from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
from .state import AgentState
from services.prompt_manager import PromptManager
from services.template_matcher import TemplateMatcher


def process_input(state: AgentState) -> AgentState:
    """处理用户输入"""
    state.current_step = "processed"
    return state


def generate_response(state: AgentState, model) -> AgentState:
    """使用 LLM 生成回复"""
    if state.messages:
        response = model.invoke(state.messages)
        state.messages.append(response)
    return state


def normalize_input(state: AgentState, model) -> AgentState:
    """将用户输入规范化为标准英文语句"""
    if not state.messages:
        return state

    last_message = state.messages[-1]
    if not isinstance(last_message, HumanMessage):
        return state

    prompt_manager = PromptManager()
    system_prompt = SystemMessage(
        content=prompt_manager.get("system", "english_normalizer")
    )

    response = model.invoke([system_prompt, HumanMessage(content=last_message.content)])
    normalized = response.content if hasattr(response, "content") else str(response)

    state.context["original_input"] = last_message.content
    state.context["normalized_input"] = normalized

    matcher = TemplateMatcher()
    state.context["table_match"] = matcher.match(normalized)
    return state