| |
| """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() |
|
|
