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import streamlit as st
from streamlit_chat import message
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import CTransformers
from langchain.llms import Replicate
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import TextLoader
from langchain.document_loaders import Docx2txtLoader
from transformers import AutoTokenizer, AutoModelForCausalLM
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import os
from dotenv import load_dotenv
import tempfile
import torch
load_dotenv()
def initialize_session_state():
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 🤗"]
if 'past' not in st.session_state:
st.session_state['past'] = ["Hey! 👋"]
def conversation_chat(query, chain, history):
result = chain({"question": query, "chat_history": history})
history.append((query, result["answer"]))
return result["answer"]
def display_chat_history(chain):
reply_container = st.container()
container = st.container()
with container:
with st.form(key='my_form', clear_on_submit=True):
user_input = st.text_input("Question:", placeholder="Ask about your Documents", key='input')
submit_button = st.form_submit_button(label='Send')
if submit_button and user_input:
with st.spinner('Generating response...'):
output = conversation_chat(user_input, chain, st.session_state['history'])
st.session_state['past'].append(user_input)
st.session_state['generated'].append(output)
if st.session_state['generated']:
with reply_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="thumbs")
message(st.session_state["generated"][i], key=str(i), avatar_style="fun-emoji")
os.environ['HuggingFaceHub_API_Token']= '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'
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Nous-Hermes-llama-2-7b")
def create_conversational_chain(vector_store):
load_dotenv()
# Create llm
llm = CTransformers(streaming=True,
model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-llama-2-7b", device_map='auto',torch_dtype=torch.float16,load_in_4bit=True),
callbacks=[StreamingStdOutCallbackHandler()],
model_type="llama", config={'max_new_tokens': 500, 'temperature': 0.01})
# llm = Replicate(
# streaming = True,
# model = "NousResearch/Llama-2-7b-hf",
# callbacks=[StreamingStdOutCallbackHandler()],
# input = {"temperature": 0.01, "max_length" :500,"top_p":1})
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
memory=memory)
return chain
def main():
load_dotenv()
# Initialize session state
initialize_session_state()
st.title("Multi-Docs ChatBot using llama-2-7b :books:")
# Initialize Streamlit
st.sidebar.title("Document Processing")
uploaded_files = st.sidebar.file_uploader("Upload files", accept_multiple_files=True)
if uploaded_files:
text = []
for file in uploaded_files:
file_extension = os.path.splitext(file.name)[1]
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_file.write(file.read())
temp_file_path = temp_file.name
loader = None
if file_extension == ".pdf":
loader = PyPDFLoader(temp_file_path)
elif file_extension == ".docx" or file_extension == ".doc":
loader = Docx2txtLoader(temp_file_path)
elif file_extension == ".txt":
loader = TextLoader(temp_file_path)
if loader:
text.extend(loader.load())
os.remove(temp_file_path)
text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=100, length_function=len)
text_chunks = text_splitter.split_documents(text)
# Create embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'})
# Create vector store
vector_store = FAISS.from_documents(text_chunks,embeddings)
# Create the chain object
chain = create_conversational_chain(vector_store)
display_chat_history(chain)
if __name__ == "__main__":
main()
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