import sys
import json
import re
from typing import List, Dict, Any, Optional

from PIL import Image
import pytesseract
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

CLS_MODEL_ID = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli"

classifier = pipeline(
    "zero-shot-classification",
    model=CLS_MODEL_ID,
    tokenizer=CLS_MODEL_ID,
    device=0 if hasattr(sys, "gettrace") is False else -1,
)


def ocr_image_to_text(image: Image.Image) -> str:

    image = image.convert("L")

    config = r"--psm 6"
    text = pytesseract.image_to_string(image, lang="rus", config=config)
    return text


def pick_total_from_text(text: str) -> Optional[float]:
    if not text:
        return None

    text = text.replace("\xa0", " ")
    text = re.sub(r"[₽€$]", " ", text)

    def _to_float(s: str) -> Optional[float]:
        s = s.replace(" ", "").replace(",", ".")
        try:
            return float(s)
        except Exception:
            return None

    pattern_num = r"(-?\d{1,3}(?:[ .,\u00A0]?\d{3})*(?:[.,]\d{2}))"

    strong_candidates = []
    medium_candidates = []
    all_candidates = []
    line_totals = []

    for line in text.splitlines():
        line_clean = line.strip()
        if not line_clean:
            continue
        lower = line_clean.lower()

        if "сдач" in lower:
            continue

        m_eq = re.search(r"=\s*([0-9][0-9 .,\u00A0]*[.,]\d{2})", line_clean)
        if m_eq:
            v_eq = _to_float(m_eq.group(1))
            if v_eq and 0 < v_eq < 1e7:
                line_totals.append(v_eq)

        nums = re.findall(pattern_num, line_clean)
        if not nums:
            continue

        for m in nums:
            v = _to_float(m)
            if not v or v <= 0 or v > 1e7:
                continue

            if any(k in lower for k in ["итог", "итого", "к оплате", "всего к оплате"]):
                strong_candidates.append(v)
            elif any(k in lower for k in ["наличн", "карта", "безнал", "оплачено"]):
                medium_candidates.append(v)

            all_candidates.append(v)

    if strong_candidates:
        return max(strong_candidates)

    if medium_candidates:
        return max(medium_candidates)

    if len(line_totals) >= 3:
        s = sum(line_totals)
        if 0 < s < 1e7:
            return s

    if all_candidates:
        return max(all_candidates)

    return None

def classify_category_zeroshot(
    receipt_text: str,
    categories: List[Dict[str, Any]],
) -> Dict[str, Any]:
    if not categories:
        return {"id": None, "name": None}

    receipt_short = receipt_text[:1500]
    labels = [cat["name"] for cat in categories]

    result = classifier(
        receipt_short,
        candidate_labels=labels,
        multi_label=False,
        hypothesis_template="Это покупка по категории {}.",
    )

    best_label = result["labels"][0]

    best_cat = next((c for c in categories if c["name"] == best_label), None)

    if best_cat is None:
        best_label_low = best_label.lower()
        for c in categories:
            if c["name"].lower() == best_label_low:
                best_cat = c
                break

    if best_cat is None:
        best_cat = categories[-1]

    return best_cat

def guess_shop_name(receipt_text: str) -> Optional[str]:
    lines = [ln.strip() for ln in receipt_text.splitlines() if ln.strip()]
    top = lines[:5]

    candidates = []
    for ln in top:
        lower = ln.lower()
        if any(x in lower for x in ["инн", "ккт", "касса", "рн кк", "россия", "г. ", "ул."]):
            continue
        if 2 <= len(ln) <= 40:
            candidates.append(ln)

    if candidates:
        return candidates[0]
    return None


def build_description(
    receipt_text: str,
    category_name: Optional[str],
    total: Optional[float],
) -> str:
    cat = category_name or "покупка"
    shop = guess_shop_name(receipt_text)

    if shop:
        return f"Покупка по категории {cat} в {shop}"
    else:
        if total is not None:
            return f"Покупка по категории {cat} на {total:.2f}"
        else:
            return f"Покупка по категории {cat}"

DEFAULT_CATEGORIES: List[Dict[str, Any]] = [
    {"id": 1, "name": "Еда"},
    {"id": 2, "name": "Спорт"},
    {"id": 3, "name": "Обучение"},
    {"id": 4, "name": "Транспорт"},
    {"id": 5, "name": "Развлечения"},
    {"id": 6, "name": "Медицина"},
    {"id": 7, "name": "Бытовые товары"},
    {"id": 8, "name": "Прочее"},
]


def extract_info(
    image_path: str,
    categories: Optional[List[Dict[str, Any]]] = None,
) -> Dict[str, Any]:

    if categories is None:
        categories = DEFAULT_CATEGORIES

    image = Image.open(image_path).convert("RGB")
    text = ocr_image_to_text(image)

    total = pick_total_from_text(text)
    best_cat = classify_category_zeroshot(text, categories)
    description = build_description(text, best_cat["name"], total)

    return {
        "total": total,
        "category_id": best_cat.get("id"),
        "category_name": best_cat.get("name"),
        "description": description,
        "raw_text": text,
    }


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: receipt_info_api.py path/to/receipt.jpg [categories.json]", file=sys.stderr)
        sys.exit(1)

    image_path = sys.argv[1]

    cats = None
    if len(sys.argv) >= 3:
        with open(sys.argv[2], "r", encoding="utf-8") as f:
            cats = json.load(f)

    info = extract_info(image_path, categories=cats)

    out = {
        "total": info["total"],
        "category_id": info["category_id"],
        "category_name": info["category_name"],
        "description": info["description"],
    }
    print(json.dumps(out, ensure_ascii=False))