i could not get the right answer.

[fengyunzaidushi]

[fengyunzaidushi]

the right answer should be 1.029

[fengyunzaidushi]

here is my code,just copy from your video

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 = "vectorstores/db_faiss"

def load_llm():
    llm = CTransformers(
        model = "/mnt/sda/huggingface_weight/llama2/llama-2-7b-chat.ggmlv3.q8_0.bin",
        model_type = "llama",
        max_new_tokens = 512,
        temperature = 0.5
    )
    return llm

st.title("Chat with CSV using llama 2")
st.markdown("<h3 ttyle='text-align:center; color:white;'>AI Anytime with</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 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 ! : wave: "]

    #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 your CSV data here (:",key='input')
            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='thumbs')

[toyash]

See if you were hit by a Tesla truck where driver was Mr Llama2_Model then you will file a case against whom?

• Against Tesla Company? Or
• Against the driver Llama2_Model?

I hope you understood whom to ask why it is not working.