import argparse
import asyncio
import os

from src.pipeline.orchestrator import PipelineOrchestrator
from src.rag.manager import RAGManager


def main():
    parser = argparse.ArgumentParser(description="D&D Helpers CLI")

    # RAG Ingestion Arguments
    parser.add_argument(
        "--ingest-pdf",
        type=str,
        help="Path to a PDF file to ingest into the RAG system",
    )
    parser.add_argument(
        "--ingest-file",
        type=str,
        help="Path to a markdown file to ingest into the RAG system",
    )
    parser.add_argument(
        "--ingest-dir",
        type=str,
        help="Path to a directory of markdown files to ingest into the RAG system",
    )

    # LLM Configuration Arguments
    parser.add_argument(
        "--llm-backend",
        type=str,
        choices=["openai", "ollama", "vllm"],
        default=os.environ.get("LLM_BACKEND", "openai"),
        help="LLM backend to use",
    )
    parser.add_argument(
        "--llm-model",
        type=str,
        default=os.environ.get("LLM_MODEL", "gpt-4o"),
        help="The model to use for processing",
    )
    parser.add_argument(
        "--llm-api-key",
        type=str,
        default=os.environ.get("OPENAI_API_KEY"),
        help="API key for the LLM backend",
    )
    parser.add_argument(
        "--llm-base-url",
        type=str,
        default=os.environ.get("OPENAI_BASE_URL"),
        help="Base URL for the LLM backend",
    )

    # STT Configuration Arguments
    parser.add_argument(
        "--whisper-model",
        type=str,
        default=os.environ.get("WHISPER_MODEL", "turbo"),
        help="The Whisper model to use for STT",
    )

    # Pipeline Execution Argument
    parser.add_argument(
        "--run-pipeline",
        action="store_true",
        help="Start the main orchestration pipeline (TUI + STT + LLM)",
    )

    args = parser.parse_args()

    llm_config = {
        "backend": args.llm_backend,
        "model": args.llm_model,
        "api_key": args.llm_api_key,
        "base_url": args.llm_base_url,
    }

    # Remove None values to allow defaults to take over if not provided
    llm_config = {k: v for k, v in llm_config.items() if v is not None}

    if args.run_pipeline:
        async def run_pipeline():
            loop = asyncio.get_event_loop()
            orchestrator = PipelineOrchestrator(loop, llm_config=llm_config, whisper_model=args.whisper_model)
            try:
                await orchestrator.run()
            except KeyboardInterrupt:
                orchestrator.stop()

        asyncio.run(run_pipeline())
        return

    rag_manager = RAGManager(llm_config=llm_config)

    if args.ingest_pdf:
        print(f"Ingesting PDF: {args.ingest_pdf}...")
        rag_manager.ingest_pdf(args.ingest_pdf)
        print("PDF ingestion complete.")

    if args.ingest_file:
        print(f"Ingesting File: {args.ingest_file}...")
        rag_manager.ingest_file(args.ingest_file)
        print("File ingestion complete.")

    if args.ingest_dir:
        print(f"Ingesting Directory: {args.ingest_dir}...")
        rag_manager.ingest_directory(args.ingest_dir)
        print("Directory ingestion complete.")

    if not any([args.ingest_pdf, args.ingest_file, args.ingest_dir, args.run_pipeline]):
        print("Hello from dnd-helpers! Use --help to see available commands.")


if __name__ == "__main__":
    main()