"""Model-serving API for fraud prediction.

Flat-layout entrypoint for deployments that keep source files at repository root.
"""

from __future__ import annotations

import json
import os
from contextlib import asynccontextmanager
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

import firebase_admin
import joblib
import numpy as np
import pandas as pd
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from firebase_admin import credentials, firestore
from pydantic import BaseModel, Field
from xgboost import XGBClassifier

from decision import build_decision_output
from risk_explainability import build_explainer, explain_prediction
from reasoning import build_reasoning_output

load_dotenv()

ROOT_DIR = Path(__file__).resolve().parent
MODEL_DIR = ROOT_DIR / "models"
if not MODEL_DIR.exists():
	MODEL_DIR = ROOT_DIR / "backend" / "models"

XGB_PKL_PATH = MODEL_DIR / "xgboost_model.pkl"
XGB_JSON_PATH = MODEL_DIR / "xgb_fraud_model.json"
PHASE6_METADATA_PATH = MODEL_DIR / "phase6_training_metadata.json"
PHASE7_METRICS_PATH = MODEL_DIR / "phase7_metrics.json"


class PredictRequest(BaseModel):
	transaction_id: str | None = None
	features: dict[str, float] = Field(..., description="Feature dict for model inference")
	meta: dict[str, Any] = Field(default_factory=dict)


class PredictData(BaseModel):
	transaction_id: str | None = None
	fraud_probability: float
	risk_score: float
	classification: str
	fraud_threshold: float
	suspicious_threshold: float
	missing_handled_as_nan: list[str]
	ignored_extra_features: list[str]
	aligned_features: dict[str, float | None]
	explainability: dict[str, Any]


class PredictResponse(BaseModel):
	status: str = "success"
	data: PredictData


class ExplainData(BaseModel):
	transaction_id: str | None = None
	classification: str
	fraud_probability: float
	risk_score: float
	explainability: dict[str, Any]


class ExplainResponse(BaseModel):
	status: str = "success"
	data: ExplainData


class ReasonResponse(BaseModel):
	status: str = "success"
	data: dict[str, Any]


class DecisionResponse(BaseModel):
	status: str = "success"
	data: dict[str, Any]


class FraudFeedbackRequest(BaseModel):
	transaction_id: str
	amount: float | None = None
	classification: str
	action: str | None = None
	risk_score: float | None = None
	fraud_probability: float | None = None
	fraud_type: str | None = None
	explainability_summary: str | None = None
	meta: dict[str, Any] = Field(default_factory=dict)


class FraudFeedbackResponse(BaseModel):
	status: str = "success"
	data: dict[str, Any]


class BatchPredictRequest(BaseModel):
	batch: list[PredictRequest] = Field(..., min_length=1, max_length=1000)


class BatchPredictData(BaseModel):
	count: int
	results: list[PredictData]


class BatchPredictResponse(BaseModel):
	status: str = "success"
	data: BatchPredictData


def _load_model() -> XGBClassifier:
	if XGB_PKL_PATH.exists():
		model = joblib.load(XGB_PKL_PATH)
		if not isinstance(model, XGBClassifier):
			raise RuntimeError(f"Unexpected model type in {XGB_PKL_PATH}: {type(model)}")
		return model

	if XGB_JSON_PATH.exists():
		model = XGBClassifier()
		model.load_model(str(XGB_JSON_PATH))
		return model

	raise FileNotFoundError(f"No model found. Checked: {XGB_PKL_PATH} and {XGB_JSON_PATH}")


def _load_feature_columns() -> list[str]:
	if PHASE6_METADATA_PATH.exists():
		metadata = json.loads(PHASE6_METADATA_PATH.read_text(encoding="utf-8"))
		columns = metadata.get("feature_columns")
		if isinstance(columns, list) and columns:
			return [str(c) for c in columns]
	raise FileNotFoundError(f"Missing or invalid feature metadata: {PHASE6_METADATA_PATH}")


def _load_thresholds() -> tuple[float, float]:
	fraud_threshold = 0.15
	suspicious_threshold = 0.05

	if PHASE7_METRICS_PATH.exists():
		metrics = json.loads(PHASE7_METRICS_PATH.read_text(encoding="utf-8"))
		fraud_threshold = float(metrics.get("best_threshold", fraud_threshold))

	suspicious_threshold = min(suspicious_threshold, fraud_threshold)
	return fraud_threshold, suspicious_threshold


def _classify(prob: float, fraud_threshold: float, suspicious_threshold: float) -> str:
	if prob >= fraud_threshold:
		return "fraud"
	if prob >= suspicious_threshold:
		return "suspicious"
	return "legitimate"


@asynccontextmanager
async def lifespan(app_: FastAPI):
	model = _load_model()
	feature_columns = _load_feature_columns()
	fraud_threshold, suspicious_threshold = _load_thresholds()
	explainer = build_explainer(model)

	if not firebase_admin._apps:
		service_account_json = os.getenv("FIREBASE_SERVICE_ACCOUNT")
		if service_account_json:
			service_account_dict = json.loads(service_account_json)
			cred = credentials.Certificate(service_account_dict)
			firebase_admin.initialize_app(cred)
		else:
			firebase_admin.initialize_app(options={"projectId": "interceptai-e82e5"})

	app_.state.model = model
	app_.state.explainer = explainer
	app_.state.feature_columns = feature_columns
	app_.state.fraud_threshold = fraud_threshold
	app_.state.suspicious_threshold = suspicious_threshold
	app_.state.db = firestore.client()
	yield


app = FastAPI(
	title="Fraud Decision Engine API",
	version="1.0.0",
	description="Fraud detection API",
	lifespan=lifespan,
)


@app.get("/health")
def health() -> dict[str, Any]:
	return {
		"status": "ok",
		"model_loaded": getattr(app.state, "model", None) is not None,
		"feature_count": len(getattr(app.state, "feature_columns", [])),
		"fraud_threshold": getattr(app.state, "fraud_threshold", None),
		"suspicious_threshold": getattr(app.state, "suspicious_threshold", None),
	}


@app.get("/model-info")
def model_info() -> dict[str, Any]:
	return {
		"model_type": type(app.state.model).__name__,
		"feature_count": len(app.state.feature_columns),
		"feature_columns": app.state.feature_columns,
		"fraud_threshold": app.state.fraud_threshold,
		"suspicious_threshold": app.state.suspicious_threshold,
	}


def _persist_fraud_transaction(user_id: str, record: dict[str, Any]) -> dict[str, Any]:
	db = app.state.db
	transaction_id = record.get("transaction_id")
	if not transaction_id:
		raise ValueError("transaction_id is required for persistence")

	doc_ref = db.collection("fraud_transaction").document(user_id).collection("transactions").document(transaction_id)
	doc_ref.set(record)

	return {
		"collection": "fraud_transaction",
		"subcollection": "transactions",
		"document_id": transaction_id,
		"user_id": user_id,
		"storage_mode": "firestore_direct",
	}


def _predict_one(payload: PredictRequest) -> PredictData:
	if not payload.features:
		raise HTTPException(status_code=422, detail="'features' must not be empty")

	required = app.state.feature_columns
	incoming = payload.features

	missing = sorted(set(required) - set(incoming))
	extra = sorted(set(incoming) - set(required))

	aligned = {col: (float(incoming[col]) if col in incoming else np.nan) for col in required}
	features_df = pd.DataFrame([aligned], columns=required).astype(np.float32)

	probability = float(app.state.model.predict_proba(features_df)[0, 1])
	risk_score = round(probability * 100, 2)
	classification = _classify(probability, app.state.fraud_threshold, app.state.suspicious_threshold)

	transaction_id = payload.transaction_id or payload.meta.get("transaction_id")
	aligned_features_json = {key: (None if pd.isna(value) else float(value)) for key, value in aligned.items()}

	explainability = explain_prediction(
		explainer=app.state.explainer,
		features_df=features_df,
		feature_columns=required,
		aligned_features=aligned_features_json,
		classification=classification,
		fraud_probability=probability,
	)

	return PredictData(
		transaction_id=transaction_id,
		fraud_probability=round(probability, 6),
		risk_score=risk_score,
		classification=classification,
		fraud_threshold=app.state.fraud_threshold,
		suspicious_threshold=app.state.suspicious_threshold,
		missing_handled_as_nan=missing,
		ignored_extra_features=extra,
		aligned_features=aligned_features_json,
		explainability=explainability,
	)


@app.post("/predict", response_model=PredictResponse)
def predict(payload: PredictRequest) -> PredictResponse:
	return PredictResponse(data=_predict_one(payload))


@app.post("/explain", response_model=ExplainResponse)
def explain(payload: PredictRequest) -> ExplainResponse:
	prediction = _predict_one(payload)
	return ExplainResponse(
		data=ExplainData(
			transaction_id=prediction.transaction_id,
			classification=prediction.classification,
			fraud_probability=prediction.fraud_probability,
			risk_score=prediction.risk_score,
			explainability=prediction.explainability,
		)
	)


@app.post("/reason", response_model=ReasonResponse)
def reason(payload: PredictRequest) -> ReasonResponse:
	prediction = _predict_one(payload)
	decision = build_decision_output(
		transaction_id=prediction.transaction_id,
		classification=prediction.classification,
		fraud_probability=prediction.fraud_probability,
		risk_score=prediction.risk_score,
		fraud_threshold=prediction.fraud_threshold,
		suspicious_threshold=prediction.suspicious_threshold,
		explainability=prediction.explainability,
	)
	reasoning = build_reasoning_output(
		transaction_id=prediction.transaction_id,
		classification=prediction.classification,
		fraud_probability=prediction.fraud_probability,
		risk_score=prediction.risk_score,
		fraud_threshold=prediction.fraud_threshold,
		suspicious_threshold=prediction.suspicious_threshold,
		explainability=prediction.explainability,
		decision_action=decision["action"],
	)
	return ReasonResponse(data=reasoning)


@app.post("/decision", response_model=DecisionResponse)
def decision(payload: PredictRequest) -> DecisionResponse:
	prediction = _predict_one(payload)
	output = build_decision_output(
		transaction_id=prediction.transaction_id,
		classification=prediction.classification,
		fraud_probability=prediction.fraud_probability,
		risk_score=prediction.risk_score,
		fraud_threshold=prediction.fraud_threshold,
		suspicious_threshold=prediction.suspicious_threshold,
		explainability=prediction.explainability,
	)
	return DecisionResponse(data=output)


@app.post("/feedback/fraud/{user_id}", response_model=FraudFeedbackResponse)
def feedback_fraud(user_id: str, payload: FraudFeedbackRequest) -> FraudFeedbackResponse:
	if payload.classification.lower() != "fraud":
		raise HTTPException(status_code=422, detail="Only fraud transactions are accepted by this endpoint")

	record = {
		"transaction_id": payload.transaction_id,
		"amount": payload.amount,
		"classification": payload.classification,
		"action": payload.action,
		"risk_score": payload.risk_score,
		"fraud_probability": payload.fraud_probability,
		"fraud_type": payload.fraud_type,
		"explainability_summary": payload.explainability_summary,
		"meta": payload.meta,
		"created_at": datetime.now(timezone.utc).isoformat(),
	}

	persistence = _persist_fraud_transaction(user_id=user_id, record=record)
	return FraudFeedbackResponse(
		data={
			"user_id": user_id,
			"saved": True,
			"record": record,
			"persistence": persistence,
		}
	)


@app.post("/predict/batch", response_model=BatchPredictResponse)
def predict_batch(payload: BatchPredictRequest) -> BatchPredictResponse:
	results = [_predict_one(item) for item in payload.batch]
	return BatchPredictResponse(data=BatchPredictData(count=len(results), results=results))