from __future__ import annotations

import hashlib
import json
from pathlib import Path
from typing import Any

import pandas as pd

from src.domain import CODE_VERSION, PRODUCT, SCHEMA_ID, SNAPSHOT_ID
from src.schemas import ChangeFinding


def input_hash(payload: dict[str, Any]) -> str:
    raw = json.dumps(payload, sort_keys=True, ensure_ascii=False, default=str)
    return hashlib.sha256(raw.encode("utf-8")).hexdigest()[:16]


def findings_frame(rows: list[ChangeFinding]) -> pd.DataFrame:
    records = []
    for item in rows:
        data = item.model_dump()
        data["observed"] = " | ".join(item.observed)
        data["inferred"] = " | ".join(item.inferred)
        data["gold_event_ids"] = ",".join(item.gold_event_ids)
        data["evidence_event_ids"] = ",".join(item.evidence_event_ids)
        data["methods_fired"] = ",".join(item.methods_fired)
        data["tag"] = item.tag.value
        data["alert_class"] = item.alert_class.value
        data["severity"] = item.severity.value
        data["confidence"] = item.confidence.value
        data["workflow"] = item.workflow.value
        data["approval_status"] = item.approval_status.value
        data["before_state"] = item.before_state.value
        data["after_state"] = item.after_state.value
        records.append(data)
    return pd.DataFrame(records)


def report_payload(result: dict[str, Any]) -> dict[str, Any]:
    return {
        "product": PRODUCT,
        "code_version": CODE_VERSION,
        "schema_id": SCHEMA_ID,
        "snapshot_id": SNAPSHOT_ID,
        "run_id": result["run_id"],
        "verdict": result["verdict"].value if hasattr(result["verdict"], "value") else result["verdict"],
        "kpis": result["kpis"].model_dump() if hasattr(result["kpis"], "model_dump") else result["kpis"],
        "comparison": [row.model_dump() if hasattr(row, "model_dump") else row for row in result.get("comparison") or []],
        "calibration": [row.model_dump() if hasattr(row, "model_dump") else row for row in result.get("calibration") or []],
        "input_hash": result["run_id"].split("-")[-1],
        "live": True,
        "disclaimer": (
            "A finding is a review ranking, not proof of current cloud exposure. "
            "Before/after is shown only when both events exist in this export. "
            "This demo never requests cloud credentials and never changes configuration. "
            "Severity is not approval status. Maintenance windows stay context."
        ),
    }


def write_downloads(result: dict[str, Any], folder: Path) -> dict[str, str]:
    folder.mkdir(parents=True, exist_ok=True)
    run_id = result["run_id"]
    files = {
        "findings": folder / f"{run_id}_findings.csv",
        "comparison": folder / f"{run_id}_comparison.csv",
        "calibration": folder / f"{run_id}_calibration.csv",
        "report": folder / f"{run_id}_report.json",
    }
    findings_frame(result["findings"]).to_csv(files["findings"], index=False)
    pd.DataFrame([row.model_dump() if hasattr(row, "model_dump") else row for row in result.get("comparison") or []]).to_csv(
        files["comparison"], index=False
    )
    pd.DataFrame([row.model_dump() if hasattr(row, "model_dump") else row for row in result.get("calibration") or []]).to_csv(
        files["calibration"], index=False
    )
    files["report"].write_text(json.dumps(result["report"], indent=2, ensure_ascii=False, default=str), encoding="utf-8")
    return {key: str(path) for key, path in files.items()}