import streamlit as st 
from dotenv import load_dotenv 
from PyPDF2 import PdfReader 
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import HuggingFaceHub
# from htmlTemplates import css,user_template,bot_template
from langchain.embeddings import HuggingFaceEmbeddings



def get_pdf_text(pdf_docs):
    text = ""

    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text+=page.extract_text()
    return text 

def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator = '\n',
        chunk_size = 1000,
        chunk_overlap = 200,
        # length_fucntion = len(text)
    )
    chuncks = text_splitter.split_text(text)
    return chuncks



def get_vectorstore(text_chunks):
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversations_chain(vectorstores):
    llm = HuggingFaceHub(repo_id ='mistralai/Mistral-7B-Instruct-v0.3',model_kwargs={"temperature":0.5, "max_length":512},huggingfacehub_api_token=API_KEY)


    memory = ConversationBufferMemory(
        memory_key = 'chat_history',return_messages = True
    )
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm = llm,
        retriever = vectorstores.as_retriever(),
        memory = memory,
    )

    return conversation_chain



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(user_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)
        else: 
            st.write(bot_template.replace(
                "{{MSG}}", message.content), unsafe_allow_html=True)





def main():
    load_dotenv()
    st.set_page_config(page_title="Chat with multiple PDFs",
                       page_icon=":books:")
    # st.write(css, unsafe_allow_html=True)

    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 PDFs :books:")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        st.subheader("Your documents")
        pdf_docs = st.file_uploader(
            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
        if st.button("Process"):
            with st.spinner("Processing"):
                # get pdf text
                raw_text = get_pdf_text(pdf_docs)

                # get the text chunks
                text_chunks = get_text_chunks(raw_text)

                # create vector store
                vectorstore = get_vectorstore(text_chunks)

                # create conversation chain
                st.session_state.conversation = get_conversations_chain(
                    vectorstore)


if __name__ == '__main__':
    main()