from datetime import datetime
import os
import streamlit as st
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import GPT4All
from streamlit_chat import message
from huggingface_hub import hf_hub_download

from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler


def get_pdf_text(pdfs):
    text = ""
    for pdf in pdfs:
        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 = HuggingFaceEmbeddings(
        model_name="all-MiniLM-L6-v2")
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore


def get_conversation_chain(vectorstore):
    callbacks = [StreamingStdOutCallbackHandler()]
    llm = GPT4All(model="/tmp/ggml-gpt4all-j-v1.3-groovy.bin",
                  max_tokens=1000, backend='gptj', callbacks=callbacks, n_batch=8, verbose=False)
    # llm = 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 user_input(user_question):
    # log user question with timestamp
    print(f"[{datetime.now()}]:{user_question}")
    with st.spinner("Thinking ..."):
        response = st.session_state.conversation({'question': user_question})
    # log bot answer with timestamp
    print(f"\n[{datetime.now()}]:{response['answer']}")
    st.session_state.chat_history = response['chat_history']
    for i, messages in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            message(messages.content, is_user=True)
        else:
            message(messages.content)


def main():
    load_dotenv()
    if "ggml-gpt4all-j-v1.3-groovy.bin" not in os.listdir("/tmp"):
        hf_hub_download(repo_id="dnato/ggml-gpt4all-j-v1.3-groovy.bin",
                        filename="ggml-gpt4all-j-v1.3-groovy.bin", local_dir="/tmp")
    st.set_page_config(page_title="Trade Document Chatbot")
    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("Query your trade documents")
    user_question = st.text_input("Ask a question about your documents...")
    if user_question and st.session_state.conversation:
        user_input(user_question)
    with st.sidebar:
        st.subheader("Your trade documents")
        pdfs = st.file_uploader(
            "Upload here", accept_multiple_files=True, type=["pdf"],)
        if st.button("Study"):
            with st.spinner("Studying ..."):
                raw_text = get_pdf_text(pdfs)
                # print(raw_text)
                chunks = get_text_chunks(raw_text)
                # print(chunks)
                vectorstore = get_vectorstore(chunks)
                # print(vectorstore)
                st.session_state.conversation = get_conversation_chain(
                    vectorstore)
                st.success("Done!")


if __name__ == '__main__':
    main()