#pip install streamlit langchain openai faiss-cpu tiktoken

import streamlit as st
from streamlit_chat import message
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.vectorstores import FAISS
import tempfile
from htmlTemplates import css, bot_template, user_template
from dotenv import load_dotenv
import os

load_dotenv('.env')

# Access the API key
api_key = os.getenv('API_KEY')
#user_api_key = "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"
#st.sidebar.text_input(
    #label="#### Your OpenAI API key 👇",
    #placeholder="Paste your openAI API key, sk-",
    #type="password")
#logo=
uploaded_file = st.sidebar.file_uploader("  UPLOAD YOUR FILE", type="csv")

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

    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
    data = loader.load()

    embeddings = OpenAIEmbeddings(openai_api_key=api_key)
    vectors = FAISS.from_documents(data, embeddings)

    chain = ConversationalRetrievalChain.from_llm(llm=ChatOpenAI(temperature=0.0, model_name='gpt-3.5-turbo', openai_api_key=api_key),
    retriever=vectors.as_retriever())

    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"]
    
    if 'history' not in st.session_state:
        st.session_state['history'] = []

    if 'generated' not in st.session_state:
        st.session_state['generated'] = ["Hello! Ask me anything about " + uploaded_file.name]

    if 'past' not in st.session_state:
        st.session_state['past'] = ["Hey!"]
        
    #container for the chat history
    response_container = st.container()
    #container for the user's text input
    container = st.container()

    # Apply CSS styles
    st.write(css, unsafe_allow_html=True)

    with container:
        with st.form(key='my_form', clear_on_submit=True):
            
            user_input = st.text_input("Query:", placeholder="Talk about your csv data here", 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)

    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")
                
#streamlit run tuto_chatbot_csv.py