"""Small USDA FoodData Central API client used by the nutrition pipeline."""

from __future__ import annotations

import json
import re
from pathlib import Path
from typing import Any
from urllib.parse import urlencode
from urllib.request import urlopen


FDC_SEARCH_URL = "https://api.nal.usda.gov/fdc/v1/foods/search"


NUTRIENT_NUMBERS = {
    "kcal": "208",
    "protein": "203",
    "fat": "204",
    "carbs": "205",
}


class FdcClient:
    def __init__(
        self,
        api_key: str,
        cache_path: Path,
        data_type: str = "Foundation,SR Legacy,FNDDS",
        page_size: int = 10,
        timeout: int = 30,
    ) -> None:
        self.api_key = api_key
        self.cache_path = cache_path
        self.data_type = data_type
        self.page_size = page_size
        self.timeout = timeout
        self.cache = self._load_cache()

    def search_first(self, query: str) -> dict[str, Any] | None:
        cache_key = f"ranked-v2|{self.data_type}|{self.page_size}|{query}".lower()
        if cache_key in self.cache:
            return self.cache[cache_key]

        params = {
            "api_key": self.api_key,
            "query": query,
            "pageSize": self.page_size,
            "dataType": self.data_type,
        }
        url = f"{FDC_SEARCH_URL}?{urlencode(params)}"
        with urlopen(url, timeout=self.timeout) as response:
            payload = json.loads(response.read().decode("utf-8"))

        foods = payload.get("foods", [])
        result = choose_best_food(query, foods)
        self.cache[cache_key] = result
        return result

    def save_cache(self) -> None:
        self.cache_path.parent.mkdir(parents=True, exist_ok=True)
        self.cache_path.write_text(
            json.dumps(self.cache, indent=2, sort_keys=True),
            encoding="utf-8",
        )

    def _load_cache(self) -> dict[str, Any]:
        if not self.cache_path.exists():
            return {}
        return json.loads(self.cache_path.read_text(encoding="utf-8"))


def extract_macros(food: dict[str, Any] | None) -> dict[str, float | None]:
    values: dict[str, float | None] = {name: None for name in NUTRIENT_NUMBERS}
    if food is None:
        return values

    nutrients = food.get("foodNutrients", [])
    by_number = {
        str(nutrient.get("nutrientNumber")): nutrient.get("value")
        for nutrient in nutrients
        if nutrient.get("nutrientNumber") is not None
    }

    for macro_name, nutrient_number in NUTRIENT_NUMBERS.items():
        value = by_number.get(nutrient_number)
        values[macro_name] = round(float(value), 2) if value is not None else None

    return values


def choose_best_food(query: str, foods: list[dict[str, Any]]) -> dict[str, Any] | None:
    if not foods:
        return None

    ranked = sorted(
        enumerate(foods),
        key=lambda item: food_match_score(query, item[1], item[0]),
        reverse=True,
    )
    return ranked[0][1]


def food_match_score(query: str, food: dict[str, Any], index: int) -> float:
    description = str(food.get("description", "")).lower()
    category = str(food.get("foodCategory", "")).lower()
    haystack = f"{description} {category}"
    tokens = tokenize(query)
    score = 0.0

    cooking_words = {"cooked", "grilled", "boiled", "raw", "roasted", "fried", "steamed"}
    for token in tokens:
        if token in haystack:
            score += 0.25 if token in cooking_words else 2.0

    if "skin" not in tokens:
        if description.startswith("chicken, skin") or " skin (" in description:
            score -= 4.0

    # Keep API ordering as a small tie-breaker after semantic token matches.
    score -= index * 0.01
    return score


def tokenize(text: str) -> list[str]:
    return [token for token in re.findall(r"[a-z0-9]+", text.lower()) if len(token) > 2]