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")

    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()

        model = "stabilityai/stablelm-zephyr-3b"
        tokenizer = AutoTokenizer.from_pretrained(model)
        pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto", do_sample=True, top_k=1, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id,offload_folder="offload")

        llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': 0})
        embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
        vectorstore = FAISS.from_documents(data, embeddings,,allow_dangerous_deserialization=True)
        vectorstore.save_local(DB_FAISS_PATH)

        # Load the saved vectorstore
        vectorstore = FAISS.load_local(DB_FAISS_PATH, embeddings)

        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()