"""
Inference server — thin HTTP wrapper around ML models.

Runs on the GPU machine. The detection pipeline calls this over HTTP,
or imports the same logic locally if GPU is on the same machine.

Config is loaded from env on startup, then editable at runtime via
GET/PUT /config. The UI config panel is just a visual editor for these
same values.

Usage:
    cd gpu && uvicorn server:app --host 0.0.0.0 --port 8000
    # or
    cd gpu && python server.py
"""

from __future__ import annotations

import base64
import io
import logging
import os
from contextlib import asynccontextmanager

import numpy as np
from fastapi import FastAPI, HTTPException
from PIL import Image
from pydantic import BaseModel

logger = logging.getLogger(__name__)

# --- Runtime config (loaded from env, mutable via API) ---
_config = {
    "device": os.environ.get("DEVICE", "auto"),
    "yolo_model": os.environ.get("YOLO_MODEL", "yolov8n.pt"),
    "yolo_confidence": float(os.environ.get("YOLO_CONFIDENCE", "0.3")),
    "vram_budget_mb": int(os.environ.get("VRAM_BUDGET_MB", "10240")),
    "strategy": os.environ.get("STRATEGY", "sequential"),
}

# --- Model registry ---
_models: dict[str, object] = {}


# --- Helpers ---

def _get_device() -> str:
    device = _config["device"]
    if device != "auto":
        return device
    try:
        import torch
        return "cuda" if torch.cuda.is_available() else "cpu"
    except ImportError:
        return "cpu"


def _get_yolo(model_name: str | None = None):
    name = model_name or _config["yolo_model"]
    if name not in _models:
        from ultralytics import YOLO
        device = _get_device()
        logger.info("Loading %s on %s", name, device)
        model = YOLO(name)
        model.to(device)
        _models[name] = model
    return _models[name]


def _decode_image(b64: str) -> np.ndarray:
    data = base64.b64decode(b64)
    img = Image.open(io.BytesIO(data)).convert("RGB")
    return np.array(img)


# --- Request/Response models ---

class DetectRequest(BaseModel):
    image: str  # base64 JPEG
    model: str | None = None  # defaults to config yolo_model
    confidence: float | None = None  # defaults to config yolo_confidence
    target_classes: list[str] | None = None


class BBox(BaseModel):
    x: int
    y: int
    w: int
    h: int
    confidence: float
    label: str


class DetectResponse(BaseModel):
    detections: list[BBox]


class ConfigUpdate(BaseModel):
    """Partial config update — only provided fields are changed."""
    device: str | None = None
    yolo_model: str | None = None
    yolo_confidence: float | None = None
    vram_budget_mb: int | None = None
    strategy: str | None = None


# --- App ---

@asynccontextmanager
async def lifespan(app: FastAPI):
    logger.info("Inference server starting (device=%s)", _get_device())
    yield
    logger.info("Inference server shutting down")
    _models.clear()


app = FastAPI(title="MPR Inference Server", lifespan=lifespan)


@app.get("/health")
def health():
    return {
        "status": "ok",
        "device": _get_device(),
        "loaded_models": list(_models.keys()),
        "vram_budget_mb": _config["vram_budget_mb"],
        "strategy": _config["strategy"],
    }


@app.get("/config")
def get_config():
    """Current runtime config. Same values the .env sets at startup."""
    return {**_config, "device_resolved": _get_device()}


@app.put("/config")
def update_config(update: ConfigUpdate):
    """Update runtime config. Only provided fields are changed."""
    changes = update.model_dump(exclude_none=True)
    if not changes:
        return _config

    # If model changed, unload the old one so it gets reloaded on next request
    if "yolo_model" in changes and changes["yolo_model"] != _config["yolo_model"]:
        old = _config["yolo_model"]
        if old in _models:
            del _models[old]
            logger.info("Unloaded %s (model changed)", old)

    _config.update(changes)
    logger.info("Config updated: %s", changes)
    return {**_config, "device_resolved": _get_device()}


@app.post("/models/unload")
def unload_model(body: dict):
    """Unload a model from memory to free VRAM."""
    name = body.get("model", "")
    if name in _models:
        del _models[name]
        logger.info("Unloaded %s", name)
        return {"status": "unloaded", "model": name}
    return {"status": "not_loaded", "model": name}


@app.post("/detect", response_model=DetectResponse)
def detect(req: DetectRequest):
    model_name = req.model or _config["yolo_model"]
    confidence = req.confidence if req.confidence is not None else _config["yolo_confidence"]

    try:
        model = _get_yolo(model_name)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to load model: {e}")

    image = _decode_image(req.image)
    results = model(image, conf=confidence, verbose=False)

    detections = []
    for r in results:
        for box in r.boxes:
            x1, y1, x2, y2 = box.xyxy[0].tolist()
            label = r.names[int(box.cls[0])]

            if req.target_classes and label not in req.target_classes:
                continue

            det = BBox(
                x=int(x1), y=int(y1),
                w=int(x2 - x1), h=int(y2 - y1),
                confidence=float(box.conf[0]),
                label=label,
            )
            detections.append(det)

    return DetectResponse(detections=detections)


if __name__ == "__main__":
    import uvicorn

    logging.basicConfig(level=logging.INFO, format="%(levelname)-7s %(name)s — %(message)s")
    host = os.environ.get("HOST", "0.0.0.0")
    port = int(os.environ.get("PORT", "8000"))
    uvicorn.run(app, host=host, port=port)