#!/usr/bin/env python3
"""图片与原始 query → scene（content、intent、note）；使用原始 Claude CLI prompt。"""

import argparse
import json
import os
import subprocess
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed

STAGE1_PROMPT = """请观察图片并结合用户的问题，以 JSON 格式回答以下三点：

1. "content"：图中是什么东西或什么场景？如果物品的具体类型会影响解决方案，请说清楚（例如螺丝是内六角还是十字的，因为这决定需要什么工具）。若某个关键特征看不清或不确定，明确写出"疑似"或"无法确定"，不要臆断。
2. "intent"：结合图片和提问，用户想解决什么问题？尽量往搜索商品上靠（如"寻找清洗白色运动鞋的用品"）；但如果靠不上（比如用户只是想了解、评价、比较、询问某属性），就别硬靠，直接输出原始意图，如"想了解这款衣服的评价如何"。
3. "note"：根据识别出的物品/场景和用户意图，指出一句解决该问题时的注意事项，重点是会影响"该选什么商品"的地方。如果没有特别需要注意的，就留空 ""。

示例：
- 菜刀图 + "生锈了怎么处理" →
  {"content": "菜刀，刀面有锈斑", "intent": "寻找给菜刀除锈的工具或用品", "note": "菜刀直接接触食物，除锈处理需满足食品安全要求，不能用工业除锈剂"}
- 内六角螺丝图 + "怎么拧下来" →
  {"content": "内六角螺丝", "intent": "寻找拧下内六角螺丝的工具", "note": "内六角螺丝需要特定规格的工具，规格不匹配无法使用"}
- 免打孔挂杆图 + "厨房墙上挂东西用啥" →
  {"content": "厨房墙面收纳挂杆", "intent": "寻找厨房墙面免打孔收纳挂杆", "note": "需免打孔安装；厨房环境需考虑承重和防油污"}
- 针织衫图 + "这种容易起球吗" →
  {"content": "针织上衣", "intent": "想了解这款衣服是否容易起球", "note": ""}

请直接输出 JSON 对象，不要输出任何其他内容。"""

SYSTEM_PROMPT = "你是一个多模态场景分析助手。用户会提供一张图片的文件路径和一个搜索查询。请先用 Read 工具读取图片，然后根据图片内容和查询回答。只输出 JSON 对象，不要输出任何解释或其他内容。"


def load_input_jsonl(path):
    with open(path, encoding="utf-8") as f:
        rows = [json.loads(line) for line in f if line.strip()]
    return [dict(row, idx=str(row["idx"])) for row in rows]


def load_done_jsonl(path):
    done = set()
    if os.path.exists(path):
        with open(path, encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if not line:
                    continue
                try:
                    done.add(str(json.loads(line)["idx"]))
                except (json.JSONDecodeError, KeyError):
                    continue
    return done


_write_lock = threading.Lock()


def append_jsonl(path, obj):
    with _write_lock:
        with open(path, "a", encoding="utf-8") as f:
            f.write(json.dumps(obj, ensure_ascii=False) + "\n")
            f.flush()


def extract_json_obj(text):
    import re
    try:
        match = re.search(r"\{.*\}", text, re.DOTALL)
        if match:
            parsed = json.loads(match.group())
            if isinstance(parsed, dict):
                return parsed
    except (json.JSONDecodeError, ValueError):
        pass
    return {"content": "未知", "intent": "未知", "note": "未知", "_raw": text}


def call_claude(image_path, query, model, timeout_seconds):
    user_prompt = f"请先用 Read 工具读取这张图片: {image_path}\n\n用户搜索查询：{query}\n\n{STAGE1_PROMPT}"

    result = subprocess.run(
        [
            "claude", "-p", user_prompt,
            "--system-prompt", SYSTEM_PROMPT,
            "--model", model,
            "--output-format", "text",
            "--allowedTools", "Read",
            "--no-session-persistence",
            "--bare",
        ],
        stdin=subprocess.DEVNULL,
        capture_output=True,
        text=True,
        timeout=timeout_seconds,
    )
    if result.returncode != 0:
        stderr = (result.stderr or "").strip()
        raise RuntimeError(f"claude -p exit {result.returncode}: {stderr}")

    return result.stdout.strip()


def call_claude_with_retry(image_path, query, model, max_retries, timeout_seconds):
    for attempt in range(1, max_retries + 1):
        try:
            raw = call_claude(image_path, query, model, timeout_seconds)
            return raw, None
        except Exception as exc:
            if attempt == max_retries:
                return None, str(exc)
            time.sleep(min(2 ** attempt, 10))
    return None, "max retries exceeded"


def run_stage1(args):
    rows = load_input_jsonl(args.input)
    done = load_done_jsonl(args.output)
    os.makedirs(os.path.dirname(os.path.abspath(args.output)), exist_ok=True)

    def process_one(row):
        image_path = os.path.abspath(os.path.join(args.image_dir, row["image_url"]))
        if not os.path.isfile(image_path):
            raise FileNotFoundError(image_path)
        raw, error = call_claude_with_retry(
            image_path, row["query"], args.model, args.max_retries, args.timeout)
        if error:
            raise RuntimeError(f"idx={row['idx']}: {error}")
        append_jsonl(args.output, {
            **row, "scene": extract_json_obj(raw), "raw_response_s1": raw,
        })

    with ThreadPoolExecutor(max_workers=args.concurrency) as pool:
        futures = [pool.submit(process_one, row) for row in rows if row["idx"] not in done]
        for future in as_completed(futures):
            future.result()
    print(f"意图分析完成：{args.output}")


def main():
    p = argparse.ArgumentParser(description=__doc__)
    p.add_argument("--input", required=True, help="JSONL: idx, query, image_url（本地图片文件名）")
    p.add_argument("--output", required=True)
    p.add_argument("--image-dir", required=True)
    p.add_argument("--model", default="claude-sonnet-4-6")
    p.add_argument("--concurrency", type=int, default=8)
    p.add_argument("--max-retries", type=int, default=3)
    p.add_argument("--timeout", type=int, default=120)
    run_stage1(p.parse_args())


if __name__ == "__main__":
    main()