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'
background_image_path = 'image1.jpg'
# Set the background image and color

#Loading the model
def load_llm():
    # Load the locally downloaded model here
  llm = CTransformers(model='TheBloke/Llama-2-7B-Chat-GGML',model_file='llama-2-7b-chat.ggmlv3.q8_0.bin',max_new_tokens=512,temperature=0.1,gpu_layers=50)
  return llm

st.title("☔ ☔Chat with CSV using Llama2 ☔ ☔")
st.markdown("<h3 style='text-align: center; color: white;'>Built by <a href=https://github.com/Sakil786/CSVQConnect>♻️ CSVQConnect GitHub ♻️ </a></h3>", unsafe_allow_html=True)
# Your background image URL goes here
#background_image_url = 'https://www.bing.com/images/search?view=detailV2&ccid=lFAWXtbv&id=BB57AC3541361FF3844CAA706B667014CB515B92&thid=OIP.lFAWXtbvpchf66BryfJQ1QHaE8&mediaurl=https%3a%2f%2fimage.freepik.com%2ffree-photo%2ftwo-llamas-andean-highland-bolivia_107467-2006.jpg&exph=418&expw=626&q=llama2+image&simid=608011097331751957&FORM=IRPRST&ck=F66D65F1AFAAA4BBCF9986ADF8ED1643&selectedIndex=4'
background_image_path = 'image1.jpg'
# Set the background image and color


uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")

if uploaded_file :
   #use tempfile because CSVLoader only accepts a file_path
    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': ','})
    data = loader.load()
    #st.json(data)
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

    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 ! What is your query 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="Search answer from 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")