app.py

import streamlit as st
from streamlit_chat import message
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import CTransformers
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
import sys

st.title("Chat with csv using Open Source Inference point")


DB_FAISS_PATH = "vectorstore/db_faiss"
loader = CSVLoader(file_path="data/2019.csv", encoding="utf-8", csv_args={'delimiter': ','})
data = loader.load()
print(data)

# Split the text into Chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
text_chunks = text_splitter.split_documents(data)

print(len(text_chunks))

# Download Sentence Transformers Embedding From Hugging Face
embeddings = HuggingFaceEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2')

# COnverting the text Chunks into embeddings and saving the embeddings into FAISS Knowledge Base
docsearch = FAISS.from_documents(text_chunks, embeddings)

docsearch.save_local(DB_FAISS_PATH)


#query = "What is the value of GDP per capita of Finland provided in the data?"

#docs = docsearch.similarity_search(query, k=3)

#print("Result", docs)

from transformers import pipeline

pipe = pipeline("text-generation",model="mistralai/Mistral-7B-v0.1",model_type="llama",max_new_tokens=512,temperature=0.1 )

qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever())

# Insert a chat message container.
with st.chat_message("user"):
   st.write("Hello 👋")
   st.line_chart(np.random.randn(30, 3))

# Display a chat input widget.
st.chat_input("Say something")

while True:
    chat_history = []
    #query = "What is the value of  GDP per capita of Finland provided in the data?"
    query = input(f"Input Prompt: ")
    if query == 'exit':
        print('Exiting')
        sys.exit()
    if query == '':
        continue
    result = qa({"question":query, "chat_history":chat_history})
    print("Response: ", result['answer'])