import streamlit as st
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 html_template import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
import os

FREE_RUN = False

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_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks

def get_vector_store(text_chunks):
    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") if FREE_RUN else OpenAIEmbeddings()
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore

def get_conversation_chain(vectorstore):
    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={
                        "temperature": 0.5, "max_length": 512}) if FREE_RUN else ChatOpenAI()
    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)

def main():
    st.set_page_config(page_title="WhisperChain 🔗", page_icon=":link:")
    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("WhisperChain 🔗")
    user_question = st.text_input("Ask a question about your documents.")

    if user_question:
        handle_userinput(user_question)

    with st.sidebar:
        
        ###
        OPENAI_API_KEY = st.sidebar.text_input("Enter OpenAI API Key", type="password")
        HUGGINGFACEHUB_API_KEY = st.sidebar.text_input("Enter Hugging Face API Key", type="password")

        if not OPENAI_API_KEY or not HUGGINGFACEHUB_API_KEY:
            st.sidebar.error("Please enter your API keys")
            st.stop()

        os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
        os.environ["HUGGINGFACEHUB_API_KEY"] = HUGGINGFACEHUB_API_KEY

        #Toggle free run
        global FREE_RUN
        FREE_RUN = st.sidebar.checkbox("Free run", value=False)
        ###

        pdf_docs = st.file_uploader(
            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)

        if st.button("Process"):
            if pdf_docs:
                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
                    vector_store = get_vector_store(text_chunks)

                    # create conversation chain
                    st.session_state.conversation = get_conversation_chain(vector_store)
            else:
                st.error("Please upload at least one PDF")

if __name__ == '__main__':
    main()