import sys
from pathlib import Path

import gradio as gr
from omegaconf import OmegaConf
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker


def initialize_search_engines(db, config):
    """
    Initialize both BM25 and Semantic search engines.

    Args:
        db: Database session.
        config: Configuration dictionary.

    Returns:
        tuple: Initialized BM25Search and SemanticSearch engines.
    """
    from search.bm25_search import BM25Search
    from search.semantic_search import SemanticSearch
    from preprocessing.mystem_tokenizer import MystemTokenizer

    custom_tokenizer = MystemTokenizer()
    bm25_search = BM25Search(
        db,
        config['index_folders']['bm25'],
        custom_tokenizer.tokenize
    )
    semantic_search = SemanticSearch(
        db,
        model=config['semantic_search']['model'],
        embeddings_file=f"{config['index_folders']['semantic']}/embeddings.npy",
        prefix=config['semantic_search']['query_prefix'])

    return bm25_search, semantic_search


def search_memes(query: str, search_type: str, num_results: int):
    """
    Search for memes using the specified search method.

    Args:
        query (str): The search query.
        search_type (str): The type of search to perform. Either 'BM25' or 'Семантический'.
        num_results (int): The number of results to return.

    Returns:
        tuple: A tuple containing the search results and search time.
    """
    if search_type == "BM25":
        results = bm25_search.search(query, num_results)
    else:
        results = semantic_search.search(query, num_results)

    output = []
    for result in results['results']:
        output.append((result['image_url'], result['text']))

    return output, f"Время поиска: {results['search_time']:.4f} секунд"


def main():
    global bm25_search, semantic_search

    # Load configuration
    config = OmegaConf.load('config.yaml')
    config = OmegaConf.to_container(config)

    # Initialize database session
    engine = create_engine(config['database']['url'])
    SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
    db = SessionLocal()

    # Initialize search engines
    bm25_search, semantic_search = initialize_search_engines(db, config)

    # Gradio interface
    with gr.Blocks() as demo:
        gr.Markdown("# Поиск мемов")
        gr.Markdown(
            "Добро пожаловать в приложение для поиска мемов! Введите запрос, выберите тип поиска и количество результатов."
        )

        with gr.Row():
            query = gr.Textbox(label="Запрос")
            search_type = gr.Radio(
                ["BM25", "Семантический"],
                label="Тип поиска",
                value="BM25"
            )
            num_results = gr.Slider(
                minimum=1,
                maximum=10,
                step=1,
                value=1,
                label="Количество результатов"
            )

        search_button = gr.Button("Найти")

        output_gallery = gr.Gallery(
            label="Результаты",
            show_label=False,
            columns=3,
            height=400
        )
        output_time = gr.Markdown()

        search_button.click(
            fn=search_memes,
            inputs=[query, search_type, num_results],
            outputs=[output_gallery, output_time]
        )

    demo.launch()


if __name__ == "__main__":
    # Set up project root path
    project_root = Path(__file__).resolve().parents[1]
    sys.path.insert(0, str(project_root))
    main()