# Langchain imports
from langchain_community.vectorstores.faiss import FAISS
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain

# Embedding and model imports
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_groq import ChatGroq

# Other
import streamlit as st
import os
import time
from PyPDF2 import PdfReader
import tempfile

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

st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap= 200)

def initialize_vector_store(option):
    if option:
        if option == "Website":
            website_link = st.text_input("Enter the website link:")
            if st.button("Submit & Process"):
                with st.spinner("Loading website content..."):
                    st.session_state.loader = WebBaseLoader(website_link)
                    st.session_state.docs = st.session_state.loader.load()
                    st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
                    st.session_state.vector = FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings)
                    st.success("Website content loaded successfully!")

        elif option == "PDF(s)":
            pdf_files = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True)
            if st.button("Submit & Process"):
                with st.spinner("Loading pdf..."):
                    st.session_state.docs = get_pdf_processed(pdf_files)
                    st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs)
                    st.session_state.vector = FAISS.from_texts(st.session_state.final_documents,st.session_state.embeddings)
                    st.success("PDF content loaded successfully!")

def get_conversational_chain():
    llm = ChatGroq(model="mixtral-8x7b-32768")
    prompt = ChatPromptTemplate.from_template(
    """
    Answer the question based on the provided context only.
    Please provide the most accurate response based on the question
    <context>
    {context}
    </context>
    Questions:{input}
    """
    )
    document_chain = create_stuff_documents_chain(llm,prompt)
    retriever = st.session_state.vector.as_retriever() if st.session_state.vector else None
    retrieval_chain = create_retrieval_chain(retriever,document_chain)
    return retrieval_chain

def user_input(prompt):
    chain = get_conversational_chain()
    start =time.process_time()
    response = chain.invoke({"input":prompt})
    st.write(response['answer'])
    st.write("Response time: ", time.process_time() - start)

    with st.expander("Did not like the response? Check out more here"):
        for i, doc in enumerate(response['context']):
            st.write(doc.page_content)
            st.write("-----------------------------")

def main():
    st.title("Ask your questions from pdf(s) or website")

    option = None
    # Prompt user to choose between PDFs or website
    option = st.radio("Choose input type:", ("PDF(s)", "Website"), index=None)
    initialize_vector_store(option)
    prompt = st.text_input("Input your question here")
    if prompt:
        user_input(prompt)


if __name__ == "__main__":
    main()