import json
import random
import time
import uuid
import requests
import redis
import meilisearch_python_sdk
from config import settings

_redis = None
_meili = None

def meili_client():
    global _meili
    if _meili is None:
        try:
            _meili = meilisearch_python_sdk.Client(settings.meili_url, settings.meili_api_key or None)
        except Exception:
            _meili = None
    return _meili

def meili_index_doc(index_uid: str, doc: dict):
    c = meili_client()
    if not c:
        return False
    try:
        idx = c.index(index_uid)
        idx.add_documents([doc], primary_key='id')
        return True
    except Exception:
        return False

def meili_search(index_uid: str, q: str, limit: int = 20):
    c = meili_client()
    if not c:
        return {"hits": []}
    try:
        idx = c.index(index_uid)
        res = idx.search(q, {'limit': limit})
        if isinstance(res, dict):
            return res
        # sdk object fallback
        return {
            'hits': getattr(res, 'hits', []),
            'estimatedTotalHits': getattr(res, 'estimated_total_hits', None),
            'processingTimeMs': getattr(res, 'processing_time_ms', None),
            'query': q,
        }
    except Exception:
        return {"hits": []}

def meili_health():
    c = meili_client()
    if not c:
        return {"ok": False, "error": "client_unavailable"}
    try:
        h = c.health()
        status = getattr(h, 'status', None)
        if status is None and isinstance(h, dict):
            status = h.get('status')
        return {"ok": True, "status": status or 'available'}
    except Exception as e:
        return {"ok": False, "error": str(e)[:160]}

def redis_client():
    global _redis
    if _redis is None:
        try:
            _redis = redis.Redis.from_url(settings.redis_url, decode_responses=True, socket_timeout=2)
            _redis.ping()
        except Exception:
            _redis = None
    return _redis

def redis_set_json(key: str, value: dict, ttl: int = 3600):
    r = redis_client()
    if not r:
        return False
    try:
        r.setex(key, ttl, json.dumps(value))
        return True
    except Exception:
        return False

def redis_get_json(key: str):
    r = redis_client()
    if not r:
        return None
    try:
        v = r.get(key)
        return json.loads(v) if v else None
    except Exception:
        return None

def redis_lock(key: str, ttl: int = 180):
    r = redis_client()
    if not r:
        return True
    try:
        return bool(r.set(key, str(int(time.time())), ex=ttl, nx=True))
    except Exception:
        return True


def _ollama_generate(model: str, payload_obj: dict, timeout: int = 45):
    payload = {"model": model, "stream": False, "prompt": json.dumps(payload_obj), "format": "json"}
    r = requests.post(f"{settings.ollama_url}/api/generate", json=payload, timeout=timeout)
    r.raise_for_status()
    return json.loads(r.json().get("response", "{}"))


def _embed(text: str):
    try:
        r = requests.post(f"{settings.ollama_url}/api/embeddings", json={"model": settings.ollama_embed_model, "prompt": text[:8000]}, timeout=30)
        r.raise_for_status()
        return r.json().get("embedding", [])
    except Exception:
        return []


def qdrant_ensure_collection(vector_size=768):
    try:
        requests.put(f"{settings.qdrant_url}/collections/{settings.qdrant_collection}", json={"vectors": {"size": vector_size, "distance": "Cosine"}}, timeout=10)
    except Exception:
        pass


def qdrant_add_memory(symbol: str, text: str, payload: dict):
    vec = _embed(text)
    if not vec:
        return False
    qdrant_ensure_collection(len(vec))
    enriched = {
        "symbol": symbol,
        "text": text[:2000],
        "memory_type": payload.get("memory_type", "decision"),
        "created_at": int(time.time()),
        **payload,
    }
    body = {
        "points": [{"id": str(uuid.uuid4()), "vector": vec, "payload": enriched}]
    }
    try:
        r = requests.put(f"{settings.qdrant_url}/collections/{settings.qdrant_collection}/points", json=body, timeout=15)
        return r.ok
    except Exception:
        return False


def qdrant_similar(symbol: str, query_text: str, limit: int = 8):
    vec = _embed(query_text)
    if not vec:
        return []
    body = {
        "vector": vec,
        "limit": limit,
        "with_payload": True,
        "filter": {
            "should": [
                {"key": "symbol", "match": {"value": symbol}},
                {"key": "memory_type", "match": {"value": "macro"}}
            ]
        }
    }
    try:
        r = requests.post(f"{settings.qdrant_url}/collections/{settings.qdrant_collection}/points/search", json=body, timeout=15)
        if not r.ok:
            return []
        out = []
        for p in r.json().get("result", []):
            pl = p.get("payload", {})
            out.append({
                "score": p.get("score", 0),
                "text": pl.get("text", ""),
                "status": pl.get("status", ""),
                "action": pl.get("action", ""),
                "confidence": pl.get("confidence", None),
                "outcome_score": pl.get("outcome_score", None),
            })
        return out
    except Exception:
        return []


def searx_news(symbol: str, limit: int = 12):
    q = f"{symbol} stock news earnings guidance analyst macro risk"
    params = {"q": q, "format": "json", "language": "en"}
    try:
        r = requests.get(settings.searx_url, params=params, timeout=20)
        r.raise_for_status()
        data = r.json()
        return [{"title": it.get("title", ""), "url": it.get("url", ""), "content": (it.get("content", "") or "")[:700]} for it in data.get("results", [])[:limit]]
    except Exception:
        return []


def extra_research(symbol: str, weak_points: list, limit: int = 6):
    q = f"{symbol} {' '.join(weak_points[:3])} SEC filing guidance risks competition"
    params = {"q": q, "format": "json", "language": "en"}
    try:
        r = requests.get(settings.searx_url, params=params, timeout=20)
        r.raise_for_status()
        data = r.json()
        return [{"title": it.get("title", ""), "url": it.get("url", ""), "content": (it.get("content", "") or "")[:700]} for it in data.get("results", [])[:limit]]
    except Exception:
        return []


def summarize_news_with_ollama(symbol: str, context_items: list):
    prompt = {"task": "Summarize market-moving info into a concise brief", "symbol": symbol, "news": context_items}
    fallback = " | ".join([(x.get("title") or "")[:90] for x in context_items[:3] if x.get("title")]) or f"No strong headlines for {symbol}"
    try:
        parsed = _ollama_generate(settings.ollama_curator_model, prompt)
        s = parsed.get("summary")
        return s if s else fallback
    except Exception:
        return fallback


def strategy_signals(symbol: str, context_items: list):
    text_blob = " ".join((x.get("title", "") + " " + x.get("content", "")) for x in context_items).lower()
    bullish = sum(k in text_blob for k in ["beat", "raise guidance", "upgrade", "buyback", "record revenue"])
    bearish = sum(k in text_blob for k in ["miss", "downgrade", "lawsuit", "probe", "cut guidance", "recall"])
    score = bullish - bearish
    action = "buy" if score >= 2 else ("sell" if score <= -2 else "hold")
    conf = min(0.85, 0.50 + abs(score) * 0.08)
    return {"strategy": "event-momentum-v1", "score": score, "action": action, "confidence": conf, "signals": {"bullish": bullish, "bearish": bearish}}


def _normalize_decision(d: dict, strategy: dict):
    action = str(d.get("action", strategy.get("action", "hold"))).lower()
    if action not in {"buy", "sell", "hold"}:
        action = strategy.get("action", "hold")
    try:
        confidence = max(0.0, min(1.0, float(d.get("confidence", strategy.get("confidence", 0.5)))))
    except Exception:
        confidence = min(strategy.get("confidence", 0.5), 0.55)
    try:
        order_usd = float(d.get("order_usd", settings.max_order_usd))
    except Exception:
        order_usd = settings.max_order_usd
    order_usd = max(1.0, min(order_usd, settings.max_order_usd))
    reason = str(d.get("reason", "normalized-decision"))[:500]
    return {
        "action": action,
        "confidence": confidence,
        "order_usd": order_usd,
        "reason": reason,
        "needs_more_research": bool(d.get("needs_more_research", False)),
        "research_topics": d.get("research_topics", []) or [],
    }


def llm_final_decision(symbol: str, context_items: list, strategy: dict, memory_hits: list):
    prompt = {
        "task": "Final trading decision. Return strict JSON.",
        "symbol": symbol,
        "constraints": {"actions": ["buy", "sell", "hold"], "max_order_usd": settings.max_order_usd, "fee_per_trade_usd": settings.fee_per_trade_usd, "slippage_bps": settings.slippage_bps, "avoid_overtrading": True},
        "strategy_prior": strategy,
        "memory_hits": memory_hits,
        "news": context_items,
        "output_schema": {"action": "buy|sell|hold", "confidence": "0-1", "order_usd": f"<= {settings.max_order_usd}", "reason": "short rationale", "needs_more_research": True, "research_topics": ["..."]},
    }
    # pass 1: strict json mode
    try:
        d = _ollama_generate(settings.ollama_decision_model, prompt, timeout=60)
        return _normalize_decision(d, strategy)
    except Exception:
        pass

    # pass 2: non-json constrained output, then parse heuristically
    try:
        text_prompt = (
            f"Symbol: {symbol}\n"
            f"Strategy prior: {strategy}\n"
            f"Return 4 lines only:\n"
            f"action: buy|sell|hold\nconfidence: 0-1\norder_usd: <= {settings.max_order_usd}\nreason: <short>\n"
        )
        r = requests.post(f"{settings.ollama_url}/api/generate", json={"model": settings.ollama_decision_model, "prompt": text_prompt, "stream": False}, timeout=45)
        if r.ok:
            raw = (r.json().get("response", "") or "").lower()
            action = "buy" if "buy" in raw else ("sell" if "sell" in raw else "hold")
            conf = 0.6 if "confidence" not in raw else strategy.get("confidence", 0.55)
            parsed = {"action": action, "confidence": conf, "order_usd": settings.max_order_usd, "reason": raw[:300]}
            return _normalize_decision(parsed, strategy)
    except Exception:
        pass

    return {
        "action": strategy.get("action", "hold"),
        "confidence": min(strategy.get("confidence", 0.5), 0.55),
        "order_usd": min(5.0, settings.max_order_usd),
        "reason": "decision-fallback-strategy",
        "needs_more_research": False,
        "research_topics": [],
    }


def alpaca_headers():
    return {"APCA-API-KEY-ID": settings.alpaca_key, "APCA-API-SECRET-KEY": settings.alpaca_secret, "Content-Type": "application/json"}


def place_order(symbol: str, action: str, order_usd: float):
    if action not in {"buy", "sell"}:
        return None
    payload = {"symbol": symbol, "side": action, "type": "market", "time_in_force": "day", "notional": round(order_usd, 2)}
    try:
        r = requests.post(f"{settings.alpaca_base}/v2/orders", headers=alpaca_headers(), json=payload, timeout=20)
        return {"ok": r.ok, "status": r.status_code, "json": r.json() if r.text else {}}
    except Exception as e:
        return {"ok": False, "status": 0, "json": {"error": str(e)}}


def account_snapshot():
    try:
        r = requests.get(f"{settings.alpaca_base}/v2/account", headers=alpaca_headers(), timeout=20)
        r.raise_for_status()
        return r.json()
    except Exception:
        return {}


def positions_snapshot():
    try:
        r = requests.get(f"{settings.alpaca_base}/v2/positions", headers=alpaca_headers(), timeout=20)
        return r.json() if r.ok else []
    except Exception:
        return []


def market_open():
    try:
        r = requests.get(f"{settings.alpaca_base}/v2/clock", headers=alpaca_headers(), timeout=20)
        return bool(r.json().get("is_open", False)) if r.ok else False
    except Exception:
        return False


def trilium_log(title: str, body: str):
    if not settings.trilium_token:
        return False
    headers = {"Authorization": settings.trilium_token, "Content-Type": "application/json"}
    payload = {"title": title[:120], "type": "text", "mime": "text/markdown", "content": body}
    try:
        # best-effort endpoints across Trilium variants
        for ep in ["/etapi/create-note", "/etapi/notes"]:
            r = requests.post(settings.trilium_url.rstrip("/") + ep, headers=headers, json=payload, timeout=15)
            if r.ok:
                return True
    except Exception:
        pass
    return False


def qdrant_memory_health():
    try:
        # collection-specific
        r = requests.get(f"{settings.qdrant_url}/collections/{settings.qdrant_collection}", timeout=10)
        if r.ok:
            j = r.json().get("result", {})
            return {
                "ok": True,
                "collection": settings.qdrant_collection,
                "points_count": j.get("points_count"),
                "indexed_vectors_count": j.get("indexed_vectors_count"),
            }
        # fallback global health/list
        r2 = requests.get(f"{settings.qdrant_url}/collections", timeout=10)
        if r2.ok:
            names=[c.get('name') for c in r2.json().get('result',{}).get('collections',[])]
            return {"ok": True, "collection": settings.qdrant_collection, "exists": settings.qdrant_collection in names, "collections": names[:20]}
        return {"ok": False, "status": r.status_code}
    except Exception as e:
        return {"ok": False, "error": str(e)[:160]}


def n8n_emit(event: dict):
    if not settings.n8n_webhook:
        return False
    try:
        r = requests.post(settings.n8n_webhook, json=event, timeout=10)
        return r.ok
    except Exception:
        return False