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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 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 langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import os
import tempfile
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 about your file"]
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():
reply_container = st.container()
container = st.container()
with container:
col1, col2 = st.columns(2)
with col1:
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')
with col2:
if st.session_state['generated']:
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")
def create_conversational_chain(vector_store, text_chunks, embeddings):
replicate_api_token = "r8_AA3K1fhDykqLa5M74E5V0w5ss1z0P9S3foWJl" # Replace with your actual token
os.environ["REPLICATE_API_TOKEN"] = replicate_api_token
print("Length of text_chunks:", len(text_chunks))
print("Content of text_chunks:", text_chunks)
print("Length of embeddings:", len(embeddings))
print("Content of embeddings:", embeddings)
llm = Replicate(
streaming=True,
model="replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781",
callbacks=[StreamingStdOutCallbackHandler()],
input={"temperature": 0.01, "max_length": 500, "top_p": 1},
replicate_api_token=replicate_api_token
)
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()
st.title("Chat With Your Doc")
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)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
vector_store = FAISS.from_documents(text_chunks, embedding=embeddings)
chain = create_conversational_chain(vector_store, text_chunks, embeddings)
display_chat_history()
if __name__ == "__main__":
main()
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