from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from qdrant_client import QdrantClient, models
from qdrant_client.models import Filter, HasIdCondition

from vacancies.conf.settings import QDRANT_URL
from vacancies.main.models import RecommendedVacancy, VacancyFeatures

qdrant_client = QdrantClient(url=QDRANT_URL)

FEATURE_NAMES = [
    "job_title", "employment_type", "work_format", "experience", "position_level", "industry", "tech_stack",
    "location", "salary_range", "languages", "education", "schedule", "additional_requirements"
]

weights = {
    "job_title": 70,
    "tech_stack": 10,
    "salary_range": 10,
}

vectors_config = {
    name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
}

if not qdrant_client.collection_exists("vacancies"):
    qdrant_client.create_collection(
        collection_name="vacancies",
        vectors_config=vectors_config,
    )
    qdrant_client.create_payload_index(
        collection_name="vacancies",
        field_name="timestamp",
        field_schema="datetime",
    )
if not qdrant_client.collection_exists("cvs"):
    qdrant_client.create_collection(
        collection_name="cvs",
        vectors_config=vectors_config,
    )

embedding = OpenAIEmbeddings(model="text-embedding-3-large")

def _prepare_texts(features):
    texts = {}
    for name in FEATURE_NAMES:
        value = features.get(name)
        if isinstance(value, list):
            text = " ".join(value) if value else ""
        else:
            text = str(value) if value else ""
        texts[name] = text
    return texts


def embed_features(features):
    features = {key: value for key, value in features.items() if value}
    features_texts = _prepare_texts(features)
    names, texts = features_texts.keys(), features_texts.values()
    vectors = dict(zip(names, embedding.embed_documents(texts)))
    return vectors


def add_vectors(collection_name: str, _id: int, features: dict, payload: dict, vectors):
    max_similarities = {}
    for name, vec in vectors.items():
        results = qdrant_client.query_points(collection_name="vacancies", query=vec, using=name, limit=100)
        for res in results.points:
            max_similarities.setdefault(res.id, {})
            max_similarities[res.id][name] = res.score

    scored = []
    for vid, feature_sims in max_similarities.items():
        total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
        scored.append({"id": vid, "score": total})

    scored.sort(key=lambda x: x["score"], reverse=True)
    if scored and scored[0]["score"] > 80:  # threshold
        return

    qdrant_client.upsert(
        collection_name=collection_name,
        points=[models.PointStruct(id=_id, vector=vectors, payload=payload)]
    )


def search_similarities(query_filter: Filter, cv_id: int):
    cv = qdrant_client.retrieve(collection_name="cvs", ids=[cv_id], with_vectors=True)[0]

    max_similarities, vacancies_content = {}, {}
    for name, vec in cv.vector.items():
        results = qdrant_client.query_points(
            collection_name="vacancies",
            query=vec,
            using=name,
            limit=100000,
            with_payload=True,
            query_filter=query_filter,
        )
        for res in results.points:
            max_similarities.setdefault(res.id, {})
            vacancies_content.setdefault(res.id, {})

            max_similarities[res.id][name] = res.score
            vacancies_content[res.id]["content"] = res.payload["content"]
            vacancies_content[res.id]["features_json"] = res.payload["features_json"]
            vacancies_content[res.id]["link"] = res.payload["link"]

    scored = []
    for vid, feature_sims in max_similarities.items():
        total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
        scored.append({
            "id": vid,
            "score": total,
            "content": vacancies_content[vid]["content"],
            "features_json": vacancies_content[vid]["features_json"],
            "link": vacancies_content[vid]["link"],
            "sims": feature_sims,
        })

    scored.sort(key=lambda x: x["score"], reverse=True)

    return scored[0]["id"], scored[0]["content"], scored[0]["link"]


def batch_extract_features(contents: list[str]) -> list[VacancyFeatures]:
    prompts = [
    f"""
    Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
    Features:
    - job_title: Должность (e.g., DevOps, Python программист)
    - employment_type: Тип занятости (e.g., Полная занятость, Частичная)
    - work_format: Формат работы (e.g., Офис, Удалённо, Гибрид)
    - experience: Опыт работы (e.g., 3-5 лет, Нет опыта)
    - position_level: Уровень позиции (e.g., Junior, Senior)
    - industry: Отрасль / Сфера деятельности (e.g., IT, Финансы)
    - tech_stack: Технологический стек / Ключевые навыки (list of strings)
    - location: География (e.g., Москва, Россия)
    - salary_range: Зарплатные ожидания / вилка (e.g., 100000-200000 руб)
    - languages: Языки (list of strings, e.g., ["Русский", "Английский"])
    - education: Образование (e.g., Высшее, Среднее специальное)
    - schedule: График работы (e.g., Полный день, Сменный)
    - additional_requirements: Дополнительные предпочтения / требования (list of strings)
    Vacancy content:
    {content}
    """
    for content in contents
    ]
    openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
    structured_llm = openai_client.with_structured_output(VacancyFeatures)
    response = structured_llm.batch(prompts)
    return response


def get_next_vacancy(customer_cv):
    recommended_vacancy_ids = RecommendedVacancy.objects.filter(
        customer=customer_cv.customer,
    ).values_list('vacancy_id', flat=True)

    query_filter = Filter(must_not=[HasIdCondition(has_id=recommended_vacancy_ids)])
    result = search_similarities(query_filter, customer_cv.id)
    if not result:
        return None

    search_result_id, vacancy_content, link = result

    recommendation = RecommendedVacancy.objects.create(
        customer=customer_cv.customer,
        vacancy_id=search_result_id,
    )

    return recommendation, vacancy_content, link