from langchain_community.document_loaders import DirectoryLoader
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.chains import (
    StuffDocumentsChain, LLMChain, ConversationalRetrievalChain
)
from langchain_core.prompts import PromptTemplate
import streamlit as st
from PyPDF2 import PdfReader

css = '''
<style>
.chat-message {
    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
}
.chat-message.user {
    background-color: #2b313e
}
.chat-message.bot {
    background-color: #475063
}
.chat-message .avatar {
  width: 20%;
}
.chat-message .avatar img {
  max-width: 78px;
  max-height: 78px;
  border-radius: 50%;
  object-fit: cover;
}
.chat-message .message {
  width: 80%;
  padding: 0 1.5rem;
  color: #fff;
}
'''

bot_template = '''
<div class="chat-message bot">
    <div class="avatar">
        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png">
    </div>
    <div class="message">{{MSG}}</div>
</div>
'''

user_template = '''
<div class="chat-message user">
    <div class="avatar">
        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
    </div>
    <div class="message">{{MSG}}</div>
</div>
'''

def get_pdf_text(pdf_files):

    text = ""

    for pdf_file in pdf_files:
        reader = PdfReader(pdf_file)
        for page in reader.pages:
            text += page.extract_text()

    return text

def get_chunk_text(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_vector_store(text_chunks):

    # For Huggingface Embeddings

    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={
        'device' : 'cpu'
    })
    vectorstore = FAISS.from_texts(texts = text_chunks, embedding = embeddings)

    return vectorstore


def get_conversation_chain(vector_store):

    # llm = HuggingFaceHub(repo_id="tiiuae/falcon-40b-instruct", model_kwargs={"temperature":0.5, "max_length":512})

    llm = CTransformers(model='llama-2-7b-chat.ggmlv3.q2_K.bin', # model available here: https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main
                model_type='llama',
                config={'max_new_tokens': 600,
                        'context_length':700,
                        'temperature': 0.01})

    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)

    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm = llm,
        retriever = vector_store.as_retriever(),
        memory = memory
    )

    return conversation_chain

def handle_user_input(question):

    response = st.session_state.conversation({'question':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)

# def main():
#     st.set_page_config(page_title='Chat with Your own 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 Your own PDFs :books:')
#     question = st.text_input("Ask anything to your PDF: ")

#     if question:
#         handle_user_input(question)

#     with st.sidebar:
#         st.subheader("Upload your Documents Here: ")
#         pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)

#         if st.button("OK"):
#             with st.spinner("Processing your PDFs..."):

#                 # Get PDF Text
#                 raw_text = get_pdf_text(pdf_files)

#                 # Get Text Chunks
#                 text_chunks = get_chunk_text(raw_text)

#                 # Create Vector Store

#                 vector_store = get_vector_store(text_chunks)
#                 st.write("DONE")

#                 # Create conversation chain

#                 st.session_state.conversation =  get_conversation_chain(vector_store)

def main():
    st.set_page_config(page_title='Chat with Your own PDFs', page_icon=':books:')

    st.write(css, unsafe_allow_html=True)

    if "conversation" not in st.session_state or st.session_state.conversation is None:
        # Initialize conversation only if it's not already present in 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 Your own PDFs :books:')
    question = st.text_input("Ask anything to your PDF: ")

    if question:
        # Check if conversation is still None (not properly initialized)
        if st.session_state.conversation is None:
            st.session_state.conversation = initialize_conversation_chain()
        handle_user_input(question)

    with st.sidebar:
        st.subheader("Upload your Documents Here: ")
        pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)

        if st.button("OK"):
            with st.spinner("Processing your PDFs..."):
                # Get PDF Text
                raw_text = get_pdf_text(pdf_files)

                # Get Text Chunks
                text_chunks = get_chunk_text(raw_text)

                # Create Vector Store
                vector_store = get_vector_store(text_chunks)
                st.write("DONE")

                # Create conversation chain
                st.session_state.conversation = get_conversation_chain(vector_store)


def initialize_conversation_chain():
    return None  # Replace with the actual initialization


main()


main()