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

api_key = st.secrets['api_key']

def main():
    load_dotenv()
    st.set_page_config(page_title="PDF Chatbot")
    st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png")

    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.title("Chat with Multiple PDFs using FLAN-T5")
    user_question = st.text_input("Ask a question about your documents:")
    if user_question:
        handle_userinput(user_question)
    
    with st.sidebar:
        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 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)
                st.success("file uploaded")

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(vectorestore):
    llm = HuggingFaceHub(
        repo_id="google/flan-t5-large",
        model_kwargs={"temperature": 0.5, "max_length": 1024},
        huggingfacehub_api_token=api_key
    )

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm = llm, retriever = vectorestore.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()