import os
import traceback
from typing import Optional, Dict, Tuple, Any, List

import gradio as gr

from inference import (
    clean_text,
    load_sentiment_model,
    infer_sentiment_single,
    _SENTIMENT_NEGATIVE_THRESHOLD,
    _SENTIMENT_POSITIVE_THRESHOLD,
)

SENTIMENT_REPO = "DanielNRU/aiforever-rubert-large-tbsa-avito-sentiment"

USE_CUDA_ENV = os.getenv("USE_CUDA", "1")
USE_CUDA = USE_CUDA_ENV == "1"


# ── Нормализация ──────────────────────────────────────────────────────────────

def normalize_tone_label(value: Any) -> str:
    if value is None:
        return "—"
    text = str(value).strip().lower()
    if not text or text in {"—", "-", "none", "null"}:
        return "—"
    if any(x in text for x in ["негатив", "negative", "neg"]):
        return "негатив"
    if any(x in text for x in ["нейтрал", "neutral", "neu"]):
        return "нейтрально"
    if any(x in text for x in ["позитив", "positive", "pos"]):
        return "позитив"
    if text in {"-1", "-1.0"}:
        return "негатив"
    if text in {"0", "0.0"}:
        return "нейтрально"
    if text in {"1", "1.0"}:
        return "позитив"
    return "—"


def normalize_prob_value(v: Any) -> Optional[float]:
    try:
        x = float(v)
    except Exception:
        return None
    if x > 1.0:
        x = x / 100.0
    if 0.0 <= x <= 1.0:
        return x
    return None


def normalize_prob_dict(probs: Any) -> Dict[str, float]:
    result: Dict[str, float] = {}
    if not isinstance(probs, dict):
        return result
    for k, v in probs.items():
        key = normalize_tone_label(k)
        if key == "—":
            continue
        val = normalize_prob_value(v)
        if val is None:
            continue
        result[key] = val
    return result


def choose_tone_from_probs(tone_probs: Dict[str, float]) -> str:
    if not tone_probs:
        return "—"
    best_label = None
    best_prob = -1.0
    for key in ["негатив", "нейтрально", "позитив"]:
        val = tone_probs.get(key)
        if val is not None and val > best_prob:
            best_prob = val
            best_label = key
    return best_label if best_label is not None else "—"


def build_probs_block(tone_probs: Dict[str, float]) -> str:
    if not tone_probs:
        return "—"
    lines = []
    for key in ["негатив", "нейтрально", "позитив"]:
        if key in tone_probs:
            try:
                lines.append(f"{key}: {tone_probs[key] * 100:.1f}%")
            except Exception:
                continue
    return "\n".join(lines) if lines else "—"


# ── Логика порогов neg_thr / pos_thr ─────────────────────────────────────────

def _apply_thresholds(
    tone_probs: Dict[str, float],
    neg_thr: Optional[float] = None,
    pos_thr: Optional[float] = None,
) -> str:
    """Определяет тональность по раздельным порогам neg_thr / pos_thr.

    Логика (A-модель всегда работает в режиме neg/pos, без tone_thr):
      - P(neg) >= neg_thr  →  'негатив'
      - P(pos) >= pos_thr  →  'позитив'
      - иначе              →  'нейтрально'

    Fallback при отсутствии порогов — argmax.
    """
    if not tone_probs:
        return "—"

    p_neg = tone_probs.get("негатив", 0.0)
    p_pos = tone_probs.get("позитив", 0.0)

    if neg_thr is not None or pos_thr is not None:
        _neg_thr = neg_thr if neg_thr is not None else 1.0
        _pos_thr = pos_thr if pos_thr is not None else 1.0
        if p_neg >= _neg_thr:
            return "негатив"
        if p_pos >= _pos_thr:
            return "позитив"
        return "нейтрально"

    # fallback argmax
    return choose_tone_from_probs(tone_probs)


# ── SentimentService ──────────────────────────────────────────────────────────

class SentimentService:
    def __init__(self, sentiment_model_dir: str, use_cuda: bool = True):
        import torch
        device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu")
        print(f"[INFO] SentimentService device: {device}")
        print(f"[INFO] Загружаем модель: {sentiment_model_dir}")
        self.model, self.tokenizer, self.max_length = load_sentiment_model(
            sentiment_model_dir, device
        )
        self.device = device

    def analyze_text(
        self,
        text: str,
        negative_threshold: Optional[float] = None,
        positive_threshold: Optional[float] = None,
    ) -> Tuple[str, Dict[str, float]]:
        """Анализирует тональность одного текста.

        Использует neg_thr/pos_thr нативно через infer_sentiment_single.
        Дополнительно применяет _apply_thresholds() для согласованности
        с SL-sentiment и HFBatchSender.
        """
        text_clean = clean_text(text)
        if not text_clean:
            return "—", {}

        _neg_thr = negative_threshold if negative_threshold is not None else _SENTIMENT_NEGATIVE_THRESHOLD
        _pos_thr = positive_threshold if positive_threshold is not None else _SENTIMENT_POSITIVE_THRESHOLD

        try:
            res: Any = infer_sentiment_single(
                text_clean,
                self.model,
                self.tokenizer,
                self.max_length,
                self.device,
                negative_threshold=_neg_thr,
                positive_threshold=_pos_thr,
            )
            print(f"[DEBUG] infer_sentiment_single raw result: {res!r}")
        except Exception as e:
            print(f"[ERROR] infer_sentiment_single exception: {e}")
            traceback.print_exc()
            return "—", {}

        if not isinstance(res, dict):
            print(f"[WARN] unexpected type from infer_sentiment_single: {type(res)}")
            return "—", {}

        tone_probs = normalize_prob_dict(res.get("tone_probs"))

        # Применяем _apply_thresholds для явного контроля (консистентно с SL-sentiment)
        tone_str = _apply_thresholds(tone_probs, neg_thr=_neg_thr, pos_thr=_pos_thr)
        if tone_str == "—":
            tone_str = normalize_tone_label(res.get("tone_str"))

        print(f"[DEBUG] final tone_str: {tone_str!r}, probs: {tone_probs}")
        return tone_str, tone_probs


_service: Optional[SentimentService] = None


def get_service() -> SentimentService:
    global _service
    if _service is None:
        print("[INFO] Инициализация SentimentService...")
        _service = SentimentService(SENTIMENT_REPO, use_cuda=USE_CUDA)
    return _service


# ── UI functions ──────────────────────────────────────────────────────────────

def analyze_single_text(text: str, neg_threshold: float, pos_threshold: float):
    text = text or ""
    if not text.strip():
        return "—", "—"

    try:
        svc = get_service()
        tone_str, tone_probs = svc.analyze_text(
            text,
            negative_threshold=float(neg_threshold),
            positive_threshold=float(pos_threshold),
        )
    except Exception as e:
        print(f"[ERROR] analyze_single_text / A-sentiment: {e}")
        traceback.print_exc()
        return "—", "—"

    tone_short = normalize_tone_label(tone_str)
    if tone_short == "—" and tone_probs:
        tone_short = choose_tone_from_probs(tone_probs)
    probs_block = build_probs_block(tone_probs)

    print(f"[DEBUG] tone_short: {tone_short!r}")
    print(f"[DEBUG] tone_probs: {tone_probs}")
    print(f"[DEBUG] probs_block:\n{probs_block}")
    return tone_short, probs_block


def analyze_single_text_alias(text: str, neg_threshold: float, pos_threshold: float):
    return analyze_single_text(text, neg_threshold, pos_threshold)


# ── Batch endpoint (Issue #222 / Шаг 8) ──────────────────────────────────────

def analyze_batch(
    texts: List[str],
    neg_thr: float = _SENTIMENT_NEGATIVE_THRESHOLD,
    pos_thr: float = _SENTIMENT_POSITIVE_THRESHOLD,
) -> List[List[str]]:
    """Батч-анализ тональности (A).

    A-модель нативно работает с neg_thr/pos_thr — tone_thr не используется.

    HFBatchSender вызывает:
        client.predict(texts, neg_thr, pos_thr, api_name='/analyze_batch')

    Returns:
        [[tone_label, probs_block], ...] той же длины что и texts.
    """
    svc = get_service()
    results: List[List[str]] = []
    for text in texts:
        if not text or not str(text).strip():
            results.append(["нейтрально", "—"])
            continue
        try:
            tone_str, tone_probs = svc.analyze_text(
                str(text),
                negative_threshold=float(neg_thr),
                positive_threshold=float(pos_thr),
            )
            tone_short = normalize_tone_label(tone_str)
            if tone_short == "—" and tone_probs:
                tone_short = choose_tone_from_probs(tone_probs)
            probs_block = build_probs_block(tone_probs)
            results.append([tone_short, probs_block])
        except Exception as e:
            print(f"[ERROR] analyze_batch / A-sentiment (item): {e}")
            results.append(["ошибка", "—"])
    return results
# ─────────────────────────────────────────────────────────────────────────────


with gr.Blocks(title="Тональность сообщения") as demo:
    gr.Markdown("# Определение тональности сообщения")
    gr.Markdown(
        "Модель: `aiforever/ruroberta-large` (fine-tuned на датасете Авито, tbsa-формат)  \n"
        f"Дефолтный порог негатива: `{_SENTIMENT_NEGATIVE_THRESHOLD}` · "
        f"Дефолтный порог позитива: `{_SENTIMENT_POSITIVE_THRESHOLD}`"
    )

    inp_text = gr.Textbox(
        label="Текст сообщения",
        placeholder="Вставьте сообщение...",
        lines=8,
    )

    with gr.Row():
        neg_threshold_slider = gr.Slider(
            minimum=0.0,
            maximum=1.0,
            value=_SENTIMENT_NEGATIVE_THRESHOLD,
            step=0.01,
            label=f"Порог негатива (neg_thr, default={_SENTIMENT_NEGATIVE_THRESHOLD})",
            info="P(neg) >= neg_thr → негатив",
        )
        pos_threshold_slider = gr.Slider(
            minimum=0.0,
            maximum=1.0,
            value=_SENTIMENT_POSITIVE_THRESHOLD,
            step=0.01,
            label=f"Порог позитива (pos_thr, default={_SENTIMENT_POSITIVE_THRESHOLD})",
            info="P(pos) >= pos_thr → позитив",
        )

    btn = gr.Button("Анализировать")

    out_tone = gr.Textbox(label="Тональность", interactive=False)
    out_details = gr.Textbox(
        label="Подробные вероятности",
        interactive=False,
        lines=6,
    )

    btn.click(
        fn=analyze_single_text,
        inputs=[inp_text, neg_threshold_slider, pos_threshold_slider],
        outputs=[out_tone, out_details],
        api_name="/analyze_single_text",
    )

    gr.Button(visible=False).click(
        fn=analyze_single_text_alias,
        inputs=[inp_text, neg_threshold_slider, pos_threshold_slider],
        outputs=[out_tone, out_details],
        api_name="analyze_single_text",
    )

    # Issue #222 / Шаг 8: батч-endpoint
    # HFBatchSender: client.predict(texts, neg_thr, pos_thr, api_name='/analyze_batch')
    gr.api(
        fn=analyze_batch,
        api_name="analyze_batch",
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)