"""
SW Identifier — FastAPI server

Routes
------
GET  /                      SPA frontend
POST /predict               Internal SPA endpoint (no auth)
POST /api/v1/predict        Public API  (requires X-API-Key header)
GET  /api/v1/keys           List API keys  (requires X-Admin-Key header)
POST /api/v1/keys           Create API key (requires X-Admin-Key header)
DELETE /api/v1/keys/{key}   Revoke API key (requires X-Admin-Key header)
GET  /docs                  OpenAPI / Swagger UI
"""
import csv
import io
import json
import logging
import os
import secrets
import sys
import time
from contextlib import asynccontextmanager
from datetime import datetime, timezone
from typing import List, Optional

import numpy as np
from PIL import Image
from fastapi import APIRouter, Depends, FastAPI, File, HTTPException, Security, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse
from fastapi.security import APIKeyHeader
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel

# ── paths ─────────────────────────────────────────────────────────────────────
BASE             = os.path.dirname(os.path.abspath(__file__))
DETECTOR_PATH    = os.path.join(BASE, "detector",             "model.pt")
SEGMENTATOR_PATH = os.path.join(BASE, "segmentator",          "model.ts")
CLASSIFIER_CKPT  = os.path.join(BASE, "classification_model", "model.ckpt")
DATABASE_PATH    = os.path.join(BASE, "classification_model", "database.pt")
STATIC_DIR       = os.path.join(BASE, "static")
TAXONS_CSV       = os.path.join(BASE, "taxons.csv")
KEYS_FILE        = os.path.join(BASE, "api_keys.json")

sys.path.insert(0, BASE)

# ── logging ───────────────────────────────────────────────────────────────────
logging.basicConfig(level=logging.WARNING)
log = logging.getLogger("sw.app")

# ── common name lookup ────────────────────────────────────────────────────────
def _load_common_names(path: str) -> dict:
    mapping = {}
    with open(path, newline="", encoding="utf-8") as f:
        for row in csv.DictReader(f):
            taxon  = row["taxon"].strip()
            common = row["common_name"].strip()
            if taxon:
                mapping[taxon] = common or taxon
    return mapping

COMMON_NAMES: dict = _load_common_names(TAXONS_CSV)

# ── API key store ─────────────────────────────────────────────────────────────
def _load_keys() -> list:
    if os.path.exists(KEYS_FILE):
        with open(KEYS_FILE, encoding="utf-8") as f:
            return json.load(f)
    return []

def _save_keys(keys: list) -> None:
    with open(KEYS_FILE, "w", encoding="utf-8") as f:
        json.dump(keys, f, indent=2)

def _valid_key_set() -> set:
    # Prefer env var (comma-separated) — required for stateless deployments
    # like HF Spaces where the filesystem is ephemeral.
    env = os.environ.get("SW_API_KEYS", "").strip()
    if env:
        return {k.strip() for k in env.split(",") if k.strip()}
    return {k["key"] for k in _load_keys()}

def _new_key(name: str) -> dict:
    return {
        "key":        "fsh_" + secrets.token_urlsafe(32),
        "name":       name,
        "created_at": datetime.now(timezone.utc).isoformat(),
    }

# Ensure at least one key exists on startup; print it once to console.
def _bootstrap_keys() -> None:
    # Skip file-based bootstrap when keys are supplied via env var.
    if os.environ.get("SW_API_KEYS", "").strip():
        return
    keys = _load_keys()
    if not keys:
        k = _new_key("default")
        _save_keys([k])
        print("\n" + "═" * 60)
        print("  No API keys found — generated a default key:")
        print(f"  {k['key']}")
        print("  Store this somewhere safe; it won't be shown again.")
        print("═" * 60 + "\n")

# Admin key — set SW_ADMIN_KEY env var, or one is auto-generated once.
_ADMIN_KEY_FILE = os.path.join(BASE, ".admin_key")

def _get_admin_key() -> str:
    env = os.environ.get("SW_ADMIN_KEY")
    if env:
        return env
    if os.path.exists(_ADMIN_KEY_FILE):
        with open(_ADMIN_KEY_FILE) as f:
            return f.read().strip()
    key = "fadm_" + secrets.token_urlsafe(32)
    with open(_ADMIN_KEY_FILE, "w") as f:
        f.write(key)
    print("\n" + "═" * 60)
    print("  Admin key (manage API keys):")
    print(f"  {key}")
    print("  Stored in .admin_key  — keep it out of version control.")
    print("═" * 60 + "\n")
    return key

ADMIN_KEY: str = ""   # set during lifespan

# ── model globals ─────────────────────────────────────────────────────────────
detector    = None
segmentator = None
classifier  = None

# ── lifespan ──────────────────────────────────────────────────────────────────
@asynccontextmanager
async def lifespan(app: FastAPI):
    global detector, segmentator, classifier, ADMIN_KEY

    _bootstrap_keys()
    ADMIN_KEY = _get_admin_key()

    from ultralytics import YOLO
    log.warning("Loading detector …")
    detector = YOLO(DETECTOR_PATH)

    log.warning("Loading segmentator …")
    from segmentator.inference import Inference as Segmentator
    segmentator = Segmentator(SEGMENTATOR_PATH)

    log.warning("Loading classifier …")
    from classification_model.inference import EmbeddingClassifier
    classifier = EmbeddingClassifier({
        "log_level": "WARNING",
        "dataset": {"path": DATABASE_PATH},
        "model": {
            "checkpoint_path":     CLASSIFIER_CKPT,
            "backbone_model_name": "beitv2_base_patch16_224.in1k_ft_in22k_in1k",
            "embedding_dim":       512,
            "num_classes":         775,
            "arcface_s":           64.0,
            "arcface_m":           0.2,
            "pooling_type":        "attention",
            "device":              "cpu",
        },
        "use_knn":                True,
        "arcface_min_score":      0.1,
        "centroid_fallback_score": 0.1,
        "topk_centroid":          5,
        "topk_neighbors":         10,
        "topk_arcface":           5,
        "centroid_threshold":     0.7,
        "neighbor_threshold":     0.8,
        "use_albumentations":     False,
    })

    log.warning("All models ready.")
    yield
    log.warning("Shutting down.")

# ── Pydantic response models ──────────────────────────────────────────────────
class BoundingBox(BaseModel):
    x1:         int
    y1:         int
    x2:         int
    y2:         int
    confidence: float

class Prediction(BaseModel):
    name:       str           # common name
    taxon:      str           # scientific name
    accuracy:   float         # confidence 0–1
    species_id: str

class Detection(BaseModel):
    bbox:        BoundingBox
    polygon:     Optional[List[List[int]]]  # [[x,y], ...] in original image coords
    predictions: List[Prediction]

class ImageSize(BaseModel):
    width:  int
    height: int

class Timing(BaseModel):
    detect_ms:   int
    segment_ms:  int
    classify_ms: int
    total_ms:    int

class PredictResponse(BaseModel):
    detections: List[Detection]
    image_size: ImageSize
    timing:     Timing

# ── shared pipeline ───────────────────────────────────────────────────────────
async def _run_pipeline(raw: bytes) -> PredictResponse:
    try:
        image_rgb = np.array(Image.open(io.BytesIO(raw)).convert("RGB"))
    except Exception as exc:
        raise HTTPException(status_code=400, detail=f"Cannot decode image: {exc}")

    h, w = image_rgb.shape[:2]
    t_start = time.perf_counter()

    # 1. Detection
    t0 = time.perf_counter()
    yolo_out = detector.predict(
        source=image_rgb, imgsz=640, conf=0.25, iou=0.45,
        device="cpu", verbose=False, save=False,
    )
    detect_ms = (time.perf_counter() - t0) * 1000
    boxes_raw = yolo_out[0].boxes.data.cpu().numpy() if yolo_out else []

    detections: List[Detection] = []
    seg_ms_total = 0.0
    cls_ms_total = 0.0

    for box in boxes_raw:
        x1 = max(0, int(box[0])); y1 = max(0, int(box[1]))
        x2 = min(w, int(box[2])); y2 = min(h, int(box[3]))
        confidence = float(box[4])
        if x2 <= x1 or y2 <= y1:
            continue

        crop_rgb = image_rgb[y1:y2, x1:x2]

        # 2. Segmentation
        polygon_coords = None
        masked_crop    = crop_rgb
        t0 = time.perf_counter()
        try:
            seg_results = segmentator.predict(crop_rgb)
            if seg_results:
                poly = seg_results[0]
                polygon_coords = [[int(px) + x1, int(py) + y1] for px, py in poly.points]
                masked_crop    = poly.mask_polygon(crop_rgb)
        except Exception as exc:
            log.warning("Segmentator error: %s", exc)
        seg_ms_total += (time.perf_counter() - t0) * 1000

        # 3. Classification
        pred_list: List[Prediction] = []
        t0 = time.perf_counter()
        try:
            preds = classifier(masked_crop)
            for p in (preds or [])[:3]:
                pred_list.append(Prediction(
                    name       = COMMON_NAMES.get(p.name, p.name),
                    taxon      = p.name,
                    accuracy   = round(float(p.accuracy), 4),
                    species_id = str(p.species_id),
                ))
        except Exception as exc:
            log.warning("Classifier error: %s", exc)
        cls_ms_total += (time.perf_counter() - t0) * 1000

        detections.append(Detection(
            bbox        = BoundingBox(x1=x1, y1=y1, x2=x2, y2=y2,
                                      confidence=round(confidence, 3)),
            polygon     = polygon_coords,
            predictions = pred_list,
        ))

    total_ms = (time.perf_counter() - t_start) * 1000
    return PredictResponse(
        detections = detections,
        image_size = ImageSize(width=w, height=h),
        timing     = Timing(
            detect_ms   = round(detect_ms),
            segment_ms  = round(seg_ms_total),
            classify_ms = round(cls_ms_total),
            total_ms    = round(total_ms),
        ),
    )

# ── auth dependencies ─────────────────────────────────────────────────────────
_api_key_header   = APIKeyHeader(name="X-API-Key",   auto_error=True)
_admin_key_header = APIKeyHeader(name="X-Admin-Key", auto_error=True)

def _require_api_key(key: str = Security(_api_key_header)):
    if key not in _valid_key_set():
        raise HTTPException(status_code=401, detail="Invalid or missing API key.")
    return key

def _require_admin_key(key: str = Security(_admin_key_header)):
    if key != ADMIN_KEY:
        raise HTTPException(status_code=401, detail="Invalid admin key.")
    return key

# ── app & middleware ──────────────────────────────────────────────────────────
app = FastAPI(
    title       = "SW Identifier API",
    description = "Fish detection, segmentation, and species classification.",
    version     = "1.0.0",
    lifespan    = lifespan,
    docs_url    = "/api/docs",
    redoc_url   = "/api/redoc",
)

app.add_middleware(
    CORSMiddleware,
    allow_origins     = ["*"],
    allow_methods     = ["GET", "POST", "DELETE"],
    allow_headers     = ["*"],
)

app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")

# ── SPA routes ────────────────────────────────────────────────────────────────
@app.get("/", response_class=HTMLResponse, include_in_schema=False)
async def root():
    with open(os.path.join(STATIC_DIR, "index.html"), encoding="utf-8") as fh:
        return fh.read()

@app.post("/predict", include_in_schema=False)
async def predict_spa(file: UploadFile = File(...)):
    """Internal endpoint used by the SPA — no auth required."""
    if not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="Upload must be an image file.")
    return await _run_pipeline(await file.read())

# ── public API v1 ─────────────────────────────────────────────────────────────
api = APIRouter(prefix="/api/v1", tags=["SW Identifier API"])

@api.post(
    "/predict",
    response_model = PredictResponse,
    summary        = "Identify fish in an image",
    description    = (
        "Upload an image. Returns every detected fish with its bounding box, "
        "segmentation polygon, and ranked species predictions.\n\n"
        "Requires an `X-API-Key` header."
    ),
)
async def predict_api(
    file: UploadFile = File(..., description="Image file (JPEG, PNG, WEBP, …)"),
    _key: str        = Depends(_require_api_key),
):
    if not file.content_type.startswith("image/"):
        raise HTTPException(status_code=400, detail="Upload must be an image file.")
    return await _run_pipeline(await file.read())


# ── key management ────────────────────────────────────────────────────────────
class KeyRecord(BaseModel):
    key:        str
    name:       str
    created_at: str

class CreateKeyRequest(BaseModel):
    name: str = "unnamed"

@api.get(
    "/keys",
    response_model = List[KeyRecord],
    summary        = "List API keys",
    description    = "Requires `X-Admin-Key` header.",
)
async def list_keys(_admin: str = Depends(_require_admin_key)):
    return _load_keys()

@api.post(
    "/keys",
    response_model = KeyRecord,
    status_code    = 201,
    summary        = "Create a new API key",
    description    = "Requires `X-Admin-Key` header.",
)
async def create_key(
    body:   CreateKeyRequest  = CreateKeyRequest(),
    _admin: str               = Depends(_require_admin_key),
):
    keys = _load_keys()
    k = _new_key(body.name)
    keys.append(k)
    _save_keys(keys)
    return k

@api.delete(
    "/keys/{key}",
    status_code = 204,
    summary     = "Revoke an API key",
    description = "Requires `X-Admin-Key` header.",
)
async def revoke_key(key: str, _admin: str = Depends(_require_admin_key)):
    keys = _load_keys()
    remaining = [k for k in keys if k["key"] != key]
    if len(remaining) == len(keys):
        raise HTTPException(status_code=404, detail="Key not found.")
    _save_keys(remaining)

app.include_router(api)

# ── entry point ───────────────────────────────────────────────────────────────
if __name__ == "__main__":
    import uvicorn
    port = int(os.environ.get("PORT", 7860))
    uvicorn.run("app:app", host="0.0.0.0", port=port, reload=False)