from dotenv import load_dotenv
import os
import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback

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


def extract_text_from_txt(txt):
    text = txt.read().decode("utf-8")
    return text


def extract_text_from_brain():
    with open('brain/brain_journal.txt', 'r', encoding='utf-8') as file:
        text = file.read()
    return text


def main():
    load_dotenv()
    hide_streamlit_style = """
                <style>
                footer {visibility: hidden;}
                </style>
                """
    st.markdown(hide_streamlit_style, unsafe_allow_html=True)
    st.title("Digital Brain Journal Search 🔍")
    st.write("Ask any questions about all the journal entries with OpenAI's Embeddings API and Langchain. The virtual brain keeps track of everything in a user's life. If you have another TXT or PDF file you'd like to search for answers, click on the dropdown and select eithter TXT or PDF option in file type.")

    # Add API key input
    api_key = st.text_input("Enter your API key:", type="password")
    os.environ["OPENAI_API_KEY"] = api_key

    if not api_key:
        st.warning("Please enter your OpenAI API key to continue.")
    else:
        file_type = st.selectbox("Choose the file type", options=["Brain", "PDF", "TXT"])

        file = None
        text = None

        if file_type == "PDF":
            file = st.file_uploader("Upload your PDF", type="pdf")
            if file is not None:
                text = extract_text_from_pdf(file)
        elif file_type == "TXT":
            file = st.file_uploader("Upload your TXT", type="txt")
            if file is not None:
                text = extract_text_from_txt(file)
        elif file_type == "Brain":
            text = extract_text_from_brain()

        if file is not None or file_type == "Brain":
            # split into chunks
            text_splitter = CharacterTextSplitter(
                separator="\n",
                chunk_size=1000,
                chunk_overlap=200,
                length_function=len
            )
            chunks = text_splitter.split_text(text)

            # create embeddings
            embeddings = OpenAIEmbeddings()
            knowledge_base = FAISS.from_texts(chunks, embeddings)

            # show user input
            user_question = st.text_area("Ask a question about your document:")

            if st.button("Submit"):
                if user_question:
                    docs = knowledge_base.similarity_search(user_question)

                    llm = OpenAI()
                    chain = load_qa_chain(llm, chain_type="stuff")
                    with get_openai_callback() as cb:
                        response = chain.run(input_documents=docs, question=user_question)
                        print(cb)

                    st.markdown("### Response:")
                    st.write(response)
                    st.write(cb)
    st.markdown("---")
    st.markdown("")
    st.markdown("<p style='text-align: center'><a href='https://github.com/Kaludii'>Github</a> | <a href='https://huggingface.co/Kaludi'>HuggingFace</a></p>", unsafe_allow_html=True)


if __name__ == '__main__':
    main()