# app/agents/classification_agent.py
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import PydanticOutputParser
from ..core.llm import llm
from ..schemas import ClassificationResult  # 导入新的Schema

# 1. 设置PydanticOutputParser
parser = PydanticOutputParser(pydantic_object=ClassificationResult)

# 2. 更新Prompt模板以要求语言，并包含格式指令
classification_template = """
You are a professional document analysis assistant. Please perform two tasks on the following text:
1.  Determine its category. The category must be one of: ["LETTER", "INVOICE", "RECEIPT", "CONTRACT", "OTHER"].
2.  Detect the primary language of the text. Return the language as a two-letter ISO 639-1 code (e.g., "en" for English, "zh" for Chinese, "es" for Spanish).

{format_instructions}

[Document Text Start]
{document_text}
[Document Text End]
"""

classification_prompt = PromptTemplate(
    template=classification_template,
    input_variables=["document_text"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

# 3. 创建新的LangChain链
classification_chain = classification_prompt | llm | parser


async def agent_classify_document_from_text(text: str) -> ClassificationResult:
    """Agent 1: Classify document and detect language from OCR-extracted text."""
    print("--- [Agent 1] Calling LLM for classification and language detection...")
    if not text.strip():
        print("--- [Agent 1] Text content is empty, classifying as 'OTHER'.")
        return ClassificationResult(category="OTHER", language="unknown")

    # 调用链并返回Pydantic对象
    result = await classification_chain.ainvoke({"document_text": text})
    return result