"""
Receipt OCR Service — FastAPI entry point.

Endpoints:
  POST /ocr          Upload a receipt image, get structured JSON back.
  GET  /health       Liveness check.

Usage:
  uvicorn main:app --host 0.0.0.0 --port 8000 --reload
"""

import asyncio
import logging
from contextlib import asynccontextmanager

from fastapi import FastAPI, File, HTTPException, Query, UploadFile
from fastapi.responses import JSONResponse

from ocr.reader import PaddleOCRReader
from ocr.parser import parse_blocks
from utils.image_prep import preprocess_image

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Global reader instance — loaded once at startup
_reader: PaddleOCRReader | None = None


async def _background_warmup():
    import numpy as np
    logger.info("Background warm-up starting...")
    try:
        await asyncio.get_event_loop().run_in_executor(
            None, lambda: _reader.extract(np.ones((200, 600, 3), dtype=np.uint8) * 255)
        )
        logger.info("Background warm-up complete.")
    except Exception as exc:
        logger.warning("Background warm-up failed (non-fatal): %s", exc)


@asynccontextmanager
async def lifespan(app: FastAPI):
    global _reader
    _reader = PaddleOCRReader()
    # Run warm-up in background so the server starts accepting connections
    # immediately. First real request after restart may still be slow (JIT),
    # but the server won't appear unresponsive during startup.
    asyncio.create_task(_background_warmup())
    yield
    _reader = None


app = FastAPI(
    title="Receipt OCR API",
    description="Extract structured data from receipt images using PaddleOCR.",
    version="1.0.0",
    lifespan=lifespan,
)


@app.get("/health")
async def health():
    return {"status": "ok", "model_loaded": _reader is not None}


@app.get("/warmup")
async def warmup():
    """Run OCR on a blank image to exercise the inference engine. Used by keepalive cron."""
    if _reader is None:
        raise HTTPException(status_code=503, detail="OCR model not loaded yet")
    import numpy as np
    _reader.extract(np.ones((200, 600, 3), dtype=np.uint8) * 255)
    return {"status": "warm"}


@app.post("/ocr")
async def ocr_receipt(
    file: UploadFile = File(..., description="Receipt image (JPEG, PNG, etc.)"),
    debug: bool = Query(False, description="Include raw OCR blocks in response"),
):
    """
    Process a receipt image and return structured JSON.

    - **file**: multipart image upload
    - **debug**: if true, includes raw OCR text blocks in the response
    """
    if _reader is None:
        raise HTTPException(status_code=503, detail="OCR model not loaded yet")

    # Validate content type loosely
    content_type = file.content_type or ""
    if content_type and not content_type.startswith("image/"):
        raise HTTPException(
            status_code=415,
            detail=f"Unsupported media type: {content_type}. Upload an image file.",
        )

    import time
    t0 = time.perf_counter()

    raw_bytes = await file.read()
    if not raw_bytes:
        raise HTTPException(status_code=400, detail="Empty file uploaded")

    try:
        image = preprocess_image(raw_bytes)
    except (ValueError, FileNotFoundError) as exc:
        raise HTTPException(status_code=400, detail=f"Image preprocessing failed: {exc}")
    t1 = time.perf_counter()
    print(f"TIMING preprocess: {t1-t0:.2f}s | image size: {image.shape[1]}x{image.shape[0]} | upload: {len(raw_bytes)//1024}KB", flush=True)

    try:
        blocks = _reader.extract(image)
    except Exception as exc:
        logger.exception("OCR extraction failed")
        raise HTTPException(status_code=500, detail=f"OCR failed: {exc}")
    t2 = time.perf_counter()
    print(f"TIMING ocr extract: {t2-t1:.2f}s | blocks found: {len(blocks)}", flush=True)

    result = parse_blocks(blocks)
    t3 = time.perf_counter()
    print(f"TIMING parse: {t3-t2:.2f}s | items: {len(result.get('line_items', []))} | total: {t3-t0:.2f}s", flush=True)

    if debug:
        result["_raw_blocks"] = blocks

    return JSONResponse(content=result)