# app/services/summary_service.py
import json
import os
from datetime import timedelta
from typing import Any

import numpy as np
from openai import AsyncOpenAI

from app.database import SessionLocal
from app.models.models import (
    ExtractedTopic,
    InfoSource,
    TargetType,
    TrendingEvent,
    UnifiedEvent,
    utcnow,
)
from app.prompts.summary_prompts import (
    SUMMARY_SYSTEM_PROMPT,
    SUMMARY_USER_PROMPT_TEMPLATE,
)
from app.services.fetcher_service import embedder_model

HOT_SCORE_THRESHOLD = int(os.getenv("HOT_SCORE_THRESHOLD", 3))
TOPIC_TAG_MIN_HOT_SCORE = int(os.getenv("TOPIC_TAG_MIN_HOT_SCORE", HOT_SCORE_THRESHOLD))
TOPIC_SIMILARITY_THRESHOLD = float(os.getenv("TOPIC_SIMILARITY_THRESHOLD", 0.82))
TOPIC_TAG_MAX_COUNT = int(os.getenv("TOPIC_TAG_MAX_COUNT", 8))
AI_API_KEY = os.getenv("AI_API_KEY", "")


deepseek_client = AsyncOpenAI(
    api_key=AI_API_KEY,
    base_url="https://api.deepseek.com",
)


async def call_llm_for_summary(platform_data_text: str) -> dict:
    """Call LLM for unified title, summary and topic candidates."""
    prompt = SUMMARY_USER_PROMPT_TEMPLATE.format(platform_data_text=platform_data_text)

    response = await deepseek_client.chat.completions.create(
        model="deepseek-chat",
        messages=[
            {"role": "system", "content": SUMMARY_SYSTEM_PROMPT},
            {"role": "user", "content": prompt},
        ],
        response_format={"type": "json_object"},
        temperature=1,
    )

    result_text = response.choices[0].message.content
    return json.loads(result_text)


def _normalize_score(raw_score: Any) -> float | None:
    try:
        score = float(raw_score)
    except (TypeError, ValueError):
        return None

    if score <= 1:
        score *= 100

    return max(0.0, min(100.0, score))


def parse_topic_keywords(llm_result: dict) -> list[dict[str, Any]]:
    """Parse topic keywords from LLM response; support list[str] and list[object]."""
    raw_topics = llm_result.get("topic_keywords") or []
    parsed: list[dict[str, Any]] = []
    seen: set[str] = set()

    for item in raw_topics:
        keyword = ""
        score = None

        if isinstance(item, str):
            keyword = item.strip()
        elif isinstance(item, dict):
            raw_keyword = (
                item.get("keyword")
                or item.get("topic_keyword")
                or item.get("name")
                or item.get("topic")
                or ""
            )
            keyword = str(raw_keyword).strip()
            score = _normalize_score(item.get("relevance_score") or item.get("score"))

        if not keyword:
            continue

        keyword = keyword[:100]
        normalized_key = keyword.casefold()
        if normalized_key in seen:
            continue

        seen.add(normalized_key)
        parsed.append({"keyword": keyword, "score": score})

    return parsed


def normalize_topic_keywords(topic_candidates: list[dict[str, Any]]) -> list[dict[str, Any]]:
    """Deduplicate semantically similar tags using embedding similarity."""
    if not topic_candidates:
        return []

    keywords = [item["keyword"] for item in topic_candidates]
    vectors = embedder_model.encode(keywords, normalize_embeddings=True, show_progress_bar=False)

    clusters: list[dict[str, Any]] = []
    for item, vector in zip(topic_candidates, vectors):
        vec = np.asarray(vector, dtype=np.float32)

        best_idx = -1
        best_sim = -1.0
        for idx, cluster in enumerate(clusters):
            sim = float(np.dot(vec, cluster["vector"]))
            if sim > best_sim:
                best_sim = sim
                best_idx = idx

        if best_idx >= 0 and best_sim >= TOPIC_SIMILARITY_THRESHOLD:
            cluster = clusters[best_idx]
            merged = cluster["vector"] * cluster["count"] + vec
            norm = float(np.linalg.norm(merged))
            if norm > 0:
                cluster["vector"] = merged / norm

            cluster["count"] += 1
            if item["score"] is not None and (
                cluster["score"] is None or item["score"] > cluster["score"]
            ):
                cluster["score"] = item["score"]

            # Prefer shorter tag as canonical keyword.
            if len(item["keyword"]) < len(cluster["keyword"]):
                cluster["keyword"] = item["keyword"]
        else:
            clusters.append(
                {
                    "keyword": item["keyword"],
                    "score": item["score"],
                    "vector": vec,
                    "count": 1,
                }
            )

    if any(cluster["score"] is not None for cluster in clusters):
        clusters.sort(key=lambda x: x["score"] if x["score"] is not None else -1.0, reverse=True)

    result = [
        {"keyword": cluster["keyword"], "score": cluster["score"]}
        for cluster in clusters[:TOPIC_TAG_MAX_COUNT]
    ]
    return result


def replace_event_topics(db, event_id: int, normalized_topics: list[dict[str, Any]]) -> None:
    """Replace EVENT tags for one unified event atomically within current transaction."""
    db.query(ExtractedTopic).filter(
        ExtractedTopic.target_type == TargetType.EVENT,
        ExtractedTopic.target_id == event_id,
    ).delete(synchronize_session=False)

    for item in normalized_topics:
        db.add(
            ExtractedTopic(
                target_type=TargetType.EVENT,
                target_id=event_id,
                topic_keyword=item["keyword"],
                relevance_score=item["score"],
            )
        )


async def generate_unified_summaries():
    """Scheduled task: refresh summaries and topic tags for hot unified events."""
    print(f"[{utcnow()}] Start unified summary generation task...")

    # 先提取需要处理的事件 ID，尽早释放 session，不长期占用 db session
    with SessionLocal() as db:
        recent_threshold = utcnow() - timedelta(days=3)
        events = db.query(UnifiedEvent).filter(
            UnifiedEvent.hot_score >= HOT_SCORE_THRESHOLD,
            UnifiedEvent.hot_score > UnifiedEvent.last_summarized_trends_count,
            UnifiedEvent.created_at >= recent_threshold,
        ).all()
        
        if not events:
            print("No events require summary update in this round.")
            return
            
        # 复制出需要的信息，脱离 session
        event_ids = [e.id for e in events]
        event_hot_scores = {e.id: e.hot_score for e in events}

    # 外层循环：针对每个 event_id 开启一个极短生命周期的 session 获取依赖数据
    for event_id in event_ids:
        platform_dict: dict[str, set[str]] = {}
        with SessionLocal() as db:
            trends = (
                db.query(TrendingEvent, InfoSource.source_name)
                .join(InfoSource, TrendingEvent.source_id == InfoSource.id)
                .filter(TrendingEvent.unified_event_id == event_id)
                .all()
            )

            if not trends:
                continue

            for trend_record, source_name in trends:
                platform_dict.setdefault(source_name, set()).add(trend_record.current_headline)

        prompt_lines = [
            f"[{platform}] {', '.join(sorted(headlines))}"
            for platform, headlines in platform_dict.items()
        ]
        platform_data_text = "\n".join(prompt_lines)

        try:
            # 大模型调用可能耗时几十秒，绝对不能把它包裹在数据库事务里
            llm_result = await call_llm_for_summary(platform_data_text)
            
            # 调用完成后，再开启一个新的极短事务，进行数据回写
            with SessionLocal() as write_db:
                event = write_db.query(UnifiedEvent).get(event_id)
                if not event:
                    continue

                if "unified_title" in llm_result and llm_result["unified_title"]:
                    event.unified_title = llm_result["unified_title"]
                if "ai_comprehensive_summary" in llm_result and llm_result["ai_comprehensive_summary"]:
                    event.ai_comprehensive_summary = llm_result["ai_comprehensive_summary"]

                hot_score = event_hot_scores.get(event_id, event.hot_score)
                if hot_score >= TOPIC_TAG_MIN_HOT_SCORE:
                    topic_candidates = parse_topic_keywords(llm_result)
                    normalized_topics = normalize_topic_keywords(topic_candidates)
                    if normalized_topics:
                        replace_event_topics(write_db, event.id, normalized_topics)

                event.last_summarized_trends_count = event.hot_score
                write_db.commit()
                
                print(
                    f"Updated event {event.id} summary"
                    f" (hot_score={event.hot_score})."
                )

        except Exception as exc:
            print(f"Event {event_id} summary generation failed: {exc}")
            continue