import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
# from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from langchain.llms import HuggingFaceHub
import os 
# from transformers import T5Tokenizer, T5ForConditionalGeneration
# from langchain.callbacks import get_openai_callback

hub_token = os.environ["HUGGINGFACE_HUB_TOKEN"]

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_vectorstore(text_chunks):
    # embeddings = OpenAIEmbeddings()
    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
    embeddings = HuggingFaceEmbeddings()
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversation_chain(vectorstore):
    # llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
    # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
    # model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")

    llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-v0.1", huggingfacehub_api_token=hub_token, model_kwargs={"temperature":0.5, "max_length":20})

    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
    reply = response.run(user_question)
    st.write(reply)
    # 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"):
            if(len(pdf_docs) == 0):
                st.error("Please upload at least one PDF")
            else:
                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)

if __name__ == '__main__':
    main()






# import os
# import getpass
# import streamlit as st
# from langchain.document_loaders import PyPDFLoader
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# from langchain.embeddings import HuggingFaceEmbeddings
# from langchain.vectorstores import Chroma
# from langchain import HuggingFaceHub
# from langchain.chains import RetrievalQA
# # __import__('pysqlite3')
# # import sys
# # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')


# # load huggingface api key
# hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]

# # use streamlit file uploader to ask user for file
# # file = st.file_uploader("Upload PDF")


# path = "Geeta.pdf"
# loader = PyPDFLoader(path)
# pages = loader.load()

# # st.write(pages)

# splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
# docs = splitter.split_documents(pages)

# embeddings = HuggingFaceEmbeddings()
# doc_search = Chroma.from_documents(docs, embeddings)

# repo_id = "tiiuae/falcon-7b"
# llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})

# from langchain.schema import retriever
# retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())

# if query := st.chat_input("Enter a question: "):
#   with st.chat_message("assistant"):
#     st.write(retireval_chain.run(query))