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from io import BytesIO
import streamlit as st
import shutil
import requests
import os
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_openai import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
import time
def getpdfdoc():
with st.spinner("Loading PDF..."):
filename = '48lawsofpower.pdf'
if os.path.exists(filename):
with open(filename, 'rb') as f:
pdf_doc = f.read()
return pdf_doc
else:
url = 'https://pgcag.files.wordpress.com/2010/01/48lawsofpower.pdf'
response = requests.get(url)
with open(filename, 'wb') as f:
f.write(response.content)
return getpdfdoc()
def extract_text_from_pdf(pdf_file_obj):
with st.spinner("Extracting text from PDF..."):
pdf_reader = PdfReader(BytesIO(pdf_file_obj))
text = ""
for page_num in range(len(pdf_reader.pages)):
page_obj = pdf_reader.pages[page_num]
text += page_obj.extract_text()
return text
def get_text_chunks(text):
with st.spinner("Splitting text into chunks..."):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
def get_vectorstore(text_chunks):
with st.spinner("Creating vectorstore..."):
st.markdown("Creating vector store")
time.sleep(10)
metadatas = [{"source": f"{i}-pl"} for i in range(len(text_chunks))]
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings, persist_directory="./data/vectorstore", metadatas=metadatas)
return vectorstore
def get_conversation_chain(vectorstore):
with st.spinner("Loading LLM..."):
llm = ChatOpenAI()
memory = ConversationBufferMemory(
memory_key='chat_history', return_messages=True)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory
)
return conversation_chain
def retrain_model():
st.session_state.conversation = None
st.session_state.chat_history = None
pdf_doc = getpdfdoc() # get pdf
raw_text = extract_text_from_pdf(pdf_doc) # get pdf text
text_chunks = get_text_chunks(raw_text) # get the text chunks
vectorstore = get_vectorstore(text_chunks) # create vector store
st.session_state.conversation = get_conversation_chain(vectorstore) # create conversation chain
def handle_userinput(user_question):
response = st.session_state.conversation({'question': user_question})
st.session_state.chat_history = response['chat_history']
for i, message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.markdown("**User:**")
st.markdown(message.content)
else:
st.markdown("**AI:**")
st.markdown(message.content)
def main():
if "conversation" not in st.session_state:
st.session_state.conversation = None
if "chat_history" not in st.session_state:
st.session_state.chat_history = None
if st.session_state.conversation is None:
if os.path.isdir("./data/vectorstore"):
if os.listdir("./data/vectorstore"):
with st.spinner("Loading vector store..."):
vectorstore = Chroma(persist_directory="./data/vectorstore", embedding_function=OpenAIEmbeddings())
st.session_state.conversation = get_conversation_chain(vectorstore)
else:
retrain_model()
else:
retrain_model()
if st.session_state.conversation is not None:
st.sidebar.button("Retrain model", on_click=retrain_model)
st.header("Ask questions from 48 Laws of Power:books:")
user_question = st.chat_input("Ask a question about your documents:")
if user_question:
handle_userinput(user_question)
if __name__ == '__main__':
main() |