"""Deterministic six-step MOF screening tools backed by precomputed tables."""
from __future__ import annotations

import math
import re
from functools import lru_cache
from pathlib import Path
from typing import Any

import joblib
import numpy as np
import pandas as pd


ROOT = Path(__file__).resolve().parent.parent
ALL_DES_ROOT = ROOT.parent
DOC_ROOT = ALL_DES_ROOT.parent
REVISE_ROOT = ALL_DES_ROOT / "0629_revise"
ORDER_ROOT = REVISE_ROOT / "Screening" / "筛选顺序" / "筛选顺序"
PRICE_ROOT = DOC_ROOT / "price_filter" / "mof_price_0707"

STEP1_FULL = REVISE_ROOT / "screen_result" / "all_mofs_sorted_by_balanced_score_with_YEYVOO_clean5b_only.csv"
STEP1_TOP20 = REVISE_ROOT / "screen_result" / "top20_percent_mofs_by_balanced_score_with_YEYVOO_clean5b_only.csv"
STEP2_DIR = ORDER_ROOT / "1重金属结果"
STEP3_SA = ORDER_ROOT / "2 配体可合成性" / "All_MOF_SA_Ranking.csv"
STEP3_TOP6000 = ORDER_ROOT / "3 水生物毒性" / "Top6000_MOF_MaxSA_Linker.csv"
STEP4_TOX = ORDER_ROOT / "3 水生物毒性" / "Strict_EasySynth_Linkers_Optimized_Predictions.csv"
STEP5_DIR = ORDER_ROOT / "4 PMT"
STEP5_DESC = STEP5_DIR / "PMT描述符.csv"
STEP6_PRICE = PRICE_ROOT / "Strict_EasySynth_Linkers_Toxicity_Ranking0705_CoPriNet_price_ranked.csv"

SIX_STEP_TOOLS = [
    "adsorption_screen",
    "heavy_metal",
    "ligand_sa",
    "aquatic_toxicity",
    "pmt",
    "price",
]

ALLOWED_METALS = {"Mg", "Al", "Ca", "Ti", "Mn", "Fe", "Cu", "Zn", "Zr", "Ag"}
ALL_METALS = {
    "Li", "Be", "Na", "Mg", "Al", "K", "Ca", "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni",
    "Cu", "Zn", "Ga", "Rb", "Sr", "Y", "Zr", "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd",
    "In", "Sn", "Cs", "Ba", "La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho",
    "Er", "Tm", "Yb", "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", "Tl", "Pb",
    "Bi", "Po", "Fr", "Ra", "Ac", "Th", "Pa", "U", "Np", "Pu",
}


def normalize_mof_name(value: Any) -> str:
    """Normalize MOF ids for table and filename matching."""
    if value is None:
        return ""
    return re.sub(r"[\s\-_()]+", "", str(value)).lower()


def _smiles_key(value: Any) -> str:
    return "" if value is None else re.sub(r"\s+", "", str(value))


@lru_cache(maxsize=16)
def _csv(path: str) -> pd.DataFrame:
    return pd.read_csv(path)


def _read(path: Path) -> pd.DataFrame:
    return _csv(str(path))


def _find_by_name(df: pd.DataFrame, name: str | None, columns: tuple[str, ...] = ("MOF_Name", "MOF")) -> pd.Series | None:
    if not name:
        return None
    key = normalize_mof_name(name)
    for column in columns:
        if column not in df.columns:
            continue
        mask = df[column].map(normalize_mof_name) == key
        if mask.any():
            return df.loc[mask].iloc[0]
    return None


def _find_by_smiles(df: pd.DataFrame, smiles: str | None, column: str = "SMILES") -> pd.Series | None:
    if not smiles or column not in df.columns:
        return None
    key = _smiles_key(smiles)
    mask = df[column].map(_smiles_key) == key
    if mask.any():
        return df.loc[mask].iloc[0]
    return None


def _num(value: Any) -> float | None:
    try:
        if pd.isna(value):
            return None
        value = float(value)
        if math.isnan(value) or math.isinf(value):
            return None
        return value
    except Exception:
        return None


def _int(value: Any) -> int | None:
    number = _num(value)
    return None if number is None else int(number)


def _jsonable(value: Any) -> Any:
    if isinstance(value, dict):
        return {str(k): _jsonable(v) for k, v in value.items()}
    if isinstance(value, list | tuple):
        return [_jsonable(v) for v in value]
    if isinstance(value, np.ndarray):
        return _jsonable(value.tolist())
    if isinstance(value, np.generic):
        return _jsonable(value.item())
    if isinstance(value, float) and (math.isnan(value) or math.isinf(value)):
        return None
    return value


def _unknown(tool: str, reason: str, step: int) -> dict[str, Any]:
    return {"tool": tool, "step": step, "status": "unknown", "reason": reason, "pass": None}


def resolve_candidate(cif_path: str | None = None, user_text: str = "") -> dict[str, Any]:
    """Resolve uploaded filename or user text to a known MOF candidate."""
    query_names: list[str] = []
    if cif_path:
        query_names.append(Path(cif_path).stem)
    query_names.extend(re.findall(r"[A-Za-z0-9]+(?:[-_][A-Za-z0-9]+)*(?:-\(id[:_]\d+\))?", user_text or ""))

    candidate: dict[str, Any] = {
        "cif_path": cif_path,
        "query_names": list(dict.fromkeys([q for q in query_names if q])),
        "matched_mof": None,
        "linker_smiles": None,
        "match_source": None,
    }

    for path, columns, source in [
        (STEP1_FULL, ("MOF_Name", "MOF"), "adsorption_screen"),
        (STEP3_SA, ("MOF", "MOF_Name"), "ligand_sa"),
        (STEP3_TOP6000, ("MOF", "MOF_Name"), "top6000_linker"),
        (STEP6_PRICE, ("MOF", "MOF_Name"), "price"),
    ]:
        if not path.exists():
            continue
        df = _read(path)
        for name in candidate["query_names"]:
            row = _find_by_name(df, name, columns)
            if row is not None:
                matched = row.get("MOF") if "MOF" in row.index else row.get("MOF_Name")
                candidate["matched_mof"] = str(matched)
                candidate["match_source"] = source
                if "SMILES" in row.index and pd.notna(row.get("SMILES")):
                    candidate["linker_smiles"] = str(row.get("SMILES"))
                return candidate

    return candidate


def adsorption_screen_tool(candidate: dict[str, Any]) -> dict[str, Any]:
    """Step 1: precomputed top-20% adsorption screen."""
    if not STEP1_FULL.exists() or not STEP1_TOP20.exists():
        return _unknown("adsorption_screen", "adsorption ranking table is missing", 1)

    full = _read(STEP1_FULL)
    top = _read(STEP1_TOP20)
    name = candidate.get("matched_mof") or next(iter(candidate.get("query_names", [])), None)
    row = _find_by_name(full, name)
    if row is None:
        return _unknown("adsorption_screen", "MOF was not found in the precomputed adsorption ranking", 1)

    mof = str(row.get("MOF_Name"))
    top_row = _find_by_name(top, mof)
    rank = _int(row.get("rank"))
    cutoff = len(top)
    passed = top_row is not None
    candidate["matched_mof"] = mof
    return {
        "tool": "adsorption_screen",
        "step": 1,
        "status": "pass" if passed else "fail",
        "pass": passed,
        "mof": mof,
        "rank": rank,
        "top20_cutoff_rank": cutoff,
        "predicted_benzene_adsorption": _num(row.get("predicted_benzene_adsorption")),
        "predicted_toluene_adsorption": _num(row.get("predicted_toluene_adsorption")),
        "balanced_score": _num(row.get("balanced_score")),
        "model": "precomputed benzene/toluene adsorption ranking from 0629_revise/screen_result",
    }


def heavy_metal_tool(candidate: dict[str, Any]) -> dict[str, Any]:
    """Step 2: allowed-metal filter from uploaded CIF."""
    cif_path = candidate.get("cif_path")
    if not cif_path:
        return _unknown("heavy_metal", "no CIF file was provided for metal parsing", 2)

    try:
        from pymatgen.core import Structure

        structure = Structure.from_file(cif_path)
        elements = sorted({str(site.specie.symbol) for site in structure})
    except Exception as exc:
        return _unknown("heavy_metal", f"failed to parse CIF metals: {exc}", 2)

    metals = [element for element in elements if element in ALL_METALS]
    illegal = [element for element in metals if element not in ALLOWED_METALS]
    passed = len(illegal) == 0
    return {
        "tool": "heavy_metal",
        "step": 2,
        "status": "pass" if passed else "fail",
        "pass": passed,
        "detected_metals": metals,
        "illegal_metals": illegal,
        "allowed_metals": sorted(ALLOWED_METALS),
        "model": "pymatgen CIF parser + fixed allowed-metal list",
    }


def ligand_sa_tool(candidate: dict[str, Any]) -> dict[str, Any]:
    """Step 3: ligand synthesizability lookup."""
    if not STEP3_SA.exists() or not STEP3_TOP6000.exists():
        return _unknown("ligand_sa", "ligand SA tables are missing", 3)

    name = candidate.get("matched_mof") or next(iter(candidate.get("query_names", [])), None)
    sa = _read(STEP3_SA)
    top = _read(STEP3_TOP6000)
    row = _find_by_name(sa, name)
    if row is None:
        return _unknown("ligand_sa", "MOF was not found in All_MOF_SA_Ranking.csv", 3)

    mof = str(row.get("MOF"))
    top_row = _find_by_name(top, mof)
    passed = top_row is not None
    smiles = str(top_row.get("SMILES")) if top_row is not None and pd.notna(top_row.get("SMILES")) else None
    candidate["matched_mof"] = mof
    if smiles:
        candidate["linker_smiles"] = smiles

    return {
        "tool": "ligand_sa",
        "step": 3,
        "status": "pass" if passed else "fail",
        "pass": passed,
        "mof": mof,
        "sa_rank": _int(row.get("SA_Rank")),
        "sa_score": _num(row.get("Max_SA")),
        "mean_sa": _num(row.get("Mean_SA")),
        "n_linker": _int(row.get("NLinker")),
        "in_top6000": passed,
        "linker_id": _int(top_row.get("Linker_ID")) if top_row is not None else None,
        "linker_smiles": smiles,
        "model": "All_MOF_SA_Ranking.csv + Top6000_MOF_MaxSA_Linker.csv lookup",
    }


def aquatic_toxicity_tool(candidate: dict[str, Any]) -> dict[str, Any]:
    """Step 4: aquatic toxicity lookup by linker SMILES."""
    if not STEP4_TOX.exists():
        return _unknown("aquatic_toxicity", "aquatic toxicity prediction table is missing", 4)
    if not candidate.get("linker_smiles"):
        ligand_sa_tool(candidate)
    smiles = candidate.get("linker_smiles")
    if not smiles:
        return _unknown("aquatic_toxicity", "no linker SMILES was available for toxicity lookup", 4)

    row = _find_by_smiles(_read(STEP4_TOX), smiles)
    if row is None:
        return _unknown("aquatic_toxicity", "linker SMILES was not found in toxicity predictions", 4)

    values = [
        _num(row.get("Predicted_Tox_IBC50")),
        _num(row.get("Predicted_Tox_IGC50")),
        _num(row.get("Predicted_Tox_LC50")),
        _num(row.get("Predicted_Tox_LC50DM")),
    ]
    present = [v for v in values if v is not None]
    return {
        "tool": "aquatic_toxicity",
        "step": 4,
        "status": "pass" if len(present) == 4 else "unknown",
        "pass": True if len(present) == 4 else None,
        "linker_smiles": smiles,
        "linker_id": _int(row.get("Linker_ID")),
        "IBC50": values[0],
        "IGC50": values[1],
        "LC50": values[2],
        "LC50DM": values[3],
        "mean_toxicity": round(sum(present) / len(present), 6) if present else None,
        "worst_toxicity": min(present) if present else None,
        "model": "Strict_EasySynth_Linkers_Optimized_Predictions.csv lookup",
    }


def pmt_tool(candidate: dict[str, Any]) -> dict[str, Any]:
    """Step 5: PMT classifier lookup + model inference from precomputed descriptors."""
    if not STEP5_DESC.exists():
        return _unknown("pmt", "PMT descriptor table is missing", 5)
    if not candidate.get("linker_smiles"):
        ligand_sa_tool(candidate)
    smiles = candidate.get("linker_smiles")
    if not smiles:
        return _unknown("pmt", "no linker SMILES was available for PMT descriptor lookup", 5)

    desc = _read(STEP5_DESC)
    row = _find_by_smiles(desc, smiles)
    if row is None:
        return _unknown("pmt", "linker SMILES was not found in PMT描述符.csv", 5)

    try:
        imputer = joblib.load(STEP5_DIR / "PMT_imputer.pkl")
        scaler = joblib.load(STEP5_DIR / "PMT_scaler.pkl")
        selector = joblib.load(STEP5_DIR / "PMT_selector.pkl")
        model = joblib.load(STEP5_DIR / "PMT_xgb_model.pkl")
        feature_row = row.drop(labels=["SMILES"], errors="ignore").to_frame().T
        feature_row = feature_row.apply(pd.to_numeric, errors="coerce")
        x = imputer.transform(feature_row)
        x = scaler.transform(x)
        x = selector.transform(x)
        proba = model.predict_proba(x)[0]
        classes = list(getattr(model, "classes_", [0, 1]))
        positive_index = classes.index(1) if 1 in classes else len(proba) - 1
        probability = float(proba[positive_index])
    except Exception as exc:
        return _unknown("pmt", f"PMT model inference failed: {exc}", 5)

    threshold = 0.4
    passed = probability >= threshold
    return {
        "tool": "pmt",
        "step": 5,
        "status": "pass" if passed else "fail",
        "pass": passed,
        "linker_smiles": smiles,
        "pmt_probability": round(probability, 6),
        "pmt_class": "non_PMT" if passed else "PMT_risk",
        "threshold": threshold,
        "threshold_note": "Class 1 is treated as non-PMT/pass; probability >= 0.4 passes.",
        "model": "PMT_xgb_model.pkl with PMT_imputer/scaler/selector",
    }


def price_tool(candidate: dict[str, Any]) -> dict[str, Any]:
    """Step 6: CoPriNet price lookup."""
    if not STEP6_PRICE.exists():
        return _unknown("price", "price ranking table is missing", 6)

    price = _read(STEP6_PRICE)
    row = _find_by_name(price, candidate.get("matched_mof"))
    if row is None and candidate.get("linker_smiles"):
        row = _find_by_smiles(price, candidate.get("linker_smiles"))
    if row is None:
        for name in candidate.get("query_names", []):
            row = _find_by_name(price, name)
            if row is not None:
                break
    if row is None:
        return _unknown("price", "MOF/linker was not found in the CoPriNet price ranking", 6)

    status = str(row.get("Price_Prediction_Status", "unknown"))
    passed = status.upper() == "OK"
    if pd.notna(row.get("SMILES")):
        candidate["linker_smiles"] = str(row.get("SMILES"))
    if pd.notna(row.get("MOF")):
        candidate["matched_mof"] = str(row.get("MOF"))
    return {
        "tool": "price",
        "step": 6,
        "status": "pass" if passed else "fail",
        "pass": passed,
        "mof": str(row.get("MOF")),
        "linker_smiles": str(row.get("SMILES")) if pd.notna(row.get("SMILES")) else None,
        "coprinet_price_rank": _int(row.get("CoPriNet_Price_Rank")),
        "usd_per_g": _num(row.get("CoPriNet_USD_per_g")),
        "usd_per_mmol": _num(row.get("CoPriNet_USD_per_mmol")),
        "price_status": status,
        "model": "CoPriNet price ranking CSV lookup; independent of PMT",
    }


def _run_tool(name: str, candidate: dict[str, Any]) -> dict[str, Any]:
    tools = {
        "adsorption_screen": adsorption_screen_tool,
        "heavy_metal": heavy_metal_tool,
        "ligand_sa": ligand_sa_tool,
        "aquatic_toxicity": aquatic_toxicity_tool,
        "pmt": pmt_tool,
        "price": price_tool,
    }
    if name not in tools:
        return _unknown(name, "unknown six-step tool name", 0)
    return _jsonable(tools[name](candidate))


def run_selected_six_step_tools(cif_path: str | None, user_text: str, tools: list[str]) -> dict[str, Any]:
    candidate = resolve_candidate(cif_path, user_text)
    results: dict[str, Any] = {}
    trace: list[dict[str, Any]] = []
    for name in tools:
        if name in {"aquatic_toxicity", "pmt", "price"} and not candidate.get("linker_smiles"):
            ligand = results.get("ligand_sa") or _run_tool("ligand_sa", candidate)
            results.setdefault("ligand_sa", ligand)
            trace.append({"agent": "Tool Execution Agent", "action": "resolved linker SMILES via ligand_sa", "output": ligand})
        result = _run_tool(name, candidate)
        results[name] = result
        trace.append({"agent": "Tool Execution Agent", "action": f"ran {name}", "output": result})

    payload = _final_payload(candidate, results, full=False)
    payload["agent_trace"] = trace + payload["agent_trace"]
    return payload


def run_six_step_screening(cif_path: str | None, user_text: str = "") -> dict[str, Any]:
    candidate = resolve_candidate(cif_path, user_text)
    results: dict[str, Any] = {}
    trace: list[dict[str, Any]] = []
    for name in SIX_STEP_TOOLS:
        result = _run_tool(name, candidate)
        results[name] = result
        trace.append({"agent": "Tool Execution Agent", "action": f"ran step {result.get('step')}: {name}", "output": result})
    payload = _final_payload(candidate, results, full=True)
    payload["agent_trace"] = trace + payload["agent_trace"]
    return payload


def _final_payload(candidate: dict[str, Any], results: dict[str, Any], full: bool) -> dict[str, Any]:
    failed = [r for r in results.values() if r.get("status") == "fail"]
    unknown = [r for r in results.values() if r.get("status") == "unknown"]
    if failed:
        failed_first = sorted(failed, key=lambda r: r.get("step", 999))[0]
        gate_status = "failed"
        failed_at_step = failed_first.get("step")
        recommendation = f"failed_at_step_{failed_at_step}_{failed_first.get('tool')}"
    elif unknown:
        gate_status = "incomplete"
        failed_at_step = None
        recommendation = "incomplete_due_to_unknown_evidence"
    else:
        gate_status = "pass_full_screening" if full else "pass_selected_tools"
        failed_at_step = None
        recommendation = gate_status

    decision_record = {
        "decision_class": gate_status,
        "recommendation": recommendation,
        "failed_at_step": failed_at_step,
        "unknown_steps": [r.get("step") for r in unknown],
        "blocking_tools": [r.get("tool") for r in failed],
        "full_screening": full,
    }
    payload = {
        "mof_id": candidate.get("matched_mof") or next(iter(candidate.get("query_names", [])), "unknown"),
        "candidate": candidate,
        "six_step": results,
        "results": results,
        "gate_status": gate_status,
        "failed_at_step": failed_at_step,
        "recommendation": recommendation,
        "decision_record": decision_record,
        "final_score": _score_from_status(gate_status),
        "explanation": _summary(results, gate_status, failed_at_step),
        "warnings": [],
        "errors": [],
        "agent_trace": [{
            "agent": "Decision Agent",
            "action": "computed deterministic six-step gate status",
            "output": decision_record,
        }],
    }
    payload["row"] = build_result_row(payload)
    payload["evidence_ledger"] = [
        {"tool": name, "status": result.get("status"), "outputs": result, "confidence": "precomputed_or_deterministic"}
        for name, result in results.items()
    ]
    return _jsonable(payload)


def _score_from_status(status: str) -> float | None:
    if status.startswith("pass"):
        return 10.0
    if status == "incomplete":
        return 5.0
    if status == "failed":
        return 0.0
    return None


def _summary(results: dict[str, Any], gate_status: str, failed_at_step: int | None) -> str:
    parts = []
    for name in SIX_STEP_TOOLS:
        result = results.get(name)
        if not result:
            continue
        parts.append(f"step {result.get('step')} {name}: {result.get('status')}")
    if failed_at_step:
        tail = f"Final gate status is failed at step {failed_at_step}."
    elif gate_status == "incomplete":
        tail = "Final gate status is incomplete because at least one required tool returned unknown."
    else:
        tail = f"Final gate status is {gate_status}."
    return "; ".join(parts + [tail])


def _fmt_status(result: dict[str, Any] | None) -> str:
    if not result:
        return "N/A"
    return str(result.get("status", "unknown"))


def build_result_row(result: dict[str, Any]) -> dict[str, Any]:
    steps = result.get("six_step") or result.get("results") or {}
    adsorption = steps.get("adsorption_screen") or {}
    metal = steps.get("heavy_metal") or {}
    sa = steps.get("ligand_sa") or {}
    tox = steps.get("aquatic_toxicity") or {}
    pmt = steps.get("pmt") or {}
    price = steps.get("price") or {}

    return {
        "MOF ID": result.get("mof_id", "unknown"),
        "Step 1 adsorption": (
            f"{_fmt_status(adsorption)}; rank={adsorption.get('rank')}; "
            f"B={adsorption.get('predicted_benzene_adsorption')}; T={adsorption.get('predicted_toluene_adsorption')}"
        ),
        "Step 2 metal": (
            f"{_fmt_status(metal)}; metals={', '.join(metal.get('detected_metals', []) or [])}; "
            f"illegal={', '.join(metal.get('illegal_metals', []) or [])}"
        ),
        "Step 3 SA": f"{_fmt_status(sa)}; rank={sa.get('sa_rank')}; score={sa.get('sa_score')}",
        "Step 4 toxicity": (
            f"{_fmt_status(tox)}; mean={tox.get('mean_toxicity')}; worst={tox.get('worst_toxicity')}"
        ),
        "Step 5 PMT": (
            f"{_fmt_status(pmt)}; class={pmt.get('pmt_class')}; prob={pmt.get('pmt_probability')}"
        ),
        "Step 6 price": (
            f"{_fmt_status(price)}; rank={price.get('coprinet_price_rank')}; USD/g={price.get('usd_per_g')}"
        ),
        "Final gate status": result.get("gate_status", "unknown"),
        "Recommendation": result.get("recommendation", "N/A"),
        "Score": result.get("final_score"),
    }