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' #Loading the model def load_llm(): # Load the locally downloaded model here llm = CTransformers( model = "TheBloke/Llama-2-7B-Chat-GGML", max_new_tokens = 512, temperature = 0.5 ) return llm st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png") st.title("Chat with CSV using Llama2") 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', model_kwargs={'device': 'cpu'}) 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 ! 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 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="Talk to 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")