# Import necessary libraries
import streamlit as st
from streamlit_chat import message
import tempfile
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.chains import ConversationalRetrievalChain

import sys
sys.path.append(r"vectorstore/db_faiss")
import dataset_utils

# Define the path for generated embeddings
DB_FAISS_PATH = 'vectorstore/db_faiss'

# Load the model of choice
def load_llm():
    llm = CTransformers(
        model="meta-llama/Llama-2-7b",
        model_type="llama",
        max_new_tokens=512,
        temperature=0.5
    )
    return llm

# Set the title for the Streamlit app
st.title("Llama2 Chat CSV - 🦜🦙")

# Create a file uploader in the sidebar
uploaded_file = st.sidebar.file_uploader("Upload File", type="csv")

# Handle file upload
if uploaded_file:
    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
        tmp_file.write(uploaded_file.getvalue())
        tmp_file_path = tmp_file.name

    # Load CSV data using CSVLoader
    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
    data = loader.load()

    # Create embeddings using Sentence Transformers
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})

    # Create a FAISS vector store and save embeddings
    db = FAISS.from_documents(data, embeddings)
    db.save_local(DB_FAISS_PATH)

    # Load the language model
    llm = load_llm()

    # Create a conversational chain
    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())

    # Function for conversational chat
    def conversational_chat(query):
        result = chain({"question": query, "chat_history": st.session_state['history']})
        st.session_state['history'].append((query, result["answer"]))
        return result["answer"]

    # Initialize chat history
    if 'history' not in st.session_state:
        st.session_state['history'] = []

    # Initialize messages
    if 'generated' not in st.session_state:
        st.session_state['generated'] = ["Hello ! Ask me(LLAMA2) about " + uploaded_file.name + " 🤗"]

    if 'past' not in st.session_state:
        st.session_state['past'] = ["Hey ! 👋"]

    # Create containers for chat history and user input
    response_container = st.container()
    container = st.container()

    # User input form
    with container:
        with st.form(key='my_form', clear_on_submit=True):
            user_input = st.text_input("Query:", placeholder="Talk to csv data 👉 (:", key='input')
            submit_button = st.form_submit_button(label='Send')

        if submit_button and user_input:
            output = conversational_chat(user_input)
            st.session_state['past'].append(user_input)
            st.session_state['generated'].append(output)

    # Display chat history
    if st.session_state['generated']:
        with response_container:
            for i in range(len(st.session_state['generated'])):
                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
                message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")