#!/usr/bin/env python3
import ast
import asyncio
import csv
from decimal import Decimal
from pathlib import Path

from template_project.adapters.unit_of_work import DefaultUnitOfWork
from template_project.application.common.embedding import Embedder
from template_project.application.common.enums import ExperienceType
from template_project.application.vacancy.entity import Vacancy, VacancyEmbedding
from template_project.ml.configuration import load_configuration as load_ml_configuration
from template_project.ml.ioc.make import make_ioc as make_ml_ioc
from template_project.web_api.configuration import load_configuration as load_backend_configuration
from template_project.web_api.ioc.make import make_ioc as make_backend_ioc


def parse_skills(skills_str: str) -> list[str]:
    try:
        skills = ast.literal_eval(skills_str)
        if isinstance(skills, list):
            return [str(skill) for skill in skills]
        return []  # noqa
    except (ValueError, SyntaxError):
        return []


def compose_embedding_text(position: str, description: str, key_skills: list[str]) -> str:
    skills_text = ", ".join(key_skills) if key_skills else ""
    parts = [position, description, skills_text]
    return " ".join(filter(None, parts))


async def main() -> None:
    backend_config_path = Path("config.toml")
    backend_configuration = load_backend_configuration(backend_config_path)
    backend_container = make_backend_ioc(backend_configuration)

    ml_config_path = Path("infrastructure/configs/ml/config.toml")
    ml_configuration = load_ml_configuration(ml_config_path)
    ml_container = make_ml_ioc(ml_configuration)

    csv_path = Path("filtered_vacancies.csv")
    max_records = 1000

    try:
        async with backend_container() as backend_request_container, ml_container() as ml_request_container:
            unit_of_work = await backend_request_container.get(DefaultUnitOfWork)
            embedder = await ml_request_container.get(Embedder)

            print(f"Загружаю первые {max_records} вакансий из {csv_path}...")

            with csv_path.open("r", encoding="utf-8") as f:
                reader = csv.DictReader(f)
                batch_size = 50
                batch = []

                for idx, row in enumerate(reader):
                    if idx >= max_records:
                        break

                    try:
                        vacancy_id_str = row.get("vacancy_id", "").strip()
                        if not vacancy_id_str:
                            continue

                        position = row.get("vacancy_nm", "").strip()
                        if not position:
                            continue

                        experience_str = row.get("experience", "").strip()
                        try:
                            experience_type = ExperienceType(experience_str)
                        except ValueError:
                            continue

                        salary_from_str = row.get("salary_from", "").strip()
                        salary_to_str = row.get("salary_to", "").strip()
                        try:
                            salary_from = Decimal(salary_from_str) if salary_from_str else Decimal(0)
                            salary_to = Decimal(salary_to_str) if salary_to_str else Decimal(0)
                        except (ValueError, TypeError):
                            continue

                        description = row.get("vacancy_description", "").strip()
                        key_skills = parse_skills(row.get("key_skills", "[]"))

                        vacancy = Vacancy.factory(
                            position=position,
                            from_salary=salary_from,
                            to_salary=salary_to,
                            experience_type=experience_type,
                            description=description,
                            key_skills=key_skills,
                        )

                        embedding_text = compose_embedding_text(position, description, key_skills)
                        embedding_vector = await embedder.encode(embedding_text)

                        embedding = VacancyEmbedding.factory(
                            vacancy_id=vacancy.id,
                            vector=embedding_vector,
                        )

                        await unit_of_work.add(vacancy, embedding)
                        batch.append((vacancy.id, position))

                        if len(batch) >= batch_size:
                            await unit_of_work.commit()
                            print(f"Загружено {len(batch)} вакансий (всего: {idx + 1})")
                            batch = []

                    except Exception as e:
                        print(f"Ошибка при обработке строки {idx + 1}: {e}")
                        continue

                if batch:
                    await unit_of_work.commit()
                    print(f"Загружено {len(batch)} вакансий (всего: {idx + 1})")

            print("Готово!")
    finally:
        await backend_container.close()
        await ml_container.close()


if __name__ == "__main__":
    asyncio.run(main())