import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import logging
def setup_logger(logger_name):
    """
    设置指定名称的logger，将其级别设置为WARNING并禁用传播
    :param logger_name: logger的名称
    """
    logger = logging.getLogger(logger_name)
    logger.setLevel(logging.WARNING)  # 设置httpcore及其子模块的级别
    logger.propagate = False  # 可选：禁用传播（防止被根logger处理）
    return logger


logger_names = ["httpx", "openai", "langsmith.client", "neo4j", "urllib3", "httpcore"]
for name in logger_names:
    setup_logger(name)

import json
from src.dialog_manager import DialogManager
from src.multi_llm_client import MultiAPIKeyChatOpenAI
from src.code_executor import CodeExecutor
from src.neo4j_raw_retriever import Neo4jRawRetriever
from src.prompt_manager import PromptManager
import yaml
from src.config import Config
from src.document_loader import load_file
from src.embedding_client import EmbeddingClient

from src.project import ProjectBuilder, ProjectToolkit
from src.project_implementation import ProjectToolkitNeo4j

success_count = 0
fail_count = 0
questions = []     
error_list = []

def main():
    global fail_count, success_count, questions, error_list

    config = Config()

    business_structure = load_file(config.business_object_structure_path)
    bowei_api_docs = load_file(config.bowei_api_docs_path)

    llm_client = MultiAPIKeyChatOpenAI(config.openai)

    llm_client_coder = MultiAPIKeyChatOpenAI(config.openai_coder)

    prompt_manager = PromptManager()

    neo4j_conf = config.neo4j_conf
    embedding_conf = config.embedding

    embedding_client = EmbeddingClient(embedding_conf)

    # 创建Neo4j检索器
    knowledge_retriever = Neo4jRawRetriever(neo4j_conf)

    ProjectBuilder.register(ProjectToolkitNeo4j, knowledge_retriever.driver)

    code_executor = CodeExecutor(prompt_manager.prompts, llm_client_coder, config.max_retries)

    dialog_manager = DialogManager(
        llm_client,
        business_structure,
        bowei_api_docs,
        code_executor,
        knowledge_retriever,
        prompt_manager,
    )

    # 加载 zhibiao.json
    with open('./tests/zhibiao.json', 'r', encoding='utf-8') as f:
        zhibiao_data = json.load(f)

    # 提取指标映射关系并批量执行
    for item in zhibiao_data:
        datasource = item['数据来源']
        if datasource == '报表指标' or datasource == '指标库':
            continue

        query = item['指标描述']['指标映射']
        #rewritten_results = dialog_manager.understand_user_question(query)
        rewritten_results = []
        if rewritten_results is None or rewritten_results == []:
            print('问题: {} 没有找到符合要求的数据'.format(query))
            fail_count += 1
            error_list.append(f"问题 {query} 调用 understand_user_question 返回空结果")
            continue

        selected_rewritten, selected_knowledge = rewritten_results[0]

        result = dialog_manager.execute_generated_code(selected_rewritten, selected_knowledge)
        if result is None or result == []:
            questions.append(selected_rewritten)
            fail_count += 1
            error_list.append(f"问题 {query}  {selected_rewritten} 调用 execute_generated_code 返回空结果")
        else:
            success_count += 1

        #print(result)

if __name__ == "__main__":
    main()

    total = success_count + fail_count
    if total > 0:
        success_rate = success_count / total
        fail_rate = fail_count / total
    else:
        success_rate = fail_rate = 0
    print(f"问题总数: {total}")
    print(f"成功比例: {success_rate * 100:.2f}%")
    print(f"失败比例: {fail_rate * 100:.2f}%")
    print("错误列表:")
    for error in error_list:
        print(error)