from fastapi import FastAPI, File, UploadFile, HTTPException, Header
from fastapi.middleware.cors import CORSMiddleware
from starlette.responses import JSONResponse
import io
import os
import uuid
from datetime import datetime, timezone

from utils.image_processing import crop_receipt_from_image
from utils.ocr import run_hf_ocr
from utils.supabase_helpers import (
    fetch_user_from_supabase_token,
    upload_bytes_to_supabase_storage,
    insert_slip_row,
)


APP_NAME = "Slip Scanner Backend"

try:
    from dotenv import load_dotenv  # type: ignore
    load_dotenv()
except Exception:
    # Optional dependency; ignore if not present
    pass

app = FastAPI(title=APP_NAME)


frontend_origin = os.getenv("FRONTEND_CORS_ORIGIN", "*")
app.add_middleware(
    CORSMiddleware,
    allow_origins=[frontend_origin] if frontend_origin != "*" else ["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/health")
def health():
    required_env = [
        "SUPABASE_URL",
        "SUPABASE_ANON_KEY",
        "SUPABASE_SERVICE_ROLE_KEY",
        "SUPABASE_STORAGE_BUCKET",
        "HUGGINGFACE_API_TOKEN",
    ]
    missing = [k for k in required_env if not os.getenv(k)]
    return {
        "service": APP_NAME,
        "status": "ok" if not missing else "degraded",
        "missing_env": missing,
    }


@app.post("/scan")
async def scan(
    file: UploadFile = File(...),
    authorization: str | None = Header(default=None, convert_underscores=False),
):
    # Validate auth header
    if not authorization or not authorization.lower().startswith("bearer "):
        raise HTTPException(status_code=401, detail="Missing or invalid Authorization header")

    user_jwt = authorization.split(" ", 1)[1]
    user = fetch_user_from_supabase_token(user_jwt)
    if not user or not user.get("id"):
        raise HTTPException(status_code=401, detail="Invalid Supabase session")

    user_id = user["id"]

    # Read upload
    original_bytes = await file.read()
    if not original_bytes:
        raise HTTPException(status_code=400, detail="Empty file upload")

    # Crop receipt via edge detection & perspective transform
    try:
        cropped_bytes, crop_meta = crop_receipt_from_image(original_bytes)
    except Exception as exc:
        raise HTTPException(status_code=422, detail=f"Failed to process image: {exc}")

    # OCR via Hugging Face Inference API
    try:
        ocr_text = run_hf_ocr(cropped_bytes)
    except Exception as exc:
        raise HTTPException(status_code=502, detail=f"OCR failed: {exc}")

    # Extract naive amount (ZAR or generic currency) and a fallback category
    amount = _extract_amount(ocr_text)
    category = _infer_category(ocr_text)

    # Upload cropped image to Supabase Storage
    bucket = os.getenv("SUPABASE_STORAGE_BUCKET", "slips")
    object_name = f"{user_id}/{uuid.uuid4()}.jpg"
    try:
        public_url = upload_bytes_to_supabase_storage(
            bucket=bucket,
            object_path=object_name,
            content_bytes=cropped_bytes,
            content_type="image/jpeg",
        )
    except Exception as exc:
        raise HTTPException(status_code=502, detail=f"Upload failed: {exc}")

    # Insert DB row into slips table
    try:
        inserted = insert_slip_row(
            {
                "user_id": user_id,
                "image_path": object_name,
                "image_url": public_url,
                "raw_text": ocr_text,
                "amount": amount,
                "category": category,
                "created_at": datetime.now(timezone.utc).isoformat(),
                "crop_confidence": crop_meta.get("confidence"),
            }
        )
    except Exception as exc:
        raise HTTPException(status_code=502, detail=f"Database insert failed: {exc}")

    return JSONResponse(
        {
            "ok": True,
            "slip": inserted,
        }
    )


def _extract_amount(text: str) -> float | None:
    import re

    if not text:
        return None
    # Look for amounts with optional currency symbol/letter (e.g., R250.00, $12.34)
    candidates: list[float] = []
    for match in re.finditer(r"(?:R|\$)?\s*(\d{1,3}(?:[\,\s]\d{3})*(?:\.\d{2})?|\d+(?:\.\d{2}))", text, flags=re.IGNORECASE):
        num_str = match.group(1).replace(",", "").replace(" ", "")
        try:
            candidates.append(float(num_str))
        except ValueError:
            continue
    return max(candidates) if candidates else None


def _infer_category(text: str) -> str:
    text_l = (text or "").lower()
    if any(k in text_l for k in ["fuel", "petrol", "gas"]):
        return "Fuel"
    if any(k in text_l for k in ["restaurant", "meal", "food", "dine"]):
        return "Food"
    if any(k in text_l for k in ["grocery", "supermarket", "market", "store"]):
        return "Grocery"
    return "Uncategorized"