"""
Логика обработки NER: загрузка моделей, обработка текста, история запросов.
"""

import time
import io
from collections import deque
from datetime import datetime

import pandas as pd
from transformers import pipeline

from config import (
    MAX_CHARS,
    MAX_BATCH_ROWS,
    HISTORY_SIZE,
    MODELS,
    ENTITY_LABELS
)

# Глобальные переменные
pipelines_cache = {}  # Кэш загруженных моделей
history = deque(maxlen=HISTORY_SIZE)  # История запросов


# ============== ЗАГРУЗКА МОДЕЛЕЙ ==============
def load_model(model_key: str):
    """Ленивая загрузка модели по ключу."""
    if model_key not in pipelines_cache:
        model_name = MODELS[model_key]
        pipelines_cache[model_key] = pipeline(
            "ner",
            model=model_name,
            aggregation_strategy="simple"
        )
    return pipelines_cache[model_key]


# ============== ВАЛИДАЦИЯ И ОБРАБОТКА ==============
def validate_input(text: str) -> tuple[bool, str]:
    """Валидация входного текста."""
    if text is None or not text.strip():
        return False, "Ошибка: введите текст для анализа."

    text = text.strip()
    if len(text) > MAX_CHARS:
        return False, f"Ошибка: текст слишком длинный ({len(text)} символов). Максимум: {MAX_CHARS}."

    return True, text


def normalize_entity_type(entity_group: str) -> str:
    """Нормализация типа сущности (убираем префиксы B-, I- и т.д.)."""
    for prefix in ["B-", "I-", "E-", "S-", "L-", "U-"]:
        if entity_group.startswith(prefix):
            return entity_group[2:]
    return entity_group


def process_entities(entities: list) -> list[dict]:
    """Обработка и нормализация списка сущностей."""
    processed = []
    for ent in entities:
        entity_type = normalize_entity_type(ent.get("entity_group", ent.get("entity", "UNKNOWN")))
        processed.append({
            "text": ent["word"],
            "type": entity_type,
            "label": ENTITY_LABELS.get(entity_type, entity_type),
            "score": round(ent["score"], 4),
            "start": ent["start"],
            "end": ent["end"]
        })
    return processed


def create_highlighted_text(text: str, entities: list) -> list:
    """Создание данных для подсветки текста."""
    if not entities:
        return [(text, None)]

    sorted_entities = sorted(entities, key=lambda x: x["start"])

    highlighted = []
    last_end = 0

    for ent in sorted_entities:
        start, end = ent["start"], ent["end"]

        if start > last_end:
            highlighted.append((text[last_end:start], None))

        entity_text = text[start:end]
        entity_type = ent["type"]
        highlighted.append((entity_text, entity_type))

        last_end = end

    if last_end < len(text):
        highlighted.append((text[last_end:], None))

    return highlighted


def entities_to_dataframe(entities: list) -> pd.DataFrame:
    """Преобразование списка сущностей в DataFrame."""
    if not entities:
        return pd.DataFrame(columns=["Текст", "Тип", "Описание", "Уверенность"])

    data = []
    for ent in entities:
        data.append({
            "Текст": ent["text"],
            "Тип": ent["type"],
            "Описание": ent["label"],
            "Уверенность": f"{ent['score']:.2%}"
        })

    return pd.DataFrame(data)


# ============== ИСТОРИЯ ЗАПРОСОВ ==============
def add_to_history(text: str, model: str, entities: list, latency: float):
    """Добавление запроса в историю."""
    timestamp = datetime.now().strftime("%H:%M:%S")
    entity_count = len(entities)
    entity_types = ", ".join(set(e["type"] for e in entities)) if entities else "—"

    history.appendleft({
        "Время": timestamp,
        "Модель": model.split()[0],
        "Текст": text[:50] + "..." if len(text) > 50 else text,
        "Найдено": entity_count,
        "Типы": entity_types,
        "Latency": f"{latency} мс"
    })


def get_history_df():
    """Получение истории запросов как DataFrame."""
    if not history:
        return pd.DataFrame(columns=["Время", "Модель", "Текст", "Найдено", "Типы", "Latency"])
    return pd.DataFrame(list(history))


def clear_history():
    """Очистка истории запросов."""
    history.clear()
    return pd.DataFrame(columns=["Время", "Модель", "Текст", "Найдено", "Типы", "Latency"]), "История очищена"


# ============== ОСНОВНЫЕ ФУНКЦИИ ОБРАБОТКИ ==============
def process_single_text(text: str, model_choice: str):
    """Обработка одиночного текста."""
    is_valid, result = validate_input(text)
    if not is_valid:
        return result, None, None, "—"

    text = result

    try:
        pipe = load_model(model_choice)

        t0 = time.time()
        raw_entities = pipe(text)
        latency = round((time.time() - t0) * 1000, 1)

        entities = process_entities(raw_entities)

        highlighted = create_highlighted_text(text, entities)
        df = entities_to_dataframe(entities)

        add_to_history(text, model_choice, entities, latency)

        status = f"Найдено сущностей: {len(entities)}"
        return status, highlighted, df, f"{latency} мс"

    except Exception as e:
        return f"Ошибка: {type(e).__name__}: {e}", None, None, "—"


def compare_models(text: str):
    """Сравнение результатов двух моделей."""
    is_valid, result = validate_input(text)
    if not is_valid:
        return result, None, None, "—", None, None, "—"

    text = result
    results = {}

    try:
        for model_key in MODELS.keys():
            pipe = load_model(model_key)

            t0 = time.time()
            raw_entities = pipe(text)
            latency = round((time.time() - t0) * 1000, 1)

            entities = process_entities(raw_entities)
            highlighted = create_highlighted_text(text, entities)
            df = entities_to_dataframe(entities)

            results[model_key] = {
                "highlighted": highlighted,
                "df": df,
                "latency": f"{latency} мс",
                "count": len(entities)
            }

        model_keys = list(MODELS.keys())
        m1, m2 = model_keys[0], model_keys[1]

        status = f"Модель 1: {results[m1]['count']} сущностей | Модель 2: {results[m2]['count']} сущностей"

        return (
            status,
            results[m1]["highlighted"],
            results[m1]["df"],
            results[m1]["latency"],
            results[m2]["highlighted"],
            results[m2]["df"],
            results[m2]["latency"]
        )

    except Exception as e:
        error_msg = f"Ошибка: {type(e).__name__}: {e}"
        return error_msg, None, None, "—", None, None, "—"


def process_batch(file, model_choice: str):
    """Пакетная обработка файла (CSV или TXT)."""
    if file is None:
        return "Ошибка: загрузите файл.", None, None

    try:
        file_path = file.name

        if file_path.endswith('.csv'):
            df_input = pd.read_csv(file_path)
            if 'text' not in df_input.columns:
                return "Ошибка: CSV должен содержать колонку 'text'.", None, None
            texts = df_input['text'].tolist()
        else:  # TXT
            with open(file_path, 'r', encoding='utf-8') as f:
                texts = [line.strip() for line in f if line.strip()]

        if len(texts) > MAX_BATCH_ROWS:
            return f"Ошибка: слишком много строк ({len(texts)}). Максимум: {MAX_BATCH_ROWS}.", None, None

        if not texts:
            return "Ошибка: файл пустой или не содержит текстов.", None, None

        pipe = load_model(model_choice)

        results = []
        t0 = time.time()

        for i, text in enumerate(texts):
            if len(text) > MAX_CHARS:
                text = text[:MAX_CHARS]

            try:
                raw_entities = pipe(text)
                entities = process_entities(raw_entities)

                per_list = [e["text"] for e in entities if e["type"] == "PER"]
                org_list = [e["text"] for e in entities if e["type"] == "ORG"]
                loc_list = [e["text"] for e in entities if e["type"] == "LOC"]
                misc_list = [e["text"] for e in entities if e["type"] == "MISC"]

                results.append({
                    "№": i + 1,
                    "Текст": text[:100] + "..." if len(text) > 100 else text,
                    "PER": ", ".join(per_list) if per_list else "—",
                    "ORG": ", ".join(org_list) if org_list else "—",
                    "LOC": ", ".join(loc_list) if loc_list else "—",
                    "MISC": ", ".join(misc_list) if misc_list else "—",
                    "Всего": len(entities)
                })
            except Exception as e:
                results.append({
                    "№": i + 1,
                    "Текст": text[:100] + "...",
                    "PER": "ОШИБКА",
                    "ORG": str(e)[:30],
                    "LOC": "—",
                    "MISC": "—",
                    "Всего": 0
                })

        total_latency = round((time.time() - t0) * 1000, 1)
        df_results = pd.DataFrame(results)

        csv_buffer = io.StringIO()
        df_results.to_csv(csv_buffer, index=False, encoding='utf-8')
        csv_content = csv_buffer.getvalue()

        status = f"Обработано: {len(texts)} текстов за {total_latency} мс"
        return status, df_results, csv_content

    except Exception as e:
        return f"Ошибка: {type(e).__name__}: {e}", None, None