"""Daily digest analyzer - fetches today's memos and generates AI summary."""

import json
import logging
import os
import sys
from datetime import datetime, timezone

# Add parent dir to path for local imports
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from tools.config import load_secrets, get_output_dir
from tools.llm import DeepSeekClient
from tools.memos_client import MemosClient
from tools.formatter import format_daily_digest

logger = logging.getLogger(__name__)

# System prompt for daily digest
DAILY_SYSTEM_PROMPT = """你是一个灵感整理助手。你的任务是将用户零散的灵感记录进行智能整理。

请严格按照要求整理，输出纯 JSON（不要代码块标记，不要额外说明）：

{
  "summary": "一段简短的今日灵感总结，50字以内",
  "categories": {
    "分类名1": ["具体灵感项1", "具体灵感项2"],
    "分类名2": ["具体灵感项3"]
  },
  "connections": ["跨主题关联发现1", "2-3条"],
  "todos": ["待办事项1", "2-3条"]
}

分类原则：根据内容自然归类，分类名不超过4个字。
关联发现：识别不同灵感之间的关联、冲突或可串联的主题。
待办：从灵感中提取可执行的事项。"""


def parse_ai_response(text):
    """Parse AI response, handling both pure JSON and code-block wrapped JSON."""
    text = text.strip()
    # Remove markdown code block wrappers if present
    if text.startswith("```"):
        lines = text.split("\n")
        # Remove first and last ``` lines
        if lines[0].startswith("```"):
            lines = lines[1:]
        if lines and lines[-1].strip() == "```":
            lines = lines[:-1]
        text = "\n".join(lines).strip()

    return json.loads(text)


def run(memos_client, llm_client, date=None):
    """Run daily digest analysis."""
    date = date or datetime.now(timezone.utc)

    logger.info("Daily digest started for %s", date.strftime("%Y-%m-%d"))

    # Step 1: Fetch today's memos
    memos = memos_client.list_memos(days=1)

    if not memos:
        logger.info("No memos today, skipping")
        # Still generate a minimal file
        content = format_daily_digest(
            date=date,
            categories={},
            summary="今天没有记录灵感。",
            connections=[],
            todos=[]
        )
        output_dir = get_output_dir("daily")
        filename = f"{date.strftime('%Y-%m-%d')}_digest.md"
        filepath = os.path.join(output_dir, filename)

        with open(filepath, "w", encoding="utf-8") as f:
            f.write(content)

        logger.info("Empty digest written to %s", filepath)
        return filepath, 0

    # Step 2: Prepare prompt
    memo_texts = []
    for m in memos:
        time_str = m["created_at"][:16].replace("T", " ")
        memo_texts.append(f"[{time_str}] {m['content']}")

    user_prompt = f"请分析以下灵感记录：\n\n" + "\n".join(memo_texts)

    # Step 3: Call DeepSeek API
    raw_response = llm_client.ask(
        system_prompt=DAILY_SYSTEM_PROMPT,
        user_prompt=user_prompt,
        temperature=0.3
    )

    # Step 4: Parse and format
    try:
        data = parse_ai_response(raw_response)
    except (json.JSONDecodeError, KeyError) as e:
        logger.error("Failed to parse AI response: %s", e)
        logger.error("Raw response: %s", raw_response[:200])
        data = {
            "summary": f"AI 解析失败，请查看原始 Memos。",
            "categories": {"未分类": [m["content"] for m in memos]},
            "connections": [],
            "todos": []
        }

    categories = data.get("categories", {})
    connections = data.get("connections", [])
    todos = data.get("todos", [])
    summary = data.get("summary", "")

    # Step 5: Format and write output
    content = format_daily_digest(
        date=date,
        categories=categories,
        summary=summary,
        connections=connections,
        todos=todos
    )

    output_dir = get_output_dir("daily")
    filename = f"{date.strftime('%Y-%m-%d')}_digest.md"
    filepath = os.path.join(output_dir, filename)

    with open(filepath, "w", encoding="utf-8") as f:
        f.write(content)

    logger.info(
        "Daily digest written to %s | %d memos | %d categories",
        filepath, len(memos), len(categories)
    )
    return filepath, len(memos)


def main():
    """CLI entry point."""
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
    )

    try:
        secrets = load_secrets()
    except FileNotFoundError as e:
        print(e)
        sys.exit(1)

    memos_client = MemosClient(
        base_url=secrets.get("memos_url", "http://localhost:5230"),
        access_token=secrets["memos_token"]
    )

    llm_client = DeepSeekClient(
        api_key=secrets["deepseek_api_key"],
        model=secrets.get("deepseek_model", "deepseek-chat")
    )

    filepath, count = run(memos_client, llm_client)
    print(f"Done: {filepath} ({count} memos)")


if __name__ == "__main__":
    main()