import logging
import os
from pathlib import Path

import pandas as pd

from common.common import configure_logging
from common.configuration import Configuration
from components.elastic import create_index_elastic_chunks, create_index_elastic_people
from components.embedding_extraction import EmbeddingExtractor
from controlpanel.components.datasets.dispatcher import Dispatcher
from components.nmd.services.acronym import AcronymService
from components.nmd.services.dataset import DatasetService
from components.nmd.services.document import DocumentService
from components.sqlite.create_database import create_database

CONFIG_PATH = os.environ.get('CONFIG_PATH', './config_dev.yaml')

config = Configuration(CONFIG_PATH)
logger = logging.getLogger(__name__)
configure_logging(config_file_path=config.common_config.log_file_path)

logger.info(f'Start work...')
logger.info(f'Use config: {os.path.abspath(CONFIG_PATH)}')

model = EmbeddingExtractor(
    config.db_config.faiss.model_embedding_path,
    config.db_config.faiss.device,
)

dispatcher = Dispatcher(model, config, logger)

acronym_service = AcronymService()
dataset_service = DatasetService(model, dispatcher, config)
document_service = DocumentService(dataset_service, config)

create_database(dataset_service, config)

current_dataset = dataset_service.get_current_dataset()

dispatcher.reset_dataset(current_dataset.dataset_id)

df = pd.read_pickle(
    Path.cwd()
    / config.db_config.files.regulations_path
    / f'{current_dataset.dataset_id}'
    / 'dataset.pkl'
)

if config.db_config.elastic.use_elastic:
    create_index_elastic_chunks(df, logger)
    create_index_elastic_people(config.db_config.elastic.people_path, logger)

logger.info('Loaded embedding model')