from ragflow_sdk import RAGFlow
import os
import re
## home
api_key = "ragflow-MyMjM2ODE2NThlMTExZjBiMzJlNzY5Mj"
base_url = "http://127.0.0.1:8099"


## 公司内网
base_url = "http://192.168.107.165:8099"
api_key = "ragflow-I5ZDNjMWNhNTdlMjExZjBiOTEwMzI0ZT"
rag_object = RAGFlow(api_key=api_key, base_url=base_url)






def choose_from_list(options, prompt):
    for idx, item in enumerate(options):
        print(f"{idx + 1}. {item}")
    while True:
        choice = input(prompt)
        if choice.isdigit() and 1 <= int(choice) <= len(options):
            return options[int(choice) - 1]
        else:
            print("输入无效，请重新输入编号。")


def select_files(file_path, file_type="pdf"):
    """
    选择file_path中的所有指定类型文件（默认pdf），
    返回文件路径列表
    """
    file_list = []
    for root, dirs, files in os.walk(file_path):
        for file in files:
            if file.lower().endswith(f".{file_type.lower()}"):
                file_list.append(os.path.join(root, file))
    return file_list


def pair_pdf_and_txt(pdf_path, txt_path):
    """
    将pdf和txt文件对齐，
    返回对齐pdf_dict和txt_dict，
    pdf_dict和txt_dict的key为文件名（不含后缀），value为文件路径
    txt_dict仅收入与pdf_dict中存在的文件，
    如果pdf_dict中有文件名没有对应的txt文件，则不收入txt_dict
    """
    pdf_files = select_files(pdf_path, "pdf")
    txt_files = select_files(txt_path, "txt")

    # 构建文件名到路径的映射
    pdf_dict = {os.path.splitext(os.path.basename(f))[0]: f for f in pdf_files}
    txt_dict_all = {os.path.splitext(os.path.basename(f))[0]: f for f in txt_files}

    # 只保留有对应txt的pdf
    pdf_dict_aligned = {}
    txt_dict_aligned = {}
    for name in pdf_dict:
        if name in txt_dict_all:
            pdf_dict_aligned[name] = pdf_dict[name]
            txt_dict_aligned[name] = txt_dict_all[name]

    return pdf_dict_aligned, txt_dict_aligned


def select_dataset(rag_object):
    """选择可用数据集"""
    datasets = rag_object.list_datasets()
    if not datasets:
        print("没有可用的数据集。")
        return None
    
    dataset_names = [ds.name for ds in datasets]
    dataset_name = choose_from_list(dataset_names, "请选择数据集编号：")
    return [ds for ds in datasets if ds.name == dataset_name][0]

def upload_or_get_document(dataset, pdf_path, display_name):
    """上传或获取已存在的文档"""
    try:
        document = dataset.list_documents(name=display_name)[0]
        print(f"文档已存在: {display_name}，跳过上传。")
        return document
    except Exception:
        try:
            with open(pdf_path, "rb") as f:
                blob = f.read()
            dataset.upload_documents([{"display_name": display_name, "blob": blob}])
            return dataset.list_documents(name=display_name)[0]
        except Exception as e:
            print(f"上传PDF失败: {pdf_path}，错误: {e}")
            return None




def divid_txt_chunk_img(txt_chunk):
    """分离文本块中的图片链接和纯文本内容
    
    输入格式示例: 
        "这是文本内容![image](路径/IMAGE1.png)更多文本![image](路径/IMAGE2.png)"
    
    返回:
        clean_text: 移除所有图片链接后的纯文本内容
        image_paths: 提取到的图片路径列表
    """
    # 正则表达式匹配Markdown图片格式: ![alt_text](path)
    pattern = r'!\[.*?\]\((.*?)\)'
    
    # 提取所有图片路径
    image_paths = re.findall(pattern, txt_chunk)
    
    # 移除所有图片标记
    clean_text = re.sub(pattern, '', txt_chunk)
    
    # 移除多余空行并清理首尾空白
    clean_text = re.sub(r'\n\s*\n', '\n\n', clean_text).strip()
    
    return clean_text, image_paths

def upload_images_to_minio(image_paths, document):
    """
    上传图片到MinIO,
    
    """




def process_txt_chunks(document, txt_path):
    """处理文本分块并添加到文档"""
    try:
        with open(txt_path, 'r', encoding='utf-8') as file:
            file_content = file.read()
        
        for num, txt_chunk in enumerate(file_content.split('\n\n')):
            if txt_chunk.strip():
                print(f"处理文本块: {txt_chunk[:30]}...")
                chunk = document.add_chunk(content=txt_chunk)
                print(f"第{num+1} Chunk添加成功! ID: {chunk.id}")
                
    except Exception as e:
        print(f"处理文本文件时出错: {txt_path}，错误: {e}")

def process_pdf_txt_pairs(pdf_dict, txt_dict, dataset):
    """处理PDF-TXT文件对"""
    for name, pdf_path in pdf_dict.items():
        display_name = os.path.basename(pdf_path)
        document = upload_or_get_document(dataset, pdf_path, display_name)
        if not document:
            continue
            
        txt_path = txt_dict.get(name)
        if txt_path:
            process_txt_chunks(document, txt_path)

def main():

    """主函数，处理PDF和TXT文件对
    
    dataset.id = bucket_name
    chunk_id = object_name
    """
    file_path = "g:\\11\\22\\规范\\"
    #pdf_dict, txt_dict = pair_pdf_and_txt(file_path, file_path)
    
    # if not pdf_dict:
    #     print("未选择任何文件。")
    #     return
        
    dataset = select_dataset(rag_object)
    print(f"选择的数据集: {dataset.name}")
    print(f"选择的数据集id: {dataset.id}")
    if not dataset:
        print("未选择数据集。")
        return
        
    #process_pdf_txt_pairs(pdf_dict, txt_dict, dataset)










if __name__ == "__main__":
    main()