import uuid
from contextlib import asynccontextmanager
from datetime import datetime, timezone

from fastapi import FastAPI, Query
from pydantic import BaseModel

from app.db.store import init_db, save_decision, get_history, get_stats
from app.engine.decision import decide
from app.engine.explain import explain
from app.nlp.emotion import detect_emotion
from app.nlp.emoji_map import sentiment_emoji, emotion_emoji
from app.nlp.language import detect_language
from app.nlp.sentiment import analyze_sentiment
from app.nlp.signals import extract_signals
from app.engine.priority import compute_priority


@asynccontextmanager
async def lifespan(app: FastAPI):
    init_db()
    yield


app = FastAPI(title="AI Decision Maker", lifespan=lifespan)


# ── Request / Response models ──────────────────────────────────────────────────

class DecideRequest(BaseModel):
    text: str


class SentimentOut(BaseModel):
    label: str
    score: float
    confidence: float


class SignalsOut(BaseModel):
    urgency: str
    intent: str
    keywords: list[str]


class DecideResponse(BaseModel):
    id: str
    timestamp: str
    text: str
    sentiment: SentimentOut
    signals: SignalsOut
    decision: str
    confidence: float
    explanation: str
    emotion: str
    emotion_score: float
    sentiment_emoji: str
    emotion_emoji: str
    priority_score: int
    priority_level: str
    factors: dict[str, int]
    detected_language: str


class HistoryRecord(BaseModel):
    id: str
    text: str
    sentiment_label: str
    score: float
    urgency: str
    intent: str
    decision: str
    confidence: float
    explanation: str
    created_at: str
    emotion: str = "neutral"
    emotion_score: float = 0.0
    priority_score: int = 0
    priority_level: str = "LOW"
    detected_language: str = "en"


class StatsResponse(BaseModel):
    total: int
    by_decision: dict[str, int]
    by_sentiment: dict[str, int]
    avg_priority_score: float = 0.0
    by_priority_level: dict[str, int] = {}


# ── Endpoints ──────────────────────────────────────────────────────────────────

@app.get("/health")
def health():
    return {"status": "ok"}


@app.post("/decide", response_model=DecideResponse)
def post_decide(body: DecideRequest):
    language        = detect_language(body.text)
    sentiment       = analyze_sentiment(body.text, language=language)
    signals         = extract_signals(body.text)
    decision_result = decide(sentiment, signals)
    explanation_text = explain(sentiment, signals, decision_result)
    emotion_result  = detect_emotion(body.text)
    priority_result = compute_priority(sentiment, signals, emotion_result)

    sent_emoji = sentiment_emoji(sentiment["label"], sentiment["confidence"])
    emo_emoji  = emotion_emoji(emotion_result["emotion"])

    record_id = str(uuid.uuid4())
    timestamp = datetime.now(timezone.utc).isoformat()

    save_decision({
        "id":                record_id,
        "text":              body.text,
        "sentiment_label":   sentiment["label"],
        "score":             sentiment["score"],
        "urgency":           signals["urgency"],
        "intent":            signals["intent"],
        "decision":          decision_result["decision"],
        "confidence":        decision_result["confidence"],
        "explanation":       explanation_text,
        "created_at":        timestamp,
        "emotion":           emotion_result["emotion"],
        "emotion_score":     emotion_result["score"],
        "priority_score":    priority_result["priority_score"],
        "priority_level":    priority_result["priority_level"],
        "detected_language": language,
    })

    return DecideResponse(
        id=record_id,
        timestamp=timestamp,
        text=body.text,
        sentiment=SentimentOut(**sentiment),
        signals=SignalsOut(**signals),
        decision=decision_result["decision"],
        confidence=decision_result["confidence"],
        explanation=explanation_text,
        emotion=emotion_result["emotion"],
        emotion_score=emotion_result["score"],
        sentiment_emoji=sent_emoji,
        emotion_emoji=emo_emoji,
        priority_score=priority_result["priority_score"],
        priority_level=priority_result["priority_level"],
        factors=priority_result["factors"],
        detected_language=language,
    )


@app.get("/history", response_model=list[HistoryRecord])
def history(limit: int = Query(default=50, ge=1, le=500)):
    return get_history(limit)


@app.get("/stats", response_model=StatsResponse)
def stats():
    return get_stats()