#!/usr/bin/env python3
"""
analyze_history.py — one-off health/calorie review of the meal-history.json log.

Reads the rolling 7-day meal log and prints, per meal and per day:
  - estimated kcal and protein (g)
  - daily totals + averages across the window
  - a rough micronutrient read (fibre, sat fat, vit C, vit A, calcium)
  - whether each day meets the protein / fibre / sat-fat targets

Nutrition is ESTIMATE-grade (see references/nutrition-db.json). Each DB item carries its
canonical `unit` and `g_per_unit`; the script converts the LOGGED qty+unit to grams, then
scales — so a veg logged in 'g' and one logged in 'count' both resolve correctly. Condiments,
oils and cooking fat are NOT in the log, so real meals run ~80-120 kcal higher per cooked meal.
This is a review tool, not a send gate. It does NOT modify any file.

Usage:
  python3 scripts/analyze_history.py            # whole logged window
  python3 scripts/analyze_history.py --days 4   # last N days only
"""
import json, os, argparse
from datetime import datetime

SKILL_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
FIELDS = ["kcal", "protein", "fibre", "satfat", "vitC", "vitA", "calcium"]


def load():
    with open(os.path.join(SKILL_DIR, "meal-history.json")) as f:
        hist = json.load(f)
    with open(os.path.join(SKILL_DIR, "references", "nutrition-db.json")) as f:
        db = json.load(f)
    entries = hist["entries"] if isinstance(hist, dict) else hist
    return entries, db


def to_grams(name, qty, unit, db):
    spec = next((db["items"][k] for k in db["items"] if k.lower() == name.lower()), None)
    if not spec:
        return 0.0
    if unit in ("g", "ml", "kg"):
        return qty * (1000 if unit == "kg" else 1)
    # count/can/serving/portion: grams = qty * g_per_unit of the canonical unit
    return qty * spec.get("g_per_unit", 100)


def item_nutrition(name, qty, unit, db):
    spec = next((db["items"][k] for k in db["items"] if k.lower() == name.lower()), None)
    if not spec:
        return {f: 0 for f in FIELDS}
    grams = to_grams(name, qty, unit, db)
    factor = grams / spec["g_per_unit"]  # g_per_unit == grams per one canonical unit
    return {f: spec.get(f, 0) * factor for f in FIELDS}


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--days", type=int, default=0, help="limit to last N days (0 = all)")
    args = ap.parse_args()
    entries, db = load()
    entries = sorted(entries, key=lambda e: e.get("date", ""))
    if args.days:
        dates = sorted({e["date"] for e in entries})[-args.days:]
        entries = [e for e in entries if e["date"] in dates]

    by_day = {}
    for e in entries:
        by_day.setdefault(e["date"], []).append(e)

    grand = {f: 0 for f in FIELDS}
    day_proteins = []
    print("=" * 78)
    print("MEAL-HISTORY HEALTH REVIEW (estimated; condiments/oil not logged => +~80-120 kcal/meal)")
    print("=" * 78)
    for d in sorted(by_day):
        day_tot = {f: 0 for f in FIELDS}
        veg = set()
        print(f"\n--- {d} ({by_day[d][0].get('weekday','?')}) ---")
        for e in by_day[d]:
            mtot = {f: 0 for f in FIELDS}
            for it in e.get("items", []):
                n = item_nutrition(it["name"], it["qty"], it.get("unit", "count"), db)
                for f in FIELDS:
                    mtot[f] += n[f]
                if it["name"] in db.get("veg_names", []):
                    veg.add(it["name"])
            for f in FIELDS:
                day_tot[f] += mtot[f]
            print(f"  {e['meal']:6} {e['name']}")
            print(f"           ~{mtot['kcal']:.0f} kcal | {mtot['protein']:.0f} g protein | "
                  f"fibre {mtot['fibre']:.0f} | satfat {mtot['satfat']:.0f} | "
                  f"vitC {mtot['vitC']:.0f} | vitA {mtot['vitA']:.0f} | Ca {mtot['calcium']:.0f}")
        for f in FIELDS:
            grand[f] += day_tot[f]
        day_proteins.append(day_tot["protein"])
        t = db["targets"]
        p_ok = "OK" if day_tot["protein"] >= t["protein_per_day_g"] else "LOW"
        sf = "HIGH" if day_tot["satfat"] > t["satfat_per_day_g"] else "ok"
        print(f"  DAY TOTAL ~{day_tot['kcal']:.0f} kcal | protein {day_tot['protein']:.0f} g [{p_ok}] | "
              f"fibre {day_tot['fibre']:.0f} | satfat {day_tot['satfat']:.0f} [{sf}] | "
              f"veg types {len(veg)}")

    n = len(by_day) or 1
    t = db["targets"]
    print("\n" + "=" * 78)
    print(f"WINDOW AVERAGE/DAY (over {len(by_day)} day(s)):")
    print(f"  kcal      ~{grand['kcal']/n:.0f}")
    print(f"  protein   {grand['protein']/n:.0f} g (target >= {t['protein_per_day_g']}) "
          f"-> days meeting: {sum(1 for p in day_proteins if p >= t['protein_per_day_g'])}/{len(day_proteins)}")
    print(f"  fibre     {grand['fibre']/n:.0f} g (target ~{t['fibre_per_day_g']})")
    print(f"  satfat    {grand['satfat']/n:.0f} g (target <= {t['satfat_per_day_g']})")
    print(f"  vitC      {grand['vitC']/n:.0f} mg (RDA ~{t['vitC_mg']})")
    print(f"  vitA      {grand['vitA']/n:.0f} mcg (RDA ~{t['vitA_mcg']})")
    print(f"  calcium   {grand['calcium']/n:.0f} mg (RDA ~{t['calcium_mg']})")
    print("=" * 78)
    print("Not medical advice — defer to the user's clinical/dietitian team.")


if __name__ == "__main__":
    main()