#! python3
import logging
import os
import sqlite3
import sys
from dotenv import load_dotenv

load_dotenv()

if (os.environ.get('LOGGING_LEVEL', 'INFO') == 'INFO'):
    logging_level = logging.INFO
else:
    logging_level = logging.DEBUG

logging.basicConfig(
    level=logging_level,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("app.log"),
        logging.StreamHandler(sys.stdout)
    ]
)
logger = logging.getLogger("knack-transform")


def setup_database_connection():
    """Create connection to the SQLite database."""
    db_path = os.environ.get('DB_PATH', '/data/knack.sqlite')
    logger.info(f"Connecting to database: {db_path}")
    return sqlite3.connect(db_path)


def table_exists(tablename: str, con: sqlite3.Connection):
    """Check if a table exists in the database."""
    query = "SELECT 1 FROM sqlite_master WHERE type='table' AND name=? LIMIT 1"
    return len(con.execute(query, [tablename]).fetchall()) > 0


def main():
    """Main entry point for the transform pipeline."""
    logger.info("Starting transform pipeline")
    
    try:
        con = setup_database_connection()
        logger.info("Database connection established")
        
        # Check if posts table exists
        if not table_exists('posts', con):
            logger.warning("Posts table does not exist yet. Please run the scraper first to populate the database.")
            logger.info("Transform pipeline skipped - no data available")
            return
        
        # Import transform nodes
        from author_node import AuthorNode
        from base import TransformContext
        import pandas as pd
        
        # Load posts data
        logger.info("Loading posts from database")
        sql = "SELECT id, author FROM posts WHERE author IS NOT NULL AND (is_cleaned IS NULL OR is_cleaned = 0) LIMIT ?"
        MAX_CLEANED_POSTS = os.environ.get("MAX_CLEANED_POSTS", 500)
        df = pd.read_sql(sql, con, params=[MAX_CLEANED_POSTS])
        logger.info(f"Loaded {len(df)} uncleaned posts with authors")
        
        if df.empty:
            logger.info("No uncleaned posts found. Transform pipeline skipped.")
            return
        
        # Create context and run author classification
        context = TransformContext(df)
        author_transform = AuthorNode(device=os.environ.get('COMPUTE_DEVICE', 'cpu'))  # Change to "cuda" or "mps" if available
        result_context = author_transform.run(con, context)

        # TODO: Create Node to compute Text Embeddings and UMAP. 
        # TODO: Create Node to pre-compute data based on visuals to reduce load time.
        
        logger.info("Transform pipeline completed successfully")
        
    except Exception as e:
        logger.error(f"Error in transform pipeline: {e}", exc_info=True)
        sys.exit(1)
    finally:
        if 'con' in locals():
            con.close()
            logger.info("Database connection closed")


if __name__ == "__main__":
    main()