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import streamlit as st | |
import openai | |
import os | |
from dotenv import load_dotenv | |
from PyPDF2 import PdfReader | |
from langchain.text_splitter import CharacterTextSplitter | |
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings | |
from langchain.vectorstores import FAISS | |
from langchain.memory import ConversationBufferMemory | |
from langchain.chains import ConversationalRetrievalChain | |
from langchain.chat_models import ChatOpenAI | |
from htmlTemplates import css, bot_template, user_template | |
from PIL import Image | |
def get_pdf_text(pdf_docs): | |
text = "" | |
for pdf in pdf_docs: | |
pdf_reader = PdfReader(pdf) | |
for page in pdf_reader.pages: | |
text += page.extract_text() | |
return text | |
# documentation for CharacterTextSplitter: | |
# https://python.langchain.com/en/latest/modules/indexes/text_splitters/examples/character_text_splitter.html | |
def get_text_chunk(text): | |
text_splitter = CharacterTextSplitter( | |
separator="\n", | |
chunk_size = 1000, | |
chunk_overlap = 200, | |
length_function = len | |
) | |
chunks = text_splitter.split_text(text) | |
return chunks | |
#embedding using openAI embedding. Warn: This will cost you money | |
def get_vectorstore_openAI(text_chunks): | |
embeddings = OpenAIEmbeddings() | |
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
return vectorstore | |
#embedding using instructor-xl with your local machine for free | |
#you can find more details at: https://huggingface.co/hkunlp/instructor-xl | |
def get_vectorstore(text_chunks): | |
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") | |
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) | |
return vectorstore | |
def get_conversation_chain(vectorstore): | |
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 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.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True) | |
else: | |
st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True) | |
def main(): | |
############################################################################## | |
#load openai api_key from .evn | |
# load_dotenv() | |
openai.api_key = os.getenv("OPENAI_API_KEY") | |
############################################################################## | |
#set up basic page | |
st.set_page_config(page_title="Chat With multiple PDFs", page_icon=":books:") | |
st.write(css, unsafe_allow_html=True) | |
#initial session_state in order to avoid refresh | |
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 | |
st.header("Chat based on PDF you provided :books:") | |
user_question = st.text_input("Ask a question about your documents:") | |
if user_question: | |
handle_userinput(user_question) | |
# Define the templates | |
with st.sidebar: | |
st.subheader("Your PDF documents") | |
pdf_docs = st.file_uploader("Upload your pdfs here and click on 'Proces'", accept_multiple_files= True) | |
#if the button is pressed | |
if st.button("Process"): | |
with st.spinner("Processing"): | |
#get pdf text | |
raw_text = get_pdf_text(pdf_docs) | |
print('raw_text is created') | |
#get the text chunks | |
text_chunks = get_text_chunk(raw_text) | |
print('text_chunks are generated') | |
#create vector store | |
vectorstore = get_vectorstore_openAI(text_chunks) | |
print('vectorstore is created') | |
#create converstion chain | |
st.session_state.conversation = get_conversation_chain(vectorstore) | |
print('conversation chain created') | |
# to run this application, you need to run "streamlit run app.py" | |
if __name__ == '__main__': | |
main() |