from __future__ import annotations

import json
from pathlib import Path
from typing import Any, Dict, List, Optional

import joblib
import numpy as np
import pandas as pd
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
import xgboost as xgb

import os
from huggingface_hub import hf_hub_download

# Compatibility shim for pickles created with newer sklearn that include _RemainderColsList
import sklearn.compose._column_transformer as _ct  # type: ignore
if not hasattr(_ct, "_RemainderColsList"):
    class _RemainderColsList(list):  # type: ignore
        pass
    _ct._RemainderColsList = _RemainderColsList

ROOT = Path(__file__).resolve().parents[1]
MODEL_DIR = Path(__file__).resolve().parent / "model"
# MODEL_PATH = MODEL_DIR / "xgboost_pipeline.pkl"
BOOSTER_PATH = MODEL_DIR / "xgboost_booster.json"
META_PATH = MODEL_DIR / "explain_meta.json"

HF_MODEL_REPO = os.getenv("HF_MODEL_REPO", "Gutema/frankscore-model-artifact")
HF_MODEL_REVISION = os.getenv("HF_MODEL_REVISION", "main")

try:
    MODEL_PATH = Path(
        hf_hub_download(
            repo_id=HF_MODEL_REPO,
            filename="xgboost_pipeline.pkl",
            revision=HF_MODEL_REVISION,
        )
    )
except Exception as e:
    raise RuntimeError(f"Failed to download model artifact from HF repo={HF_MODEL_REPO}: {e}") from e

if not META_PATH.exists():
    raise FileNotFoundError(f"Explainability meta missing at {META_PATH}")
if not BOOSTER_PATH.exists():
    raise FileNotFoundError(f"Booster file missing at {BOOSTER_PATH}")

if not MODEL_PATH.exists():
    raise FileNotFoundError(f"Model file missing at {MODEL_PATH}")
if not META_PATH.exists():
    raise FileNotFoundError(f"Explainability meta missing at {META_PATH}")
if not BOOSTER_PATH.exists():
    raise FileNotFoundError(f"Booster file missing at {BOOSTER_PATH}")

PIPELINE = joblib.load(MODEL_PATH)
META = json.loads(META_PATH.read_text())

EXPECTED_FEATURES = list(getattr(PIPELINE, "feature_names_in_", []))
PREPROCESS = PIPELINE.named_steps.get("preprocess") if hasattr(PIPELINE, "named_steps") else None
if PREPROCESS is None:
    raise RuntimeError("Pipeline missing 'preprocess' step; cannot infer columns.")

if not EXPECTED_FEATURES:
    EXPECTED_FEATURES = list(getattr(PREPROCESS, "feature_names_in_", []))
    if not EXPECTED_FEATURES:
        raise RuntimeError("Unable to determine expected feature names from the pipeline.")

_col_map = {name: cols for name, _, cols in getattr(PREPROCESS, "transformers_", [])}
NUM_FEATURES = list(_col_map.get("num", []))
CAT_FEATURES = list(_col_map.get("cat", []))
PRE_FEATURE_NAMES = META.get("pre_feature_names") or list(getattr(PREPROCESS, "get_feature_names_out", lambda: [])())
RAW_FEATURE_SET = set((META.get("raw_num_cols") or []) + (META.get("raw_cat_cols") or []))
FEATURE_GROUPS = {
    "Repayment Activity": [
        "num_previous_defaults",
        "past_default_rate",
        "repayment_consistency",
        "repayment_intensity",
    ],
    "Loan Amount & Burden": [
        "Total_Amount",
        "Total_Amount_to_Repay",
        "amount_bucket",
        "burden_percentile",
        "daily_burden",
        "duration",
        "duration_bucket",
        "interest_rate",
        "amount_ratio",
        "burden_ratio",
        "lender_exposure_ratio",
    ],
    "Borrowing History": [
        "account_age_days",
        "avg_past_amount",
        "avg_past_daily_burden",
        "avg_time_bw_loans",
        "borrower_history_strength",
        "days_since_last_loan",
        "loan_frequency_per_year",
        "num_previous_loans",
        "std_past_amount",
        "std_past_daily_burden",
        "trend_in_amount",
        "trend_in_burden",
        "lender_id",
        "lender_risk_profile",
    ],
    "Spending & Transactions": [
        "latest_amount_ma3",
        "days_to_local_festival",
        "days_to_salary_day",
        "month",
        "quarter",
        "week_of_year",
    ],
}
FEATURE_GROUP_LOOKUP: Dict[str, str] = {}
for group, variables in FEATURE_GROUPS.items():
    for var in variables:
        FEATURE_GROUP_LOOKUP[var] = group

app = FastAPI(title="FrankScore", version="1.0.0")


class PredictionRequest(BaseModel):
    records: List[Dict[str, Any]] = Field(..., description="List of borrower feature dictionaries")


class PredictionResponse(BaseModel):
    probabilities: List[float]


class ScoreRequest(BaseModel):
    probabilities: List[float] = Field(..., description="Probabilities of default (0-1)")


class ScoreResponse(BaseModel):
    scores: List[float]


class ExplainRequest(BaseModel):
    records: List[Dict[str, Any]]
    top_k: Optional[int] = Field(default=10, ge=1, le=100, description="Number of top features to return per record")


class FeatureContribution(BaseModel):
    feature: str
    shap_value: float


class GroupContribution(BaseModel):
    group: str
    total_shap_value: float
    percentage: float
    direction: str
    label: str
    features: List[FeatureContribution]


class ExplainItem(BaseModel):
    probability: float
    base_value: float
    group_contributions: List[GroupContribution]


class ExplainResponse(BaseModel):
    explanations: List[ExplainItem]


class PredictExplainItem(BaseModel):
    probability: float
    score: float
    base_value: float
    group_contributions: List[GroupContribution]


class PredictExplainResponse(BaseModel):
    results: List[PredictExplainItem]


def prepare_frame(records: List[Dict[str, Any]]) -> pd.DataFrame:
    if not records:
        raise HTTPException(status_code=400, detail="No records provided.")
    df = pd.DataFrame(records)
    for col in EXPECTED_FEATURES:
        if col not in df.columns:
            df[col] = np.nan
    df = df[EXPECTED_FEATURES]
    if NUM_FEATURES:
        df[NUM_FEATURES] = df[NUM_FEATURES].apply(pd.to_numeric, errors="coerce")
    if CAT_FEATURES:
        df[CAT_FEATURES] = df[CAT_FEATURES].astype("object")
    return df


def pd_to_score(p: np.ndarray, base_score: float = 50, base_odds: float = 9, pdo: float = 20) -> np.ndarray:
    p = np.clip(p, 1e-6, 1 - 1e-6)
    B = pdo / np.log(2)
    A = base_score - B * np.log(base_odds)
    odds = (1 - p) / p
    score = A + B * np.log(odds)
    return np.clip(score, 0, 100)


def _sanitize_feature_name(name: str) -> str:
    sanitized = name
    for ch, repl in {"[": "", "]": "", "<": "lt", ">": "gt", " ": "_", ",": "_", "=": "_"}.items():
        sanitized = sanitized.replace(ch, repl)
    return sanitized


def _base_feature_name(name: str) -> str:
    base = name
    if "__" in base:
        base = base.split("__", 1)[1]
    if base in RAW_FEATURE_SET:
        return base
    parts = base.split("_")
    while len(parts) > 1:
        candidate = "_".join(parts[:-1])
        if candidate in RAW_FEATURE_SET:
            return candidate
        parts = parts[:-1]
    return base


def _label_for_percentage(pct: float) -> str:
    if pct >= 30:
        return "Exceptional"
    if pct >= 20:
        return "Very Good"
    if pct >= 10:
        return "Good"
    if pct >= 5:
        return "Bad"
    return "Very Bad"


def _direction_for_value(val: float) -> str:
    if val > 0:
        return "raises risk"
    if val < 0:
        return "reduces risk"
    return "neutral"


def _build_group_contribs(
    group_totals: Dict[str, float],
    group_details: Dict[str, List[FeatureContribution]],
    top_k: Optional[int],
) -> List[GroupContribution]:
    denom = sum(abs(v) for v in group_totals.values())
    if denom == 0:
        denom = 1e-12  # avoid division by zero; all percentages become ~0
    group_contribs: List[GroupContribution] = []
    for grp, total in sorted(group_totals.items(), key=lambda kv: abs(kv[1]), reverse=True):
        feats = sorted(group_details.get(grp, []), key=lambda fc: abs(fc.shap_value), reverse=True)
        if top_k:
            feats = feats[:top_k]
        pct = abs(total) / denom * 100
        group_contribs.append(
            GroupContribution(
                group=grp,
                total_shap_value=total,
                percentage=pct,
                direction=_direction_for_value(total),
                label=_label_for_percentage(pct),
                features=feats,
            )
        )
    return group_contribs


def get_booster():
    if not hasattr(get_booster, "_booster"):
        booster = xgb.Booster()
        booster.load_model(str(BOOSTER_PATH))
        base_score = booster.attr("base_score")
        if base_score:
            try:
                float(base_score)
            except ValueError:
                cleaned = base_score.strip("[]")
                try:
                    cleaned_val = str(float(cleaned))
                except Exception:
                    cleaned_val = "0.5"
                booster.set_param({"base_score": cleaned_val})
                booster.set_attr(base_score=cleaned_val)
        get_booster._booster = booster
    return get_booster._booster


@app.post("/predict", response_model=PredictionResponse)
def predict(req: PredictionRequest) -> PredictionResponse:
    frame = prepare_frame(req.records)
    probas = PIPELINE.predict_proba(frame)[:, 1]
    return PredictionResponse(probabilities=probas.tolist())


@app.get("/health")
def health() -> Dict[str, str]:
    return {"status": "ok", "model_path": str(MODEL_PATH)}


@app.post("/score", response_model=ScoreResponse)
def score(req: ScoreRequest) -> ScoreResponse:
    if not req.probabilities:
        raise HTTPException(status_code=400, detail="No probabilities provided.")
    arr = np.array(req.probabilities, dtype=float)
    scores = pd_to_score(arr)
    return ScoreResponse(scores=scores.tolist())


@app.post("/explain", response_model=ExplainResponse)
def explain(req: ExplainRequest) -> ExplainResponse:
    if not req.records:
        raise HTTPException(status_code=400, detail="No records provided.")
    frame = prepare_frame(req.records)
    probas = PIPELINE.predict_proba(frame)[:, 1]
    booster = get_booster()
    X_proc = PREPROCESS.transform(frame)
    feat_names = np.array(PRE_FEATURE_NAMES) if PRE_FEATURE_NAMES else np.array([f"f{i}" for i in range(X_proc.shape[1])])
    sanitized_names = [_sanitize_feature_name(n) for n in feat_names]
    dmat = xgb.DMatrix(X_proc, feature_names=sanitized_names)
    contribs = booster.predict(dmat, pred_contribs=True)
    if contribs.shape[1] != X_proc.shape[1] + 1:
        raise HTTPException(status_code=500, detail="Unexpected contribution shape from booster.")
    base_vals = contribs[:, -1]
    feat_contribs = contribs[:, :-1]
    explanations: List[ExplainItem] = []
    for i in range(feat_contribs.shape[0]):
        row_vals = feat_contribs[i]
        group_totals: Dict[str, float] = {}
        group_details: Dict[str, List[FeatureContribution]] = {}
        for name, val in zip(feat_names, row_vals):
            base = _base_feature_name(str(name))
            group = FEATURE_GROUP_LOOKUP.get(base, "Other")
            group_totals[group] = group_totals.get(group, 0.0) + float(val)
            group_details.setdefault(group, []).append(
                FeatureContribution(feature=str(name), shap_value=float(val))
            )
        group_contribs = _build_group_contribs(group_totals, group_details, req.top_k)
        explanations.append(
            ExplainItem(
                probability=float(probas[i]),
                base_value=float(base_vals[i]),
                group_contributions=group_contribs,
            )
        )
    return ExplainResponse(explanations=explanations)


@app.post("/predict_explain", response_model=PredictExplainResponse)
def predict_explain(req: ExplainRequest) -> PredictExplainResponse:
    if not req.records:
        raise HTTPException(status_code=400, detail="No records provided.")
    frame = prepare_frame(req.records)
    probas = PIPELINE.predict_proba(frame)[:, 1]
    booster = get_booster()
    X_proc = PREPROCESS.transform(frame)
    feat_names = np.array(PRE_FEATURE_NAMES) if PRE_FEATURE_NAMES else np.array([f"f{i}" for i in range(X_proc.shape[1])])
    sanitized_names = [_sanitize_feature_name(n) for n in feat_names]
    dmat = xgb.DMatrix(X_proc, feature_names=sanitized_names)
    contribs = booster.predict(dmat, pred_contribs=True)
    if contribs.shape[1] != X_proc.shape[1] + 1:
        raise HTTPException(status_code=500, detail="Unexpected contribution shape from booster.")
    base_vals = contribs[:, -1]
    feat_contribs = contribs[:, :-1]
    items: List[PredictExplainItem] = []
    for i in range(feat_contribs.shape[0]):
        row_vals = feat_contribs[i]
        group_totals: Dict[str, float] = {}
        group_details: Dict[str, List[FeatureContribution]] = {}
        for name, val in zip(feat_names, row_vals):
            base = _base_feature_name(str(name))
            group = FEATURE_GROUP_LOOKUP.get(base, "Other")
            group_totals[group] = group_totals.get(group, 0.0) + float(val)
            group_details.setdefault(group, []).append(
                FeatureContribution(feature=str(name), shap_value=float(val))
            )
        group_contribs = _build_group_contribs(group_totals, group_details, req.top_k)
        score_val = int(round(float(pd_to_score(np.array([probas[i]]))[0])))
        items.append(
            PredictExplainItem(
                probability=float(probas[i]),
                score=score_val,
                base_value=float(base_vals[i]),
                group_contributions=group_contribs,
            )
        )
    return PredictExplainResponse(results=items)