"""
Phase 2.1.1: 工序聚类（workflow 形式）

直接读取 source.json + case_detailed.json，调用 LLM 生成 blueprint，
输出到 blueprint_temp.json
"""

import asyncio
import json
from pathlib import Path
from typing import Any, Dict

from examples.process_pipeline.script.llm_helper import call_llm_with_retry
from examples.process_pipeline.script.validate_schema import validate_blueprint_temp


def load_prompt_template(prompt_name: str) -> str:
    base_dir = Path(__file__).parent.parent
    prompt_path = base_dir / "prompts" / f"{prompt_name}.prompt"
    with open(prompt_path, "r", encoding="utf-8") as f:
        content = f.read()
    if content.startswith("---"):
        parts = content.split("---", 2)
        if len(parts) >= 3:
            content = parts[2]
    content = content.replace("$system$", "").replace("$user$", "")
    return content.strip()


async def cluster_processes(
    source_file: Path,
    detailed_file: Path,
    output_file: Path,
    requirement: str,
    llm_call,
    model: str = "anthropic/claude-sonnet-4-6",
) -> Dict[str, Any]:
    """
    读取 source.json + case_detailed.json，调用 LLM 生成 blueprint
    """
    # 读取输入文件
    source_data = {}
    detailed_data = {}

    if source_file.exists():
        with open(source_file, "r", encoding="utf-8") as f:
            source_data = json.load(f)

    if detailed_file.exists():
        with open(detailed_file, "r", encoding="utf-8") as f:
            detailed_data = json.load(f)

    # 构造输入数据摘要
    cases_summary = []
    for case in detailed_data.get("cases", []):
        case_id = case.get("case_id", f"{case.get('platform', '')}_{case.get('channel_content_id', '')}")
        workflow = case.get("workflow")
        steps_text = ""
        if workflow and "工序步骤" in workflow:
            steps = workflow["工序步骤"]
            steps_text = " → ".join([s.get("步骤描述", "") for s in steps])

        # 从 source.json 获取原始帖子信息
        body_text = ""
        for src in source_data.get("sources", []):
            src_id = f"{src.get('platform', '')}_{src.get('channel_content_id', '')}"
            if src_id == case_id:
                body_text = src.get("post", {}).get("body_text", "")[:500]
                break

        cases_summary.append({
            "case_id": case_id,
            "title": case.get("title", ""),
            "source_url": case.get("source_url", ""),
            "steps": steps_text,
            "body_preview": body_text,
        })

    cases_json = json.dumps(cases_summary, ensure_ascii=False, indent=2)
    print(f"Loaded {len(cases_summary)} cases for process clustering")

    # 加载 prompt
    try:
        prompt_template = load_prompt_template("process_cluster")
        prompt = prompt_template.replace("%requirement%", requirement)
        prompt = prompt.replace("%cases_data%", cases_json)
    except Exception:
        prompt = f"""你是一个 AI 创作工作流分析师。请按照核心创作手段对以下案例进行聚类。

需求：{requirement}

案例数据：
{cases_json}

输出 JSON 格式：
{{"clusters": [{{"cluster_id": "A", "cluster_name": "...", "工序步骤": [{{"步骤序号": 1, "步骤描述": "..."}}], "关联案例": ["bili_BV1xxx"]}}]}}"""

    messages = [{"role": "user", "content": prompt}]

    cluster_data, total_cost = await call_llm_with_retry(
        llm_call=llm_call,
        messages=messages,
        model=model,
        temperature=0.2,
        max_tokens=4000,
        max_retries=3,
        validate_fn=validate_blueprint_temp,
        task_name="P2.1.1_ClusterProcesses",
    )

    if cluster_data is None:
        cluster_data = {"clusters": []}

    output_file.parent.mkdir(parents=True, exist_ok=True)
    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(cluster_data, f, ensure_ascii=False, indent=2)

    return {
        "clusters": len(cluster_data.get("clusters", [])),
        "total_cost": total_cost,
    }