"""
HuggingFace Sentiment Analysis
─────────────────────────────
เรียก HuggingFace Inference API เพื่อวิเคราะห์ความรู้สึกของข้อความภาษาไทย

Model ที่แนะนำ:
  - airesearch/wangchanberta-base-att-spm-uncased  (ภาษาไทย ทั่วไป)
  - pythainlp/thai-sentiment-analysis               (ถ้ามี fine-tune)
  - อาจใช้ multilingual model: cardiffnlp/twitter-xlm-roberta-base-sentiment
"""

import httpx
import logging
from app.config import settings
from app.models import SentimentResult

logger = logging.getLogger(__name__)

HF_API_URL = (
    f"https://api-inference.huggingface.co/models/{settings.HF_MODEL_NAME}"
)

# Label mapping จาก model output → ชื่อ standard
LABEL_MAP = {
    # WangchanBERTa / Thai models
    "pos":      "positive",
    "neu":      "neutral",
    "neg":      "negative",
    # cardiffnlp / XLM-RoBERTa
    "positive": "positive",
    "neutral":  "neutral",
    "negative": "negative",
    # LABEL_0/1/2 fallback (ขึ้นกับ model)
    "LABEL_0":  "negative",
    "LABEL_1":  "neutral",
    "LABEL_2":  "positive",
}


async def analyze_sentiment(text: str) -> SentimentResult:
    """
    ส่งข้อความไปวิเคราะห์ที่ HuggingFace แล้วคืน SentimentResult

    HuggingFace Inference API คืนรูปแบบ:
    [[{"label": "pos", "score": 0.91}, {"label": "neg", "score": 0.05}, ...]]
    """
    headers = {"Authorization": f"Bearer {settings.HF_API_TOKEN}"}
    payload = {"inputs": text, "options": {"wait_for_model": True}}

    try:
        async with httpx.AsyncClient(timeout=15.0) as client:
            response = await client.post(HF_API_URL, headers=headers, json=payload)
            response.raise_for_status()
            data = response.json()

        # Flatten nested list ที่ HF คืนมา
        scores = data[0] if isinstance(data[0], list) else data

        # แปลง label และสร้าง raw_scores dict
        raw_scores: dict[str, float] = {}
        for item in scores:
            normalized = LABEL_MAP.get(item["label"], item["label"].lower())
            raw_scores[normalized] = round(item["score"], 4)

        # หา label ที่มี score สูงสุด
        best_label = max(raw_scores, key=raw_scores.get)

        return SentimentResult(
            label=best_label,
            score=raw_scores[best_label],
            raw_scores=raw_scores,
        )

    except httpx.HTTPStatusError as e:
        logger.error(f"HuggingFace API error {e.response.status_code}: {e.response.text}")
        return _fallback_result()

    except httpx.TimeoutException:
        logger.error("HuggingFace API timeout")
        return _fallback_result()

    except Exception as e:
        logger.error(f"Unexpected error in sentiment analysis: {e}")
        return _fallback_result()


def _fallback_result() -> SentimentResult:
    """คืน neutral เมื่อ API ล้มเหลว เพื่อไม่ให้ระบบหยุด"""
    return SentimentResult(
        label="neutral",
        score=0.0,
        raw_scores={"positive": 0.0, "neutral": 0.0, "negative": 0.0},
    )