app.py

import streamlit as st
import tempfile
import pandas as pd 
from langchain import HuggingFacePipeline 
from transformers import AutoTokenizer 
from langchain.embeddings import HuggingFaceEmbeddings 
from langchain.document_loaders.csv_loader import CSVLoader 
from langchain.vectorstores import FAISS 
from langchain.chains import RetrievalQA 
import transformers 
import torch 
import textwrap 

def main():
    st.set_page_config(page_title="Talk with BORROWER data")
    st.title("Talk with BORROWER data")
    uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")

    query = st.text_input("Send a Message")
    if st.button("Submit Query", type="primary"):
        DB_FAISS_PATH = "vectorstore/db_faiss"


        loader = CSVLoader(file_path="./borrower_data.csv", encoding="utf-8", csv_args={
                    'delimiter': ','})
        data = loader.load()
        st.write(data)

        model = "daryl149/llama-2-7b-chat-hf" 
        tokenizer = AutoTokenizer.from_pretrained(model) 
        pipeline = transformers.pipeline("text-generation", #task 
                                          model=model, 
                                          tokenizer=tokenizer, 
                                          torch_dtype=torch.bfloat16, 
                                          trust_remote_code=True, 
                                          device_map="auto",  
                                          do_sample=True, 
                                          top_k=5, 
                                          num_return_sequences=1, 
                                          eos_token_id=tokenizer.eos_token_id 
        ) 

        llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0}) 
        embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') 
        vectorstore = FAISS.from_documents(data, embeddings)
        vectorstore.save_local(DB_FAISS_PATH)
        chain=retrievalQA.from_chain_type(llm=llm, chain_type = "stuff",return_source_documents=True, retriever=vectorstore.as_retriever()) 
        result=chain(query)
        st.write(result['result'])

if __name__ == '__main__':
    main()