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

DB_FAISS_PATH ='vectorstore/db_faiss'

#Loading the model
def load_llm():
    llm = CTransformers(
        model= "TheBloke/Llama-2-7B-Chat-GGML",
        max_new_tokens=512,
        temperature=0.5
    )
    return llm

st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png")
st.title("Chat with CSV using Llama2")

uploaded_file = st.sidebar.file_uploader("Upload your data", type="csv")
if uploaded_file is not None:
    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", csv_args={
        'delimiter': ',',  # default value
    })
    data = loader.load()
    embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
                                     model_kwargs={'device': 'cpu'})
    db = FAISS.from_documents(data, embeddings)
    db.save_local(DB_FAISS_PATH)
    llm = load_llm()
    chain=ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.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()

    with container:
        with st.form(key='my_form', clear_on_submit=True):
            user_input = st.text_input('Query:', placeholder="Talk to 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")