import uuid
from execution_tracker import load_executed_from_gsheet, log_progress_to_gsheet
from pipeline_processor import process_single_row, configure_row_settings
from cleanup_manager import process_delete_entries
from src.config import get_config_value
from src.logger_config import logger

async def run_plain_video_workflow(commit=False, job_index=None, total_jobs=None):
    """Generate plain videos (music-synced, no AI content) with progress tracking."""
    
    # Run delete cleanup first
    await process_delete_entries()

    if total_jobs is None or job_index is None:
        total_jobs = 1
        job_index = 0

    try:
        n = int(get_config_value("generation_count", 100))
    except ValueError:
        n = 100
    logger.info(f"Creating {n} plain videos...")

    # Load how many already completed from Google Sheets (filtered by SETUP_TYPE and job_index)
    # setup_type = get_config_value("setup_type")
    executed = load_executed_from_gsheet(job_index=job_index)
    completed = len(executed)
    if get_config_value("test_automation"):
        completed = 0

    # --- ASSIGN SLICE TO THIS JOB ---
    per_job = n / total_jobs
    start_index = int(per_job * job_index)
    end_index   = int(per_job * (job_index + 1))

    # But skip those already completed
    start_index = max(start_index, completed)

    logger.debug(
        f"[Job {job_index}/{total_jobs}] Assigned range: {start_index} → {end_index}, "
        f"completed={completed}"
    )

    # Run only the assigned range
    generated_results = []
    
    for i in range(start_index, min(n, end_index)):
        logger.debug("=" * 200)
        row = { "TTS Script (AI Avatar)": uuid.uuid4().hex[:8] }

        # Configure row settings (includes setting current_audio_index)
        configure_row_settings(row, i)
        results = await process_single_row(row)

        for result in results:
            if result.get("success", False):
                log_progress_to_gsheet(result, job_index, commit)
                
            # Collect for summary table
            generated_results.append({
                "local_path": result.get("local_path", "N/A"),
                "gcs_path": result.get("gcs_filename", "N/A"),
                "drive_path": result.get("final_url", "N/A")  # final_url serves as drive/public URL
            })

    logger.info(f"[Job {job_index}] Finished slice.")
    return generated_results