from dotenv import load_dotenv
load_dotenv()

import json
import sys


from app.observability import init_observability
from app.settings import init_settings

import nest_asyncio
nest_asyncio.apply()

from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import SimpleDirectoryReader
from llama_index.core.evaluation import DatasetGenerator

import prompts  

init_settings()
init_observability()

# 读取所有文档（即所有表格）
documents = SimpleDirectoryReader("D:/LLM_model/text2sql/zjdataai-app-test/backend/data-test").load_data()

# 定义表格名称和索引的对应关系
table_names = {
    "工程信息表": 0,
    "其他费用表": 1,
    "取费表": 2,
    "项目划分表": 3,
    "项目划分_费用预览表": 4,
    "总算表": 5,
    "工程量表": 6
}

# 定义中文提示词和Python代码中提示词名称的映射
prompt_mapping = {
    "普通属性": "Attribute_Prompt",
    "金额查询": "Amount_Prompt",
    "单位换算": "Units_Prompt",
    "重名项目划分": "Name_Prompt",
    "总体金额查询": "All_Amount_Prompt"
}

# 定义表格与其对应的查询类别
table_prompt_mapping = {
    "工程信息表": ["普通属性", "单位换算"],
    "其他费用表": ["金额查询", "单位换算"],
    "取费表": ["金额查询"],
    "总算表": ["金额查询", "总体金额查询"],
    "工程量表": ["普通属性", "重名项目划分"]
}

# 根据表格名称选择特定的表格
def select_document(documents, table_name):
    if table_name not in table_names:
        raise ValueError(f"未找到名为 '{table_name}' 的表格")
    index = table_names[table_name]
    return [documents[index]]  # 返回一个包含所选表格的列表

# 选择提示词
def select_prompt(prompt_category):
    prompt_name = prompt_mapping.get(prompt_category)
    if not prompt_name:
        raise ValueError(f"未找到名为 '{prompt_category}' 的提示词")
    try:
        return getattr(prompts, prompt_name)
    except AttributeError:
        raise ValueError(f"未找到提示词 '{prompt_name}' 对应的函数")

# 生成问题和答案
def generate_questions_from_document(document, quest_prompt, num_questions):
    question_generator = DatasetGenerator.from_documents(
        documents=document,
        question_gen_query=quest_prompt,
        num_questions_per_chunk=num_questions
    )

    eval_questions = question_generator.generate_questions_from_nodes(num_questions)
    print(eval_questions)

    qa_pairs = []
    for qa in eval_questions:
        if ',' in qa:
            question, answer = qa.split(",", 1)
            qa_pairs.append({
                "question": question.strip(),
                "answer": answer.strip()
            })
        else:
            print(f"无法处理的问题和答案: {qa}")

    return qa_pairs

# 主函数，控制生成多个表格的问题和使用多个提示词，并将结果合并到一个文件中
def main(documents, table_names_input, prompt_categories_input, num_questions_per_prompt):
    if table_names_input == "all":
        selected_tables = list(table_prompt_mapping.keys())
    else:
        selected_tables = table_names_input.strip('[]').split(',')

    all_results = {}

    for table_name in selected_tables:
        table_name = table_name.strip()  # 去掉前后空格
        document = select_document(documents, table_name)

        if prompt_categories_input == "all":
            selected_prompts = table_prompt_mapping[table_name]
        else:
            selected_prompts = prompt_categories_input.strip('[]').split(',')
            selected_prompts = [p.strip() for p in selected_prompts]  # 去掉前后空格

        for prompt_category in selected_prompts:
            if prompt_category not in table_prompt_mapping[table_name]:
                print(f"跳过表格 '{table_name}' 的提示词 '{prompt_category}'，因为该表中不包含该类别的信息")
                continue

            quest_prompt = select_prompt(prompt_category).format(num_questions_per_chunk=num_questions_per_prompt)
            qa_pairs = generate_questions_from_document(document, quest_prompt, num_questions_per_prompt)

            label = f"test:{table_name}_{prompt_category}"
            all_results[label] = qa_pairs

    # 自动生成输出文件名
    output_file = "combined_test.json"

    with open(output_file, "w", encoding="utf-8") as f:
        json.dump(all_results, f, ensure_ascii=False, indent=4)

    print(f"All questions and answers have been saved to '{output_file}'")

# 获取命令行参数
if __name__ == "__main__":
    if len(sys.argv) != 4:
        print("Usage: python script.py <table_names_input> <prompt_categories_input> <num_questions_per_prompt>")
    else:
        table_names_input = sys.argv[1]
        prompt_categories_input = sys.argv[2]
        num_questions_per_prompt = int(sys.argv[3])

        main(documents, table_names_input, prompt_categories_input, num_questions_per_prompt)