from __future__ import annotations
import os
import json
from datetime import datetime
import pandas as pd

# 書き込み可能な場所をデフォルトにする（HFでは /app がReadOnlyな場合がある）
DEFAULT_WRITABLE_DIR = "/tmp/adcopy_data"
DATA_DIR = os.environ.get("DATA_DIR", DEFAULT_WRITABLE_DIR)

LOG_PATH = os.path.join(DATA_DIR, "events.csv")
META_PATH = os.path.join(DATA_DIR, "meta.json")

SCHEMA = [
    "ts", "date", "medium", "creative", "is_control",
    "impressions", "clicks", "conversions", "cost", "features_json"
]

def _ensure_storage():
    os.makedirs(DATA_DIR, exist_ok=True)
    if not os.path.exists(LOG_PATH):
        pd.DataFrame(columns=SCHEMA).to_csv(LOG_PATH, index=False)
    if not os.path.exists(META_PATH):
        with open(META_PATH, "w", encoding="utf-8") as f:
            json.dump({"created_at": datetime.utcnow().isoformat()}, f)

# インポート時に準備
_ensure_storage()

def read_events() -> pd.DataFrame:
    _ensure_storage()
    df = pd.read_csv(LOG_PATH)
    if df.empty:
        return df
    df["date"] = pd.to_datetime(df["date"]).dt.date.astype(str)
    df["is_control"] = df["is_control"].fillna(0).astype(int)
    for col in ["impressions", "clicks", "conversions"]:
        df[col] = df[col].fillna(0).astype(int)
    df["cost"] = df["cost"].fillna(0.0).astype(float)
    df["features_json"] = df["features_json"].fillna("{}")
    return df

def append_events(rows: pd.DataFrame) -> None:
    _ensure_storage()
    for c in SCHEMA:
        if c not in rows.columns:
            if c == "features_json":
                rows[c] = "{}"
            elif c == "ts":
                rows[c] = datetime.utcnow().isoformat()
            elif c == "date":
                rows[c] = datetime.utcnow().date().isoformat()
            elif c in ("impressions", "clicks", "conversions", "is_control"):
                rows[c] = 0
            elif c == "cost":
                rows[c] = 0.0
            else:
                rows[c] = None
    rows = rows[SCHEMA]
    rows.to_csv(LOG_PATH, mode="a", header=False, index=False)

def aggregate(levels=("medium", "creative")) -> pd.DataFrame:
    _ensure_storage()
    df = read_events()
    if df.empty:
        return pd.DataFrame(columns=[*levels, "is_control", "impressions", "clicks", "conversions", "cost"])
    g = df.groupby([*levels, "is_control"], dropna=False).agg(
        impressions=("impressions", "sum"),
        clicks=("clicks", "sum"),
        conversions=("conversions", "sum"),
        cost=("cost", "sum"),
    ).reset_index()
    return g