try:
        from services.database import get_conn, get_config
        from services.causal_graphs import get_template
        import openai

        key = get_config("openai_api_key") or ""
        if not key:
            raise HTTPException(400, "Clé OpenAI manquante dans la configuration")

        conn = get_conn()
        t = get_template(conn, template_id)
        conn.close()
        if not t:
            raise HTTPException(404, f"Template {template_id} non trouvé")

        gj = body.current_graph if body.current_graph.get("nodes") else t["graph_json"]

        # Compact node/edge summary for context
        node_summary = "\n".join(
            f"  {n['id']} ({n['type']}) — {n['label']}"
            for n in gj.get("nodes", [])
        )
        edge_summary = "\n".join(
            f"  {e['from']} → {e['to']}  sign={e.get('sign','?')} lag={e.get('lag_days','?')} [{e.get('label','')}]"
            for e in gj.get("edges", [])
        )

        system_prompt = (
            "You are a causal graph patch generator for the OpenFin macro-finance platform.\n"
            "Convert the user's natural language modification request into graph patch operations.\n"
            "Return ONLY the grammar lines — no explanation, no markdown, no fences.\n\n"
            + GRAMMAR_SPEC
        )

        user_prompt = (
            f'Current graph nodes:\n{node_summary}\n\n'
            f'Current graph edges:\n{edge_summary}\n\n'
            f'Modification request:\n{body.prompt}\n\n'
            "Return ONLY the grammar patch lines, one per line."
        )

        client = openai.OpenAI(api_key=key)
        resp = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user",   "content": user_prompt},
            ],
            temperature=0.15,
            max_tokens=800,
        )
        grammar = (resp.choices[0].message.content or "").strip()
        # Strip any accidental markdown fences
        grammar = "\n".join(
            l for l in grammar.splitlines()
            if not l.strip().startswith("```")
        )
        return {"grammar": grammar}

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] ai_to_grammar {template_id}: {e}")
        raise HTTPException(500, str(e))


@router.post("/api/causal-lab/template/{template_id}/ai-modify")
def ai_modify_template(template_id: int, body: AiModifyRequest):
    """
    GPT-4o modifie un graph_json selon un prompt utilisateur.
    Retourne le graph_json modifié sans sauvegarder — l'utilisateur valide puis sauvegarde.
    """
    try:
        from services.database import get_conn, get_config
        from services.causal_graphs import get_template
        import openai

        key = get_config("openai_api_key") or ""
        if not key:
            raise HTTPException(400, "Clé OpenAI manquante dans la configuration")

        conn = get_conn()
        t = get_template(conn, template_id)
        conn.close()
        if not t:
            raise HTTPException(404, f"Template {template_id} non trouvé")

        # Use the graph sent by client (latest in-memory edits) or fall back to DB
        gj = body.current_graph if body.current_graph.get("nodes") else t["graph_json"]

        system_prompt = (
            "You are a causal graph editor for the OpenFin macro-finance platform.\n"
            "You receive a causal graph in JSON and a modification request in natural language.\n"
            "Return ONLY the complete modified graph_json as a valid JSON object — no explanation, no markdown.\n\n"
            "Graph format:\n"
            "{\n"
            '  "nodes": [{"id": str, "label": str, "type": "macro_event|observable|latent|market_asset",\n'
            '             "x": int, "y": int, "formula": str_opt, "unit": str_opt, "instrument": str_opt}],\n'
            '  "edges": [{"from": str, "to": str, "style": "solid|dashed", "strength": 1|2|3,\n'
            '             "sign": "positive|negative|neutral", "label": str_opt,\n'
            '             "lag_days": int_opt, "decay_days": float_opt}],\n'
            '  "coefficients": {"key": {"value": float, "calibrated": null, "description": str}},\n'
            '  "instruments": [str],\n'
            '  "input_mapping": {"node_id": {"source": "user_input|surprise|actual_value|impact_score_scaled",\n'
            '                               "key": str_opt, "field": str_opt}}\n'
            "}\n\n"
            "Rules:\n"
            "- Preserve existing node x/y positions unless layout is clearly broken\n"
            "- All edge from/to must reference valid node ids\n"
            "- instruments list must include instrument field of all market_asset nodes\n"
            "- Return the COMPLETE graph_json (not just the changed parts)"
        )

        user_prompt = (
            f'Current graph — template "{t["name"]}" ({t["category"]}, sub_type: {t.get("sub_type","")}):\n'
            f"{json.dumps(gj, ensure_ascii=False, indent=2)}\n\n"
            f"Modification request:\n{body.prompt}\n\n"
            "Return the complete modified graph_json as JSON only."
        )

        client = openai.OpenAI(api_key=key)
        resp = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user",   "content": user_prompt},
            ],
            response_format={"type": "json_object"},
            temperature=0.25,
            max_tokens=3000,
        )
        raw = resp.choices[0].message.content or "{}"
        modified = json.loads(raw)

        if "nodes" not in modified or "edges" not in modified:
            raise ValueError("L'IA n'a pas retourné un graph_json valide (nodes/edges manquants)")

        return {"graph_json": modified, "template_name": t["name"]}

    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[causal_lab] ai_modify_template {template_id}: {e}")
        raise HTTPException(500, str(e))