import streamlit as st
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import PyPDFLoader
import tempfile
import os


# PDF 문서로부터 텍스트를 추출하는 함수입니다.
def get_pdf_text(pdf_docs):
    temp_dir = tempfile.TemporaryDirectory()
    temp_filepath = os.path.join(temp_dir.name, pdf_docs.name)
    with open(temp_filepath, "wb") as f:
        f.write(pdf_docs.getvalue())
    pdf_loader = PyPDFLoader(temp_filepath)
    pdf_doc = pdf_loader.load()
    return pdf_doc


# 텍스트 파일을 처리하는 함수입니다.
def get_text_file(docs):
    text = docs.getvalue().decode("utf-8")
    return [text]


# CSV 파일을 처리하는 함수입니다.
def get_csv_file(docs):
    import pandas as pd
    csv_text = docs.getvalue().decode("utf-8")
    csv_data = pd.read_csv(pd.compat.StringIO(csv_text))
    csv_columns = csv_data.columns.tolist()
    csv_rows = csv_data.to_dict(orient='records')
    csv_texts = [', '.join([f"{col}: {row[col]}" for col in csv_columns]) for row in csv_rows]
    return csv_texts


# JSON 파일을 처리하는 함수입니다.
def get_json_file(docs):
    import json
    json_text = docs.getvalue().decode("utf-8")
    json_data = json.loads(json_text)
    json_texts = [item.get('text', '') for item in json_data]
    return json_texts


# 문서들을 처리하여 텍스트 청크로 나누는 함수입니다.
def get_text_chunks(documents):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    return text_splitter.split_documents(documents)


# 텍스트 청크들로부터 벡터 스토어를 생성하는 함수입니다.
def get_vectorstore(text_chunks):
    embeddings = OpenAIEmbeddings()
    vectorstore = FAISS.from_documents(text_chunks, embeddings)
    return vectorstore


# 대화 체인을 생성하는 함수입니다.
def get_conversation_chain(vectorstore):
    gpt_model_name = 'gpt-3.5-turbo'
    llm = ChatOpenAI(model_name=gpt_model_name)
    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever=vectorstore.as_retriever(),
        memory=memory
    )
    return conversation_chain


# 사용자 입력을 처리하는 함수입니다.
def handle_userinput(user_question):
    response = st.session_state.conversation({'question': user_question})
    st.session_state.chat_history = response['chat_history']

    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            st.write(f"<div>{message.content}</div>", unsafe_allow_html=True)
        else:
            st.write(f"<div>{message.content}</div>", unsafe_allow_html=True)


def main():
    st.set_page_config(page_title="Chat with multiple Files", page_icon=":books:")

    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None

    st.header("Chat with multiple Files :")
    user_question = st.text_input("Ask a question about your documents:")

    with st.sidebar:
        st.subheader("Your documents")
        docs = st.file_uploader(
            "Upload your files here and click on 'Process'",
            accept_multiple_files=True
        )

        if st.button("Process"):
            with st.spinner("Processing"):
                doc_list = []

                for file in docs:
                    if file.type == 'text/plain':
                        doc_list.extend(get_text_file(file))
                    elif file.type == 'application/pdf':
                        doc_list.extend(get_pdf_text(file))
                    elif file.type == 'text/csv':
                        doc_list.extend(get_csv_file(file))
                    elif file.type == 'application/json':
                        doc_list.extend(get_json_file(file))

                text_chunks = get_text_chunks(doc_list)
                vectorstore = get_vectorstore(text_chunks)
                st.session_state.conversation = get_conversation_chain(vectorstore)

    if user_question and st.session_state.conversation:  # 대화 체인이 있을 때만 사용자 입력 처리
        handle_userinput(user_question)


if __name__ == '__main__':
    main()