import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmltemp import css, bot_template, user_template
from langchain.llms import HuggingFaceHub


def main():
    load_dotenv()
    st.set_page_config(page_title="PDF Chatbot", page_icon="📚")
    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 your PDFs 📚")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        st.sidebar.info("""Note: I haven't used any GPU for this project so It can take 
        long time to process large PDFs. Also this is POC project and can be easily upgraded
        with better model and resources.  """)

        st.subheader("Your PDFs")
        pdf_docs = st.file_uploader(
            "Upload your PDFs here", 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_conversation_chain(vectorstore)


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 = RecursiveCharacterTextSplitter(
        separators=["\n\n", "\n", "."], chunk_size=900, chunk_overlap=200, length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks


def get_vectorstore(text_chunks):
    embeddings = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-base-en-v1.5")
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversation_chain(vectorstore):
    llm = HuggingFaceHub(
        repo_id="google/flan-t5-large",
        model_kwargs={"temperature": 0.5, "max_length": 1024},
        
    )

    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


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
            )


if __name__ == "__main__":
    main()