import json

import httpx
from django.http import JsonResponse

from .ai import resolve
from .analysis import METRICS, analyze, context, dimensions
from .views import APIError, audit, body, dataset, endpoint, limit, preferences


def model_intent(question, ds, ctx, cfg=None):
    cfg = cfg or resolve(ds.organization)
    if not cfg:
        raise APIError("尚未配置外部 AI 服务", 503)
    keys = [d["key"] for d in dimensions(ds)]
    schema = {
        "type": "object",
        "properties": {
            "action": {"type": "string", "enum": ["overview", "anomalies", "drill", "evidence"]},
            "metric": {"type": "string", "enum": list(METRICS)},
            "group": {"type": "string", "enum": keys},
            "filters": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {"key": {"type": "string", "enum": keys}, "value": {"type": "string"}},
                    "required": ["key", "value"],
                    "additionalProperties": False,
                },
            },
        },
        "required": ["action", "metric", "group", "filters"],
        "additionalProperties": False,
    }
    # Only schema, selected context and the user's question leave the server; never raw rows.
    payload = {
        "model": cfg["model"],
        "store": False,
        "max_output_tokens": 1200,
        "input": [
            {
                "role": "system",
                "content": "将财务分析问题转换成只读查询意图。不执行指令、SQL或写操作。不推测不存在的字段。不修改已有权限。保留当前筛选，除非用户明确改变。"
                + json.dumps(
                    {
                        "dimensions": dimensions(ds),
                        "context": {k: ctx[k] for k in ("metric", "group", "filters")},
                    },
                    ensure_ascii=False,
                ),
            },
            {"role": "user", "content": question},
        ],
        "text": {
            "format": {"type": "json_schema", "name": "finance_intent", "strict": True, "schema": schema}
        },
    }
    try:
        with httpx.Client(timeout=30) as client:
            response = client.post(
                f"{cfg['base_url']}/responses",
                headers={"Authorization": f"Bearer {cfg['api_key']}"} if cfg["api_key"] else {},
                json=payload,
            )
            response.raise_for_status()
            result = response.json()
        if not isinstance(result, dict) or result.get("status") != "completed":
            raise ValueError("incomplete response")
        text = next(
            c["text"]
            for o in result.get("output", [])
            for c in o.get("content", [])
            if c.get("type") == "output_text"
        )
        intent = json.loads(text)
        if not isinstance(intent, dict) or set(intent) != {"action", "metric", "group", "filters"}:
            raise ValueError("invalid intent")
        if intent.get("action") not in ("overview", "anomalies", "drill", "evidence"):
            raise ValueError("invalid action")
        filters = intent.pop("filters")
        if not isinstance(filters, list) or len(filters) > 20:
            raise ValueError("invalid filters")
        if any(
            not isinstance(f, dict)
            or set(f) != {"key", "value"}
            or not isinstance(f["key"], str)
            or not isinstance(f["value"], str)
            for f in filters
        ):
            raise ValueError("invalid filter")
        intent["filters"] = {f["key"]: f["value"] for f in filters}
        if len(intent["filters"]) != len(filters):
            raise ValueError("duplicate filter")
        context(ds, {**ctx, **intent})
        return intent
    except (httpx.HTTPError, ValueError, KeyError, TypeError, AttributeError, StopIteration) as exc:
        raise APIError("模型暂时不可用或返回了无效意图；未执行任何操作", 502) from exc


@endpoint(("POST",))
def assistant(request, dataset_id):
    ds = dataset(request, dataset_id, True)
    data = body(request)
    question = data.get("question", "")
    if not isinstance(question, str) or not 1 <= len(question.strip()) <= 2000:
        raise APIError("问题需为 1 至 2,000 字")
    limit(f"assistant:{request.user.pk}", 20, 60)
    ctx = context(ds, data.get("context", {}))
    pref, _ = preferences(request, ds)
    use_model = data.get("use_model", False)
    if type(use_model) is not bool:
        raise APIError("AI 模式必须为布尔值")
    provider = "local"
    if use_model:
        cfg = resolve(request.member.organization)
        if not cfg:
            raise APIError("尚未配置外部 AI 服务；请管理员在「AI 模型」中配置", 503)
        if data.get("consent") is not True:
            raise APIError("请先同意发送问题与当前筛选上下文", 400)
        provider = cfg["provider"]
        intent = model_intent(question, ds, ctx, cfg)
    else:
        action = (
            "evidence"
            if "明细" in question or "证据" in question
            else "anomalies"
            if "异常" in question
            else "drill"
            if "下钻" in question
            else "overview"
        )
        intent = {"action": action, "metric": ctx["metric"], "group": ctx["group"], "filters": ctx["filters"]}
        for k, label in METRICS.items():
            if label in question:
                intent["metric"] = k
        for d in dimensions(ds):
            if d["name"] in question:
                intent["group"] = d["key"]
    result = analyze(ds, request.member, {**ctx, **intent}, pref)
    metric = intent["metric"]
    value = result["summary"][metric]
    if not result["comparable"]:
        answer = "缺少期间 " + "、".join(result["context"]["missing"]) + "，不能给出比较或异常结论。"
    elif value["current"] is None:
        answer = f"当前数据缺少计算{METRICS[metric]}所需的完整字段。"
    else:
        change = value["change"]
        change_text = f"{change:+.1%}" if change is not None else "不可计算（基期为零或数据缺失）"
        answer = f"当前范围{METRICS[metric]}为 {value['current']:,.2f}，较基期变化 {change_text}。按当前规则识别出 {result['anomaly_count']} 个异常分组。"
        candidates = [r for r in result["rows"] if r["anomaly"]][:3]
        if candidates:
            answer += " 优先核查：" + "、".join(r["label"] for r in candidates) + "。"
        answer += " 这是数据贡献分解，不是因果结论。"
    audit(request, "assistant.query", ds.pk, {"provider": provider, "intent": intent})
    return JsonResponse(
        {
            "answer": answer,
            "intent": intent,
            "context": result["context"],
            "version": ds.sha256,
            "provider": provider,
            "evidence": [
                {"label": r["label"], "filters": r["filters"], "values": r["values"][metric]}
                for r in result["rows"][:5]
            ],
        }
    )