import os
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings

embedding_path = "/data/Z_LLM_data/Embed_data/bge-m3"
embeddings = HuggingFaceEmbeddings(model_name=embedding_path)

def Mixed_retrieval(input_path):
    file_name = os.path.splitext(os.path.basename(input_path))[0]
    faiss_archived = f"./faiss_data/{file_name}"

    txt_list = []
    with open(input_path, 'r', encoding='utf-8') as file:
        txt_list = [line.strip() for line in file]
    vectorstore_txt_faiss = FAISS.from_texts(txt_list, embeddings)
    vectorstore_txt_faiss.save_local(faiss_archived)

    # vectorstore_txt_faiss = FAISS.load_local(vectorstore_txt_faiss,
    #                                          embeddings=embeddings,
    #                                          allow_dangerous_deserialization=True)

    retriever_txt_faiss1 = vectorstore_txt_faiss.as_retriever(search_kwargs={"k": 5})
    retriever_txt_faiss2 = vectorstore_txt_faiss.as_retriever(
        search_type="mmr",
        search_kwargs={"k": 5,  # 检索结果
                       "fetch_k": 2,  # 候选结果数量
                       "lambda_mult": 0.1}  # 平衡指数，1为相关性；0为多样性
    )
    retriever_txt_faiss3 = vectorstore_txt_faiss.as_retriever(
        search_type="similarity_score_threshold",
        search_kwargs={"score_threshold": 0.3}
    )

    return retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3

def interface_search(input_str, retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3):
    index_keyword1 = []
    for i in retriever_txt_faiss1.invoke(input_str):
        index_keyword1.append(i.page_content)
    index_keyword2 = []
    for i in retriever_txt_faiss2.invoke(input_str):
        index_keyword2.append(i.page_content)
    index_keyword3 = []
    for i in retriever_txt_faiss3.invoke(input_str):
        index_keyword3.append(i.page_content)

    return list(set(index_keyword1) & set(index_keyword2) & set(index_keyword3))


def Building_search_dictionary(input_csv_path1, input_csv_path2, index_keyword):
    import pandas as pd

    df1 = pd.read_csv(input_csv_path1, encoding='utf-8')
    df2 = pd.read_csv(input_csv_path2, encoding='utf-8', names=['path', 'id'])
    #df2 = pd.read_csv(input_csv_path2, encoding='utf-8')

    matching_path = df1.loc[df1['name'] == index_keyword, 'index']

    # print(matching_path)

    # print(matching_path.tolist()[0] )

    # todo: bug修改: 避免matching_path和matching_ids没有映射
    if matching_path.empty:
        return(None, None)
    else:
        matching_ids = df2.loc[df2['path'] == matching_path.tolist()[0], 'id']

    # print(matching_ids)
    if matching_ids.empty:
        return (matching_path.tolist()[0], None)
    else:
        return (matching_path.tolist()[0], int(matching_ids.values[0]))


def Official_website_kg_search(input_id):
    # info = WikijsTool.get_all_documents()

    import re
    from bs4 import BeautifulSoup
    from booway_kg_api.WikijsTool import WikijsTool

    html_text = WikijsTool.query_doc_info(input_id)['content']
    cleaned_img_text = re.sub(r'<img\s+[^>]*>', '', html_text)

    soup = BeautifulSoup(cleaned_img_text, "html.parser")
    plain_text = soup.get_text()

    return plain_text