import os
import json

import streamlit as st
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_groq import ChatGroq
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain

from vectorize_documents import embeddings


working_dir = os.path.dirname(os.path.abspath(__file__))
config_data = json.load(open(f"{working_dir}/config.json"))
GROQ_API_KEY = config_data["GROQ_API_KEY"]
os.environ["GROQ_API_KEY"]= GROQ_API_KEY


def setup_vectorstore():
    persist_directory = f"{working_dir}/vector_db_dir"
    embeddings = HuggingFaceEmbeddings()
    vectorstore = Chroma(persist_directory=persist_directory,
                         embedding_function=embeddings)
    return vectorstore

def chat_chain(vectorstore):
    llm = ChatGroq(
        model = "llama-3.1-70b-versatile",
        temperature = 0
    )
    retriever = vectorstore.as_retriever()
    memory = ConversationBufferMemory(
        llm = llm,
        output_key = "answer",
        memory_key = "chat_history",
        return_messages = True
    )
    chain = ConversationalRetrievalChain.from_llm(
        llm=llm,
        retriever = retriever,
        chain_type = "stuff",
        memory = memory,
        verbose=True,
        return_source_documents= True
    )
    return chain

st.set_page_config(
    page_title="WhatsApp FAQ AI",
    page_icon="🤖AI",
    layout="centered"
)

st.title("🤖AI WhatsApp FAQ")

if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

if "vectorstore" not in st.session_state:
    st.session_state.vectorstore = setup_vectorstore()

if "conversational_chain" not in st.session_state:
    st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)

for message in st.session_state.chat_history:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])
user_input = st.chat_input("Ask AI....")

if user_input:
    st.session_state.chat_history.append({"role":"user", "content":user_input})

    with st.chat_message("user"):
        st.markdown(user_input)

    with st.chat_message("assistant"):
        response = st.session_state.conversational_chain({"question":user_input})
        assistant_response = response["answer"]
        st.markdown(assistant_response)
        st.session_state.chat_history.append({"role":"assistant","content": assistant_response})