# csv_util.py

import os
import pandas as pd
import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger(__name__)

CSV_COLUMNS = ["DateTime", "Currency", "Impact", "Event", "Actual", "Forecast", "Previous", "Detail"]

def ensure_csv_header(csv_file):
    if not os.path.exists(csv_file) or os.path.getsize(csv_file) == 0:
        pd.DataFrame(columns=CSV_COLUMNS).to_csv(csv_file, index=False)

def read_existing_data(csv_file):
    ensure_csv_header(csv_file)
    try:
        df = pd.read_csv(csv_file, dtype=str)
        for col in CSV_COLUMNS:
            if col not in df.columns:
                df[col] = ""
        return df[CSV_COLUMNS]
    except Exception:
        return pd.DataFrame(columns=CSV_COLUMNS)

def write_data_to_csv(df: pd.DataFrame, csv_file: str):
    df = df.sort_values(by="DateTime", ascending=True)
    df.to_csv(csv_file, index=False)

def merge_new_data(existing_df, new_df):
    if existing_df.empty:
        return new_df

    def add_unique_key(df):
        df = df.copy()
        df['unique_key'] = (
            df["DateTime"].astype(str).str.strip() + "_" +
            df["Currency"].astype(str).str.strip() + "_" +
            df["Event"].astype(str).str.strip()
        )
        return df

    existing_df = add_unique_key(existing_df)
    new_df = add_unique_key(new_df)
    existing_df.set_index('unique_key', inplace=True)
    new_df.set_index('unique_key', inplace=True)
    new_rows_list = []
    for key, new_row in new_df.iterrows():
        if key in existing_df.index:
            existing_detail = str(existing_df.at[key, "Detail"]).strip() if pd.notna(existing_df.at[key, "Detail"]) else ""
            new_detail = str(new_row["Detail"]).strip() if pd.notna(new_row["Detail"]) else ""
            if not existing_detail and new_detail:
                existing_df.at[key, "Detail"] = new_detail
        else:
            new_rows_list.append(new_row)
    if new_rows_list:
        new_rows_df = pd.DataFrame(new_rows_list)
        existing_df = pd.concat([existing_df, new_rows_df])
    merged_df = existing_df.reset_index(drop=True)
    merged_df = merged_df[CSV_COLUMNS]
    return merged_df