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import streamlit as st
import os
from streamlit_chat import message
import tempfile
#from langchain_community.documentloader.csv_loader import CSVLoader
from langchain_community.document_loaders.csv_loader import CSVLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
#from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
#from langchain_community.llms import CTransformers
from langchain_community.llms.ctransformers import CTransformers
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
#from langchain.chains.conversational_retrieval.base import ConversationalRetreievalChain
DB_FAISS_PATH = 'vectorstore/db_faiss'
TEMP_DIR = 'temp'
if not os.path.exists(TEMP_DIR):
os.makedirs(TEMP_DIR)
def load_llm():
# load model from hugging face repo
llm = CTransformers(
model = 'TheBloke/Llama-2-7B-Chat-GGML',
model_type = 'llama',
max_new_token = 512,
temperature = 0.5
)
return llm
st.title("Chat with CSV using Llma 2")
st.markdown("<h1 style='text-align: center; color: blue;'>Chat with your PDF 📄 </h1>", unsafe_allow_html=True)
st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/DrKareemKAmal'>MindSparks ❤️ </a></h3>", unsafe_allow_html=True)
uploaded_file = st.sidebar.file_uploader('Upload your data', type=['csv'])
if uploaded_file:
# with tempfile.NamedTemporaryFile(delete=False)as temp_file :
# temp_file.write(uploaded_file.getvalue())
# tempfile_path = temp_file.name
file_path = os.path.join(TEMP_DIR, uploaded_file.name)
with open(file_path, "wb") as f:
f.write(uploaded_file.getvalue())
st.write(f"Uploaded file: {uploaded_file.name}")
st.write("Processing CSV file...")
loader = CSVLoader(file_path = file_path, encoding = 'utf-8',
csv_args = {'delimiter': ','} )
data = loader.load()
#st.json(data)
text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500 , chunk_overlap = 20)
text_chunks = text_splitter.split_documents(data)
st.write (f"Total text chunks : {len(text_chunks)}")
embeddings = HuggingFaceEmbeddings(
model_name = 'sentence-transformers/all-MiniLM-L6-v2',
# model_kwargs = {'device': 'cpu'}
)
db = FAISS.from_documents(text_chunks, 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 = st.container()
with container :
with st.form(key = 'my_form',
clear_on_submit=True):
user_input = st.text_input('Query:', placeholder= "Talk to youur CSV Data here ")
submit_button = st.form_submit_button(label = 'chat')
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='thumb')
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