import argparse
import logging
import os
import sys
import sqlite3
import pandas as pd

# Assuming logging_utils.py is in the same directory
from logging_utils import setup_logging

class Migrator:
    """
    Reads 1-minute candle data from CSV files and migrates it into an
    SQLite database for improved performance and easier access.
    """

    def __init__(self, log_level: str):
        setup_logging(log_level, 'Migrator')
        self.source_folder = os.path.join("_data", "candles")
        self.db_path = os.path.join("_data", "market_data.db")

    def run(self):
        """
        Main execution function to find all CSV files and migrate them to the database.
        """
        if not os.path.exists(self.source_folder):
            logging.error(f"Source data folder '{self.source_folder}' not found. "
                          "Please ensure data has been fetched first.")
            sys.exit(1)

        csv_files = [f for f in os.listdir(self.source_folder) if f.endswith('_1m.csv')]
        
        if not csv_files:
            logging.warning("No 1-minute CSV files found in the source folder to migrate.")
            return

        logging.info(f"Found {len(csv_files)} source CSV files to migrate to SQLite.")

        # Connect to the SQLite database (it will be created if it doesn't exist)
        with sqlite3.connect(self.db_path) as conn:
            for file_name in csv_files:
                coin = file_name.split('_')[0]
                table_name = f"{coin}_1m"
                file_path = os.path.join(self.source_folder, file_name)
                
                logging.info(f"Migrating '{file_name}' to table '{table_name}'...")
                
                try:
                    # 1. Load the entire CSV file into a pandas DataFrame.
                    df = pd.read_csv(file_path)
                    
                    if df.empty:
                        logging.warning(f"CSV file '{file_name}' is empty. Skipping.")
                        continue

                    # 2. Convert the timestamp column to a proper datetime object.
                    df['datetime_utc'] = pd.to_datetime(df['datetime_utc'])
                    
                    # 3. Write the DataFrame to the SQLite database.
                    # 'replace' will drop the table first if it exists and create a new one.
                    # This is ideal for a migration script to ensure a clean import.
                    df.to_sql(
                        table_name, 
                        conn, 
                        if_exists='replace',
                        index=False # Do not write the pandas DataFrame index as a column
                    )
                    
                    # 4. (Optional but Recommended) Create an index on the timestamp for fast queries.
                    logging.debug(f"Creating index on 'datetime_utc' for table '{table_name}'...")
                    conn.execute(f"CREATE INDEX IF NOT EXISTS idx_{table_name}_time ON {table_name}(datetime_utc);")

                    logging.info(f"Successfully migrated {len(df)} rows to '{table_name}'.")

                except Exception as e:
                    logging.error(f"Failed to process and migrate file '{file_name}': {e}")
    
        logging.info("--- Database migration complete ---")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Migrate 1-minute candle data from CSV files to an SQLite database.")
    parser.add_argument(
        "--log-level",
        default="normal",
        choices=['off', 'normal', 'debug'],
        help="Set the logging level for the script."
    )
    args = parser.parse_args()
    
    migrator = Migrator(log_level=args.log_level)
    migrator.run()