"""
pipeline_service/intent_classifier.py — Reusable intent classification.

DSPY can call: await intent_classify(model_name, message, intents, context)
Registered on ServerEnv in pipeline_service/init.py
"""

import json
import logging

logger = logging.getLogger("pipeline.intent_classifier")

INTENT_PROMPT = """你是一个意图分类器。分析用户输入，返回 JSON。

可用意图：
{intent_list}

当前上下文：{context_str}

返回纯 JSON（不要 markdown 包裹）：
{{"intent":"...","confidence":0.0-1.0,"params":{{"key":"value"...}},"missing_info":"..."}}"""


async def intent_classify(intents: list, message: str, context: dict = None) -> dict:
    """Classify user intent. Returns {intent, confidence, params, missing_info}.

    Args:
        intents: [{"name":"new_project","description":"创建新项目"},...]
        message: user input text
        context: {"project_name":"...","iteration_name":"..."}
    """
    if not intents:
        return {"intent": "chat", "confidence": 0.5, "params": {}, "missing_info": ""}

    intent_list = "\n".join(
        f"- {i['name']}: {i.get('description','')}" for i in intents
    )
    ctx_parts = []
    if context:
        for k, v in context.items():
            if v:
                ctx_parts.append(f"{k}={v}")
    context_str = ", ".join(ctx_parts) if ctx_parts else "无上下文"

    prompt = INTENT_PROMPT.format(intent_list=intent_list, context_str=context_str)

    try:
        from pipeline_service.llm_bridge import llm_call
        raw = await llm_call(
            prompt=f"{prompt}\n\n用户输入：{message}",
            model=None,
            temperature=0.2,
        )
        raw = raw.strip()
        if raw.startswith("```"):
            raw = raw.split("\n", 1)[1].rsplit("```", 1)[0]
        return json.loads(raw)
    except Exception as e:
        logger.warning("intent_classify failed: %s", e)
        return {"intent": "chat", "confidence": 0.3, "params": {}, "missing_info": ""}